6. Model Physics

As stated in chapter [chap:coupling], the total parameterization package in CAM6.0 consists of a sequence of components, indicated by

P = \{ M,R,S,T \} ~,

where M denotes (Moist) precipitation processes, R denotes clouds and Radiation, S denotes the Surface model, and T denotes Turbulent mixing. Each of these in turn is subdivided into various components: M includes an optional dry adiabatic adjustment normally applied only in the stratosphere, moist penetrative convection, shallow convection, and large-scale stable condensation; R first calculates the cloud parameterization followed by the radiation parameterization; S provides the surface fluxes obtained from land, ocean and sea ice models, or calculates them based on specified surface conditions such as sea surface temperatures and sea ice distribution. These surface fluxes provide lower flux boundary conditions for the turbulent mixing T which is comprised of the planetary boundary layer parameterization, vertical diffusion, and gravity wave drag.

The updating described in the preceding paragraph of all variable except temperature is straightforward. Temperature, however, is a little more complicated and follows the general procedure described by Boville and [BB03] involving dry static energy. The state variable updated after each time-split parameterization component is the dry static energy s_i. Let i be the index in a sequence of I time-split processes. The dry static energy at the end of the ith process is s_i. The dry static energy is updated using the heating rate Q calculated by the ith process:

s_i = s_{i-1} + \left(\Delta t\right) Q_i(s_{i-1},T_{i-1},\Phi_{i-1},q_{i-1}, ...)

In processes not formulated in terms of dry static energy but rather in terms of a temperature tendency, the heating rate is given by Q_i = \left( T_i - T_{i-1} \right) / \left( C_p \Delta t \right).

The temperature, T_i, and geopotential, \Phi_i, are calculated from s_i by inverting the equation for s

s = C_pT + gz = C_pT + \Phi

with the hydrostatic equation

\Phi_k = \Phi_s + R\sum_{l=k}^{K} H_{kl}{T_v}_l

substituted for \Phi. The temperature tendencies for each process are also accumulated over the processes. For processes formulated in terms of dry static energy the temperature tendencies are calculated from the dry static energy tendency. Let \Delta T_i / \Delta t denote the total accumulation at the end of the ith process. Then

\frac{\Delta T_i}{\Delta t} = \frac{\Delta T_{i-1}}{\Delta t}
+ \frac{\Delta s_i}{\Delta t} / C_p

\frac{\Delta s_i}{\Delta t} / C_p =
\frac{\left( s_i - s_{i-1}\right)}{\Delta t} / C_p

which assumes \Phi is unchanged. Note that the inversion of s for T and \Phi changes T and \Phi. This is not included in the {\Delta T_i / \Delta t} above for processes formulated to give dry static energy tendencies.. In processes not formulated in terms of dry static energy but rather in terms of a temperature tendency, that tendency is simply accumulated.

After the last parameterization is completed, the dry static energy of the last update is saved. This final column energy is saved and used at the beginning of the next physics calculation following the Finite Volume dynamical update to calculate the global energy fixer associated with the dynamical core. The implication is that the energy inconsistency introduced by sending the T described above to the FV rather than the T returned by inverting the dry static energy is included in the fixer attributed to the dynamics. The accumulated physics temperature tendency is also available after the last parameterization is completed, {\Delta T_I / \Delta t}. An updated temperature is calculated from it by adding it to the temperature at the beginning of the physics.

T_I = T_0 + \frac{\Delta T_I}{\Delta t}*\Delta t

This temperature is converted to virtual potential temperature and passed to the Finite Volume dynamical core. The temperature tendency itself is passed to the spectral transform Eulerian and semi-Lagrangian dynamical cores. The inconsistency in the use of temperature and dry static energy apparent in the description above should be eliminated in future versions of the model.

6.1. Conversion to and from dry and wet mixing ratios for trace constituents in the model

There are trade offs in the various options for the representation of trace constituents \chi in any general circulation model:

  1. When the air mass in a model layer is defined to include the water vapor, it is frequently convenient to represent the quantity of trace constituent as a “moist” mixing ratio \chi^m, that is, the mass of tracer per mass of moist air in the layer. The advantage of the representation is that one need only multiply the moist mixing ratio by the moist air mass to determine the tracer air mass. It has the disadvantage of implicitly requiring a change in \chi^m whenever the water vapor q changes within the layer, even if the mass of the trace constituent does not.
  2. One can also utilize a “dry” mixing ratio \chi^d to define the amount of constituent in a volume of air. This variable does not have the implicit dependence on water vapor, but does require that the mass of water vapor be factored out of the air mass itself in order to calculate the mass of tracer in a cell.

NCAR atmospheric models have historically used a combination of dry and moist mixing ratios. Physical parameterizations (including convective transport) have utilized moist mixing ratios. The resolved scale transport performed in the Eulerian (spectral), and semi-Lagrangian dynamics use dry mixing ratios, specifically to prevent oscillations associated with variations in water vapor requiring changes in tracer mixing ratios. The finite volume dynamics module utilizes moist mixing ratios, with an attempt to maintain internal consistency between transport of water vapor and other constituents.

There is no “right” way to resolve the requirements associated with the simultaneous treatment of water vapor, air mass in a layer and tracer mixing ratios. But the historical treatment significantly complicates the interpretation of model simulations, and in the latest version of CAM we have also provided an “alternate” representation. That is, we allow the user to specify whether any given trace constituent is interpreted as a “dry” or “wet” mixing ratio through the specification of an “attribute” to the constituent in the physics state structure. The details of the specification are described in the users manual, but we do identify the interaction between state quantities here.

At the end of the dynamics update to the model state, the surface pressure, specific humidity, and tracer mixing ratios are returned to the model. The physics update then is allowed to update specific humidity and tracer mixing ratios through a sequence of operator splitting updates but the surface pressure is not allowed to evolve. Because there is an explicit relationship between the surface pressure and the air mass within each layer we assume that water mass can change within the layer by physical parameterizations but dry air mass cannot. We have chosen to define the dry air mass in each layer at the beginning of the physics update as

\delta p^d_{i,k} = (1-q^0_{i,k}) \delta^m_{i,k}

for column i, level k. Note that the specific humidity used is the value defined at the beginning of the physics update. We define the transformation between dry and wet mixing ratios to be

\chi^d_{i,k} = (\delta p^d_{i,k} / \delta p^m_{i,k}) \chi^m_{i,k}

We note that the various physical parameterizations that operate on tracers on the model (convection, turbulent transport, scavenging, chemistry) will require a specification of the air mass within each cell as well as the value of the mixing ratio in the cell. We have modified the model so that it will use the correct value of \delta p depending on the attribute of the tracer, that is, we use couplets of (\chi^m, \delta p^m) or (\chi^d, \delta p^d) in order to assure that the process conserves mass appropriately.

We note further that there are a number of parameterizations (convection, vertical diffusion) that transport species using a continuity equation in a flux form that can be written generically as

{\partial \chi \over \partial t} = {\partial F(\chi) \over \partial p}

where F indicates a flux of \chi. For example, in convective transports F(\chi) might correspond to M_u \chi where M_u is an updraft mass flux. In principle one should adjust M_u to reflect the fact that it may be moving a mass of dry air or a mass of moist air. We assume these differences are small, and well below the errors required to produce equation (1) in the first place. The same is true for the diffusion coefficients involved in turbulent transport. All processes using equations of such a form still satisfy a conservation relationship

{\partial \over \partial t} \sum_k{\chi_k \delta p_k}  = F_{kbot} - F_{ktop}

provided the appropriate \delta p is used in the summation.

6.2. Deep Convection

The process of deep convection is treated with a parameterization scheme developed by [ZM95] and modified with the addition of convective momentum transports by [RR08] and a modified dilute plume calculation following [RB86][RB92]. The scheme is based on a plume ensemble approach where it is assumed that an ensemble of convective scale updrafts (and the associated saturated downdrafts) may exist whenever the atmosphere is conditionally unstable in the lower troposphere. The updraft ensemble is comprised of plumes sufficiently buoyant so as to penetrate the unstable layer, where all plumes have the same upward mass flux at the bottom of the convective layer. Moist convection occurs only when there is convective available potential energy (CAPE) for which parcel ascent from the sub-cloud layer acts to destroy the CAPE at an exponential rate using a specified adjustment time scale. For the convenience of the reader we will review some aspects of the formulation, but refer the interested reader to [ZM95] for additional detail, including behavioral characteristics of the parameterization scheme. Evaporation of convective precipitation is computed following the procedure described in section conv_evap.

The large-scale budget equations distinguish between a cloud and sub-cloud layer where temperature and moisture response to convection in the cloud layer is written in terms of bulk convective fluxes as

c_p \left( \frac{\partial T}{\partial t} \right)_{cu} =
- \frac{1}{\rho} \frac{\partial}{\partial z} \left(
M_u S_u + M_d S_d - M_c S \right) + L(C - E) \,

\left( \frac{\partial q}{\partial t} \right)_{cu} =
- \frac{1}{\rho} \frac{\partial}{\partial z} \left(
M_u q_u + M_d q_d - M_c q \right) + E - C

for z\ge z_b, where z_b is the height of the cloud base. For z_s<z<z_b, where z_s is the surface height, the sub-cloud layer response is written as

c_p {\left( \rho \frac{\partial T}{\partial t} \right)}_{m} =
- \frac{1}{z_b-z_s} \left( M_b [S(z_b) - S_u (z_b)]
+ M_d [S(z_b) - S_d (z_b)] \right) \,

{\left(\rho \frac{\partial q}{\partial t} \right)}_{m}  =
- \frac{1}{z_b-z_s} \left( M_b [q(z_b) - q_u (z_b)]
+ M_d [q(z_b) - q_d (z_b)] \right)

where the net vertical mass flux in the convective region, M_c, is comprised of upward, M_u, and downward, M_d, components, C and E are the large-scale condensation and evaporation rates, S, S_u, S_d, q, q_u, q_d, are the corresponding values of the dry static energy and specific humidity, and M_b is the cloud base mass flux.

6.2.1. Updraft Ensemble

The updraft ensemble is represented as a collection of entraining plumes, each with a characteristic fractional entrainment rate \lambda. The moist static energy in each plume h_c is given by

\frac{\partial h_c}{\partial z} = \lambda (h - h_c), \quad

Mass carried upward by the plumes is detrained into the environment in a thin layer at the top of the plume, z_D, where the detrained air is assumed to have the same thermal properties as in the environment (S_c=S). Plumes with smaller \lambda penetrate to larger z_D. The entrainment rate \lambda_D for the plume which detrains at height z is then determined by solving (6) , with lower boundary condition h_c(z_b)=h_b:

  \frac{\partial h_c}{\partial (z-z_b)} &=& \lambda_D (h - h_b) -
    \lambda_D (h_c - h_b) \\
  \frac{\partial (h_c - h_b)}{\partial (z-z_b)} - \lambda_D (h_c -
    h_b) &=& \lambda_D (h - h_b) \\
  \frac{\partial (h_c - h_b)e^{\lambda_D(z-z_b)}}{\partial (z-z_b)}
    &=& \lambda_D (h - h_b)e^{\lambda_D(z-z_b)} \\
  (h_c - h_b)e^{\lambda_D(z-z_b)} &=& \int_{z_b}^z \lambda_D (h -
    h_b)e^{\lambda_D(z^\prime-z_b)} dz^\prime \\
  (h_c - h_b) &=&\lambda_D \int_{z_b}^z (h -
    h_b)e^{\lambda_D(z^\prime-z)} dz^\prime

Since the plume is saturated, the detraining air must have h_c=h^*, so that

(h_b - h^*) =\lambda_D \int_{z_b}^z (h_b -
    h)e^{\lambda_D(z^\prime-z)} dz^\prime

Then, \lambda_D is determined by solving (7) iteratively at each z.

The top of the shallowest of the convective plumes, z_0 is assumed to be no lower than the mid-tropospheric minimum in saturated moist static energy, h^*, ensuring that the cloud top detrainment is confined to the conditionally stable portion of the atmospheric column. All condensation is assumed to occur within the updraft plumes, so that C = C_u. Each plume is assumed to have the same value for the cloud base mass flux M_b, which is specified below. The vertical distribution of the cloud updraft mass flux is given by

M_u = M_b \int^{\lambda_D}_0 \frac{1}{\lambda_0} e^{\lambda (z -
      z_b)}d\lambda = M_b \frac{e^{\lambda_D (z - z_b)} - 1}{\lambda_0
      (z - z_b)}


where \lambda_0 is the maximum detrainment rate, which occurs for the plume detraining at height z_0, and \lambda_D is the entrainment rate for the updraft that detrains at height z. Detrainment is confined to regions where \lambda_D decreases with height, so that the total detrainment D_u = 0 for z < z_0. Above z_0,

D_u = - \frac{M_b}{\lambda_0} \frac{\partial \lambda_D}{\partial z}


The total entrainment rate is then just given by the change in mass flux and the total detrainment,

E_u = \frac{\partial M_u}{\partial z} - D_u


The updraft budget equations for dry static energy, water vapor mixing ratio, moist static energy, and cloud liquid water, \ell, are:

\frac{\partial}{\partial z} \left ( M_u S_u \right ) = \left ( E_u -  D_u \right ) S + \rho LC_u

\frac{\partial}{\partial z}  \left ( M_u q_u \right ) = E_u q - D_u q^* + \rho C_u

\frac{\partial}{\partial z} \left ( M_u h_u \right )  =  E_u h - D_u  h^*

\frac{\partial}{\partial z} \left ( M_u \ell \right ) =  - D_u \ell_d + \rho C_u - \rho R_u ~,

where (13) is formed from (11) and (12) and detraining air has been assumed to be saturated (q=q^* and h=h^*). It is also assumed that the liquid content of the detrained air is the same as the ensemble mean cloud water (\ell_d = \ell). The conversion from cloud water to rain water is given by

\rho R_u = c_0 M_u \ell

following Lord, Chao, and Arakawa (1982), with c_0 = 2 \times 10^{-3}\ {\rm m}^{-1}.

Since M_u, E_u and D_u are given by (8) - (10), and h and h^* are environmental profiles, (13) can be solved for h_u, given a lower boundary condition. The lower boundary condition is obtained by adding a 0.5 K temperature perturbation to the dry (and moist) static energy at cloud base, or h_u = h +
c_p\times 0.5 at z=z_b. Below the lifting condensation level (LCL), S_u and q_u are given by (11) and (12) . Above the LCL, q_u is reduced by condensation and S_u is increased by the latent heat of vaporization. In order to obtain to obtain a saturated updraft at the temperature implied by S_u, we define \Delta T as the temperature perturbation in the updraft, then:

h_u = S_u + L q_u

S_u = S + c_p \Delta T

q_u = q^* + \frac{d q^*}{dT}\Delta  T ~.

Substituting (17) and (18) into (16) ,

  h_u &=& S + L q^* + c_p \left(1 + \frac{L}{c_p}\frac{d q^*}{dT}
         \right)\Delta T  \\
      &=& h^* + c_p\left(1+\gamma \right)\Delta T  \\
  \gamma &\equiv& \frac{L}{c_p}\frac{d q^*}{dT} \\
  \Delta T &=& \frac{1}{c_p}\frac{h_u - h^*}{1+\gamma}

The required updraft quantities are then

  S_u &=& S + \frac{h_u - h^*}{1+\gamma}  \\ q_u &=& q^*
  + \frac{\gamma}{L} \frac{h_u - h^*}{1+\gamma}

With S_u given by (20) , (11) can be solved for C_u, then (14) and (15) can be solved for \ell and R_u.

The expressions above require both the saturation specific humidity to be

q^* = \frac{\epsilon e^*}{p-e^*}, \qquad e^* < p

where e^* is the saturation vapor pressure, and its dependence on temperature (in order to maintain saturation as the temperature varies) to be

\frac{d q^*}{d T} &=& \frac{\epsilon}{p-e^*} \frac{d e^*}{d T}
     - \frac{\epsilon e^*}{(p-e^*)^2}\frac{d (p-e^*)}{d T} \\
   &=& \frac{\epsilon}{p-e^*}\left(1 + \frac{1}{p-e^*}\right) \frac{d
          e^*}{d T} \\
   &=& \frac{\epsilon}{p-e^*}\left(1 + \frac{q^*}{\epsilon e^*}\right)
          \frac{d e^*}{d T}

The deep convection scheme does not use the same approximation for the saturation vapor pressure e^* as is used in the rest of the model. Instead,

e^* = c_1 \exp\left[\frac{c_2(T - T_f)}{(T-T_f+c_3)} \right]


where c_1=6.112, c_2=17.67, c_3=243.5 K and T_f=273.16 K is the freezing point. For this approximation,

  \frac{d e^*}{d T} &=& e^* \frac{d}{dT} \left[\frac{c_2(T -
    T_f)}{(T-T_f+c_3)} \right] \\ &=& e^*
            - \frac{c_2(T - T_f)}{(T-T_f+c_3)^2} \right] \\ &=& e^*
    \frac{c_2 c_3}{(T-T_f+c_3)^2}  \\

  \frac{d q^*}{d T}
    &=& q^*\left(1+ \frac{q^*}{\epsilon e^*}\right) \frac{c_2

We note that the expression for \gamma in the code gives

\frac{d q^*}{d T} = \frac{c_p}{L}\gamma
      = q^*\left(1+ \frac{q^*}{\epsilon}\right) \frac{\epsilon

The expressions for {d q^*}/{d T} in (24) and (25) are not identical. Also, T-T_f+c_3 \neq T and c_2
c_3 \neq \epsilon L/R.

6.2.2. Downdraft Ensemble

Downdrafts are assumed to exist whenever there is precipitation production in the updraft ensemble where the downdrafts start at or below the bottom of the updraft detrainment layer. Detrainment from the downdrafts is confined to the sub-cloud layer, where all downdrafts have the same mass flux at the top of the downdraft region. Accordingly, the ensemble downdraft mass flux takes a similar form to (8) but includes a “proportionality factor” to ensure that the downdraft strength is physically consistent with precipitation availability. This coefficient takes the form

\alpha = \mu \left [ \frac{P}{P + E_d} \right ]

where P is the total precipitation in the convective layer and E_d is the rain water evaporation required to maintain the downdraft in a saturated state. This formalism ensures that the downdraft mass flux vanishes in the absence of precipitation, and that evaporation cannot exceed some fraction, \mu, of the precipitation, where \mu = 0.2.

6.2.3. Closure

The parameterization is closed, i.e., the cloud base mass fluxes are determined, as a function of the rate at which the cumulus consume convective available potential energy (CAPE). Since the large-scale temperature and moisture changes in both the cloud and sub-cloud layer are linearly proportional to the cloud base updraft mass flux (see eq. (2)(5)), the CAPE change due to convective activity can be written as

\left( \frac{\partial A}{\partial t} \right)_{cu} = -M_b F


where F is the CAPE consumption rate per unit cloud base mass flux. The closure condition is that the CAPE is consumed at an exponential rate by cumulus convection with characteristic adjustment time scale \tau = 7200 s:

M_b = \frac{A}{\tau F}

6.2.4. Numerical Approximations

The quantities M_{u,d}, \ell, S_{u,d}, q_{u,d}, h_{u,d} are defined on layer interfaces, while D_u, C_u, R_u are defined on layer midpoints. S, q, h, \gamma are required on both midpoints and interfaces and the interface values \psi^{k\pm} are determined from the midpoint values \psi^k as

\psi^{k-} = \log\left(\frac{\psi^{k-1}}{\psi^k}\right)
               \frac{\psi^{k-1} \psi^k}{\psi^{k-1} - \psi^k}


All of the differencing within the deep convection is in height coordinates. The differences are naturally taken as

\frac{\partial \psi}{\partial z} = \frac{\psi^{k-} - \psi^{k+}}{z^{k-}
- z^{k+}}

where \psi^{k-} and \psi^{k+} represent values on the upper and lower interfaces, respectively for layer k. The convention elsewhere in this note (and elsewhere in the code) is \delta^k\psi = \psi^{k+}
- \psi^{k-}. Therefore, we avoid using the compact \delta^k notation, except for height, and define

d^kz \equiv z^{k-} - z^{k+} = -\delta^k z

so that d^kz corresponds to the variable dz(k) in the deep convection code.

Although differences are in height coordinates, the equations are cast in flux form and the tendencies are computed in units \rm kg\ m^{-3}\
s^{-1}. The expected units are recovered at the end by multiplying by g\delta z/\delta p.

The environmental profiles at midpoints are

  S^k &=& c_p T^k + g z^k \\ h^k &=& S^k + L q^k \\ h^{*k} &=& S^k + L
  q^{*k} \\ q^{*k} &=& \epsilon e^{*k} / (p^k - e^{*k}) \\ e^{*k} &=&
  c_1 \exp\left[\frac{c_2(T^k - T_f)}{(T^k-T_f+c_3)} \right] \\
  \gamma^k &=& q^{*k}\left(1+ \frac{q^{*k}}{\epsilon}\right)
                    \frac{\epsilon L^2}{c_pR{T^k}^2}

The environmental profiles at interfaces of S, q, q^*, and \gamma are determined using (29) if |\psi^{k-1}-\psi^{k}| is large enough. However, there are inconsistencies in what happens if |\psi^{k-1}-\psi^{k}| is not large enough. For S and q the condition is

\psi^{k-} = (\psi^{k-1}+\psi^k)/2, \quad
    \frac{|\psi^{k-1}-\psi^{k}|}{\max(\psi^{k-1}-\psi^{k})} \leq

For q^* and \gamma the condition is

\psi^{k-} = \psi^{k}, \quad |\psi^{k-1}-\psi^{k}| \leq 10^{-6}

Interface values of h are not needed and interface values of h^* are given by

  h^{*k-} &=& S^{k-} + L q^{*k-}

The unitless updraft mass flux (scaled by the inverse of the cloud base mass flux) is given by differencing (8) as

M_u^{k-} = \frac{1}{\lambda_0(z^{k-}-z_b)} \left( e^{\lambda_D^k
               (z^{k-}-z_b)} -1 \right)

with the boundary condition that M_u^{M+} =1. The entrainment and detrainment are calculated using

  m_u^{k-} &=& \frac{1}{\lambda_0(z^{k-}-z_b)} \left(
               e^{\lambda_D^{k+1} (z^{k-}-z_b)} -1 \right) \\
  E_u^k &=& \frac{m_u^{k-} - M_u^{k+}}{d^kz} \\ D_u^k &=&
  \frac{m_u^{k-} - M_u^{k-}}{d^kz}

Note that M_u^{k-} and m_u^{k-} differ only by the value of \lambda_D.

The updraft moist static energy is determined by differencing (13)

\frac{M_u^{k-}h_u^{k-} - M_u^{k+}h_u^{k+}}{d^kz} = E_u^k h^k - D_u^k  h^{*k}

h_u^{k-} = \frac{1}{M_u^{k-}}\left[M_u^{k+} h_u^{k+} + d^kz\left( E_u^k h^k - D_u^k h^{*k} \right)\right] ~,

with h_u^{M-} = h^M + c_p/2, where M is the layer of maximum h.

Once h_u is determined, the lifting condensation level is found by differencing (11) and (12) similarly to (13) :

S_u^{k-} = \frac{1}{M_u^{k-}}\left[M_u^{k+} S_u^{k+} + d^kz\left( E_u^k S^k - D_u^k S^{k} \right)\right]

q_u^{k-} = \frac{1}{M_u^{k-}}\left[M_u^{k+} q_u^{k+} + d^kz\left(  E_u^k q^k - D_u^k q^{*k} \right)\right]

The detrainment of S_u is given by D_u^kS^k not by D_u^kS_u^k, since detrainment occurs at the environmental value of S. The detrainment of q_u is given by D_u^k q^{*k}, even though the updraft is not yet saturated. The LCL will usually occur below z_0, the level at which detrainment begins, but this is not guaranteed.

The lower boundary conditions, S_u^{M-} = S^M + c_p/2 and q_u^{M-}= q^M, are determined from the first midpoint values in the plume, rather than from the interface values of S and q. The solution of (32) and (33) continues upward until the updraft is saturated according to the condition

  q_u^{k-} &>& q^{*}(T_u^{k-}), \\ T_u^{k-} &=& \frac{1}{c_p}\left(
  S_u^{k-} - gz^{k-}\right)

The condensation (in units of m^{-1}) is determined by a centered differencing of (11) :

\frac{M_u^{k-}S_u^{k-} - M_u^{k+}S_u^{k+}}{d^kz} = (E_u^k - D_u^k)
    S^k + L C_u^k

  C_u^k &=& \frac{1}{L} \left[ \frac{M_u^{k-}S_u^{k-} -
          - (E_u^k - D_u^k) S^k \right]

The rain production (in units of m^{-1}) and condensed liquid are then determined by differencing (14) as

\frac{M_u^{k-}\ell^{k-} - M_u^{k+}\ell^{k+}}{d^kz} = -D_u^k
    \ell^{k+} + C_u^k - R_u^k

and (15) as

R_u^k = c_0 M_u^{k-} \ell^{k-}


  M_u^{k-}\ell^{k-} &=& M_u^{k+}\ell^{k+} - d^kz \left( D_u^k
    \ell^{k+} - C_u^k + c_0 M_u^{k-} \ell^{k-} \right) \\
  M_u^{k-}\ell^{k-} \left(1 + c_0 d^kz \right) &=& M_u^{k+}\ell^{k+} +
    d^kz \left( D_u^k \ell^{k+} - C_u^k \right) \\
  \ell^{k-} &=& \frac{1}{M_u^{k-}\left(1 + c_0 d^kz \right)} \left[
    M_u^{k+}\ell^{k+} - d^kz \left(D_u^k \ell^{k+} - C_u^k \right)

6.2.5. Deep Convective Momentum Transports

Sub-grid scale Convective Momentum Transports (CMT) have ben added to the existing deep convection parameterization following Richter and Rasch (2008) and the methodology of Gregory, Kershaw, and Inness (1997). The sub-grid scale transport of momentum can be cast in the same manner as (3) . Expressing the grid mean horizontal velocity vector, \boldsymbol{V}, tendency due to deep convection transport following Kershaw and Gregory (1997) gives

\left( \frac{\partial \boldsymbol{V}}{\partial t} \right)_{cu} &=
- \frac{1}{\rho} \frac{\partial}{\partial z} \left(
M_u \boldsymbol{V}_u + M_d \boldsymbol{V}_d - M_c \boldsymbol{V} \right)

and neglecting the contribution from the environment the updraft and downdraft budget equation can similarly be written as

-\frac{\partial}{\partial z} \left ( M_u  \boldsymbol{V}_u \right ) &=& E_u  \boldsymbol{V}-D_u\boldsymbol{V}_u  + \boldsymbol{P}^u_G   \\
-\frac{\partial}{\partial z} \left ( M_d  \boldsymbol{V}_d \right ) &=& E_d \boldsymbol{V} + \boldsymbol{P}^d_G

where \boldsymbol{P}^u_G and \boldsymbol{P}^d_G the updraft and downdraft pressure gradient sink terms parameterized from Gregory, Kershaw, and Inness (1997) as

\boldsymbol{P}^u_G   = -C_u M_u\frac{\partial \boldsymbol{V}}{\partial z}

\boldsymbol{P}^d_G   = -C_d M_d\frac{\partial \boldsymbol{V}}{\partial z}.

C_u and C_d are tunable parameters. In the CAM6.0 implementation we use C_u = C_d = 0.4. The value of C_u and C_d control the strength of convective momentum transport. As these coefiicients increase so do the pressure gradient terms, and convective momentum transport decreases.

6.2.6. Deep Convective Tracer Transport

The CAM6.0 provides the ability to transport constituents via convection. The method used for constituent transport by deep convection is a modification of the formulation described in Zhang and McFarlane (1995).

We assume the updrafts and downdrafts are described by a steady state mass continuity equation for a “bulk” updraft or downdraft

{\partial (M_x q_x) \over \partial p} = E_x q_e - D_x

The subscript x is used to denote the updraft (u) or downdraft (d) quantity. M_x here is the mass flux in units of Pa/s defined at the layer interfaces, q_x is the mixing ratio of the updraft or downdraft. q_e is the mixing ratio of the quantity in the environment (that part of the grid volume not occupied by the up and downdrafts). E_x and D_x are the entrainment and detrainment rates (units of s^{-1}) for the up- and down-drafts. Updrafts are allowed to entrain or detrain in any layer. Downdrafts are assumed to entrain only, and all of the mass is assumed to be deposited into the surface layer.

Equation (38) is first solved for up and downdraft mixing ratios q_u and q_d, assuming the environmental mixing ratio q_e is the same as the gridbox averaged mixing ratio \bar q.

Given the up- and down-draft mixing ratios, the mass continuity equation used to solve for the gridbox averaged mixing ratio \bar q is

{\partial \bar q \over \partial t} = {\partial \over \partial p} (M_u
(q_u-\bar q) + M_d (q_d-\bar q))

These equations are solved for in subroutine CONVTRAN. There are a few numerical details employed in CONVTRAN that are worth mentioning here as well.

  • mixing quantities needed at interfaces are calculated using the geometric mean of the layer mean values.
  • simple first order upstream biased finite differences are used to solve (38) and (39).
  • fluxes calculated at the interfaces are constrained so that the resulting mixing ratios are positive definite. This means that this parameterization is not suitable for moving mixing ratios of quantities meant to represent perturbations of a trace constituent about a mean value (in which case the quantity can meaningfully take on positive and negative mixing ratios). The algorithm can be modified in a straightforward fashion to remove this constraint, and provide meaningful transport of perturbation quantities if necessary. the reader is warned however that there are other places in the model code where similar modifications are required because the model assumes that all mixing ratios should be positive definite quantities.

6.3. Evaporation of convective precipitation

The CAM6.0 employs a [Sun88] style evaporation of the convective precipitation as it makes its way to the surface. This scheme relates the rate at which raindrops evaporate to the local large-scale subsaturation, and the rate at which convective rainwater is made available to the subsaturated model layer

E_{r_k} = K_E \; (1 - \text{RH}_k) \; {(\hat{R}_{r_k})}^{1/2}


where \text{RH}_k is the relative humidity at level k, \hat{R}_{r_k} denotes the total rainwater flux at level k (which can be different from the locally diagnosed rainwater flux from the convective parameterization, as will be shown below), the coefficient K_E takes the value 0.2 \cdot 10^{-5} (kg m^{-2} s^{-1})^{-1/2}s^{-1}, and the variable E_{r_k} has units of s^{-1}. The evaporation rate E_{r_k} is used to determine a local change in q_k and T_k, associated with an evaporative reduction of \hat{R}_{r_k}. Conceptually, the evaporation process is invoked after a vertical profile of R_{r_k} has been evaluated. An evaporation rate is then computed for the uppermost level of the model for which R_{r_k} \not= 0 using (40) , where in this case R_{r_k} \equiv \; \hat{R}_{r_k}. This rate is used to evaluate an evaporative reduction in R_{r_k} which is then accumulated with the previously diagnosed rainwater flux in the layer below,

\hat{R}_{r_{k+1}} = \hat{R}_{r_k} - \left({{\Delta p_k} \over g}\right) \; E_{r_k} + R_{r_{k+1}}


A local increase in the specific humidity q_k and a local reduction of T_k are also calculated in accordance with the net evaporation

q_k = q_k + E_{r_k} \; 2 \Delta t \;



T_k = T_k - \left( {L \over c_p} \right) E_{r_k} \; 2 \Delta t \;


The procedure, (40) -(43) , is then successively repeated for each model level in a downward direction where the final convective precipitation rate is that portion of the condensed rainwater in the column to survive the evaporation process

P_s = \left( \hat{R}_{r_{K}} - \left({{\Delta p_K} \over g}\right) \;  E_{r_K} \right) /\rho_{H_{2}0}


In global annually averaged terms, this evaporation procedure produces a very small reduction in the convective precipitation rate where the evaporated condensate acts to moisten the middle and lower troposphere.

6.4. Prognostic Condensate and Precipitation Parameterization

6.4.1. Introductory comments

The parameterization of non-convective cloud processes in CAM6.0 is described in [RKristjansson98] and [ZLB+03]. The original formulation is introduced in Rasch and Kristjánsson (1998). Revisions to the parameterization to deal more realistically with the treatment of the condensation and evaporation under forcing by large scale processes and changing cloud fraction are described in Zhang et al. (2003). The equations used in the formulation are discussed here. The papers contain a more thorough description of the formulation and a discussion of the impact on the model simulation.

The formulation for cloud condensate combines a representation for condensation and evaporation with a bulk microphysical parameterization closer to that used in cloud resolving models. The parameterization replaces the diagnosed liquid water path of CCM3 with evolution equations for two additional predicted variables: liquid and ice phase condensate. At one point during each time step, these are combined into a total condensate and partitioned according to temperature (as described in section microscale), but elsewhere function as independent quantities. They are affected by both resolved (advective) and unresolved (convective, turbulent) processes. Condensate can evaporate back into the environment or be converted to a precipitating form depending upon its in-cloud value and the forcing by other atmospheric processes. The precipitate may be a mixture of rain and snow, and is treated in diagnostic form, its time derivative has been neglected.

The parameterization calculates the condensation rate more consistently with the change in fractional cloudiness and in-cloud condensate than the previous CCM3 formulation. Changes in water vapor and heat in a grid volume are treated consistently with changes to cloud fraction and in-cloud condensate. Condensate can form prior to the onset of grid-box saturation and can require a significant length of time to convert (via the cloud microphysics) to a precipitable form. Thus a substantially wider range of variation in condensate amount than in the CCM3 is possible.

The new parameterization adds significantly to the flexibility in the model and to the range of scientific problems that can be studied. This type of scheme is needed for quantitative treatment of scavenging of atmospheric trace constituents and cloud aqueous and surface chemistry. The addition of a more realistic condensate parameterization closely links the radiative properties of the clouds and their formation and dissipation. These processes must be treated for many problems of interest today (e.g. anthropogenic aerosol-climate interactions).

The parameterization has two components: 1) a macroscale component that describes the exchange of water substance between the condensate and the vapor phase and the associated temperature change arising from that phase change Zhang et al. (2003); and 2) a bulk microphysical component that controls the conversion from condensate to precipitate (Rasch and Kristjánsson 1998). These components are discussed in the following two sections.

6.5. Cloud Microphysics

The base parameterization of stratiform cloud microphysics is described by Gettelman and Morrison (2015), and is version 2 of the scheme described by Morrison and Gettelman (2008). Details of the CAM implementation are described by Gettelman et al (2015) and [GMG08]. Modifications to handle ice nucleation and ice supersaturation are described by Gettelman and others (2010).

The scheme seeks the following:

  • A more flexible, self-consistent, physically-based treatment of cloud physics.
  • A reasonable level of simplicity and computational efficiency.
  • Treatment of both number concentration and mixing ratio of cloud particles to address indirect aerosol effects and cloud-aerosol interaction.
  • Representation of precipitation number concentration, mass, and phase to better treat wet deposition and scavenging of aerosol and chemical species.
  • The achievement of equivalent or better results relative to the CAM3 microphysics parameterization when compared to observations.

The novel aspects of the scheme are an explicit representation of sub-grid cloud water distribution for calculation of the various microphysical process rates, and the diagnostic two-moment treatment of rain and snow.

6.5.1. Overview of the microphysics scheme

The two-moment scheme is based loosely on the approach of Morrison, Curry, and Khvorostyanov (2005). This scheme predicts the number concentrations (Nc, Ni) and mixing ratios (qc, qi) of cloud droplets (subscript c) and cloud ice (subscript i). Hereafter, unless stated otherwise, the cloud variables Nc, Ni, qc, and qi represent grid-averaged values; prime variables represent mean in-cloud quantities (e.g., such that Nc = Fcld NcÕ, where Fcld is cloud fraction); and double prime variables represent local in-cloud quantities. The treatment of sub-grid cloud variability is detailed in section 2.1.

The cloud droplet and ice size distributions \phi are represented by gamma functions:

\phi(D)=N_0 D^\mu \exp^{-\lambda D}

where Dis diameter, N_0 is the ÔinterceptÕ parameter, \lambda is the slope parameter, and \mu = 1 / \eta^2 -1 is the spectra shape parameter; \eta is the relative radius dispersion of the size distribution. The parameter \eta for droplets is specified following Martin, Johnson, and Spice (1994). Their observations of maritime versus continental warm stratocumulus have been approximated by the following \eta -
N^{\prime\prime}_c relationship:

\eta = 0.0005714 N^{\prime\prime}_c + 0.2714

where N^{\prime\prime}_c has units of cm^{-3}. The upper limit for \eta is 0.577, corresponding with aN^{\prime\prime}_c of 535 cm^{-3}. Note that this expression is uncertain, especially when applied to cloud types other than those observed by Martin, Johnson, and Spice (1994). In the current version of the scheme, \mu= 0 for cloud ice.

The spectral parameters N_0 and \lambda are derived from the predicted N^{\prime\prime} and q^{\prime\prime} and specified \mu:

\lambda = \left[\frac{\pi \rho N^{\prime\prime}\Gamma(\mu + 4)}{6q^{\prime\prime}\Gamma(\mu +1)}\right]^{(1/3)}

N_0 = \frac{N^{\prime\prime}\lambda^{\mu + 1}}{\Gamma(\mu +1)}

where \Gamma is the Euler gamma function. Note that (47) and (48) assume spherical cloud particles with bulk density \rho = 1000 kg m^{-3} for droplets and \rho= 500 kg m^{-3} for cloud ice following Reisner, Rasmussen, and Bruintjes (1998).

The effective size for cloud ice needed by the radiative transfer scheme is obtained directly by dividing the third and second moments of the size distribution given by (45) and accounting for differenceds in cloud ice density and that of pure ice. After rearranging terms, this yields

d_ei = \frac{3 \rho}{\lambda \rho _i}

where \rho _i = 917 kg m-2 is the bulk density of pure ice. Note that optical properties for cloud droplets are calculated using a lookup table from the N_0 and \lambda parameters. The droplet effective radius, which is used for output purposes only, is given by

r_ec = \frac{\Gamma(\mu+4)}{2\lambda\Gamma(\mu +3)}

The time evolution of q and N is determined by grid-scale advection, convective detrainment, turbulent diffusion, and several microphysical processes:

\frac{\partial N}{\partial t} + \frac{1}{\rho} \nabla \cdot [\rho \mathbf{u} N] =  \left(\frac{\partial N}{\partial t}\right)_{nuc} + \left(\frac{\partial N}{\partial t}\right)_{evap} + \left(\frac{\partial N}{\partial t}\right)_{auto} + \left(\frac{\partial N}{\partial t}\right)_{acer} + \left(\frac{\partial N}{\partial t}\right)_{accs} + \left(\frac{\partial N}{\partial t}\right)_{het} +\left(\frac{\partial N}{\partial t}\right)_{hom} + \left(\frac{\partial N}{\partial t}\right)_{mlt} + \left(\frac{\partial N}{\partial t}\right)_{mult} + \left(\frac{\partial N}{\partial t}\right)_{sed} + \left(\frac{\partial N}{\partial t}\right)_{det} +D(N)

\frac{\partial q}{\partial t} + \frac{1}{\rho} \nabla \cdot [\rho \mathbf{u} q] =  \left(\frac{\partial q}{\partial t}\right)_{cond} + \left(\frac{\partial q}{\partial t}\right)_{evap} + \left(\frac{\partial q}{\partial t}\right)_{auto} + \left(\frac{\partial q}{\partial t}\right)_{acer} + \left(\frac{\partial q}{\partial t}\right)_{accs} + \left(\frac{\partial q}{\partial t}\right)_{het} +\left(\frac{\partial q}{\partial t}\right)_{hom} + \left(\frac{\partial q}{\partial t}\right)_{mlt} + \left(\frac{\partial q}{\partial t}\right)_{mult} + \left(\frac{\partial q}{\partial t}\right)_{sed} + \left(\frac{\partial q}{\partial t}\right)_{det} +D(N)

where t is time, \mathbf{u} is the 3D wind vector, \rho is the air density, and D is the turbulent diffusion operator. The symbolic terms on the right hand side of (51) and (52) represent the grid-average microphysical source/sink terms for N and q. Note that the source/sink terms for q and N are considered separately for cloud water and ice (giving a total of four rate equations), but are generalized here using (51) and (52) for conciseness. These terms include activation of cloud condensation nuclei or deposition/condensation-freezing nucleation on ice nuclei to form droplets or cloud ice (subscript nuc; N only); ice multiplication via rime-splintering on snow (subscript mult); condensation/deposition (subscript cond; q only), evaporation/sublimation (subscript evap), autoconversion of cloud droplets and ice to form rain and snow (subscript auto), accretion of cloud droplets and ice by rain (subscript accr), accretion of cloud droplets and ice by snow (subscript accs), heterogeneous freezing of droplets to form ice (subscript het), homogeneous freezing of cloud droplets (subscript hom), melting (subscript mlt), ice multiplication (subsrcipt mult), sedimentation (subscript sed), and convective detrainment (subscript det). The formulations for these processes are detailed in section 3. Numerical aspects in solving (51) and (52) are detailed in section 4. Sub-grid cloud variability

Sub-grid variability is considered for cloud water but neglected for cloud ice and precipitation at present; furthermore, we neglect sub-grid variability of droplet number concentration for simplicity. We assume that the PDF of in-cloud cloud water, P(q_c^{\prime\prime}), follows a gamma distribution function based on observations of optical depth in marine boundary layer clouds (Barker 1996; Barker, Weilicki, and Parker 1996; McFarlane and Klein 1999):

P(q_c^{\prime\prime}) = \frac{q_c^{\prime\prime \nu -1 } \alpha^\nu}{\Gamma(\nu)} \exp^{-\alpha q_c^{\prime\prime}}

where \nu = 1/\sigma^2;\sigma^2 is the relative variance (i.e., variance divided by q_c^{\prime 2}); and \alpha = \nu
/q_c^{\prime} (q_c^{\prime} is the mean in-cloud cloud water mixing ratio). Note that this PDF is applied to all cloud types treated by the stratiform cloud scheme; the appropriateness of such a PDF for stratiform cloud types other than marine boundary layer clouds (e.g., deep frontal clouds) is uncertain given a lack of observations.

Satellite retrievals described by Barker, Weilicki, and Parker (1996) suggest that \nu >
1 in overcast conditions and \nu \sim 1 (corresponding to an exponential distribution) in broken stratocumulus. The model assumes a constant \nu = 1 for simplicity.

A major advantage of using gamma functions to represent sub-grid variability of cloud water is that the grid-average microphysical process rates can be derived in a straightforward manner as follows. For any generic local microphysical process rate M_p =
xq_c^{\prime\prime y}, replacing q_c^{\prime\prime} with P(q_c^{\prime\prime}) from (53) and integrating over the PDF yields a mean in-cloud process rate

M_p^{\prime} = x \frac{\Gamma(\nu + y)}{\Gamma(\nu)\nu^y}q_c^{\prime y}

Thus, each cloud water microphysical process rate in (51) and (52) is multiplied by a factor

E = \frac{\Gamma(\nu + y)}{\Gamma(\nu)\nu^y} Diagnostic treatment of precipitation

As described by Ghan and Easter (1992), diagnostic treatment of precipitation allows for a longer time step, since prognostic precipitation is constrained by the Courant criterion for sedimentation. Furthermore, the neglect of horizontal advection of precipitation in the diagnostic approach is reasonable given the large grid spacing (\sim 100 km) and long time step (\sim15-40 min) of GCMs. A unique aspect of this scheme is the diagnostic treatment of both precipitation mixing ratio q_p and number concentration N_p. Considering only the vertical dimension, the grid-scale time rates of change of q_p and N_p are:

\frac{\partial q_p}{\partial t} = \frac{1}{\rho} \frac{\partial(V_q \rho q_p)}{\partial z} + S_q

\frac{\partial N_p}{\partial t} = \frac{1}{\rho} \frac{\partial(V_N \rho N_p)}{\partial z} + S_N

where z is height, V_q and V_N are the mass- and number-weighted terminal fallspeeds, respectively, and S_q and S_N are the grid-mean source/sink terms for q_p and N_p, respectively:

S_q=  \left(\frac{\partial q_p}{\partial t}\right)_{auto} + \left(\frac{\partial q_p}{\partial t}\right)_{accw} + \left(\frac{\partial q_p}{\partial t}\right)_{acci} + \left(\frac{\partial q_p}{\partial t}\right)_{het} + \left(\frac{\partial q_p}{\partial t}\right)_{hom} + \left(\frac{\partial q_p}{\partial t}\right)_{mlt} + \left(\frac{\partial q_p}{\partial t}\right)_{mult} +\left(\frac{\partial q_p}{\partial t}\right)_{evap} + \left(\frac{\partial q_p}{\partial t}\right)_{coll}

S_N=  \left(\frac{\partial N_p}{\partial t}\right)_{auto} + \left(\frac{\partial N_p}{\partial t}\right)_{het} + \left(\frac{\partial N_p}{\partial t}\right)_{hom} + \left(\frac{\partial N_p}{\partial t}\right)_{mlt} + \left(\frac{\partial N_p}{\partial t}\right)_{evap} + \left(\frac{\partial N_p}{\partial t}\right)_{self} +\left(\frac{\partial N_p}{\partial t}\right)_{coll}

The symbolic terms on the right-hand sides of (58) and (59) are autoconversion (subscript auto), accretion of cloud water (subscript accw), accretion of cloud ice (subscript acci), heterogeneous freezing (subscript het), homogeneous freezing (subscript hom), melting (subscript mlt), ice multiplication via rime splintering (subsrcipt mult; qp only), evaporation (subscript evap), and self-collection (subscript self; collection of rain drops by other rain drops, or snow crystals by other snow crystals; Np only), and collection of rain by snow (subscript coll). Formulations for these processes are described in section 3.

In the diagnostic treatment , (\partial q_p / \partial t ) =0 and (\partial N_p / \partial t ) =0 . This allows (56) and (57) to be expressed as a function of z only. The q_p and N_p are therefore determined by discretizing and numerically integrating (56) and (57) downward from the top of the model atmosphere following Ghan and Easter (1992):

\rho_{a,k} V_{q,k} q_{p,k} = \rho_{a,k+1} V_{q,k+1} q_{p,k+1} + \frac{1}{2} [ \rho_{a,k} S_{q,k} \delta Z_{k} +  \rho_{a,k+1} S_{q,k+1} \delta Z_{k+1}]

\rho_{a,k} V_{N,k} N_{p,k} = \rho_{a,k+1} V_{N,k+1} N_{p,k+1} + \frac{1}{2} [ \rho_{a,k} S_{N,k} \delta Z_{k} +  \rho_{a,k+1} S_{N,k+1} \delta Z_{k+1}]

where k is the vertical level (increasing with height, i.e., k+1 is the next vertical level above k). Since V_{q,k}, S_{q,k}, V_{N,k}, and S_{N,k} depend on q_{p,k} and N_{p,k}, (60) and (61) must be solved by iteration or some other method. The approach of Ghan and Easter (1992) uses values of q_{p,k} and N_{p,k} from the previous time step as provisional estimates in order to calculate V_{q,k}, V_{N,k}, S_{p,k}, and S_{N,k}. “Final” values of q_{p,k} and N_{p,k} are calculated from these values of V_{q,k}, V_{N,k}, S_{q,k} and S_{N,k} using (60) and (61). Here we employ another method that obtains provisional values of q_{p,k} and N_{p,k} from (60) and (61) assuming V_{q,k} \sim V_{q,k+1} and V_{N,k} \sim V_{N,k+1}. It is also assumed that all source/sink terms in S_{q,k} and S_{N,q} can be approximated by the values at k+1, except for the autoconversion, which can be obtained directly at the k level since it does not depend on q_{p,k} or N_{p,k}. If there is no precipitation flux from the level above, then the provisional q_{p.k} and N_{p,k} are calculated using autoconversion at the k level in S_{q,k} and S_{N,k}; V_{q,k} and V_{N,k} are estimated assuming newly-formed rain and snow particles have fallspeeds of 0.45 m/s for rain and 0.36 m/s for snow.

Rain and snow are considered separately, and both may occur simultaneously in supercooled conditions (hereafter subscript p for precipitation is replaced by subscripts r for rain and s for snow). The rain/snow particle size distributions are given by (45), with the shape parameter \mu = 0, resulting in Marshall-Palmer (exponential) size distributions. The size distribution parameters \lambda and N_0 are similarly given by (47) and (48) with \mu = 0. The bulk particle density (parameter \rho in (47)) is \rho = 1000 kg m^{-3} for rain and \rho = 100 kg m^{-3} for snow following Reisner, Rasmussen, and Bruintjes (1998). Cloud and precipitation particle terminal fallspeeds

The mass- and number-weighted terminal fallspeeds for all cloud and precipitation species are obtained by integration over the particle size distributions with appropriate weighting by number concentration or mixing ratio:

V_N = \frac{\int_0^\infty \left(\frac{\rho_a}{\rho_{a0}}\right)^{0.54} aD^b \phi (D) \mathrm{d}D}{\int_0^\infty \phi (D) \mathrm{d}D} = \frac{\left(\frac{\rho_a}{\rho_{a0}}\right)^{0.54} a\Gamma ( 1 + b + \mu)}{\lambda^b \Gamma (\mu + 1)}

V_q = \frac{\int_0^\infty \frac{\pi \rho}{6} \left(\frac{\rho_a}{\rho_{a0}}\right)^{0.54} aD^{b+3} \phi (D) \mathrm{d}D}{\int_0^\infty \frac{\pi \rho}{6} D^3 \phi (D) \mathrm{d}D} = \frac{\left(\frac{\rho_a}{\rho_{a0}}\right)^{0.54} a\Gamma ( 4 + b + \mu)}{\lambda^b \Gamma (\mu + 4)}

where \rho^{a0} is the reference air density at 850 mb and 0 C, a and b are empirical coefficients in the diameter-fallspeed relationship V=aD^b , where V is terminal fallspeed for an individual particle with diameter D. The air density correction factor is from Heymsfield and Banseemer (2007). V_N and V_q are limited to maximum values of 9.1 m/s for rain and 1.2 m/s for snow. The a and b coefficients for each hydrometeor species are given in Table 2. Note that for cloud water fallspeeds, sub-grid variability of q is considered by appropriately multiplying the V_N and V_q by the factor E given by (55). Ice Cloud Fraction

Several modifications have been made to the determination of diagnostic fractional cloudiness in the simulations. The ice and liquid cloud fractions are now calculated separately. Ice and liquid cloud can exist in the same grid box. Total cloud fraction, used for radiative transfer, is determined assuming maximum overlap between the two.

The diagnostic ice cloud fraction closure is constructed using a total water formulation of the Slingo (1987) scheme. There is an indirect dependence of prognostic cloud ice on the ice cloud fraction since the in-cloud ice content is used for all microphysical processes involving ice. The new formulation of ice cloud fraction (CF_i) is calculated using relative humidity (RH) based on total ice water mixing ratio, including the ice mass mixing ratio (q_i) and the vapor mixing ratio (q_v). The RH based on total ice water (RH_{ti}) is then RH_{ti} = (q_v+q_i)/q_{sat} where q_{sat} is the saturation vapor mixing ratio over ice. Because this is for ice clouds only, we do not include q_l (liquid mixing ratio). We have tested that the inclusion of q_l does not substantially impact the scheme (since there is little liquid present in this regime).

Ice cloud fraction is then given by CF_i= min(1,RH_d^2) where

RH_d = max\left(0,\frac{RH_{ti} - RHi_{min}}{RHi_{max}-RHi_{min}}\right)

RHi_{max} and RHi_{min} are prescribed maximum and minimum threshold humidities with respect to ice, set at RHi_{max}=1.1 and RHi_{min}=0.8. These are adjustable parameters that reflect assumptions about the variance of humidity in a grid box. The scheme is not very sensitive to RHi_{min}. RHi_{max} affects the total ice supersaturation and ice cloud fraction.

With RHi_{max} = 1 and q_i = 0 the scheme reduces to the Slingo (1987) scheme. RH_{ti} is preferred over RH in RH_d because when q_i increases due to vapor deposition, it reduces q_v, and without any precipitation or sedimentation the decrease in RH would change diagnostic cloud fraction, whereas RH_{ti} is constant.

6.5.2. Radiative Treatment of Ice

The simulations use a self consistent treatment of ice in the radiation code. The radiation code uses as input the prognostic effective diameter of ice from the cloud microphysics (give eq. # from above). Ice cloud optical properties are calculated based on the modified anomalous diffraction approximation (MADA), described in Mitchell (2000; Mitchell 2002) and Mitchell et al. (2006). The mass-weighted extinction (volume extinction coefficient/ice water content) and the single scattering albedo, \omega_0, are evaluated using a look-up table. For solar wavelengths, the asymmetry parameter g is determined as a function of wavelength and ice particle size and shape as described in Mitchell, Macke, and Liu (1996a) and Nousiainen and McFarquhar (2004) for quasi-spherical ice crystals. For terrestrial wavelengths, g was determined following Yang et al. (2005). An ice particle shape recipe was assumed when calculating these optical properties. The recipe is described in Mitchell, d’Entremont, and Lawson (2006) based on mid-latitude cirrus cloud data from Lawson et al. (2006) and consists of 50% quasi-spherical and 30% irregular ice particles, and 20% bullet rosettes for the cloud ice (i.e. small crystal) component of the ice particle size distribution (PSD). Snow is also included in the radiation code, using the diagnosed mass and effective diameter of falling snow crystals (MG2008). For the snow component, the ice particle shape recipe was based on the crystal shape observations reported in Lawson et al. (2006) at -45:math:^circC: 7% hexagonal columns, 50% bullet rosettes and 43% irregular ice particles.

6.5.3. Formulations for the microphysical processes Activation of cloud droplets

Activation of cloud droplets, occurs on a multi-modal lognormal aerosol size distribution based on the scheme of Abdul-Razzak and Ghan (2000). Activation of cloud droplets occurs if N_c decreases below the number of active cloud condensation nuclei diagnosed as a function of aerosol chemical and physical parameters, temperature, and vertical velocity (see Abdul-Razzak and Ghan (2000)), and if liquid condensate is present. We use the existing Nc as a proxy for the number of aerosols previously activated as droplets since the actual number of activated aerosols is not tracked as a prognostic variable from time step to time step (for coupling with prescribed aerosol scheme). This approach is similar to that of Lohmann et al. (1999).

Since local rather than grid-scale vertical velocity is needed for calculating droplet activation, a sub-grid vertical velocity w_{sub} is derived from the square root of the Turbulent Kinetic Energy (TKE) following Morrison and Pinto (2005):

w_{sub} = \sqrt{\frac{2}{3} TKE}

where TKE is defined using a steady state energy balance eqn (62) and (70) in Bretherton and Park (2009))

In regions with weak turbulent diffusion, a minimum sub-grid vertical velocity of 10 cm/s is assumed. Some models use the value of wÕ at cloud base to determine droplet activation in the cloud layer (e.g., Lohmann et al. (1999)); however, because of coarse vertical and horizontal resolution and difficulty in defining the cloud base height in GCMÕs, we apply the w_{sub} calculated for a given layer to the droplet activation for that layer. Note that the droplet number may locally exceed the number activated for a given level due to advection of Nc. Some models implicitly assume that the timescale for droplet activation over a cloud layer is equal to the model time step (e.g., Lohmann et al. (1999)), which could enhance sensitivity to the time step. This timescale can be thought of as the timescale for recirculation of air parcels to regions of droplet activation (i.e., cloud base), similar to the timescale for large eddy turnover; here, we assume an activation timescale of 20 min. Primary ice nucleation

Ice crystal nucleation is based on Liu et al. (2007), which includes homogeneous freezing of sulfate competing with heterogeneous immersion freezing on mineral dust in ice clouds (with temperatures below -37:math:^circC) (Liu and Penner 2005). Because mineral dust at cirrus levels is very likely coated (Wiacek and Peter 2009), deposition nucleation is not explicitly included in this work for pure ice clouds. Immersion freezing is treated for cirrus (pure ice), but not for mixed phase clouds. The relative efficiency of immersion versus deposition nucleation in mixed phase clouds is an unsettled problem, and the omission of immersion freezing in mixed phase clouds may not be appropriate (but is implicitly included in the deposition/condensation nucleation: see below). Deposition nucleation may act at temperatures lower than immersion nucleation (i.e. T<-25:math:^circC) (Field et al. 2006), and immersion nucleation has been inferred to dominate in mixed phase clouds (Ansmann and others 2008; Ansmann et al. 2009; Hoose and Kristjansson 2010). We have not treated immersion freezing on soot because while Liu and Penner (2005) assumed it was an efficient mechanism for ice nucleation, more recent studies (Kärcher et al. 2007) indicate it is still highly uncertain.

In the mixed phase cloud regime (-37:math:<T<0^\circC), deposition/condensation nucleation is considered based on Meyers, DeMott, and Cotton (1992), with a constant nucleation rate for T<-20:math:^circC. The Meyers, DeMott, and Cotton (1992) parameterization is assumed to treat deposition/condensation on dust in the mixed phase. Since it is based on observations taken at water saturation, it should include all important ice nucleation mechanisms (such as the immersion and deposition nucleation discussed above) except contact nucleation, though we cannot distinguish all the specific processes. Meyers, DeMott, and Cotton (1992) has been shown to produce too many ice nuclei during the Mixed Phase Arctic Clouds Experiment (MPACE) by Prenni et al. (2007). Contact nucleation by mineral dust is included based on Young (1974) and related to the coarse mode dust number. It acts in the mixed phase where liquid droplets are present and and includes Brownian diffusion as well as phoretic forces. Hallet-Mossop secondary ice production due to accretion of drops by snow is included following Cotton et al. (1986).

In the Liu and Penner (2005) scheme, the number of ice crystals nucleated is a function of temperature, humidity, sulfate, dust and updraft velocity, derived from fitting the results from cloud parcel model experiments. A threshold RH_w for homogeneous nucleation was fitted as a function of temperature and updraft velocity (see Liu et al. (2007), equation 6). For driving the parameterization, the sub-grid velocity for ice (w_{sub}) is derived following ewuation (64). A minimum of 0.2 m s^{-1} is set for ice nucleation.

It is also implicitly assumed that there is some variation in humidity over the grid box. For purposes of ice nucleation, nucleation rates for a grid box are estimated based on the ‘most humid portion’ of the grid-box. This is assumed to be the grid box average humidity plus a fixed value (20% RH). This implies that the ‘local’ threshold supersaturation for ice nucleation will be reached at a grid box mean value 20% lower than the RH process threshold value. This represents another gross assumption about the RH variability in a model grid box and is an adjustable parameter in the scheme. In the baseline case, sulfate for homogeneous freezing is taken as the portion of the Aitken mode particles with radii greater than 0.1 microns, and was chosen to better reproduce observations (this too can be adjusted to alter the balance of homogeneous freezing). The size represents the large tail of the Aitken mode. In the upper troposphere there is little sulfate in the accumulation mode (it falls out), and almost all sulfate is in the Aitken mode. Deposition/sublimation of ice

Several cases are treated below that involve ice deposition in ice-only clouds or mixed-phase clouds in which all liquid water is depleted within the time step. Case [1] Ice only clouds in which q_v > q_{vi}* where q_v is the grid mean water vapor mixing ratio and q_{vi}* is the local vapor mixing ratio at ice saturation (q_{sat}). Case [2] is the same as case [1] (q_v > q_{vi}*) but there is existing liquid water depleted by the Bergeron-Findeisen process (ber). Case [3], liquid water is depleted by the Bergeron-Findeisen process and the local liquid is less than local ice saturation (q_v* \le q_{vi}*). In Case [4] q_v < q_{vi}* so sublimation of ice occurs.

Case [1]: If the ice cloud fraction is larger than the liquid cloud fraction (including grid cells with ice but no liquid water), or if all new and existing liquid water in mixed-phase clouds is depleted via the Bergeron-Findeisen process within the time step, then vapor depositional ice growth occurs at the expense of water vapor. In the case of a grid cell where ice cloud fraction exceeds liquid cloud fraction, vapor deposition in the pure ice cloud portion of the cell is calculated similarly to eq. [21] in MG08:

\left(\frac{\partial q_i}{\partial t }\right)_{dep}=\frac{( q_v-q_{vi}*)}{\Gamma_p \tau}, q_v > q_{vi}*

where \Gamma_p = 1 + \frac{L_s}{c_p}\frac{dq_{vi}}{dT} is the psychrometric correction to account for the release of latent heat, L_s is the latent heat of sublimation, c_p is the specific heat at constant pressure, \frac{dq_{vi}}{dT} is the change of ice saturation vapor pressure with temperature, and \tau is the supersaturation relaxation timescale associated with ice deposition given by eq. 22 in MG08 (a function of ice crystal surface area and the diffusivity of water vapor in air). The assumption for pure ice clouds is that the in-cloud vapor mixing ratio for deposition is equal to the grid-mean value. The same assumption is used in Liu et al. (2007), and while it is uncertain, it is the most straightforward. Thus we do not consider sub-grid variability of water vapor for calculating vapor deposition in pure ice-clouds.

The form of the deposition rate in equation (65) differs from that used by Rotstayn, Ryan, and Katzfey (2000) and Liu et al. (2007) because they considered the increase in ice mixing ratio q_i due to vapor deposition during the time step, and formulated an implicit solution based on this consideration (see eq. (5) in Rotstayn, Ryan, and Katzfey (2000)). However, these studies did not consider sinks for the ice due to processes such as sedimentation and conversion to precipitation when formulating their implicit solution; these sink terms may partially (or completely) balance the source for the ice due to vapor deposition. Thus, we use a simple explicit forward-in-time solution that does not consider changes of q_i within the microphysics time step.

Case [2]: When all new and existing liquid water is depleted via the Bergeron-Findeisen process (ber) within the time step, the vapor deposition rate is given by a weighted average of the values for growth in mixed phase conditions prior to the depletion of liquid water (first term on the right hand side) and in pure ice clouds after depletion (second term on the right hand side):

\left(\frac{\partial q_i}{\partial t }\right)_{dep}=\frac{q_c*}{\Delta t} + \left(1- \frac{q_c*}{\Delta t}\left(\frac{\partial q_i}{\partial t}\right)_{ber}^{-1}\right)\left(\frac{( q_v*-q_{vi}*)}{\Gamma_p \tau}\right), q_v > q_{vi}*

where q_c* is the sum of existing and new liquid condensate mixing ratio, \Delta t is the model time step, \left(\frac{\partial q_i}{\partial t}\right)_{ber} is the ice deposition rate in the presence of liquid water (i.e., assuming vapor mixing ratio is equal to the value at liquid saturation) as described above, and q_v* is an average of the grid-mean vapor mixing ratio and the value at liquid saturation.

Case [3]: If q_v* \leq q_{vi}* then it is assumed that no additional ice deposition occurs after depletion of the liquid water. The deposition rate in this instance is given by:

\left(\frac{\partial q_i}{\partial t}\right)_{dep}=\left(\frac{q_c*}{\Delta t}\right), q_v* \leq q_{vi}*

Case [4]: Sublimation of pure ice cloud occurs when the grid-mean water vapor mixing ratio is less than value at ice saturation. In this case the sublimation rate of ice is given by:

\left(\frac{\partial q_i}{\partial t}\right)_{sub}=\frac{( q_v-q_{vi}*)}{\Gamma_p \tau}, q_v < q_{vi}*

Again, the use of grid-mean vapor mixing ratio in equation (68) follows the assumption of Liu et al. (2007) that the in-cloud q_v is equal to the grid box mean in pure ice clouds. Grid-mean deposition and sublimation rates are given by the in-cloud values for pure ice or mixed-phase clouds described above, multiplied by the appropriate ice or mixed-phase cloud fraction. Finally, ice deposition and sublimation are limited to prevent the grid-mean mixing ratio from falling below the value for ice saturation in the case of deposition and above this value in the case of sublimation.

Cloud water condensation and evaporation are given by the bulk closure scheme within the cloud macrophysics scheme, and therefore not described here. Conversion of cloud water to rain

Autoconversion of cloud droplets and accretion of cloud droplets by rain is given by a version of the Khairoutdinov and Kogan (2000) scheme that is modified here to account for sub-grid variability of cloud water within the cloudy part of the grid cell as described previously in section 2.1. Note that the Khairoutdinov and Kogan scheme was originally developed for boundary layer stratocumulus, but is applied here to all stratiform cloud types.

The grid-mean autoconversion and accretion rates are found by replacing the qc in Eqs. (29) and (33) of Khairoutdinov and Kogan (2000) with P(q_c^{\prime\prime}) given by equation (53) here, integrating the resulting expressions over the cloud water PDF, and multiplying by the cloud fraction. This yields

\left(\frac{\partial q_c}{\partial t}\right)_{auto} = -F_{cld} \frac{\Gamma(\nu + 2.47)}{\Gamma(\nu)\nu^{2.47}} 1350 q_c^{\prime 2.47} N_c^{\prime -1.79}

\left(\frac{\partial q_c}{\partial t}\right)_{accr} = -F_{cld} \frac{\Gamma(\nu + 1.15)}{\Gamma(\nu)\nu^{1.15}} 67 (q_c^{\prime} q_r^{\prime})^{1.15}

The changes in qr due to autoconversion and accretion are given by (\partial q_r / \partial t)_{auto} = -(\partial q_c / \partial t)_{auto} and (\partial q_r / \partial t)_{accr} = -(\partial q_c / \partial t)_{accr}. The changes in N_c and N_r due to autoconversion and accretion (\partial N_c / \partial t)_{auto}, (\partial N_r / \partial t)_{auto}, (\partial N_c / \partial t)_{accr}, are derived from Eqs. (32) and (35) in Khairoutdinov and Kogan (2000). Since accretion is nearly linear with respect to q_c, sub-grid variability of cloud water is much less important for accretion than it is for autoconversion.

Note that in the presence of a precipitation flux into the layer from above, new drizzle drops formed by cloud droplet autoconversion would be accreted rapidly by existing precipitation particles (rain or snow) given collection efficiencies near unity for collision of drizzle with rain or snow (e.g., Pruppacher and Klett (1997)). This may be especially important in models with low vertical resolution, since they cannot resolve the rapid growth of precipitation that occurs over distances much less than the vertical grid spacing. Thus, if the rain or snow mixing ratio in the next level above is greater than 10-6 g kg-1, we assume that autoconversion produces an increase in rain mixing ratio but not number concentration (since the newly-formed drops are assumed to be rapidly accreted by the existing precipitation). Otherwise, autoconversion results in a source of both rain mixing ratio and number concentration. Conversion of cloud ice to snow

The autoconversion of cloud ice to form snow is calculated by integration of the cloud ice mass- and number-weighted size distributions greater than some specified threshold size, and transferring the resulting mixing ratio and number into the snow category over some specified timescale, similar to Ferrier (1994). The grid-scale changes in qi and Ni due to autoconversion are

\left(\frac{\partial q_i}{\partial t}\right)_{auto} = -F \frac{\pi \rho_i N_{0i}}{6 \tau_{auto}}  \left[ \frac{D_{cs}^3}{\lambda_i} +  \frac{3D_{cs}^2}{\lambda_i^2}  + \frac{6D_{cs}}{\lambda_i^3} +\frac{6D}{\lambda_i^4} \right] \exp^{- \lambda_i D_{cs}}

\left(\frac{\partial N_i}{\partial t}\right)_{auto} = -F \frac{N_{0i}}{\lambda_i \tau_{auto}}  \exp^{- \lambda_i D_{cs}}

where D_{cs} = 200 \mum is the threshold size separating cloud ice from snow, \rho_i is the bulk density of cloud ice, and \tau_{auto} = 3 min is the assumed autoconversion timescale. Note that this formulation assumes the shape parameter \mu = 0 for the cloud ice size distribution; different formulation must be used for other values of \mu. The changes in q_s and N_s due to autoconversion are given by (\partial q_s / \partial t)_{auto} = -(\partial q_i / \partial t)_{auto} and (\partial N_s / \partial t)_{auto} = -(\partial N_i / \partial t)_{auto} .

Accretion of q_i and N_i by snow (\partial q_i / \partial t)_{accs}, (\partial N_i/ \partial t)_{accs}, (\partial q_s / \partial t)_{acci}, and (\partial q_s / \partial t)_{acci} = -(\partial q_i / \partial t)_{accs} , are given by the continuous collection equation following Lin, Farley, and Orville (1983), which assumes that the fallspeed of snow \gg cloud ice fallspeed. The collection efficiency for collisions between cloud ice and snow is 0.1 following Reisner, Rasmussen, and Bruintjes (1998). Newly- formed snow particles formed by cloud ice autoconversion are not assumed to be rapidly accreted by existing snowflakes, given aggregation efficiencies typically much less than unity (e.g., Field, Heymsfield, and Bansemer (2007)). Other collection processes

The accretion of q_c and N_c by snow (\partial q_c / \partial t)_{accs}, (\partial N_c/ \partial t)_{accs}, and (\partial q_s / \partial t)_{accw} = -(\partial q_c / \partial t)_{accs} are given by the continuous collection equation. The collection efficiency for droplet-snow collisions is a function of the Stokes number following Thompson, Rasmussen, and Manning (2004) and thus depends on droplet size. Self-collection of snow, (\partial N_s/ \partial t)_{self} follows Reisner, Rasmussen, and Bruintjes (1998) using an assumed collection efficiency of 0.1. Self-collection of rain(\partial N_r/ \partial t)_{self} follows Beheng (1994). Collisions between rain and cloud ice, cloud droplets and cloud ice, and self-collection of cloud ice are neglected for simplicity. Collection of q_r and N_r by snow in subfreezing conditions, (\partial q_r / \partial t)_{coll} = -(\partial q_s / \partial t)_{coll} and (\partial N_r/ \partial t)_{coll}, is given by Ikawa and Saito (1990) assuming collection efficiency of unity. Freezing of cloud droplets and rain and ice multiplication

Heterogeneous freezing of cloud droplets and rain to form cloud ice and snow, respectively, occurs by immersion freezing following Bigg (1953), which has been utilized in previous microphysics schemes (e.g., Reisner, Rasmussen, and Bruintjes (1998), see Eq. A.22, A.55, A.56; Morrison, Curry, and Khvorostyanov (2005); Thompson et al. (2008)). Here the freezing rates are integrated over the mass- and number-weighted cloud droplet and rain size distributions and the impact of sub-grid cloud water variability is included as described previously. Homogeneous freezing of cloud droplets to form cloud ice occurs instantaneously at -40:math:^circC. All rain is assumed to freeze instantaneously at -5:math:^circC.

Contact freezing of cloud droplets by mineral dust is included based on Young (1974) and related to the coarse mode dust number. It acts in the mixed phase where liquid droplets are present and includes Brownian diffusion as well as phoretic forces. Hallet-Mossop ice multiplication (secondary ice production) due to accretion of drops by snow is included following Cotton et al. (1986). This represents a sink term for snow mixing ratio and source term for cloud ice mixing ratio and number concentration. Melting of cloud ice and snow

For simplicity, detailed formulations for heat transfer during melting of ice and snow are not included. Melting of cloud ice occurs instantaneously at 0^\circC. Melting of snow occurs instantaneously at +2^\circC. We have tested the sensitivity of both single- column and global results to changing the specified snow melting temperature from +2^\circ to 0^\circC and found no significant changes. Evaporation/sublimation of precipitation

Evaporation of rain and sublimation of snow, (\partial q_s / \partial t)_{evap} and (\partial q_r / \partial t)_{evap}, are given by diffusional mass balance in subsaturated conditions Lin, Farley, and Orville (1983), including ventilation effects. Evaporation of precipitation occurs within the region of the grid cell containing precipitation but outside of the cloudy region. The fraction of the grid cell with evaporation of precipitation is therefore , where F_{pre} is the precipitation fraction. F_{pre} is calculated assuming maximum cloud overlap between vertical levels, and neglecting tilting of precipitation shafts due to wind shear (F_{pre} = F_{cld} at cloud top). The out-of-cloud water vapor mixing ratio is given by

q_{clr} = \frac{q_v - F_{cld} q_s(T)}{1-F_{cld}}, F_{cld} < 1

where q_s(T) is the in-cloud water vapor mixing ratio after bulk condensation/evaporation of cloud water and ice as described previously. As in the older CAM3 microphysics parameterization, condensation/deposition onto rain/snow is neglected. Following Morrison, Curry, and Khvorostyanov (2005), the evaporation/sublimation of N_r and N_s, (\partial N_r / \partial t)_{evap} and (\partial N_s / \partial t)_{evap} , is proportional to the reduction of q_r and q_s during evaporation/sublimation. Sedimentation of cloud water and ice

The time rates of change of q and N for cloud water and cloud ice due to sedimentation, (\partial q_c / \partial t)_{sed} , (\partial q_i / \partial t)_{sed}, (\partial N_c / \partial t)_{sed}, and (\partial N_i / \partial t)_{sed} , are calculated with a first-order forward-in-time-backward-in-space scheme. Numerical stability for cloud water and ice sedimentation is ensured by sub-stepping the time step, although these numerical stability issues are insignificant for cloud water and ice because of the low terminal fallspeeds (\ll 1 m/s). We assume that the sedimentation of cloud water and ice results in evaporation/sublimation when the cloud fraction at the level above is larger than the cloud fraction at the given level (i.e., a sedimentation flux from cloudy into clear regions), with the evaporation/condensate rate proportional to the difference in cloud fraction between the levels. Convective detrainment of cloud water and ice

The ratio of ice to total cloud condensate detrained from the convective parameterizations, Fdet, is a linear function of temperature between -40:math:^circ C and -10:math:^circ C; F_{det} = 1 at T < -40:math:^circ C, and Fdet = 0 at T > -10:math:^circ C. Detrainment of number concentration is calculated by assuming a mean volume radius of 8 and 32 micron for droplets and cloud ice, respectively. Numerical considerations

To ensure conservation of both q and N for each species, the magnitudes of the various sink terms are reduced if the provisional q and N are negative after stepping forward in time. This approach ensures critical water and energy balances in the model, and is similar to the approach employed in other bulk microphysics schemes (e.g., Reisner, Rasmussen, and Bruintjes (1998). Inconsistencies are possible because of the separate treatments for N and q, potentially leading to unrealistic mean cloud and precipitation particle sizes. For consistency, N is adjusted if necessary so that mean (number-weighted) particle diameter ( ) remains within a specified range of values for each species. Limiting to a maximum mean diameter can be thought of as an implicit parameterization of particle breakup.

For the diagnostic precipitation, the source terms for q and N at a given vertical level are adjusted if necessary to ensure that the vertical integrals of the source terms (from that level to the model top) are positive. In other words, we ensure that at any given level, there isnÕt more precipitation removed (both in terms of mixing ratio and number concentration) than is available falling from above (this is also the case in the absence of any sources/sinks at that level). This check and possible adjustment of the precipitation and cloud water also ensures conservation of the total water and energy. Our simple adjustment procedure to ensure conservation could potentially result in sensitivity to time step, although as described in section 3, time truncation errors are minimized with appropriate sub-stepping.

Melting rates of cloud ice and snow are limited so that the temperature of the layer does not decrease below the melting point (i.e., in this instance an amount of cloud ice or snow is melted so that the temperature after melting is equal to the melting point). A similar approach is applied to ensure that homogeneous freezing does increase the temperature above homogeneous freezing threshold.

6.6. Parameterization of Cloud Fraction

Cloud amount (or cloud fraction), and the associated optical properties, are evaluated via a diagnostic method in CAM6.0. The basic approach is similar to that employed in CAM3. The diagnosis of cloud fraction is a generalization of the scheme introduced by Slingo (1987), with variations described in Hack et al. (1993; Kiehl et al. 1998), and Rasch and Kristjánsson (1998). Cloud fraction depends on relative humidity, atmospheric stability, water vapor and convective mass fluxes. Three types of cloud are diagnosed by the scheme: low-level marine stratus ({{\mathcal C}}_{st}), convective cloud ({{\mathcal C}}_{cir}), and layered cloud ({{\mathcal C}}_c). Layered clouds form when the relative humidity exceeds a threshold value which varies according to pressure. The diagnoses of these cloud types are described in more detail in the following paragraphs.

Marine stratocumulus clouds are diagnosed using an empirical relationship between marine stratocumulus cloud fraction and the stratification between the surface and 700mb derived by Klein and Hartmann (1993). The CCM3 parameterization for stratus cloud fraction over oceans has been replaced with

{{\mathcal C}}_{st} = \min\biggl\lbrace 1., \max\bigl[0.,
            (\theta_{700}-\theta_s)*.057-.5573 \bigr] \biggr\rbrace

\theta_{700} and \theta_s are the potential temperatures at 700 mb and the surface, respectively. The cloud is assumed to be located in the model layer below the strongest stability jump between 750 mb and the surface. If no two layers present a stability in excess of -0.125 K/mb, no cloud is diagnosed. In areas where terrain filtering has produced non-zero ocean elevations, the sea surface temperature used for this computation is reduced from the true sea surface elevation to the model surface elevation according to the lapse rate of the U.S. Standard Atmosphere (-6.5 ^\circC/km).

Convective cloud fraction in the model is related to updraft mass flux in the deep and shallow cumulus schemes according to a functional form suggested by Xu and Krueger (1991):

{{\mathcal C}}_{shallow} = k_{1,shallow} ln(1.0+k_2 M_{c,shallow},0.3 )

{{\mathcal C}}_{deep}     = k_{1_deep} ln(1.0+k_2 M_{c,deep},0.6 )

where k_{1,shallow} and k_{1,deep} are adjustable parameters given in Appendix [adjustableparameters], k_2 = 500, and M_c is the convective mass flux at the given model level. The combined convective cloud fraction C_{cir}, is further approximated as

{{\mathcal C}}_{cir} ={\rm min} \left(0.8, {{\mathcal C}}_{shallow} + {{\mathcal C}}_{deep} \right).

The remaining cloud types are diagnosed on the basis of relative humidity, according to

{{\mathcal C}}_c = \left( \frac{RH - RH_{\min}} {1 - RH_{\min}} \right)^{2}

The threshold relative humidity RH_{\min} is set according to pressure p as

RH_{\min} =
    RH_{\min}^{low} & p > 750 mb \\ RH_{\min}^{low} +
    (RH_{\min}^{high}-RH_{\min}^{low})\frac{p - 750 mb}{p_{mid}-750
    mb} & p_{mid} < p < 750 mb \\ RH_{\min}^{high} & p < p_{mid}

where p_{mid} in an adjustable parameter denoting the minimum pressure for a linear ramp from the low cloud threshold to the high cloud threshold. At present this ramp is implemented only in one configuration of the model; other versions have a step function achieved by setting p_{mid} = 750 mb. RH_{\min}^{low}, RH_{\min}^{high}, and p_{mid} are specified as in Appendix [adjustableparameters]. Also, the parameter RH_{\min}^{low} is adjusted over land by -0.10. This distinction is made to account for the increased sub-grid-scale variability of the water vapor field due to inhomogeneities in the land surface properties and subgrid orographic effects. In CAM6.0 a modification is made to the layered cloud fraction to prevent extensive cloud decks that have zero or near-zero condensate in cold climates. The adjustment is based on Vavrus and Waliser (2008) and reduces the diagnosed low cloud fraction if grid mean water vapor is less than 3 g/kg according to

C_c^{low} = C_c^{low}max(0.15,min(1,{{\mathcal C}}{q_{v}}{0.003}))

This modifiation has a significant impact during winter time in high latitude regions.

The total cloud {{\mathcal C}}_{tot} within each volume is then diagnosed as

{{\mathcal C}}_{tot} = {\rm min} \left( {\rm max}\left( {{\mathcal C}}_{c},{{\mathcal C}}_{st}\right)
                               + {{\mathcal C}}_{cir},  1 \right) .

This is equivalent to a maximum overlap assumption of cloud types within each gridbox. The condensate value is assumed uniform within any and all types of cloud within each grid box. In order to prevent inconsistent values of total cloud fraction and condensate being passed to the radiation parameterization in the CAM6.0 a second updated cloud fraction calculation is performed. Cloud fraction and therefore relative humidity are now consitent with condensate values on entry to the radiation parameterization. This vastly reduces the frequency of ’empty clouds’ seen in the CAM3, where cloud condesate was zero and yet cloud had been diagnosed to exists due to an inconsistant relative humidity.

6.7. Aerosols

Two different modal representations of the aerosol were implemented in CAM5. A 7-mode version of the modal aerosol model (MAM-7) serves as a benchmark for the further simplification. It includes Aitken, accumulation, primary carbon, fine dust and sea salt and coarse dust and sea salt modes (Predicted species for interstitial and cloud-borne component of each aerosol mode in MAM-7. Standard deviation for each mode is 1.6 (Aitken), 1.8 (accumulation), 1.6 (primary carbon), 1.8 (fine and coarse soil dust), and 2.0 (fine and coarse sea salt)). Within a single mode, for example the accumulation mode, the mass mixing ratios of internally-mixed sulfate, ammonium, secondary organic aerosol (SOA), primary organic matter (POM) aged from the primary carbon mode, black carbon (BC) aged from the primary carbon mode, sea salt, and the number mixing ratio of accumulation mode particles are predicted. Primary carbon (OM and BC) particles are emitted to the primary carbon mode and aged to the accumulation mode due to condensation of \mathrm{H_{2}SO_{4}}, \mathrm{NH_{3}} and SOA (gas) and coagulation with Aitken and accumulation mode (see section below).

Aerosol particles exist in different attachment states. We mostly think of aerosol particles that are suspended in air (either clear or cloudy air), and these are referred to as interstitial aerosol particles. Aerosol particles can also be attached to (or contained within) different hydrometeors, such as cloud droplets. In CAM5, the interstitial aerosol particles and the aerosol particles in stratiform cloud droplets (referred to as cloud-borne aerosol particles) are both explicitly predicted, as in (???). The interstitial aerosol particle species are stored in the {q} array of the state variable and are transported in 3 dimensions. The cloud-borne aerosol particle species are stored in the {qqcw} array of the physics buffer and are not transported (except for vertical turbulent mixing), which saves computer time but has little impact on their predicted values (???).

Aerosol water mixing ratio associated with interstitial aerosol for each mode is diagnosed following Kohler theory (see water uptake below), assuming equilibrium with the ambient relative humidity. It also is not transported in 3 dimensions, and is held in the {qaerwat} array of the physics buffer.

The size distributions of each mode are assumed to be log-normal, with the mode dry or wet radius varying as number and total dry or wet volume change, and standard deviation prescribed as given in Predicted species for interstitial and cloud-borne component of each aerosol mode in MAM-7. Standard deviation for each mode is 1.6 (Aitken), 1.8 (accumulation), 1.6 (primary carbon), 1.8 (fine and coarse soil dust), and 2.0 (fine and coarse sea salt). The total number of transported aerosol species is 31 for MAM-7. The transported gas species are \mathrm{SO_{2}}, \mathrm{H_{2}O_{2}}, DMS, \mathrm{H_{2}SO_{4}}, \mathrm{NH_{3}}, and SOA (gas).

For long-term (multiple century) climate simulations a 3-mode version of MAM (MAM-3) is also developed which has only Aitken, accumulation and coarse modes (aero_species_mam3). For MAM-3 the following assumptions are made: (1) primary carbon is internally mixed with secondary aerosol by merging the primary carbon mode with the accumulation mode; (2) the coarse dust and sea salt modes are merged into a single coarse mode based on the assumption that the dust and sea salt are geographically separated. This assumption will impact dust loading over the central Atlantic transported from Sahara desert because the assumed internal mixing between dust and sea salt there will increase dust hygroscopicity and thus wet removal; (3) the fine dust and sea salt modes are similarly merged with the accumulation mode; and (4) sulfate is partially neutralized by ammonium in the form of \mathrm{NH_{4}HSO_{4}}, so ammonium is effectively prescribed and \mathrm{NH_{3}} is not simulated. We note that in MAM-3 we predict the mass mixing ratio of sulfate aerosol in the form of \mathrm{NH_{4}HSO_{4}} while in MAM-7 it is in the form of \mathrm{SO_{4}}. The total number of transported aerosol tracers in MAM-3 is 15.

The time evolution of the interstitial aerosol mass (\mathrm{M^{a}_{i,j}}) and number (\mathrm{N^{a}_{j}}) for the i-th species and j-th mode is described in the following equations:

&&\frac{\partial M^{a}_{i,j}}{\partial t} + \frac{1}{\rho} \nabla \cdot [\rho \mathbf{u} M^{a}_{i,j}] =
\left(\frac{\partial M^{a}_{i,j}}{\partial t}\right)_{conv} +
\left(\frac{\partial M^{a}_{i,j}}{\partial t}\right)_{diffus}  \\ \nonumber
+\left(\frac{\partial M^{a}_{i,j}}{\partial t}\right)_{nuc} +
\left(\frac{\partial M^{a}_{i,j}}{\partial t}\right)_{cond} +
\left(\frac{\partial M^{a}_{i,j}}{\partial t}\right)_{activ}  +
\left(\frac{\partial M^{a}_{i,j}}{\partial t}\right)_{resus} \\ \nonumber
+\left(\frac{\partial M^{a}_{i,j}}{\partial t}\right)_{emis} +
\left(\frac{\partial M^{a}_{i,j}}{\partial t}\right)_{sedime} +
\left(\frac{\partial M^{a}_{i,j}}{\partial t}\right)_{drydep} +
\left(\frac{\partial M^{a}_{i,j}}{\partial t}\right)_{imp\_scav} \\ \nonumber\end{aligned}

&&\frac{\partial N^{a}_{j}}{\partial t} + \frac{1}{\rho} \nabla \cdot [\rho \mathbf{u} N^{a}_{j}] =
\left(\frac{\partial N^{a}_{j}}{\partial t}\right)_{conv} +
\left(\frac{\partial N^{a}_{j}}{\partial t}\right)_{diffus}  \\ \nonumber
+\left(\frac{\partial N^{a}_{j}}{\partial t}\right)_{nuc} +
\left(\frac{\partial N^{a}_{j}}{\partial t}\right)_{coag} +
\left(\frac{\partial N^{a}_{j}}{\partial t}\right)_{activ}  +
\left(\frac{\partial N^{a}_{j}}{\partial t}\right)_{resus} \\ \nonumber
+\left(\frac{\partial N^{a}_{j}}{\partial t}\right)_{emis} +
\left(\frac{\partial N^{a}_{i,j}}{\partial t}\right)_{sedime} +
\left(\frac{\partial N^{a}_{j}}{\partial t}\right)_{drydep} +
\left(\frac{\partial N^{a}_{j}}{\partial t}\right)_{imp\_scav} \\ \nonumber\end{aligned}

Similarly, the time evolution for the cloud-borne aerosol mass (\mathrm{M^{c}_{i,j}}) and number (\mathrm{N^{c}_{j}}) is described as:

&&\frac{\partial M^{c}_{i,j}}{\partial t} =
\left(\frac{\partial M^{c}_{i,j}}{\partial t}\right)_{conv} +
\left(\frac{\partial M^{c}_{i,j}}{\partial t}\right)_{diffus}  \\ \nonumber
+\left(\frac{\partial M^{c}_{i,j}}{\partial t}\right)_{chem} +
\left(\frac{\partial M^{c}_{i,j}}{\partial t}\right)_{activ} +
\left(\frac{\partial M^{c}_{i,j}}{\partial t}\right)_{resus} \\ \nonumber
+\left(\frac{\partial M^{c}_{i,j}}{\partial t}\right)_{sedime} +
\left(\frac{\partial M^{c}_{i,j}}{\partial t}\right)_{drydep} +
\left(\frac{\partial M^{c}_{i,j}}{\partial t}\right)_{nuc\_scav} \\ \nonumber\end{aligned}

&&\frac{\partial N^{c}_{j}}{\partial t}  =
\left(\frac{\partial N^{c}_{j}}{\partial t}\right)_{conv} +
\left(\frac{\partial N^{c}_{j}}{\partial t}\right)_{diffus}  \\ \nonumber
+\left(\frac{\partial N^{c}_{j}}{\partial t}\right)_{activ} +
\left(\frac{\partial N^{c}_{j}}{\partial t}\right)_{resus} \\ \nonumber
+\left(\frac{\partial N^{c}_{j}}{\partial t}\right)_{sedime} +
\left(\frac{\partial N^{c}_{j}}{\partial t}\right)_{drydep} +
\left(\frac{\partial N^{c}_{j}}{\partial t}\right)_{nuc\_scav} \\ \nonumber\end{aligned}

where t is time, \mathbf{u} is the 3D wind vector, and \rho is the air density. The symbolic terms on the right hand side represent the source/sink terms for \mathrm{M_{i,j}} and \mathrm{N_{j}} (???).

6.7.1. Emissions

Anthropogenic (defined here as originating from industrial, domestic and agriculture activity sectors) emissions are from the (???) IPCC AR5 emission data set. Emissions of black carbon (BC) and organic carbon (OC) represent an update of (???) and (???). Emissions of sulfur dioxide are an update of Smith, Pitcher, and Wigley (2001; ???).

The IPCC AR5 emission data set includes emissions for anthropogenic aerosols and precursor gases: \mathrm{SO_{2}}, primary OM (POM), and BC. However, it does not provide injection heights and size distributions of primary emitted particles and precursor gases for which we have followed the AEROCOM protocols (???). We assumed that 2.5% by molar of sulfur emissions are emitted directly as primary sulfate aerosols and the rest as \mathrm{SO_{2}} (???). Sulfur from agriculture, domestic, transportation, waste, and shipping sectors is emitted at the surface while sulfur from energy and industry sectors is emitted at 100-300 m above the surface, and sulfur from forest fire and grass fire is emitted at higher elevations (0-6 km). Sulfate particles from agriculture, waste, and shipping (surface sources), and from energy, industry, forest fire and grass fire (elevated sources) are put in the accumulation mode, and those from domestic and transportation are put in the Aitken mode. POM and BC from forest fire and grass fire are emitted at 0-6 km, while those from other sources (domestic, energy, industry, transportation, waste, and shipping) are emitted at surface. Injection height profiles for fire emissions are derived from the corresponding AEROCOM profiles, which vary spatially and temporally. Mass emission fluxes for sulfate, POM and BC are converted to number emission fluxes for Aitken and accumulation mode at surface or at higher elevations based on AEROCOM prescribed lognormal size distributions as summarized in Table table_aerocom_emis.

The IPCC AR5 data set also does not provide emissions of natural aerosols and precursor gases: volcanic sulfur, DMS, \mathrm{NH_{3}}, and biogenic volatile organic compounds (VOCs). Thus AEROCOM emission fluxes, injection heights and size distributions for volcanic \mathrm{SO_{2}} and sulfate and for DMS flux at surface are used. The emission flux for \mathrm{NH_{3}} is prescribed from the MOZART-4 data set (???). Emission fluxes for isoprene, monoterpenes, toluene, big alkenes, and big alkanes, which are used to derive SOA (gas) emissions (see below), are prescribed from the MOZART-2 data set (???). These emissions represent late 1990’s conditions. For years prior to 2000, we use anthropogenic non-methane volatile organic compound (NMVOC) emissions from IPCC AR5 data set and scale the MOZART toluene, bigene, and big alkane emissions by the ratio of year-of-interest NMVOC emissions to year 2000 NMVOC emissions.

The emission of sea salt aerosols from the ocean follows the parameterization by (???) for aerosols with geometric diameter < 2.8 \mum. The total particle flux {F_{0}} is described by

\frac{dF_{0}}{dlogD_{p}}=\Phi W =(A_{k}T_{w}+B_{k})W

where {D_{p}} is the particle diameter, {T_{w}} is the water temperature and {A_{k}} and {B_{k}} are coefficients dependent on the size interval. {W} is the white cap area:

W=3.84\times 10^{-4}U^{3.41}_{10}

where {U_{10}} is the wind speed at 10 m. For aerosols with a geometric diameter > 2.8 \mum, sea salt emissions follow the parameterization by (???)

\frac{dF_{0}}{dlogr}=1.373U^{3.41}_{10}r^{-3}(1+0.0057r^{1.05})\times 10^{1.19e^{-B^{2}}}

where {r} is the radius of the aerosol at a relative humidity of 80% and {B}=(0.380-log{r})/0.650. All sea salt emissions fluxes are calculated for a size interval of d\mathrm{log}{D}_{p}=0.1 and then summed up for each modal size bin. The cut-off size range for sea salt emissions in MAM-7 is 0.02-0.08 (Aitken), 0.08-0.3 (accumulation), 0.3-1.0 (fine sea salt), and 1.0-10 \mum (coarse sea salt); for MAM-3 the range is 0.02-0.08 (Aitken), 0.08-1.0 (accumulation), and 1.0-10 \mum (coarse).

Dry, unvegetated soils, in regions of strong winds generate soil particles small enough to be entrained into the atmosphere, and these are referred to here at desert dust particles. The generation of desert dust particles is calculated based on the Dust Entrainment and Deposition Model, and the implementation in the Community Climate System Model has been described and compared to observations (???; ???; ???). The only change to the CAM5 source scheme from the previous studies is the increase in the threshold for leaf area index for the generation of dust from 0.1 to 0.3 \mathrm{m^{2}/m^{2}}, to be more consistent with observations of dust generation in more productive regions (???). The cut-off size range for dust emissions is 0.1-2.0 \mum (fine dust) and 2.0-10 \mum (coarse dust) for MAM-7; and 0.1-1.0 \mum (accumulation), and 1.0-10 \mum (coarse) for MAM-3.

6.7.2. Chemistry

Simple gas-phase chemistry is included for sulfate aerosol. This includes (1) DMS oxidation with OH and \mathrm{NO_{3}} to form \mathrm{SO_{2}}; (2) \mathrm{SO_{2}} oxidation with OH to form \mathrm{H_{2}SO_{4}} (gas); (3) \mathrm{H_{2}O_{2}} production (\mathrm{HO_{2}}+\mathrm{HO_{2}}); and (4) \mathrm{H_{2}O_{2}} loss (\mathrm{H_{2}O_{2}} photolysis and \mathrm{H_{2}O_{2}}+OH). The rate coefficients for these reactions are provided from the MOZART model (???). Oxidant concentrations (\mathrm{O_{3}}, OH, \mathrm{HO_{2}}, and \mathrm{NO_{3}}) are temporally interpolated from monthly averages taken from MOZART simulations (???).

\mathrm{SO_{2}} oxidation in bulk cloud water by \mathrm{H_{2}O_{2}} and \mathrm{O_{3}} is based on the MOZART treatment (???). The {p}H value in the bulk cloud water is calculated from the electroneutrality equation between the bulk cloud-borne \mathrm{SO_{4}} and \mathrm{NH_{4}} ion concentrations (summation over modes), and ion concentrations from the dissolution and dissociation of trace gases based on the Henry’s law equilibrium. Irreversible uptake of \mathrm{H_{2}SO_{4}} (gas) to cloud droplets is also calculated (???). The sulfate produced by \mathrm{SO_{2}} aqueous oxidation and \mathrm{H_{2}SO_{4}} (gas) uptake is partitioned to the cloud-borne sulfate mixing ratio in each mode in proportion to the cloud-borne aerosol number of the mode (i.e., the cloud droplet number associated with each aerosol mode), by assuming droplets associated with each mode have the same size. For MAM-7, changes to aqueous \mathrm{NH_{4}} ion from dissolution of \mathrm{NH_{3}} (g) are similarly partitioned among modes. \mathrm{SO_{2}} and \mathrm{H_{2}O_{2}} mixing ratios are at the same time reduced due to aqueous phase consumption.

6.7.3. Secondary Organic Aerosol

The simplest treatment of secondary organic aerosol (SOA), which is used in many global models, is to assume fixed mass yields for anthropogenic and biogenic precursor VOC’s, then directly emit this mass as primary aerosol particles. MAM adds one additional step of complexity by simulating a single lumped gas-phase SOA (gas) species. Fixed mass yields for five VOC categories of the MOZART-4 gas-phase chemical mechanism are assumed, as shown in Table table_soa_yields. These yields have been increased by an additional 50% for the purpose of reducing aerosol indirect forcing by increasing natural aerosols. The total yielded mass is emitted as the SOA (gas) species. MAM then calculates condensation/evaporation of the SOA (gas) to/from several aerosol modes. The condensation/evaporation is treated dynamically, as described later. The equilibrium partial pressure of SOA (gas), over each aerosol mode m is expressed in terms of Raoult’s Law as:


where {{A}_{m}^{SOA}} is SOA mass concentration in mode {m}, {{A}_{m}^{POA}} is the primary organic aerosol (POA) mass concentration in mode {m} (10% of which is assumed to be oxygenated), and {{P}^{0}} is the mean saturation vapor pressure of SOA whose temperature dependence is expressed as:

P^{0}(T)=P^{0}(298K) \times exp[\frac{-\Delta H_{vap}}{R}(\frac{1}{T}-\frac{1}{298})]

where {{P}^{0}} (298 K) is assumed at \mathrm{1\times10^{-10}} atm and the mean enthalpy of vaporization \Delta{H}_{vap} is assumed at 156 kJ \mathrm{mol^{-1}}.

Treatment of the gaseous SOA and explicit condensation/evaporation provides (1) a realistic method for calculating the distribution of SOA among different modes and (2) a minimal treatment of the temperature dependence of the gas/aerosol partitioning.

6.7.4. Nucleation

New particle formation is calculated using parameterizations of binary \mathrm{H_{2}SO_{4}}-\mathrm{H_{2}O} homogeneous nucleation, ternary \mathrm{H_{2}SO_{4}}-\mathrm{NH_{3}}-\mathrm{H_{2}O} homogeneous nucleation, and boundary layer nucleation. A binary parameterization (???) is used in MAM-3, which does not predict \mathrm{NH_{3}}, while a ternary parameterization (???) is used in MAM-7. The boundary layer parameterization, which is used in both versions, uses the empirical 1st order nucleation rate in \mathrm{H_{2}SO_{4}} from (???), with a first order rate coefficient of \mathrm{1.0\times10^{-6} s^{-1}} as in (???). The new particles are added to the Aitken mode, and we use the parameterization of (???) to account for loss of the new particles by coagulation as they grow from critical cluster size to Aitken mode size.

6.7.5. Condensation

Condensation of \mathrm{H_{2}SO_{4}} vapor, \mathrm{NH_{3}} (MAM-7 only), and the SOA (gas) to various modes is treated dynamically, using standard mass transfer expressions (???) that are integrated over the size distribution of each mode (???). An accommodation coefficient of 0.65 is used for \mathrm{H_{2}SO_{4}} (???), and currently, for the other species too. \mathrm{H_{2}SO_{4}} and \mathrm{NH_{3}} condensation are treated as irreversible. \mathrm{NH_{3}} uptake stops when the \mathrm{NH_{4}}/\mathrm{SO_{4}} molar ratio of a mode reaches 2. SOA (gas) condensation is reversible, with the equilibrium vapor pressure over particles given by Eq. (4.296).

In MAM-7, condensation onto the primary carbon mode produces aging of the particles in this mode. Various treatments of the aging process have been used in other models (Cooke and Wilson 1996; ???; ???; ???). In CAM5 a criterion of 3 mono-layers of sulfate is used to convert a fresh POM/BC particle to the aged accumulation mode. Using this criterion, the mass of sulfate required to age all the particles in the primary carbon mode, {M_{SO4,age-all}}, is computed. If {M_{SO4,cond}} condenses on the mode during a time step, we assume that a fraction {f_{age}} = {M_{SO4,cond}} / {M_{SO4,age-all}} has been aged. This fraction of the POM, BC, and number in the mode is transferred to the accumulation mode, along with the condensed soluble species. SOA is included in the aging process. The SOA that condenses in a time step is scaled by its lower hygroscopicity to give a condensed \mathrm{SO_{4}} equivalent.

The two continuous growth processes (condensation and aqueous chemistry) can result in Aitken mode particles growing to a size that is nominally within the accumulation mode size range. Most modal aerosol treatments thus transfer part of the Aitken mode number and mass (those particles on the upper tail of the distribution) to the accumulation mode after calculating continuous growth (???).

6.7.6. Coagulation

Coagulation of the Aitken, accumulation, and primary carbon modes is treated. Coagulation within each of these modes reduces number but leaves mass unchanged. For coagulation of Aitken with accumulation mode and of primary-carbon with accumulation mode, mass is transferred from Aitken or primary-carbon mode to the accumulation mode. For coagulation of Aitken with primary-carbon mode in MAM-7, Aitken mass is first transferred to the primary-carbon mode. This ages some of the primary-carbon particles. An aging fraction is calculated as with condensation, then the Aitken mass and the aged fraction of the primary-carbon mass and number are transferred to the accumulation mode. Coagulation rates are calculated using the fast/approximate algorithms of the Community Multiscale Air Quality (CMAQ) model, version 4.6 (???).

6.7.7. Water Uptake

Water uptake is based on the equilibrium Kohler theory (Ghan and Zaveri 2007) using the relative humidity and the volume mean hygroscopicity for each mode to diagnose the wet volume mean radius of the mode from the dry volume mean radius. The hygroscopity of each component is listed in Table Subgrid Vertical Transport and Activation/Resuspension. The hygroscopicities here are equivalent to the \kappa parameters of (???). Note that the measured solubility of dust varies widely, from 0.03 to 0.26 (???).


l c c r Emission Source &


deviation, s_{g}



Number mode




Forest fire/grass fire & 1.8 & 0.080 & 0.134


& See note & See note & 0.134
Forest fire/grass fire/waste & 1.8 & 0.080 & 0.134
Energy/industry/shipping & See note & See note & 0.261
Domestic/transportation & 1.8 & 0.030 & 0.0504
Continuous volcano, 50% in Aitken mode & 1.8 & 0.030 & 0.0504
Continuous volcano, 50% in accum. mode & 1.8 & 0.080 & 0.134
Species Mass yield Reference
Big Alkanes 5% (???)
Big Alkenes 5% assumed
Toluene 15% (???)
Isoprene 4% (???)
Monoterpenes 25% (???)

Table: Assumed SOA (gas) yields

Seasalt sulfate nitrate ammonium SOA POM BC dust
1.16 0.507 0.507 0.507 0.14 0.10 10^{-10} 0.068

Table: Hygroscopicity of aerosol components

6.7.8. Subgrid Vertical Transport and Activation/Resuspension

The vertical transport of interstitial aerosols and trace gases by deep convective clouds, using updraft and downdraft mass fluxes from the Zhang-McFarlane parameterization, is described in (???). Currently this vertical transport is calculated separately from wet removal, but a more integrated treatment is planned. Cloud-borne aerosols, which are associated with large-scale stratiform cloud, are assumed to not interact with the convective clouds. Vertical transport by shallow convective clouds is treated similarly, using mass fluxes from the shallow convection parameterization. Turbulent transport of the aerosol is given a special treatment with respect to other tracers. To strengthen the coupling between turbulent transport and aerosol activation in stratiform clouds, the implicit time integration scheme used for turbulent transport of heat, energy, and momentum is replaced by an explicit scheme for droplets and aerosol. A sub-timestep is calculated for each column based on the minimum turbulent transport time in the column. Turbulent transport is integrated over the sub-time steps using a forward time integration scheme.

Aerosol activation converts particles from the interstitial attachment state to the cloud-borne state. In stratiform cloud, activation is treated consistently with droplet nucleation, so that the total number of particles activated and transferred to the cloud-borne state equals to the number of droplets nucleated. Activation is parameterized in terms of updraft velocity and the properties of all of the aerosol modes (???), with both mass and number transferred to the cloud-borne state. The updraft velocity is approximated by the square root of the turbulence kinetic energy, with a minimum value of 0.2 m \mathrm{s^{-1}}. Activation is assumed to occur as updrafts carry air into the base of the cloud (Ghan et al. 1997) and as cloud fraction increases (???). In addition, activation is assumed to occur as air is continuously cycled through clouds, assuming a cloud regeneration time scale of one hour. Consider a model time step of 20 minutes, so that 1/3 of the cloud is regenerated in a time step. We essentially dissipate then reform 1/3 of cloud each time step. During dissipation, grid-cell mean cloud droplet number is reduced by 1/3, and 1/3 of the cloud-borne aerosols are resuspended and converted to the interstitial state. During regeneration, interstitial aerosols are activated in the “new” cloud, and cloud droplet number is increased accordingly. The regeneration has small impact on shallow boundary layer clouds, but it noticeably increases droplet number in deeper free-tropospheric clouds where vertical turbulence mixing is slow. Particles are resuspended as aerosol when droplets evaporate. This process is assumed to occur as droplets are transferred below or above cloud and as clouds dissipate.

6.7.9. Wet Deposition

Aerosol wet removal is calculated using the CAM3.5 wet removal routine (Rasch et al. 2000; Barth et al. 2000) with modifications for the consistency with cloud macro- and microphysics. The routine treats in-cloud scavenging (the removal of cloud-borne aerosol particles) and below-cloud scavenging (the removal of interstitial aerosol particles by precipitation particles through impaction and Brownian diffusion).

For in-cloud scavenging, the stratiform and convective cloud fraction, cloud water, and precipitation production profiles are used to calculate first-order loss rate profiles for cloud-water. These cloud-water first-order loss rates are multiplied by “solubility factors” to obtain aerosol first-order loss rates, which are applied to the aerosol profiles. The solubility factors can be interpreted as (the fraction of aerosols that are in cloud drops) \times (an additional tuning factor). In CAM3.5, where the cloud-borne aerosol is not explicitly calculated, a value of 0.3 is used for solubility factors for all aerosol types and sizes. Different values are used for the MAM. The stratiform in-cloud scavenging only affects the stratiform-cloud-borne aerosol particles, and these have solubility factors of 1.0. It does not affect the interstitial aerosol particles, and these have solubility factors of 0.0.

For convective in-cloud scavenging of MAM aerosols, both a solubility factor and a within-convective-cloud activation fraction are passed to the wet removal routine. For the stratiform-cloud-borne aerosol particles, there is no wet removal by convective clouds, and these factors are zero. For interstitial (with respect to stratiform cloud) aerosol, the solubility factor is 0.5, and the activation fractions are 0.0 for the primary carbon mode, 0.4 for the fine and coarse dust modes, and 0.8 for other modes. The lower values reflect lower hygroscopity. These factors are applied to both number and mass species within each mode, with one exception. In MAM-3, different activation fractions are applied to the dust and sea salt of the coarse mode (0.4 and 0.8 respectively), and a weighted average is applied to the coarse mode sulfate and number.

For below-cloud scavenging, the first-order removal rate is equal to [(solubility factor) \times (scavenging coefficient) \times (precipitation rate) ]. Again, the solubility factor can be viewed as a tuning factor. In CAM3.5, a solubility factor of 0.3 and a scavenging coefficient of 0.1 \mathrm{mm^{-1}} are used for all aerosols. In MAM, the scavenging coefficient for interstitial aerosol is explicitly calculated as in (???) and thus varies strongly with particle size, with lowest values for the accumulation mode; and the solubility factor is 0.1. For stratiform-cloud-borne aerosol, there is no below-cloud scavenging, and the solubility factor is 0.0.

Aerosol that is scavenged at one altitude can be resuspended at a lower altitude if precipitation evaporates. In CAM5, as in CAM3.5, this process is treated for aerosol removed by stratiform in-cloud scavenging. A fraction of the in-cloud scavenged aerosol is resuspended, and the resuspended fraction is equal to the fraction of precipitation that evaporates below cloud.

6.7.10. Dry Deposition

Aerosol dry deposition velocities are calculated using the (???) parameterization with the CAM5 land-use and surface layer information. Gravitational settling velocities are calculated at layers above the surface (???). Both velocities depend on particle wet size and are different for mass and number and between modes. The velocities for cloud-borne aerosols are calculated based on droplet sizes. Aerosol mixing ratio changes and fluxes from dry deposition and sedimentation throughout a vertical column are then calculated using the CAM5 dust deposition/sedimentation routine.


Predicted species for interstitial and cloud-borne component of each aerosol mode in MAM-7. Standard deviation for each mode is 1.6 (Aitken), 1.8 (accumulation), 1.6 (primary carbon), 1.8 (fine and coarse soil dust), and 2.0 (fine and coarse sea salt)


Predicted species for interstitial and cloud-borne component of each aerosol mode in MAM-3. Standard deviation for each mode is 1.6 (Aitken), 1.8 (accumulation) and 1.8 (coarse mode)

6.8. Condensed Phase Optics

Condensed phase (aerosols, liquid cloud droplets, hydrometeors, and ice crystal) optics are provided as a mass-specific quantities in m^2/kg. These optics are specified for each band of the shortwave and longwave radiation code. For the shortwave, unscaled extinction, single-scattering albedo, and asymmetry parameter are specified. For the longwave, the mass-specific absorption is specified. Vertical optical depths are computed by multiplying by the mass-specific quantities by the vertical mass path of the corresponding material.

For clouds, the in-cloud values of the mixing ratios are used to compute the in-cloud values of cloud optical depths. The radiation does not use grid-cell average optical depths of clouds.

6.8.1. Tropospheric Aerosol Optics

While the radiation code supports a range of possible aerosol packages, the modal aerosol package is the default configuration, and we will discuss the optics treatment used in that package. Aerosol optical properties for each mode are parameterized in terms of wet refractive index and wet surface mode radius of the mode, as described by (Ghan and Zaveri 2007), except that volume mixing rather than the Maxwell-Garnett mixing rule is used to calculate the wet refractive index for mixtures of insoluble and soluble particles (We found little difference between the volume mixing treatment and the Maxwell-Garnett mixing rule.) Refractive indices for water and for most aerosol components are taken from OPAC (Koepke and Schult 1998), but for black carbon the value (1.95,0.79i) from (Bond and Bergstrom 2006) is used for solar wavelengths. Densities for each component are listed in Table table_aerdensity.

Sea salt Sulfate Nitrate Ammonium SOA POA BC Dust
1900 1770 1770 1770 1000 1000 1700 2600

Table Density (kg/m:math:^3) of aerosol material.

The wet volume mean radius for each mode is calculated from the dry volume mean radius using equilibrium Kohler theory (Ghan and Zaveri 2007), the relative humidity and the volume mean hygroscopicity. The hygroscopicity of each component is listed in Table [table:aerhygro]. Note that the measured solubility of dust varies widely, from 0.03 to 0.26 (Koehler et al. 2009). The wet surface mode radius is calculated from the wet volume mean radius assuming a wet lognormal size distribution with the same geometric standard deviation as the dry size distribution. The geometric standard deviation is assumed to be constant for each mode.

Sea salt Sulfate Nitrate Ammonium SOA POA BC Dust
1.16 0.507 0.507 0.507 0.14 1.e-10 1.e-10 0.068

Table: Hygroscopicity of aerosol components.

6.8.2. Stratospheric Volcanic Aerosol Optics

specifies the volcanic aerosol as a mass mixing ratio q_V of wet volcanic aerosol to dry air as a function of height, latitude, longitude and time. also specifies a geometric mean radius r_g of the volcanic aerosol. The volcanic optics are stored as a lookup table as a function of geometric mean radius.

The size distribution is defined by a log-normal size distribution with a geometric mean radius r_g and geometric standard deviation \sigma_g. For the standard version of the optics,

\sigma_g &=& 1.8 \\
\mu&=&\ln(r_g )\\
\mu &\in& [\mu_{\mathrm{min}}, \mu_{\mathrm{max}} ]\\
\mu_{\mathrm{min}}&=&\ln (0.01*10^{-6} \exp(-5/2 * (\ln \sigma_g)^2)) \\
\mu_{\mathrm{max}}&=&\ln (2.00*10^{-6} \exp(-5/2 * (\ln \sigma_g)^2)) \end{aligned}

In other words, r_{\mathrm{eff}} spans the range [0.01,2.0] \mum. The density of the sulfuric acid / water mixture at 75% / 25% at 215K is

\rho = 1.75*10^3 \;\mathrm{kg/m}^3

The index of refraction is that specified by Biermann (Biermann, Luo, and Peter 2000–1AD) and is available from the HITRAN (???) database. The index at 75%/25% weight percent (sulfuric acid to water) and at 215K is used.

The incomplete gamma weight,

L(r)=\int_0^{r} r^{*2}n(r^*) dr^* / \int_0^\infty r^{*2}n(r^*) dr^*

can be used to define the mass-specific aerosol extinction, scattering, and asymmetric scattering,

b_{\mathrm{ext}}=\frac{3}{4 \rho \; r_{\mathrm{eff}}} \int_{0}^{\infty}q_{\mathrm{ext}}(r) dL(r) \\
b_{\mathrm{sca}}=\frac{3}{4 \rho \; r_{\mathrm{eff}}} \int_{0}^{\infty}q_{\mathrm{sca}}(r) dL(r) \\
b_{\mathrm{asm}}=\frac{3}{4 \rho \; r_{\mathrm{eff}}} \int_{0}^{\infty}q_{\mathrm{gqsc}}(r) dL(r) \\
b_{\mathrm{abs}}=\frac{3}{4 \rho \; r_{\mathrm{eff}}} \int_{0}^{\infty}  (q_{\mathrm{ext}}(r) - q_{\mathrm{sca}}(r)) dL(r)\end{aligned}

where q_{\mathrm{ext}}(r), q_{\mathrm{sca}}(r), q_{\mathrm{gqsc}}(r) are efficiencies obtained from the MIEV0 program of Wiscombe (Wiscombe 1996).

These mass-specific properties are averaged over each frequency band of RRTMG and parameterized in a lookup table with \mu = \ln(r_g) as the dependent variable.

The vertical optical depths are derived as the product of vertical mass path with mass-specific aerosol properties at runtime.

\tau_\mathrm{ext} = q_V*\frac{\Delta P_\mathrm{dry} }{ g} * b_{\mathrm{ext}}(\mu)

where q_V is the mixing ratio of volcanic aerosol. The corresponding scattering optical depth, asymmetric scattering optical depth, and absorption optical depth are derived similarly.

6.8.3. Liquid Cloud Optics

For liquid clouds specifies the fraction of each grid cell occupied by liquid cloud droplets C_\mathrm{liq}, the ratio of mass of condensed water to wet air in the cloud q_\mathrm{liq}, and the number-size distribution in terms of the 2 parameters, \mu and \lambda of the gamma distribution,

n(D) = \frac{dN}{dD} = \frac{\lambda^{\mu+1} }{\Gamma(\mu+1)} D^{\mu} e^{- \lambda D }

where D is the diameter of the droplets.

Both the parameters, \mu and \lambda have limited ranges:

2.< \mu <15.

\frac{\mu+1}{50*10^{-6} \mathrm{m}} < \lambda&<\frac{\mu+1}{2*10^{-6} \mathrm{m}}

The liquid cloud optics are specified in terms of a lookup table in \mu and 1/\lambda. These optics are computed as size-distribution and spectral-band averages of the quantities (e.g., Q_\mathrm{ext}) computed by the MIEV0 program (Wiscombe 1996).

The size-integrated mass-specific extinction coefficient, k_\mathrm{ext}, (units m^{2}/kg) is given by:

k_\mathrm{ext}(\nu) = \frac{\frac{\pi}{4} \int_{0}^{\infty}\; D^2\; Q_\mathrm{ext}(D;\nu,m)\; n(D)\; dD}
               {\frac{\pi}{6} \rho_w \int_{0}^{\infty}\; D^3\; n(D)\; dD}

The corresponding quantities are used to compute mass-specific absorption in the longwave as well as single-scattering albedo and asymmetry parameter.

The in-cloud optical depth is then given by:

\tau_\mathrm{liq}(\nu) = k_\mathrm{ext}(\nu) \; q_\mathrm{liq} \; \frac{\Delta P}{g}

where q_\mathrm{liq} is the ratio of droplet mass to dry air mass.

For RRTMG, the wavenumber average values of \tau_\mathrm{liq}, \tau_\mathrm{liq}\omega_\mathrm{liq}, \tau_\mathrm{liq}\omega_\mathrm{liq} g_\mathrm{liq} on each SW band, and the wavenumber average value of the absorption optical depth, \tau_\mathrm{liq}(1-\omega_\mathrm{liq}), on each longwave band.

In-cloud water path variability is not treated by the optics.

6.8.4. Ice Cloud Optics

specifies an in-cloud ice water path, an ice cloud fraction, and an effective diameter for ice particles in the cloud. The optics for ice clouds are constructed as a lookup table as a function of effective diameter for each of the shortwave and longwave bands in the radiation code.

Ice cloud optical properties have been derived using two approaches: (1) calculations of single ice crystal scattering properties based on electrodynamic theory, followed by their application to assumed ice particle size distributions (PSD) and the representation of PSD optical properties through the effective diameter (D_e) of the PSD, and (2) parameterization of scattering/absorption processes in terms of ice particle shape and size, and integrating these expressions over the PSD to produce analytical expressions of PSD optical properties in terms of ice crystal and PSD parameters. In the latter case, the PSD extinction and absorption coefficients can be expressed as explicit functions of the ice particle projected area- and mass-dimension power laws and the PSD parameters of the gamma form. The modified anomalous diffraction approximation (MADA) uses this second approach to calculate ice cloud optical properties. The development of MADA was motivated by a desire to explicitly represent ice optical properties in terms of the ice PSD and ice crystal shape parameters, given that the ice PSD optical properties cannot be uniquely defined by D_e(Mitchell 2002).

MADA was developed from van de Hulst’s anomalous diffraction theory or ADT (Hulst 1957) through a series of physical insights, which are:

  1. The effective photon path through a particle by which its scattering properties can be predicted is given by the ratio of particle projected area/particle volume (Bryant and Latimer 1969; Mitchell and Arnott 1994), where volume is defined as particle mass/bulk density of ice (0.917 g/cm^3).
  2. The processes of internal reflection and refraction can be viewed as extending the photon path and can be parameterized using a MADA framework (Mitchell, Macke, and Liu 1996b).
  3. The maximum contribution of wave resonance or photon tunneling to absorption and extinction can be estimated as a linear function of the real part of the refractive index for ice, n_r. Photon tunneling can then be parameterized in terms of n_r, size parameter x and the other MADA parameters described above (Mitchell 2000).
  4. Edge effects as surface wave phenomena pertain only to extinction and can be represented in terms of the size parameter x as described by (Wu 1956) and modified by (Mitchell 2000). Based on a laboratory ice cloud study (Mitchell et al. 2001), edge effects for non-spherical ice crystals do not appear significant.

The first insight greatly simplified van de Hulst’s ADT, resulting in analytic and integrable expressions for the PSD extinction and absorption coefficients as shown in (Mitchell and Arnott 1994). This simplified ADT may be more accurate than the original ADT (Mitchell et al. 2006). This simplified ADT provided an analytical framework on which the other three insights or processes were expressed. These processes were represented analytically for a single ice particle, and then integrated over the PSD to produce extinction and absorption coefficients that account for these processes. These coefficients were formulated in terms of ice particle shape (i.e. the ice particle area- and mass-dimension power laws) and the three gamma PSD parameters. The basic MADA equations formulated for ice clouds are given in the appendix of (Mitchell 2002). Details regarding their derivation and their physical basis are described in (Mitchell 2000) and (Mitchell, Macke, and Liu 1996b).

The asymmetry parameter g is not treated by MADA, but was parameterized for solar wavelengths as a function of wavelength and ice particle shape and size, based on ray-tracing calculations by Andreas Macke, as described in (Mitchell, Macke, and Liu 1996b). The g parameterization for quasi-spherical ice particles is based on the phase function calculations of (Nousiainen and McFarquhar 2004). These parameterizations relate g for a PSD to the ice particle size that divides the PSD into equal projected areas (since scattering depends on projected area). For terrestrial radiation, g values for ice are based on the g parameterization described in (Yang et al. 2005). Tests of MADA

While this treatment of ice optical properties began and evolved through van de Hulst’s original insights formulated in ADT, optical properties predicted by MADA closely agree with those predicted by other ice optics schemes based on electrodynamic theory. As described in (Mitchell et al. 2001; Mitchell et al. 2006), MADA has been tested in a laboratory ice cloud experiment where the MADA extinction error was 3% on average relative to the FTIR measured extinction efficiency over the 2-14 \mum wavelength range. These same laboratory PSD were used to calculate the absorption efficiencies using MADA and T-matrix, which differed by 6% on average over the wavelength range 2-18 \mum (size parameter range 2-22). In corresponding T-matrix calculations of the single-scattering albedo, the mean MADA error was 2.5%. In another test, MADA absorption errors relative to the Finite Difference Time Domain (FDTD) method (i.e. (Yang et al. 2005) over the wavelength range 3-100 \mum were no greater than 15% for six ice particle shapes. Finally, the absorption coefficients predicted by MADA and the (Fu, Yang, and Sun 1998) and the (Yang et al. 2005) ice optics schemes generally agreed within 5%. Application to

The MADA-based ice optics scheme described above is not used explicitly in , but was used to generate a look-up table of optical properties as a function of effective diameter, D_e. The PSD optical properties consist of the mass-normalized extinction coefficient (volume extinction coefficient / ice water content), the single-scattering albedo and the asymmetry parameter for bands covering all solar and terrestrial wavelengths. The radiation bands coincide with those used in RRTMG. The ice refractive index values used are from (Warren and Brandt 2008). Since MADA is formulated to accept any ice particle shape ÒrecipeÓ, a shape recipe corresponding to that observed for mid-latitude cirrus clouds at -45\,^{\circ}\mathrm{C} (see (Lawson et al. 2006)) was assumed for ice particles larger than 60 \mum: 7% hexagonal columns, 50% bullet rosettes and 43% irregular ice particles. At smaller sizes, the shape recipe consists of 50% quasi-spherical, 30% irregular and 20% bullet rosette ice crystals, based on in-situ measurements in tropical cirrus [P. Lawson, 2005, personal communication].

The effective diameter is defined in a way that is universal for both ice and water clouds, which is essentially the photon path characterizing the PSD (Mitchell 2002):

De = \frac{3}{2} \frac{\mathrm{IWC}}{\rho_i A}

where \mathrm{IWC} is the ice water content (g/cm:math:^3), \rho_i is the bulk ice density (0.917 g/cm^3) and A is the total projected area of the PSD (cm:math:^2/cm:math:^3).

6.8.5. Snow Cloud Optics

specifies snow as a cloud fraction of snow, an effective diameter of snow, and an in-cloud mass mixing ratio of snow. The snow optics are identical to the optics for ice clouds.

6.9. Radiative Transfer

Radiative transfer calculations in the longwave and shortwave are provided by the radiation code RRTMG (Iacono et al. 2008; Mlawer et al. 1997). This is an accelerated and modified version of the correlated k-distribution model, RRTM. The condensed phase radiative parameterizations are external to the radiation package, however the gas optics and radiative transfer solver are provided within RRTMG.

6.9.1. Combination of Aerosol Radiative Properties

The number N_a of aerosol species is arbitrary; however in the standard configuration there are 3 modes. The radiative properties are combined before being passed to the radiative transfer solver. If the extinction optical depth of species i in band b is \tau_{ib} and the single-scattering albedo is \omega_{ib} and the asymmetry parameter is g_{ib} then the aerosol optics are combined as follows:

\tau_b &=& \sum_{i=1}^{N_a} \tau_{ib} \\
\omega_b&=&\sum_{i=1}^{N_a}  \tau_{ib} \omega_{ib} / \tau_b\\
g_b&=&\sum_{i=1}^{N_a}  \tau_{ib} \omega_{ib} g_{ib} / (\tau_b \omega_b)\end{aligned}

where \tau_b is the total aerosol extinction optical depth in band b, \omega_b is the total single-scattering albedo in band b, and g_b is the asymmetry parameter in band b.

6.9.2. Combination of Cloud Optics

are specifies three different types of clouds: ice clouds, liquid clouds, and snow clouds. Each of these clouds has a separate cloud fraction C_\mathrm{liq},C_\mathrm{ice},C_\mathrm{snow}, as well as an in-cloud radiative characterization in terms of optical depths \tau_i, single-scattering albedo \omega_i and asymmetry parameter g_i. The optics are smeared together into a total cloud fraction C as follows:

C &=& \max\{C_\mathrm{liq},C_\mathrm{ice},C_\mathrm{snow} \} \\
\tau_c &=& \sum_\mathrm{t \in type} \tau_t * C_t / C \\
\omega_c&=&\sum_\mathrm{t\in type}  \tau_{tb} \omega_{tb} C_t / (\tau_c C)\\
g_c&=&\sum_\mathrm{t \in type}  \tau_{tb} \omega_{tb} g_{tb} C_t / (\tau_c \omega_c C)\end{aligned}

where C,\tau_c, \omega_c, g_c are the combined cloud radiative parameters.

6.9.3. Radiative Fluxes and Heating Rates

Radiative fluxes and heating rates in are calculated using RRTMG(Iacono et al. 2008).

This model utilizes the correlated k-distribution technique to calculate irradiance and heating rate efficiently in broad spectral intervals, while realizing the objective of retaining a high level of accuracy relative to measurements and high-resolution line-by-line models. Sub-grid cloud characterization in RRTMG is treated in both the longwave and shortwave spectral regions with McICA, the Monte-Carlo Independent Column Approximation (Pincus and Morcrette 2003), using the maximum-random cloud overlap assumption.

The thermodynamic state, gas concentrations, cloud fraction, condensed phase optics, and aerosol properties are specified elsewhere. The surface model provides both the surface albedo, area-averaged for each atmospheric column, and the upward longwave surface flux, which incorporates the surface emissivity, for input to the radiation. The bulk aerosol package of CAM4 continues to be supported by this radiation code as an option, however a description of this optional configuration is not provided in this document.

To provide fluxes at the top of the atmosphere, RRTMG uses with an additional layer above the model top in both the longwave and shortwave. This extra layer is specified by replicating the composition of the highest layer into a layer that extends from the top of the model to 10^{-4} hPa. RRTMG does not treat non-LTE (local thermodynamic equilibrium) effects in the upper atmosphere. It provides accurate fluxes and heating rates up to about 0.1 hPa, above which non-LTE effects become more significant. Shortwave Radiative Transfer

RRTMG divides the solar spectrum into 14 shortwave bands that extend over the spectral range from 0.2 \mum to 12.2 \mum (820 to 50000 cm^{-1}). Modeled sources of extinction (absorption and scattering) are H2O, O3, CO2, O2, CH4, N2, clouds, aerosols, and Rayleigh scattering. The model uses a two-stream \delta-Eddington approximation assuming homogeneously mixed layers, while accounting for both absorption and scattering in the calculation of reflectance and transmittance. The model distinguishes the direct solar beam from scattered (diffuse) radiation. The scattering phase function is parameterized using the Henyey-Greenstein approximation to represent the forward scattering fraction as a function of the asymmetry parameter. This Òdelta-scalingÓ is applied to the total irradiance as well as to the direct and diffuse components. The latter are consistent with the direct and diffuse components of the surface albedo, which are applied to the calculation of surface reflectance.

The shortwave version of RRTMG used in CAM5 is derived from RRTM_SW (Clough et al. 2005). It utilizes a reduced complement of 112 quadrature points (g-points) to calculate radiative transfer across the 14 spectral bands, which is half of the 224 g-points used in RRTM_SW, to enhance computational performance with little impact on accuracy. The number of g-points needed within each band varies depending on the strength and complexity of the absorption in each spectral interval. Total fluxes are accurate to within 1-2 W/m^2 relative to the standard RRTM_SW (using DISORT with 16 streams) in clear sky and in the presence of aerosols and within 6 W/m^2 in overcast sky. RRTM_SW with DISORT is itself accurate to within 2 W/m^2 of the data-validated multiple scattering model, CHARTS (Moncet and Clough 1997). Input absorption coefficient data for the k-distributions used by RRTMG are obtained directly from the line-by-line radiation model LBLRTM (Clough et al. 2005).

RRTMG shortwave utilizes McICA, the Monte-Carlo Independent Column Approximation, to represent sub-grid scale cloud variability such as cloud fraction and cloud overlap. An external sub-column generator is used to define the stochastic cloud arrays used by the McICA technique.

The Kurucz solar source function is used in the shortwave model, which assumes a total solar irradiance (TSI) at the top of the atmosphere of 1368.22 W/m^2. However, this value is scaled in each spectral band through the specification of a time-varying solar spectral irradiance as discussed below. The TSI assumed in each RRTMG shortwave band is listed in the table below, along with the spectral band boundaries in \mum and wavenumbers.

Shortwave radiation is only calculated by RRTMG when the cosine of the zenith angle is larger than zero, that is, when the sun is above the horizon.

Band Band Band Band Band Solar
Index Min Max Min Max Irradiance
  (\mum) (\mum) (cm:math:^{-1}) (cm:math:^{-1}) (W/m:math:^2)
1 3.077 3.846 2600 3250 12.11
2 2.500 3.077 3250 4000 20.36
3 2.150 2.500 4000 4650 23.73
4 1.942 2.150 4650 5150 22.43
5 1.626 1.942 5150 6150 55.63
6 1.299 1.626 6150 7700 102.93
7 1.242 1.299 7700 8050 24.29
8 0.778 1.242 8050 12850 345.74
9 0.625 0.778 12850 16000 218.19
10 0.442 0.625 16000 22650 347.20
11 0.345 0.442 22650 29000 129.49
12 0.263 0.345 29000 38000 50.15
13 0.200 0.263 38000 50000 3.08
14 3.846 12.195 820 2600 12.89

Table: RRTMG_SW spectral band boundaries and the solar irradiance in each band. Longwave Radiative Transfer

The infrared spectrum in RRTMG is divided into 16 longwave bands that extend over the spectral range from 3.1 \mum to 1000.0 \mum (10 to 3250 cm^{-1}). The band boundaries are listed in the table below. The model calculates molecular, cloud and aerosol absorption and emission. Scattering effects are not presently included. Molecular sources of absorption are H2O, CO2, O3, N2O, CH4, O2, N2 and the halocarbons CFC-11 and CFC-12. CFC-11 is specified by CAM5 as a weighed sum of multiple CFCs (other than CFC-12). The water vapor continuum is treated with the CKD_v2.4 continuum model. For completeness, band 16 includes a small adjustment to add the infrared contribution from the spectral interval below 3.1 \mum.

The longwave version of RRTMG (Iacono et al. 2008; Iacono et al. 2003; Iacono et al. 2000) used in CAM5 has been modified from RRTM_LW (Mlawer et al. 1997) to enhance its computational efficiency with minimal effect on the accuracy. This includes a reduction in the total number of g-points from 256 to 140. The number of g-points used within each band varies depending on the strength and complexity of the absorption in each band. Fluxes are accurate to within 1.0 W/m^2 at all levels, and cooling rate generally agrees within 0.1 K/day in the troposphere and 0.3 K/day the stratosphere relative to the line-by-line radiative transfer model, LBLRTM (Clough et al. 2005; Clough and Iacono 1995). Input absorption coefficient data for the k-distributions used by RRTMG are obtained directly from LBLRTM.

This model also utilizes McICA, the Monte-Carlo Independent Column Approximation (Pincus and Morcrette 2003), to represent sub-grid scale cloud variability such as cloud fraction and cloud overlap. An external sub-column generator is used to define the stochastic cloud arrays needed by the McICA technique.

Within the longwave radiation model, the surface emissivity is assumed to be 1.0. However, the radiative surface temperature used in the longwave calculation is derived with the Stefan-Boltzmann relation from the upward longwave surface flux that is input from the land model. Therefore, this value may include some representation of surface emissivity less than 1.0 if this condition exists in the land model. RRTMG longwave also provides the capability of varying the surface emissivity within each spectral band, though this feature is not presently utilized.

Longwave radiative transfer is performed over a single (diffusivity) angle (secant =1.66) for one upward and one downward calculation. RRTMG includes an accuracy adjustment in profiles with very high water vapor that slightly varies the diffusivity angle in some bands as a function of total column water vapor.

Band Band Band Band Band
Index Min Max Min Max
  (\mum) (\mum) (cm:math:^{-1}) (cm:math:^{-1})
1 28.57 1000.0 10 350
2 20.00 28.57 350 500
3 15.87 20.00 500 630
4 14.29 15.87 630 700
5 12.20 14.29 700 820
6 10.20 12.20 820 980
7 9.26 10.20 980 1080
8 8.47 9.26 1080 1180
9 7.19 8.47 1180 1390
10 6.76 7.19 1390 1480
11 5.56 6.76 1480 1800
12 4.81 5.56 1800 2080
13 4.44 4.81 2080 2250
14 4.20 4.44 2250 2380
15 3.85 4.20 2380 2600
16 3.08 3.85 2600 3250

Table: [table:LWBands]RRTMG_LW spectral band boundaries.

6.9.4. Surface Radiative Properties

For the shortwave, the surface albedoes are specified at every grid point at every time step. The albedoes are partitioned for the spectral ranges [2.0, 0.7]\mum and [0.7,12.0]:math:mum. In addition they are partitioned between the direct and diffuse beam.

In the longwave, the surface is assumed to have an emissivity of 1.0 within the radiation model. However, the radiative surface temperature used in the longwave calculation is derived with the Stefan-Boltzmann relation from the upward longwave surface flux that is input from the surface models. Therefore, this value may include some representation of surface emissivity less than 1.0, if this condition exists in surface models (e.g. the land model).

6.9.5. Time Sampling

Both the shortwave and longwave radiation is computed at hourly intervals by default. The heating rates and fluxes are assumed to be constant between time steps.

6.9.6. Diurnal Cycle and Earth Orbit

In CAM6.0, the diurnal cycle and earth orbit is computed using the method of (Berger 1978). Using this formulation, the insolation can be determined for any time within 10^6 years of 1950 AD. The insolation at the top of the model atmosphere is given by

S_I = {S_0\,{\rho^{-2}}\,\cos\mu} ,

where S_0 is the solar constant, \mu is the solar zenith angle, and \rho^{-2} is the distance factor (square of the ratio of mean to actual distance that depends on the time of year). A time series of the solar spectral irradiance at 1 a.u. for 1870-2100 based upon (Wang, Lean, and Sheeley 2005) is included with the standard model and is in section sec-lean.

We represent the annual and diurnal cycle of solar insolation with a repeatable solar year of exactly 365 days and with a mean solar day of exactly 24 hours, respectively. The repeatable solar year does not allow for leap years. The expressions defining the annual and diurnal variation of solar insolation are:

\cos\mu & =& \sin\phi \sin\delta - \cos\phi \cos\delta \cos(H) \\
\delta & =& \arcsin(\sin\epsilon\sin\lambda) \\
\rho & =& \frac{1-e^2}{1+e\,\cos(\lambda - {\tilde\omega})} \\
{\tilde\omega}& =& \Pi + \psi\end{aligned}


\phi & = &{\rm latitude~in~radians} \nonumber \nonumber \\
\delta & =& {\rm solar~declination~in~radians} \nonumber \\
 H & =& {\rm hour~angle~of~sun~during~the~day} \nonumber \\
 \epsilon & =& {\rm obliquity} \nonumber \\
\lambda & =& {\rm true~longitude~of~the~earth~relative~to~vernal~equinox} \\
e & =& {\rm eccentricity~factor} \nonumber \\
{\tilde\omega}& = &{\rm longitude~of~the~perihelion}+ 180^\circ \nonumber \\
\Pi & =& {\rm longitude~of~perihelion~based~on~the~fixed~equinox} \nonumber \\
\psi & = &{\rm general~precession} \nonumber\end{aligned}

The hour angle H in the expression for \cos\mu depends on the calendar day d as well as model longitude:

H = 2\,\pi\left(d + \frac{\theta}{360^\circ}\right) ,

where \theta = model longitude in degrees starting from Greenwich running eastward. Note that the calendar day d varies continuously throughout the repeatable year and is updated every model time step. The values of d at 0 GMT for January 1 and December 31 are 0 and 364, respectively. This would mean, for example, that a model calendar day d having no fraction (such as 182.00) would refer to local midnight at Greenwich, and to local noon at the date line (180^\circ longitude).

The obliquity \epsilon may be approximated by an empirical series expansion of solutions for the Earth’s orbit

\epsilon = \epsilon^* + \sum_{j=1}^{47} A_j\,\cos\left(f_j\,t +
               \delta_j\right) \\

where A_j, f_j, and \delta_j are determined by numerical fitting. The term \epsilon^* = 23.320556^\circ, and t is the time (in years) relative to 1950 AD.

Since the series expansion for the eccentricity e is slowly convergent, it is computed using

e = \sqrt{\left(e\cos\Pi\right)^2+\left(e\sin\Pi\right)^2}

The terms on the right-hand side may also be written as empirical series expansions:

\sin\end{array}\right\rbrace}\Pi = \sum_{j=1}^{19} M_j\,{\left\lbrace\begin{array}{c}\cos\\

where M_j, g_j, and \beta_j are estimated from numerical fitting. Once these series have been computed, the longitude of perihelion \Pi is calculated using

\Pi = \arctan\left(\frac{e\,\sin\Pi}{e\,\cos\Pi}\right)

The general precession is given by another empirical series expansion

\psi = \tilde\psi\,t + \zeta + \sum_{j=1}^{78} F_j\,\sin\left(f'_j\,t
       + \delta'_j\right)

where \tilde\psi = 50.439273'', \zeta = 3.392506^\circ, and F_j, f'_j, and \delta'_j are estimated from the numerical solution for the Earth’s orbit.

The calculation of \lambda requires first determining two mean longitudes for the orbit. The mean longitude \lambda_{m0} at the time of the vernal equinox is :

\lambda_{m0} & = &
   2\left\lbrace \left(\frac{e}{2} + \frac{e^3}{8}\right)
                       (1+\beta)\sin({\tilde\omega}) \right.\nonumber \\
   & & \phantom{-2\left\lbrace\right.}
   & & \phantom{-2\left\lbrace\right.}
       \sin(3\,{\tilde\omega}) \right\rbrace \nonumber\end{aligned}

where \beta = \sqrt{1-e^2}. The mean longitude is

\lambda_m = \lambda_{m0} + \frac{2\,\pi\,(d-d_{ve})}{365}

where d_{ve}=80.5 is the calendar day for the vernal equinox at noon on March 21. The true longitude \lambda is then given by:

\lambda = \lambda_m & + & \left(2\,e -
   \frac{e^3}{4}\right)\sin(\lambda_m-{\tilde\omega}) \nonumber \\ & + &
   \frac{5\,e^2}{4}\,\sin\left[2(\lambda_m-{\tilde\omega})\right] \\ & + &
   \frac{13\,e^3}{12}\sin\left[3(\lambda_m-{\tilde\omega})\right] \nonumber\end{aligned}

The orbital state used to calculate the insolation is held fixed over the length of the model integration. This state may be specified in one of two ways. The first method is to specify a year for computing t. The value of the year is held constant for the entire length of the integration. The year must fall within the range of 1950 \pm
10^6. The second method is to specify the eccentricity factor e, longitude of perihelion {\tilde\omega}- 180^\circ, and obliquity \epsilon. This set of values is sufficient to specify the complete orbital state. Settings for AMIP II style integrations under 1995 AD conditions are \epsilon = 23.4441, e = 0.016715, and {\tilde\omega}-
180 = 102.7.

6.9.7. Solar Spectral Irradiance

The reference spectrum assumed by RRTMG is the Kurucz spectrum. specifies the solar spectral irradiance in a file, based on the work of Lean (Wang, Lean, and Sheeley 2005). The Kurucz spectrum can be seen in figure [fig:kurucz]. The Lean data seen in figure [fig:lean] is time-varying and the graphed values are an average over one solar cycle. These two spectra postulate different values of the total solar irradiance. A graph of the relative difference between them can be seen in figure fig_rel_sol.

Solar Irradiance Kurucz Lean
Total 1368.60 1366.96
In RRTMG bands 1368.14 1366.39
>12195 nm 0.46 0.46
[120,200] nm 0 0.11
EUV 0 0.0047


RRTMG \lambda_{high}, \lambda_{low}, Kurucz Lean Lean Relative Lean(t) Max % Lean(t) Max
Band Index nm nm W/m^2 W/m^2
  • Kurucz
% Variation \DeltaFlux
14 12195 3846 12.79 12.78 -0.01 -0.08 0.16 0.020
1 3846 3077 12.11 11.99 -0.12 -1.00 0.02 0.003
2 3077 2500 20.36 20.22 -0.14 -0.69 0.03 0.007
3 2500 2151 23.73 23.49 -0.24 -1.02 0.02 0.005
4 2151 1942 22.43 22.17 -0.26 -1.17 0.01 0.003
5 1942 1626 55.63 55.61 -0.02 -0.04 0.02 0.011
6 1626 1299 102.9 102.9 0.0
0.02 0.019
7 1299 1242 24.29 24.79 0.50 2.06 0.04 0.011
8 1242 778 345.7 348.9 3.2 0.93 0.06 0.226
9 778 625 218.1 218.2 0.1 0.05 0.11 0.238
10 625 441 347.2 344.9 -2.3 -0.67 0.13 0.463
11 441 345 129.5 130.0 0.5 0.39 0.26 0.340
12 345 263 50.15 47.41 -2.74 -5.78 0.45 0.226
13 263 200 3.120 3.129 0.009 0.29 4.51 0.141

Table: Band-level ratio of Solar Irradiances, based on average of one solar cycle


Kurucz spectrum. ssf in W/m^2/nm. Source Data: AER. Range from [20, 20000] nm.


Lean spectrum. Average over 1 solar cycle, May 1, 1996 to Dec 31, 2006. Source Data: Marsh. ssf in W/m^2/nm. Range from [120, 99975] nm.


Relative difference, \frac{\tt{Lean} - \tt{Kurucz}}{.5(\tt{Lean} + \tt{Kurucz}) } between spectra. RRTMG band boundaries are marked with vertical lines.

The heating in each band b is scaled by the ratio, \frac{{\tt Lean}(t)_b}{{\tt Kurucz}_b}, where {\tt Kurucz}_b is assumed by RRTMG as specified in table tbl_flux_diff, and {\tt Lean}(t)_b is the solar irradiance specified by the time-dependent solar spectral irradiance file. {\tt Lean}(t)_{14} includes the Lean irradiance longward of 12195 nm to capture irradiance in the very far infrared.

6.10. Surface Exchange Formulations

The surface exchange of heat, moisture and momentum between the atmosphere and land, ocean or ice surfaces are treated with a bulk exchange formulation. We present a description of each surface exchange separately. Although the functional forms of the exchange relations are identical, we present the descriptions of these components as developed and represented in the various subroutines in . The differences in the exchange expressions are predominantly in the definition of roughness lengths and exchange coefficients. The description of surface exchange over ocean follows from Bryan et al. (1996), and the surface exchange over sea ice is discussed in the sea-ice model documentation. Over lakes, exchanges are computed by a lake model embedded in the land surface model described in the following section.

6.10.1. Land

In , the NCAR Land Surface Model (LSM) (Bonan 1996) has been replaced by the Community Land Model CLM2 (Bonan et al. 2002). This new model includes components treating hydrological and biogeochemical processes, dynamic vegetation, and biogeophysics. Because of the increased complexity of this new model and since a complete description is available online, users of interested in CLM should consult this documentation at . A discussion is provided here only of the component of CLM which controls surface exchange processes.

Land surface fluxes of momentum, sensible heat, and latent heat are calculated from Monin-Obukhov similarity theory applied to the surface (i.e. constant flux) layer. The zonal \tau_x and meridional \tau_y momentum fluxes (kg m:math:{}^{-1}s{}^{-2}), sensible heat H (W m:math:{}^{-2}) and water vapor E (kg m:math:{}^{-2}s{}^{-1}) fluxes between the surface and the lowest model level z_1 are:

\tau_x &= - \rho_1 \overline {(u'w')} & &= - \rho_1 u_*^2 (u_1 /V_a )
&&= \rho_1 \frac{{u_s - u_1 }}{{r_{am} }} \\ \tau_y &= - \rho_1
\overline {(v'w')} & &= - \rho_1 u_*^2 (v_1 /V_a ) &&= \rho_1
\frac{{v_s - v_1 }}{{r_{am} }} \\ H &= \phantom{-}\rho_1 c_p
(\overline {w'\theta '} )& &= - \rho_1 c_p u_* \theta_* &&= \rho_1 c_p
\frac{{\theta_{s} - \theta_1 }} {{r_{ah} }} \\ E &= \phantom{-}\rho_1
(\overline {w'q'} )& &= - \rho_1 u_* q_* &&= \rho_1 \frac{{q_{s} - q_1
}}{{r_{aw} }}\end{aligned}

r_{am} &= V_a /u_*^2 \\ r_{ah} &= (\theta_1 - \theta_s )/u_* \theta_*
\\ r_{aw} &= (q_1 - q_s )/u_* q_*\end{aligned}

where \rho_1, u_1, v_1, \theta_1 and q_1 are the density (kg m:math:^{-3}), zonal wind (m s:math:^{-1}), meridional wind (m s:math:^{-1}), air potential temperature (K), and specific humidity (kg kg:math:^{-1}) at the lowest model level. By definition, the surface winds u_s and v_s equal zero. The symbol \theta_1 represents temperature, and q_1 is specific humidity at surface. The terms r_{am}, r_{ah}, and r_{aw} are the aerodynamic resistances (s m:math:^{-1}) for momentum, sensible heat, and water vapor between the lowest model level at height z_1 and the surface at height z_{0m}+d [z_{0h}+d]. Here z_{0m} [z_{0h}] is the roughness length (m) for momentum [scalar] fluxes, and d is the displacement height (m).

For the vegetated fraction of the grid, \theta_s = T_{af} and q_s = q_{af}, where T_{af} and q_{af} are the air temperature and specific humidity within canopy space. For the non-vegetated fraction, \theta_s = T_g and q_s = q_g, where T_g and q_g are the air temperature and specific humidity at ground surface. These terms are described by Dai et al. (2001). Roughness lengths and zero-plane displacement

The aerodynamic roughness z_{0m} is used for wind, while the thermal roughness z_{0h} is used for heat and water vapor. In general, z_{0m} is different from z_{0h}, because the transfer of momentum is affected by pressure fluctuations in the turbulent waves behind the roughness elements, while for heat and water vapor transfer no such dynamical mechanism exists. Rather, heat and water vapor must ultimately be transferred by molecular diffusion across the interfacial sublayer. Over bare soil and snow cover, the simple relation from Zilitinkevich (1970) can be used (Zeng and Dickinson 1998):

\ln \frac{{z_{0m} }} {{z_{0h} }} & = a\left( {\frac{{u_* z_{0m} }}
{\nu }} \right)^{0.45} \\ a &= 0.13 \\ \nu &= 1.5 \times 10^{ - 5}
{\text{m}}^2 {\text{s}}^{-1}\end{aligned}

Over canopy, the application of energy balance

R_n - H - L_v\,E = 0

(where R_n is the net radiation absorbed by the canopy) is equivalent to the use of different z_{0m} versus z_{0h} over bare soil, and hence thermal roughness is not needed over canopy (Zeng, Zhao, and Dickinson 1998).

The roughness z_{0m} is proportional to canopy height, and is also affected by fractional vegetation cover, leaf area index, and leaf shapes. The roughness is derived from the simple relationship z_{0m}
= 0.07\,h_c, where h_c is the canopy height. Similarly, the zero-plane displacement height d is proportional to canopy height, and is also affected by fractional vegetation cover, leaf area index, and leaf shapes. The simple relationship d/h_c = 2/3 is used to obtain the height. Monin-Obukhov similarity theory

A length scale (the Monin-Obukhov length) L is defined by

L = \frac{{\theta_v u_*^2 }} {{kg\theta_{v*}}}

where k is the von Kàrman constant, and g is the gravitational acceleration. L > 0 indicates stable conditions, L < 0 indicates unstable conditions, and L = \infty applies to neutral conditions. The virtual potential temperature \theta_v is defined by

\theta_v = \theta_1 (1 + 0.61q_1 ) = T_a \left( {\frac{{p_s }} {{p_l
}}} \right)^{R/c_p } (1 + 0.61q_1 )

where T_1 and q_1 are the air temperature and specific humidity at height z_1 respectively, \theta_1 is the atmospheric potential temperature, p_l is the atmospheric pressure, and p_s is the surface pressure. The surface friction velocity u_* is defined by

u_*^2 = [\overline {u'w'}^2 + \overline {v'w'}^2 ]^{1/2}

The temperature scale \theta_* and \theta_{ * v} and a humidity scale q_* are defined by

\theta_* &= - \overline {w'\theta '} /u_* \\ q_* &= - \overline {w'q'}
/u_* \\ \theta_{v * } &= - \overline {w'\theta '_v } /u_* \nonumber \\
& \approx - (\overline {w'\theta '} + 0.61\overline \theta \overline
{w'q'} )/u_* \\ & = \theta_* + 0.61\overline \theta q_* \nonumber\end{aligned}

(where the mean temperature \overline \theta serves as a reference temperature in this linearized form of \theta_v ).

The stability parameter is defined as

\varsigma = \frac{{z_1 - d}}{L}\quad ,

with the restriction that - 100 \leqslant \varsigma \leqslant 2. The scalar wind speed is defined as

V_a^2 &= u_1^2 + v_1^2 + U_c^2 \\ U_c &= \left\{ \begin{array}{ll}
  0.1\>{\text{ms}}^{-1} & {\text{, if }}\varsigma \geqslant {\text{0
  (stable)}} \hfill \\ \beta w_* = \beta \left( {z_i \frac{g}
{{\theta_v }}\theta_{v * } u_*} \right)^{1/3} & {\text{, if
}}\varsigma < {\text{0 (unstable)}}\,. \hfill \\
\end{array}  \right.\end{aligned}

Here w_* is the convective velocity scale, z_i is the convective boundary layer height, and \beta = 1. The value of z_i is taken as 1000 m

The flux-gradient relations are given by:

\frac{{k(z_1 - d)}} {{\theta_* }}\frac{{\partial \theta }} {{\partial
z}} &= \phi_h (\varsigma ) \\ \frac{{k(z_1 - d)}} {{q_*
}}\frac{{\partial q}} {{\partial z}} &= \phi_q (\varsigma ) \\
\phi_h &= \phi_q \\
  \phi_m (\varsigma ) &=
            \left\{\begin{array}{ll} (1 - 16\varsigma )^{ - 1/4} &
                                     \mbox{for}~\varsigma < 0 \\ 1 +
                                     5\varsigma & \mbox{for}~0 <
                                     \varsigma < 1
            \end{array}\right. \\
  \phi_h (\varsigma ) &=
            \left\{\begin{array}{ll} (1 - 16\varsigma )^{ - 1/2} &
                                     \mbox{for}~\varsigma < 0 \\ 1 +
                                     5\varsigma & \mbox{for}~0 <
                                     \varsigma < 1

Under very unstable conditions, the flux-gradient relations are taken from Kader and Yaglom (1990):

\phi_m &= 0.7k^{2/3} ( - \varsigma )^{1/3} \\ \phi_h & = 0.9k^{4/3} (
- \varsigma )^{ - 1/3}\end{aligned}

To ensure the functions \phi_m (\varsigma ) and \phi_h (\varsigma
) are continuous, the simplest approach (i.e., without considering any transition regions) is to match the above equations at \varsigma_m = - 1.574 for \phi_m (\varsigma ) and \varsigma_h = -
0.465 for \phi_h (\varsigma ) .

Under very stable conditions (i.e., \varsigma > 1 ), the relations are taken from Holtslag, Bruijn, and Pan (1990):

\phi_m = \phi_h = 5 + \varsigma

Integration of the wind profile yields:

V_a &= \frac{{u_* }} {k}f_M (\varsigma ) &&  \\
f_M (\varsigma ) &= \left\{ {\left[ {\ln \left( {\frac{{\varsigma_m L}}
{{z_{0m} }}} \right) - \psi_m (\varsigma_m )} \right] + 1.14[( -
\varsigma )^{1/3} - ( - \varsigma_m )^{1/3} ]} \right\}\,, & \varsigma
&< \varsigma_m = - 1.574 \\
f_M (\varsigma ) &= \left[ {\ln \left( {\frac{{z_1 - d}} {{z_{0m} }}} \right) - \psi_m
(\varsigma ) + \psi_m \left( {\frac{{z_{0m} }} {L}} \right)}
\right]\,, & \varsigma_m &< \varsigma < 0 \\
f_M (\varsigma ) &= \left[ {\ln \left( {\frac{{z_1 - d}} {{z_{0m} }}}
\right) + 5\varsigma } \right]\,, & 0 &< \varsigma < 1 \\
f_M (\varsigma ) &= \left\{ {\left[ {\ln
\left( {\frac{L} {{z_{0m} }}} \right) + 5} \right] + [5\ln (\varsigma
) + \varsigma - 1]} \right\}\,, & \varsigma &> 1

Integration of the potential temperature profile yields:

\theta_1 - \theta_s & = \frac{{\theta_* }} {k}f_T (\varsigma ) &&
\\ f_T (\varsigma ) & = \left\{ {\left[ {\ln \left(
{\frac{{\varsigma_h L}} {{z_{0h} }}} \right) - \psi_h (\varsigma_h )}
\right] + 0.8[( - \varsigma_h )^{ - 1/3} - ( - \varsigma )^{ - 1/3} ]}
\right\}\,, & \varsigma & < \varsigma_h = - 0.465 \\
f_T (\varsigma ) & = \left[ {\ln \left(
{\frac{{z_1 - d}} {{z_{0h} }}} \right) - \psi_h (\varsigma ) + \psi_h
\left( {\frac{{z_{0h} }} {L}} \right)} \right] \,, & \varsigma_h & <
\varsigma < 0 \\ f_T (\varsigma ) & = \left[
{\ln \left( {\frac{{z_1 - d}} {{z_{0h} }}} \right) + 5\varsigma }
\right] \,, & 0 & < \varsigma < 1 \\ f_T
(\varsigma ) & = \left\{ {\left[ {\ln \left( {\frac{L} {{z_{0h} }}}
\right) + 5} \right] + [5\ln (\varsigma ) + \varsigma - 1]} \right\}
\,, & \varsigma & > 1 \end{aligned}

The expressions for the specific humidity profiles are the same as those for potential temperature except that (\theta_1 - \theta_s ), \theta_* and z_{0h} are replaced by (q_1 - q_s ), q_* and z_{0q} respectively. The stability functions for \varsigma < 0 are

  \psi_m & = 2\ln\left( {\frac{{1 + \chi }}{2}} \right) + \ln\left(
                {\frac{{1 + \chi^2 }}{2}} \right) -
              2\tan^{ - 1} \chi + \frac{\pi }{2} \\ \psi_h & = \psi_q
  = 2\ln\left( {\frac{{1 + \chi^2 }}{2}} \right) \\
\intertext{where} \chi &= (1 - 16\varsigma )^{1/4}\end{aligned}

Note that the CLM code contains extra terms involving z_{0m}
/\varsigma, z_{0h} /\varsigma, and z_{0q} /\varsigma for completeness. These terms are very small most of the time and hence are omitted in Eqs. (81) and (82).

In addition to the momentum, sensible heat, and latent heat fluxes, land surface albedos and upward longwave radiation are needed for the atmospheric radiation calculations. Surface albedos depend on the solar zenith angle, the amount of leaf and stem material present, their optical properties, and the optical properties of snow and soil. The upward longwave radiation is the difference between the incident and absorbed fluxes. These and other aspects of the land surface fluxes have been described by Dai et al. (2001).

6.10.2. Ocean

The bulk formulas used to determine the turbulent fluxes of momentum (stress), water (evaporation, or latent heat), and sensible heat into the atmosphere over ocean surfaces are

( \boldsymbol{\tau}, E, H) = \rho_A \left|\Delta\,{{\boldsymbol{v}}}\right|(C_D
      \Delta\,{{\boldsymbol{v}}}, C_E \Delta\,q, C_p C_H \Delta\theta),

where \rho_A is atmospheric surface density and C_p is the specific heat. Since does not allow for motion of the ocean surface, the velocity difference between surface and atmosphere is \Delta\,{{\boldsymbol{v}}}= {{\boldsymbol{v}}}_A, the velocity of the lowest model level. The potential temperature difference is \Delta\theta =
\theta_A - T_s, where T_s is the surface temperature. The specific humidity difference is \Delta\,q = q_A - q_s(T_s), where q_s(T_s) is the saturation specific humidity at the sea-surface temperature.

In (83) , the transfer coefficients between the ocean surface and the atmosphere are computed at a height Z_A and are functions of the stability, \zeta:

C_{(D,E,H)} = \kappa^2 {\left[\ln\left(\frac{Z_A}{Z_{0m}}\right) -
                              - \psi_{(m,s,s)}\right]}^{-1}

where \kappa = 0.4 is von Kármán’s constant and Z_{0(m,e,h)} is the roughness length for momentum, evaporation, or heat, respectively. The integrated flux profiles, \psi_m for momentum and \psi_s for scalars, under stable conditions (\zeta >
0) are

\psi_m(\zeta) = \psi_s(\zeta) = -5 \zeta.

For unstable conditions (\zeta < 0), the flux profiles are

\psi_m(\zeta) = &2 \ln[0.5(1 + X)] + \ln[0.5(1 + X^2 )] \nonumber \\ &
- 2 \tan^{-1} X + 0.5 \pi,

\psi_s(\zeta) = 2 \ln[0.5(1 + X^2 )],

X = (1 - 16 \zeta)^{1/4} .

The stability parameter used in (85)(88) is

\zeta = \frac{\kappa\,g\,Z_A}{u^{*2}}\left(\frac{\theta^*}{\theta_v} +
                                     \frac{Q^*}{(\epsilon^{-1} +

where the virtual potential temperature is \theta_v = \theta_A(1 +
\epsilon q_A); q_A and \theta_A are the lowest level atmospheric humidity and potential temperature, respectively; and \epsilon =
0.606. The turbulent velocity scales in (89) are

u^* = &C_D^{1/2} |\Delta\,{{\boldsymbol{v}}}|, \nonumber\\ (Q^*,\theta^*) =
(\Delta\,q,\Delta\theta). \end{aligned}

Over oceans, Z_{0e} = 9.5 \times 10^{-5} m under all conditions and Z_{0h} = 2.2 \times 10^{-9} m for \zeta > 0, Z_{0h} = 4.9 \times
10^{-5} m for \zeta \le 0, which are given in Large and Pond (1982). The momentum roughness length depends on the wind speed evaluated at 10 m as

Z_{om} &= 10\,\exp\left[-\kappa{\left(\frac{c_4}{U_{10}} + c_5 +
U_{10} &= U_A {\left[1 +
     \frac{\sqrt{C_{10}^N}}{\kappa}\ln\left(\frac{Z_A}{10} -
     \psi_m\right)\right]}^{-1}\,, \end{aligned}

where c_4 = 0.0027  m s{}^{-1}, c_5 = 0.000142, c_6 =
0.0000764 m:math:{}^{-1} s, and the required drag coefficient at 10-m height and neutral stability is C^{N}_{10} = c_4 U^{-1}_{10} + c_5 +
c_6 U_{10} as given by Large, McWilliams, and Doney (1994).

The transfer coefficients in (83) and (84) depend on the stability following (85)(88) , which itself depends on the surface fluxes (89) and (90) . The transfer coefficients also depend on the momentum roughness, which itself varies with the surface fluxes over oceans (91) . The above system of equations is solved by iteration.

6.10.3. Sea Ice

The fluxes between the atmosphere and sea ice are described in detail in the sea-ice model documentation.

6.11. Dry Adiabatic Adjustment

If a layer is unstable with respect to the dry adiabatic lapse rate, dry adiabatic adjustment is performed. The layer is stable if

\frac{\partial T}{\partial p} < \frac{\kappa T}{p}.

In finite–difference form, this becomes

T_{k+1} - T_k &< C1_{k+1} (T_{k+1} + T_k) + \delta ,
\\[-1.0em] \intertext{where}\nonumber\\[-2.0em] C1_{k+1}&
= \frac{\kappa (p_{k+1} - p_k)}{2 p_{k+1/2}}

If there are any unstable layers in the top three model layers, the temperature is adjusted so that (93) is satisfied everywhere in the column. The variable \delta represents a convergence criterion. The adjustment is done so that sensible heat is conserved,

c_p(\hat{T}_k \Delta p_k + \hat{T}_{k+1} \Delta p_{k+1}) = c_p (T_k
\Delta p_k + T_{k+1} \Delta p_{k+1}) ,

and so that the layer has neutral stability:

\hat{T}_{k+1} - \hat{T}_k = C1_{k+1} (\hat{T}_{k+1} + \hat{T}_k)\, .

As mentioned above, the hats denote the variables after adjustment. Thus, the adjusted temperatures are given by

\hat{T}_{k+1} &= \frac{\Delta p_k}{\Delta p_{k+1} + \Delta p_k
C2_{k+1}} T_k + \frac{\Delta p_{k+1}}{\Delta p_{k+1} + \Delta p_k
C2_{k+1}} T_{k+1},  \\[-1.0em]
\intertext{and}\nonumber\\[-2.0em] \hat{T}_k &= C2_{k+1} \hat{T}_{k+1}
, \\[-1.0em] \intertext{where}\nonumber\\[-2.0em]
C2_{k+1} &= \frac{1 - C1_{k+1}}{1 + C1_{k+1}}

Whenever the two layers undergo dry adjustment, the moisture is assumed to be completely mixed by the process as well. Thus, the specific humidity is changed in the two layers in a conserving manner to be the average value of the original values,

\hat{q}_{k+1} = \hat{q}_k = (q_{k+1} \Delta p_{k+1} + q_k \Delta
p_k)/(\Delta p_{k+1} + \Delta p_k) .

The layers are adjusted iteratively. Initially, \delta = 0.01 in the stability check (93) . The column is passed through from k=1 to a user-specifiable lower level (set to 3 in the standard model configuration) up to 15 times; each time unstable layers are adjusted until the entire column is stable. If convergence is not reached by the 15th pass, the convergence criterion is doubled, a message is printed, and the entire process is repeated. If \delta exceeds 0.1 and the column is still not stable, the model stops.

As indicated above, the dry convective adjustment is only applied to the top three levels of the standard model. The vertical diffusion provides the stabilizing vertical mixing at other levels. Thus, in practice, momentum is mixed as well as moisture and potential temperature in the unstable case.

6.12. Prognostic Greenhouse Gases

The principal greenhouse gases whose longwave radiative effects are included in  are H_2O, CO_2, O_3, CH_4, N_2O, CFC11, and CFC12. The prediction of water vapor is described elsewhere in this chapter, and CO_2 is assumed to be well mixed. Monthly O_3 fields are specified as input, as described in chapter [datafiles]. The radiative effects of the other four greenhouse gases (CH_4, N_2O, CFC11, and CFC12) may be included in  through specified concentration distributions (Kiehl et al. 1998) or prognostic concentrations (Boville et al. 2001).

The specified distributions are globally uniform in the troposphere. Above a latitudinally and seasonally specified tropopause height, the distributions are zonally symmetric and decrease upward, with a separate latitude-dependent scale height for each gas.

Prognostic distributions are computed following Boville et al. (2001). Transport equations for the four gases are included, and losses have been parameterized by specified zonally symmetric loss frequencies: \partial q / \partial t =  - \alpha ( y, z, t ) q. Monthly averaged loss frequencies, \alpha, are obtained from the two-dimensional model of Garcia and Solomon (1994).

We have chosen to specify globally uniform surface concentrations of the four gases, rather than their surface fluxes. The surface sources are imperfectly known, particularly for CH_4 and N_2O in preindustrial times. Even given constant sources and reasonable initial conditions, obtaining equilibrium values for the loading of these gases in the atmosphere can take many years.  was designed for tropospheric simulation with relatively coarse vertical resolution in the upper troposphere and lower stratosphere. It is likely that the rate of transport into the stratosphere will be misrepresented, leading to erroneous loading and radiative forcing if surface fluxes are specified. Specifying surface concentrations has the advantage that we do not need to worry much about the atmospheric lifetime. However, we cannot examine observed features such as the interhemispheric gradient of the trace gases. For climate change experiments, the specified surface concentrations are varied but the stratospheric loss frequencies are not.

Oxidation of CH_4 is an important source of water vapor in the stratosphere, contributing about half of the ambient mixing ratio over much of the stratosphere. Although CH_4 is not generally oxidized directly into water vapor, this is not a bad approximation, as shown by Le Texier, Solomon, and Garcia (1988). In , it is assumed that the water vapor (volume mixing ratio) source is twice the CH_4 sink. This approach was also taken by Mote et al. (1993) for middle atmosphere studies with an earlier version of the CCM. This part of the water budget is of some importance in climate change studies, because the atmospheric CH_4 concentrations have increased rapidly with time and this increase is projected to continue into the next century (e.g., Alcamo et al. (1995)) The representation of stratospheric water vapor in  is necessarily crude, since there are few levels above the tropopause. However, the model is capable of capturing the main features of the CH_4 and water distributions.