NCAR CESM2 Large Ensemble Cloud-Optimized Dataset
Contents
NCAR CESM2 Large Ensemble Cloud-Optimized Dataset¶
Overview¶
The National Center for Atmospheric Research partnered with the IBS Center for Climate Physics in South Korea to generate the CESM2 Large Ensemble which consists of 100 members at 1 degree spatial resolution covering the period 1850-2100 under CMIP6 historical and SSP370 future radiative forcing scenarios. Data sets from this ensemble were made available via the Climate Data Gateway on June 14th, 2021.
Acknowledgement¶
When presenting results based on the CESM2 Large Ensemble in either oral or written form, please acknowledge the CESM2 Large Ensemble Community Project and supercomputing resources provided by the IBS Center for Climate Physics in South Korea, and reference the DOI of the Rodgers et al. (2021) - preprint.
Experiment Explanation¶
As part of the CESM2 Large Ensemble project, it was decided to use two separate sets of biomass burning emissions forcing files, with the first 50 members having been forced with CMIP6 protocols identical to those used in Danabasoglu et al. (2020) in the presentation paper for CESM2. For the second set of 50 ensemble members, the pertinent species for biomass burning fluxes from the CMIP6 protocols were smoothed with an 11-year running mean filter, impacting the fluxes over the years 1990-2020. The first set of 50 ensemble members is denoted CMIP6, and the second set of 50 SMBB, for smoothed biomass burning. All smoothing was done only after projecting fields onto the CESM2 grid. The biomass burning fluxes are represented in Fig. S2, and the difference in ensemble mean response in Northern Hemisphere sea ice and surface temperature in Fig. S3, of the preprint of the presentation paper. The enhanced biomass burning for the CMIP6 versus SMBB groupings has an impact on large-scale climate over 1990-2020, as indicated through the acceleration of Arctic sea ice loss during September as well as through warming over the Northern Hemisphere and the tropical Pacific.
Unlike the CESM1 Large Ensemble, the CESM2 Large Ensemble uses a combination of different oceanic and atmospheric initial states to create ensemble spread as follows.
Ensemble Member Description¶
Members 1-10: These will begin from years 1001, 1021, 1041, 1061, 1081, 1101, 1121, 1141, 1161, and 1181 of the 1400-year pre-industrial control simulation. This segment of the control simulation was chosen to minimize drift.
Members 11-90: These begin from 4 pre-selected years of the pre-industrial control simulation based on the phase of the Atlantic Meridional Overturning Circulation (AMOC). For each of the 4 initial states, there will be 20 ensemble members created by randomly perturbing the atmospheric temperature field by order 10^-14K. The chosen start dates (model years 1231, 1251, 1281, and 1301) sample AMOC and Sea Surface Height (SSH) in the Labrador Sea at their maximum, minimum and transition states.
Members 91-100: These will begin from years 1011, 1031, 1051, 1071, 1091, 1111, 1131, 1151, 1171, and 1191 of the 1400-year pre-industrial control simulation. This set will include the extensive “MOAR” output, which can be used to drive regional climate models.
The initialization design will allow assessment of oceanic (AMOC) and atmospheric contributions to ensemble spread, and the impact of AMOC initial-condition memory on the global earth system.
Here are a couple of graphics from the official CESM2-LE Documentation, helping to illustrate the experiment design.
Data¶
Zarr format: The CESM2-LE data on AWS are structured according to the Zarr storage format. There are independent Zarr stores for each component, frequency, experiment, and variable. The naming convention is: {component}/{frequency}/cesm2LE-{experiment}-{forcing_variant}-{variable}.zarr where:
component = atm (atmosphere), lnd (land), ocn (ocean), ice
frequency = monthly, daily, or hourly6
experiment = historical (1850 to 2015) or ssp370 (2015 to 2100)
forcing_variant = the biomass forcing variant, cmip6 (the default in the cmip6 runs) or smbb (smoothed biomass burning)
variable = one of the variable names listed in the tables below
Within each of these Zarr stores are the datasets with an additional member_id dimension
Data Catalog¶
import pandas as pd
from ipyaggrid import Grid
from IPython.display import HTML
df = pd.read_csv(
"https://ncar-cesm2-lens.s3-us-west-2.amazonaws.com/catalogs/aws-cesm2-le.csv"
)
column_defs = [
{"headerName": "variable", "field": "variable", "rowGroup": False},
{"headerName": "long_name", "field": "long_name", "rowGroup": False},
{"headerName": "component", "field": "component", "rowGroup": False},
{"headerName": "experiment", "field": "experiment", "rowGroup": False},
{"headerName": "forcing_variant", "field": "forcing_variant", "rowGroup": False},
{"headerName": "frequency", "field": "frequency", "rowGroup": False},
{
"headerName": "vertical_levels",
"field": "vertical_levels",
"rowGroup": False,
},
{
"headerName": "spatial_domain",
"field": "spatial_domain",
"rowGroup": False,
},
{"headerName": "units", "field": "units", "rowGroup": False},
{"headerName": "start_time", "field": "start_time", "rowGroup": False},
{"headerName": "end_time", "field": "end_time", "rowGroup": False},
{"headerName": "path", "field": "path", "rowGroup": False},
]
grid_options = {
"columnDefs": column_defs,
"enableSorting": True,
"enableFilter": True,
"enableColResize": True,
"enableRangeSelection": True,
}
css_rules = """
.ag-row-hover {
background-color: lightblue !important;
}
.ag-column-hover {
background-color: powderblue;
}
.ag-row-hover .ag-column-hover {
background-color: deepskyblue !important;
}
"""
g = Grid(
grid_data=df,
grid_options=grid_options,
quick_filter=True,
show_toggle_edit=False,
export_csv=False,
export_excel=False,
theme="ag-theme-blue",
show_toggle_delete=False,
columns_fit="auto",
index=False,
keep_multiindex=False,
css_rules=css_rules,
)
html = g.export_html(build=True)
HTML(html)
How is the “member_id” Assembled?¶
Within this analysis ready, cloud native format, we use CMIP standard naming conventions for this dataset. Below are a couple of graphics describing the convention, and how we map from the {control_branch_year}.{ensemble_number} (ex. 1231.001) syntax from above, to the CMIP standard.
Adding a “Helper Function” to Map from Control Branch Year + Member Number to Member ID¶
Below is a sample function, written in Python, to help with mapping from one to the other! Feel free to use it in your analysis.
You will need to select the “plus” button here to expand it!
def add_cmip_member_id(member_id):
member_id_mapping = {'1001.001':'r1i1001p1f1',
'1021.002':'r2i1021p1f1',
'1041.003':'r3i1041p1f1',
'1061.004':'r4i1061p1f1',
'1081.005':'r5i1081p1f1',
'1101.006':'r6i1101p1f1',
'1121.007':'r7i1121p1f1',
'1141.008':'r8i1141p1f1',
'1161.009':'r9i1161p1f1',
'1181.010':'r10i1181p1f1',
'1011.001':'r1i1011p1f2',
'1031.002':'r2i1031p1f2',
'1051.003':'r3i1051p1f2',
'1071.004':'r4i1071p1f2',
'1091.005':'r5i1091p1f2',
'1111.006':'r6i1111p1f2',
'1131.007':'r7i1131p1f2',
'1151.008':'r8i1151p1f2',
'1171.009':'r9i1171p1f2',
'1191.010':'r10i1191p1f2',
'1231.001':'r1i1231p1f1',
'1231.002':'r2i1231p1f1',
'1231.003':'r3i1231p1f1',
'1231.004':'r4i1231p1f1',
'1231.005':'r5i1231p1f1',
'1231.006':'r6i1231p1f1',
'1231.007':'r7i1231p1f1',
'1231.008':'r8i1231p1f1',
'1231.009':'r9i1231p1f1',
'1231.010':'r10i1231p1f1',
'1231.011':'r11i1231p1f2',
'1231.012':'r12i1231p1f2',
'1231.013':'r13i1231p1f2',
'1231.014':'r14i1231p1f2',
'1231.015':'r15i1231p1f2',
'1231.016':'r16i1231p1f2',
'1231.017':'r17i1231p1f2',
'1231.018':'r18i1231p1f2',
'1231.019':'r19i1231p1f2',
'1231.020':'r20i1231p1f2',
'1251.001':'r1i1251p1f1',
'1251.002':'r2i1251p1f1',
'1251.003':'r3i1251p1f1',
'1251.004':'r4i1251p1f1',
'1251.005':'r5i1251p1f1',
'1251.006':'r6i1251p1f1',
'1251.007':'r7i1251p1f1',
'1251.008':'r8i1251p1f1',
'1251.009':'r9i1251p1f1',
'1251.010':'r10i1251p1f1',
'1251.011':'r11i1251p1f2',
'1251.012':'r12i1251p1f2',
'1251.013':'r13i1251p1f2',
'1251.014':'r14i1251p1f2',
'1251.015':'r15i1251p1f2',
'1251.016':'r16i1251p1f2',
'1251.017':'r17i1251p1f2',
'1251.018':'r18i1251p1f2',
'1251.019':'r19i1251p1f2',
'1251.020':'r20i1251p1f2',
'1281.001':'r1i1281p1f1',
'1281.002':'r2i1281p1f1',
'1281.003':'r3i1281p1f1',
'1281.004':'r4i1281p1f1',
'1281.005':'r5i1281p1f1',
'1281.006':'r6i1281p1f1',
'1281.007':'r7i1281p1f1',
'1281.008':'r8i1281p1f1',
'1281.009':'r9i1281p1f1',
'1281.010':'r10i1281p1f1',
'1281.011':'r11i1281p1f2',
'1281.012':'r12i1281p1f2',
'1281.013':'r13i1281p1f2',
'1281.014':'r14i1281p1f2',
'1281.015':'r15i1281p1f2',
'1281.016':'r16i1281p1f2',
'1281.017':'r17i1281p1f2',
'1281.018':'r18i1281p1f2',
'1281.019':'r19i1281p1f2',
'1281.020':'r20i1281p1f2',
'1301.001':'r1i1301p1f1',
'1301.002':'r2i1301p1f1',
'1301.003':'r3i1301p1f1',
'1301.004':'r4i1301p1f1',
'1301.005':'r5i1301p1f1',
'1301.006':'r6i1301p1f1',
'1301.007':'r7i1301p1f1',
'1301.008':'r8i1301p1f1',
'1301.009':'r9i1301p1f1',
'1301.010':'r10i1301p1f1',
'1301.011':'r11i1301p1f2',
'1301.012':'r12i1301p1f2',
'1301.013':'r13i1301p1f2',
'1301.014':'r14i1301p1f2',
'1301.015':'r15i1301p1f2',
'1301.016':'r16i1301p1f2',
'1301.017':'r17i1301p1f2',
'1301.018':'r18i1301p1f2',
'1301.019':'r19i1301p1f2',
'1301.020':'r20i1301p1f2',
}
return member_id_mapping[member_id]
Data Citation¶
Data are freely available and reusable under the terms of the CC-BY-4.0 license. See Terms of Use. If you use these data, we request that you provide attribution in any derived products. The original, complete LENS dataset and the AWS-hosted subset have different DOIs (Digital Object Identifiers) to reflect their differing scope and format, so please cite whichever version of the dataset used, as well as the Rodgers et al. (2021) paper:
Original dataset: https://doi.org/10.5194/esd-2021-50 Rodgers, K. B., Lee, S.-S., Rosenbloom, N., Timmermann, A., Danabasoglu, G., Deser, C., Edwards, J., Kim, J.-E., Simpson, I., Stein, K., Stuecker, M. F., Yamaguchi, R., Bodai, T., Chung, E.-S., Huang, L., Kim, W., Lamarque, J.-F., Lombardozzi, D., Wieder, W. R., and Yeager, S. G.: Ubiquity of human-induced changes in climate variability, Earth Syst. Dynam. Discuss. [preprint], https://doi.org/10.5194/esd-2021-50, in review, 2021.
Rodgers et al. (2021) paper: https://doi.org/10.5194/esd-2021-50 Rodgers, K. B., Lee, S.-S., Rosenbloom, N., Timmermann, A., Danabasoglu, G., Deser, C., Edwards, J., Kim, J.-E., Simpson, I., Stein, K., Stuecker, M. F., Yamaguchi, R., Bodai, T., Chung, E.-S., Huang, L., Kim, W., Lamarque, J.-F., Lombardozzi, D., Wieder, W. R., and Yeager, S. G.: Ubiquity of human-induced changes in climate variability, Earth Syst. Dynam. Discuss. [preprint], https://doi.org/10.5194/esd-2021-50, in review, 2021.
Contact¶
If you have questions or want to submit a data request, please reach out to us on our GitHub Discussions page