TY - JOUR
T1 - The Community Land Model Version 5
T2 - Description of New Features, Benchmarking, and Impact of Forcing Uncertainty
AU - Lawrence, David M.
AU - Fisher, Rosie A.
AU - Koven, Charles D.
AU - Oleson, Keith W.
AU - Swenson, Sean C.
AU - Bonan, Gordon
AU - Collier, Nathan
AU - Ghimire, Bardan
AU - van Kampenhout, Leo
AU - Kennedy, Daniel
AU - Kluzek, Erik
AU - Lawrence, Peter J.
AU - Li, Fang
AU - Li, Hongyi
AU - Lombardozzi, Danica
AU - Riley, William J.
AU - Sacks, William J.
AU - Shi, Mingjie
AU - Vertenstein, Mariana
AU - Wieder, William R.
AU - Xu, Chonggang
AU - Ali, Ashehad A.
AU - Badger, Andrew M.
AU - Bisht, Gautam
AU - van den Broeke, Michiel
AU - Brunke, Michael A.
AU - Burns, Sean P.
AU - Buzan, Jonathan
AU - Clark, Martyn
AU - Craig, Anthony
AU - Dahlin, Kyla
AU - Drewniak, Beth
AU - Fisher, Joshua B.
AU - Flanner, Mark
AU - Fox, Andrew M.
AU - Gentine, Pierre
AU - Hoffman, Forrest
AU - Keppel-Aleks, Gretchen
AU - Knox, Ryan
AU - Kumar, Sanjiv
AU - Lenaerts, Jan
AU - Leung, L. Ruby
AU - Lipscomb, William H.
AU - Lu, Yaqiong
AU - Pandey, Ashutosh
AU - Pelletier, Jon D.
AU - Perket, Justin
AU - Randerson, James T.
AU - Ricciuto, Daniel M.
AU - Sanderson, Benjamin M.
AU - Slater, Andrew
AU - Subin, Zachary M.
AU - Tang, Jinyun
AU - Thomas, R. Quinn
AU - Val Martin, Maria
AU - Zeng, Xubin
N1 - Funding Information:
We would like to thank the reviewers for their insightful comments and helpful suggestions that improved the clarity and presentation of the manuscript. The CESM project is supported primarily by the National Science Foundation (NSF). This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the NSF under Cooperative Agreement 1852977. Computing and data storage resources, including the Cheyenne supercomputer (doi: 10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. D. M. L. was supported in part by the RUBISCO Scientific Focus Area (SFA), which is sponsored by the Regional and Global Climate Modeling (RGCM) Program in the Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research in the U.S. Department of Energy Office of Science. D. K. and P. G. were supported by Columbia University Presidential Fellowship. G. B., D. L. L., W. R. W., and R. Q. T. were supported by the U.S. Department of Agriculture NIFA Award 2015-67003-23485. W. R. W. and G. K. A. were supported by the NASA Interdisciplinary Science Program Award NNX17AK19G. J. B. F. and M. S. carried out the research in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. California Institute of Technology. Government sponsorship acknowledged. All rights reserved. J. B. F. and M. S. were supported in part by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Terrestrial Ecosystem Science program under Awards DE-SC0008317 and DE-SC0016188; the National Science Foundation Ecosystem Science program (DEB-1153401); and NASA's CARBON and TE programs. All model data are archived and publicly available at the UCAR/NCAR Climate Data Gateway (https://doi.org/10.5065/d6154fwh).
Funding Information:
We would like to thank the reviewers for their insightful comments and helpful suggestions that improved the clarity and presentation of the manuscript. The CESM project is supported primarily by the National Science Foundation (NSF). This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the NSF under Cooperative Agreement 1852977. Computing and data storage resources, including the Cheyenne supercomputer (doi: 10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. D. M. L. was supported in part by the RUBISCO Scientific Focus Area (SFA), which is sponsored by the Regional and Global Climate Modeling (RGCM) Program in the Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research in the U.S. Department of Energy Office of Science. D. K. and P. G. were supported by Columbia University Presidential Fellowship. G. B., D. L. L., W. R. W., and R. Q. T. were supported by the U.S. Department of Agriculture NIFA Award 2015‐67003‐23485. W. R. W. and G. K. A. were supported by the NASA Interdisciplinary Science Program Award NNX17AK19G. J. B. F. and M. S. carried out the research in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. California Institute of Technology. Government sponsorship acknowledged. All rights reserved. J. B. F. and M. S. were supported in part by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Terrestrial Ecosystem Science program under Awards DE‐SC0008317 and DE‐SC0016188; the National Science Foundation Ecosystem Science program (DEB‐1153401); and NASA's CARBON and TE programs. All model data are archived and publicly available at the UCAR/NCAR Climate Data Gateway ( https://doi.org/10.5065/d6154fwh ).
Publisher Copyright:
©2019. The Authors.
PY - 2019/12/1
Y1 - 2019/12/1
N2 - The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time-evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5.
AB - The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time-evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5.
KW - Earth System Modeling
KW - benchmarking
KW - carbon and nitrogen cycling
KW - global land model
KW - hydrology
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UR - http://www.scopus.com/inward/citedby.url?scp=85076429883&partnerID=8YFLogxK
U2 - 10.1029/2018MS001583
DO - 10.1029/2018MS001583
M3 - Article
AN - SCOPUS:85076429883
VL - 11
SP - 4245
EP - 4287
JO - Journal of Advances in Modeling Earth Systems
JF - Journal of Advances in Modeling Earth Systems
SN - 1942-2466
IS - 12
ER -