Land

Land#

The land component of CESM is the Community Land Model (CLM), also refered to as the Community Terrestrial Systems Model (CTSM).

It can be useful for people interested in land science to run simulations with only an active land components and atmospheric forcing. In this exercise, you will learn how to run one of these land-only simulations.

These exercises were created by Adrianna Foster and Erik Kluzek.

Learning Goals#

  • Students will learn what an “I” compset is, the types of forcing available to run one, and how to run one.

  • Students will learn how to run a default CLM (“big leaf”) case with satellite phenology and one with prognostic BGC and crops and compare the two experiments.

  • Students will learn how to run FATES and compare to big leaf CLM output.

  • Students will learn how to modify an input parameter file and investigate its effect on output.

Exercise Details#

  • This exercise uses the same code base as the rest of the tutorial.

  • You will be using the I2000Clm60Sp, IHistClm60BgcCrop, and I2000Clm60FatesSpCrujraRsGs compsets at the f19_f19_mt233 resolution.

  • You will run a CLM SP simulation, a CLM BGC simulation, and two FATES SP simulations.

  • The first two use the GSWP3 atmospheric forcing and the last two use CRUJRA-2024 forcing.

  • You will modify a json input file.

  • You will use the xarray and matplotlib libraries to evaluate how the simulations differ.

Useful CLM and FATES references#

CESM CLM/CTSM User’s Guide

CESM CLM/CTSM Discussion Forum

FATES Users Guide

What is an I case?#

“I” compsets have active CLM with a data atmosphere (DATM). The sea ice, ocean, atmosphere, and wave models are not active. There are three types of options for I compsets:

  • SP: Satellite Phenology (with CLM big leaf)

  • BGC: Biogeochemistry (with CLM big leaf)

  • FATES: The FATES cohort vegetation model instead of big leaf, either SP or BGC

We will use the Satellite Phenology option for our first simulation.

As explained earlier, you can get information on compsets by using “query_config in the cime/scripts directory under your CESM checkout.

cd /glade/u/home/$USER/code/my_cesm_code
cime/scripts/query_config --compsets clm | grep "I2000Clm60Sp "

Which will return the following:

I2000Clm60Sp : 2000_DATM%GSWP3v1_CLM60%SP_SICE_SOCN_MOSART_SGLC_SWAV

Which breaks down into:

Field

Description

2000

year 2000 conditions

DATM%GSWP3v1

Data Atmosphere Model using GSWP3v1 forcing

CLM60%SP

CTSM with clm6_0 physics defaults for big-leaf model with Satellite Phenology

SICE

Stub Sea-ice

SOCN

Stub Ocean

MOSART

MOSART River model

SGLC

Stub Glacier model

SWAV

Stub Ocean Wave model

Post processing and viewing your output#

You will use python and the xarray and matplotlib libraries to evaluate how the experiments differ.

  1. You can read in multiple history files using the xarray command:

ds = open_mfdataset(files)

  1. Look at differences between the same variable on two different xarray datasets:

gpp_diff = ds_1.GPP - ds_2.GPP

  1. Examine variables using the .plot() function

ds.GPP.plot()
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