The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty

被引：1044

作者：

Lawrence, David M. ^{[1
]}

Fisher, Rosie A. ^{[1
]}

Koven, Charles D. ^{[2
]}

Oleson, Keith W. ^{[1
]}

Swenson, Sean C. ^{[1
]}

Bonan, Gordon ^{[1
]}

Collier, Nathan ^{[3
]}

Ghimire, Bardan ^{[2
]}

van Kampenhout, Leo ^{[4
]}

Kennedy, Daniel ^{[5
]}

Kluzek, Erik ^{[1
]}

Lawrence, Peter J. ^{[1
]}

Li, Fang ^{[6
]}

Li, Hongyi ^{[7
]}

Lombardozzi, Danica ^{[1
]}

Riley, William J. ^{[2
]}

Sacks, William J. ^{[1
]}

Shi, Mingjie ^{[8
,9
]}

Vertenstein, Mariana ^{[1
]}

Wieder, William R. ^{[1
,18
]}

Xu, Chonggang ^{[10
]}

Ali, Ashehad A. ^{[11
]}

Badger, Andrew M. ^{[12
]}

Bisht, Gautam ^{[2
]}

van den Broeke, Michiel ^{[4
]}

Brunke, Michael A. ^{[13
]}

Burns, Sean P. ^{[14
,35
]}

Buzan, Jonathan ^{[15
]}

Clark, Martyn ^{[1
]}

Craig, Anthony ^{[1
]}

Dahlin, Kyla ^{[16
]}

Drewniak, Beth ^{[17
]}

Fisher, Joshua B. ^{[8
,9
]}

Flanner, Mark ^{[19
]}

Fox, Andrew M. ^{[20
]}

Gentine, Pierre ^{[5
]}

Hoffman, Forrest ^{[3
]}

Keppel-Aleks, Gretchen ^{[21
]}

Knox, Ryan ^{[2
]}

Kumar, Sanjiv ^{[22
]}

Lenaerts, Jan ^{[23
]}

Leung, L. Ruby ^{[24
]}

Lipscomb, William H. ^{[1
]}

Lu, Yaqiong ^{[25
]}

Pandey, Ashutosh ^{[22
]}

Pelletier, Jon D. ^{[26
]}

Perket, Justin ^{[1
,27
]}

Randerson, James T. ^{[28
]}

Ricciuto, Daniel M. ^{[29
]}

Sanderson, Benjamin M. ^{[30
]}

机构：

[1] Natl Ctr Atmospher Res, Climate & Global Dynam Lab, POB 3000, Boulder, CO 80307 USA

[2] Lawrence Berkeley Natl Lab, Climate & Ecosyst Sci Div, Berkeley, CA USA

[3] Oak Ridge Natl Lab, Comp Sci & Engn Div, Oak Ridge, TN USA

[4] Univ Utrecht, Inst Marine & Atmospher Res Utrecht, Utrecht, Netherlands

[5] Columbia Univ, Dept Earth & Environm Engn, New York, NY USA

[6] Chinese Acad Sci, Int Ctr Climate & Environm Sci, Inst Atmospher Phys, Beijing, Peoples R China

[7] Univ Houston, Dept Civil & Environm Engn, Houston, TX 77204 USA

[8] CALTECH, Jet Prop Lab, Pasadena, CA USA

[9] Univ Calif Los Angeles, Joint Inst Reg Earth Syst Sci & Engn, Los Angeles, CA USA

[10] Los Alamos Natl Lab, Los Alamos, NM USA

[11] Harvard Univ, Dept Organism & Evolutionary Biol, Cambridge, MA 02138 USA

[12] Cooperat Inst Res Environm Bethental Sci, Boulder, CO USA

[13] Univ Arizona, Dept Hydrol & Atmospher Sci, Tucson, AZ USA

[14] Univ Colorado, Dept Geog, Boulder, CO 80309 USA

[15] Purdue Univ, Dept Earth Atmospher & Planetary Sci, W Lafayette, IN 47907 USA

[16] Michigan State Univ, Dept Geog Environm & Spatial Sci, E Lansing, MI 48824 USA

[17] Argonne Natl Lab, Environm Sci Div, 9700 S Cass Ave, Argonne, IL 60439 USA

[18] Univ Colorado, Inst Arctic & Alpine Res, Boulder, CO 80309 USA

[19] Univ Michigan, Dept Climate & Space Sci & Engn, Ann Arbor, MI 48109 USA

[20] Univ Arizona, Sch Nat Resources & Environm, Tucson, AZ USA

[21] Univ Michigan, Climate & Space Sci & Engn, Ann Arbor, CA USA

[22] Auburn Univ, Sch Forestry & Wildlife Sci, Auburn, AL 36849 USA

[23] Univ Colorado, Dept Atmospher & Ocean Sci, Boulder, CO 80309 USA

[24] Pacific Northwest Natl Lab, Atmospher Sci & Global Change Div, Richland, WA 99352 USA

[25] Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu, Sichuan, Peoples R China

[26] Univ Arizona, Dept Geosci, Tucson, AZ 85721 USA

[27] NASA, Goddard Space Flight Ctr, Greenbelt, MD USA

[28] Univ Calif Irvine, Dept Earth Syst Sci, Irvine, CA USA

[29] Oak Ridge Natl Lab, Environm Sci Div, Oak Ridge, TN USA

[30] CERFACS, Toulouse, France

[31] Natl Snow & Ice Data Ctr, Boulder, CO USA

[32] Energy & Environm Econ, San Francisco, CA USA

[33] Virginia Polytech Inst & State Univ, Dept Forest Resources & Environm Conservat, Blacksburg, VA 24061 USA

[34] Univ Sheffield, Leverhulme Ctr Climate Change Mitigat, Anim & Plant Sci Dept, Sheffield, S Yorkshire, England

[35] Natl Ctr Atmospher Res, Mesoscale & Microscale Meteorol Lab, POB 3000, Boulder, CO 80307 USA

来源：

JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS | 2019年 / 11卷 / 12期

基金：

美国国家航空航天局; 美国国家科学基金会;

关键词：

global land model; Earth System Modeling; carbon and nitrogen cycling; hydrology; benchmarking; NET PRIMARY PRODUCTIVITY; CARBON USE EFFICIENCY; GLOBAL SOIL-MOISTURE; WATER-USE EFFICIENCY; EARTH SYSTEM MODEL; ICE-SHEET MODEL; INTERCOMPARISON PROJECT; DATA ASSIMILATION; PHOTOSYNTHETIC CAPACITY; TERRESTRIAL ECOSYSTEMS;

D O I：

10.1029/2018MS001583

中图分类号：

P4 [大气科学（气象学）];

学科分类号：

0706 ; 070601 ;

摘要：

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.

引用

页码：4245 / 4287

页数：43

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