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Title: 2016 International Land Model Benchmarking (ILAMB) Workshop Report

Abstract

As Earth system models become increasingly complex, there is a growing need for comprehensive and multi-faceted evaluation of model projections. To advance understanding of biogeochemical processes and their interactions with hydrology and climate under conditions of increasing atmospheric carbon dioxide, new analysis methods are required that use observations to constrain model predictions, inform model development, and identify needed measurements and field experiments. Better representations of biogeochemistry–climate feedbacks and ecosystem processes in these models are essential for reducing uncertainties associated with projections of climate change during the remainder of the 21st century.

Authors:
 [1];  [2];  [3];  [4];  [2];  [5];  [6];  [7];  [8];  [9];  [10];  [11];  [12];  [13];  [14];  [15];  [4];  [16];  [17];  [18] more »;  [19];  [20];  [21];  [22];  [22];  [23];  [24];  [25];  [26];  [27];  [28];  [2];  [1];  [29];  [30];  [31];  [32];  [29];  [2];  [33];  [34];  [26];  [35];  [35] « less
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  3. Univ. of Michigan, Ann Arbor, MI (United States)
  4. National Center for Atmospheric Research, Boulder, CO (United States)
  5. Univ. of California, Irvine, CA (United States)
  6. Stanford Univ., Stanford, CA (United States); Lund Univ., Lund (Sweden)
  7. Univ. of New South Wales, Sydney, NSW (Australia)
  8. Univ. of California, Berkeley, CA (United States)
  9. UK Met Office, Exeter, EX1 3PB (United Kingdom)
  10. Joint Global Change Research Institute, Pacific Northwest National Lab. (PNNL), College Park, MD (United States)
  11. Macquarie Univ., NSW (Australia)
  12. Colorado State Univ., Fort Collins, CO (United States)
  13. Univ. of Wisconsin, Madison, WI (United States)
  14. Deutsches Zentrum fuer Luft- und Raumfahrt (DLR), Oberpfaffenhofen (Germany)
  15. California Inst. of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Lab.
  16. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  17. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  18. Stockholm Univ. (Sweden)
  19. Univ. of Illinois, Urbana, IL (United States)
  20. NASA Goddard Institute for Space Studies, Columbia Univ., New York, NY (United States)
  21. University of Tokyo, Bunkyo-ku, Tokyo (Japan)
  22. NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States)
  23. Tsinghua Univ., Beijing (China). Dept. of Hydraulic Engineering
  24. Univ. of Oklahoma, Norman, OK (United States)
  25. Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States)
  26. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  27. Argonne National Lab. (ANL), Argonne, IL (United States)
  28. Harvard Univ., Cambridge, MA (United States)
  29. Univ. of Colorado, Boulder, CO (United States). National Snow and Ice Data Center, Cooperative Institute for Research in Environmental Sciences
  30. Woods Hole Research Center, Falmouth, MA (United States)
  31. Brookhaven National Lab. (BNL), Upton, NY (United States)
  32. Geophysical Fluid Dynamics Laboratory, Princeton Univ., Princeton, NJ (United States)
  33. Univ. of Edinburgh, Scotland (United Kingdom). School of GeoSciences and NERC National Centre for Earth Observation
  34. Univ. of Oklahoma, Norman, OK (United States); East China Normal Univ. (ECNU), Shanghai (China). Tiantong National Forest Ecosystem Observation and Research Station, School of Ecological and Environmental Sciences
  35. US Department of Energy, Germantown, MD (United States)
Publication Date:
Research Org.:
USDOE Office of Science, Washington, DC (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1330803
Report Number(s):
DOE/SC-0186
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Hoffman, Forrest M., Koven, Charles D., Keppel-Aleks, Gretchen, Lawrence, David M., Riley, William J., Randerson, James T., Ahlström, Anders, Abramowitz, Gabriel, Baldocchi, Dennis D., Best, Martin J., Bond-Lamberty, Benjamin, De Kauwe, Martin G., Denning, A. Scott, Desai, Ankur R., Eyring, Veronika, Fisher, Joshua B., Fisher, Rosie A., Gleckler, Peter J., Huang, Maoyi, Hugelius, Gustaf, Jain, Atul K., Kiang, Nancy Y., Kim, Hyungjum, Koster, Randal D., Kumar, Sujay V., Li, Hongyi, Luo, Yiqi, Mao, Jiafu, McDowell, Nathan G., Mishra, Umakant, Moorcroft, Paul R., Pau, George S.H., Ricciuto, Daniel M., Schaefer, Kevin, Schwalm, Christopher R., Serbin, Shawn P., Shevliakova, Elena, Slater, Andrew G., Tang, Jinyun, Williams, Mathew, Xia, Jianyang, Xu, Chonggang, Joseph, Renu, and Koch, Dorothy. 2016 International Land Model Benchmarking (ILAMB) Workshop Report. United States: N. p., 2017. Web. doi:10.2172/1330803.
Hoffman, Forrest M., Koven, Charles D., Keppel-Aleks, Gretchen, Lawrence, David M., Riley, William J., Randerson, James T., Ahlström, Anders, Abramowitz, Gabriel, Baldocchi, Dennis D., Best, Martin J., Bond-Lamberty, Benjamin, De Kauwe, Martin G., Denning, A. Scott, Desai, Ankur R., Eyring, Veronika, Fisher, Joshua B., Fisher, Rosie A., Gleckler, Peter J., Huang, Maoyi, Hugelius, Gustaf, Jain, Atul K., Kiang, Nancy Y., Kim, Hyungjum, Koster, Randal D., Kumar, Sujay V., Li, Hongyi, Luo, Yiqi, Mao, Jiafu, McDowell, Nathan G., Mishra, Umakant, Moorcroft, Paul R., Pau, George S.H., Ricciuto, Daniel M., Schaefer, Kevin, Schwalm, Christopher R., Serbin, Shawn P., Shevliakova, Elena, Slater, Andrew G., Tang, Jinyun, Williams, Mathew, Xia, Jianyang, Xu, Chonggang, Joseph, Renu, & Koch, Dorothy. 2016 International Land Model Benchmarking (ILAMB) Workshop Report. United States. doi:10.2172/1330803.
Hoffman, Forrest M., Koven, Charles D., Keppel-Aleks, Gretchen, Lawrence, David M., Riley, William J., Randerson, James T., Ahlström, Anders, Abramowitz, Gabriel, Baldocchi, Dennis D., Best, Martin J., Bond-Lamberty, Benjamin, De Kauwe, Martin G., Denning, A. Scott, Desai, Ankur R., Eyring, Veronika, Fisher, Joshua B., Fisher, Rosie A., Gleckler, Peter J., Huang, Maoyi, Hugelius, Gustaf, Jain, Atul K., Kiang, Nancy Y., Kim, Hyungjum, Koster, Randal D., Kumar, Sujay V., Li, Hongyi, Luo, Yiqi, Mao, Jiafu, McDowell, Nathan G., Mishra, Umakant, Moorcroft, Paul R., Pau, George S.H., Ricciuto, Daniel M., Schaefer, Kevin, Schwalm, Christopher R., Serbin, Shawn P., Shevliakova, Elena, Slater, Andrew G., Tang, Jinyun, Williams, Mathew, Xia, Jianyang, Xu, Chonggang, Joseph, Renu, and Koch, Dorothy. Sat . "2016 International Land Model Benchmarking (ILAMB) Workshop Report". United States. doi:10.2172/1330803. https://www.osti.gov/servlets/purl/1330803.
@article{osti_1330803,
title = {2016 International Land Model Benchmarking (ILAMB) Workshop Report},
author = {Hoffman, Forrest M. and Koven, Charles D. and Keppel-Aleks, Gretchen and Lawrence, David M. and Riley, William J. and Randerson, James T. and Ahlström, Anders and Abramowitz, Gabriel and Baldocchi, Dennis D. and Best, Martin J. and Bond-Lamberty, Benjamin and De Kauwe, Martin G. and Denning, A. Scott and Desai, Ankur R. and Eyring, Veronika and Fisher, Joshua B. and Fisher, Rosie A. and Gleckler, Peter J. and Huang, Maoyi and Hugelius, Gustaf and Jain, Atul K. and Kiang, Nancy Y. and Kim, Hyungjum and Koster, Randal D. and Kumar, Sujay V. and Li, Hongyi and Luo, Yiqi and Mao, Jiafu and McDowell, Nathan G. and Mishra, Umakant and Moorcroft, Paul R. and Pau, George S.H. and Ricciuto, Daniel M. and Schaefer, Kevin and Schwalm, Christopher R. and Serbin, Shawn P. and Shevliakova, Elena and Slater, Andrew G. and Tang, Jinyun and Williams, Mathew and Xia, Jianyang and Xu, Chonggang and Joseph, Renu and Koch, Dorothy},
abstractNote = {As Earth system models become increasingly complex, there is a growing need for comprehensive and multi-faceted evaluation of model projections. To advance understanding of biogeochemical processes and their interactions with hydrology and climate under conditions of increasing atmospheric carbon dioxide, new analysis methods are required that use observations to constrain model predictions, inform model development, and identify needed measurements and field experiments. Better representations of biogeochemistry–climate feedbacks and ecosystem processes in these models are essential for reducing uncertainties associated with projections of climate change during the remainder of the 21st century.},
doi = {10.2172/1330803},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Apr 01 00:00:00 EDT 2017},
month = {Sat Apr 01 00:00:00 EDT 2017}
}

Technical Report:

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  • As Earth system models become increasingly complex, there is a growing need for comprehensive and multi-faceted evaluation of model projections. To advance understanding of biogeochemical processes and their interactions with hydrology and climate under conditions of increasing atmospheric carbon dioxide, new analysis methods are required that use observations to constrain model predictions, inform model development, and identify needed measurements and field experiments. Better representations of biogeochemistry–climate feedbacks and ecosystem processes in these models are essential for reducing uncertainties associated with projections of climate change during the remainder of the 21st century.
  • The need to capture important climate feedbacks in general circulation models (GCMs) has resulted in efforts to include atmospheric chemistry and land and ocean biogeochemistry into the next generation of production climate models, called Earth System Models (ESMs). While many terrestrial and ocean carbon models have been coupled to GCMs, recent work has shown that such models can yield a wide range of results (Friedlingstein et al., 2006). This work suggests that a more rigorous set of global offline and partially coupled experiments, along with detailed analyses of processes and comparisons with measurements, are needed. The Carbon-Land Model Intercomparison Projectmore » (C-LAMP) was designed to meet this need by providing a simulation protocol and model performance metrics based upon comparisons against best-available satellite- and ground-based measurements (Hoffman et al., 2007). Recently, a similar effort in Europe, called the International Land Model Benchmark (ILAMB) Project, was begun to assess the performance of European land surface models. These two projects will now serve as prototypes for a proposed international land-biosphere model benchmarking activity for those models participating in the IPCC Fifth Assessment Report (AR5). Initially used for model validation for terrestrial biogeochemistry models in the NCAR Community Land Model (CLM), C-LAMP incorporates a simulation protocol for both offline and partially coupled simulations using a prescribed historical trajectory of atmospheric CO2 concentrations. Models are confronted with data through comparisons against AmeriFlux site measurements, MODIS satellite observations, NOAA Globalview flask records, TRANSCOM inversions, and Free Air CO2 Enrichment (FACE) site measurements. Both sets of experiments have been performed using two different terrestrial biogeochemistry modules coupled to the CLM version 3 in the Community Climate System Model version 3 (CCSM3): the CASA model of Fung, et al., and the carbon-nitrogen (CN) model of Thornton. Comparisons of the CLM3 offline results against observational datasets have been performed and are described in Randerson et al. (2009). CLM version 4 has been evaluated using C-LAMP, showing improvement in many of the metrics. Efforts are now underway to initiate a Nitrogen-Land Model Intercomparison Project (N-LAMP) to better constrain the effects of the nitrogen cycle in biosphere models. Presented will be new results from C-LAMP for CLM4, initial N-LAMP developments, and the proposed land-biosphere model benchmarking activity.« less
  • As a contribution to International Land Model Benchmarking (ILAMB) Project, we are providing new analysis approaches, benchmarking tools, and science leadership. The goal of ILAMB is to assess and improve the performance of land models through international cooperation and to inform the design of new measurement campaigns and field studies to reduce uncertainties associated with key biogeochemical processes and feedbacks. ILAMB is expected to be a primary analysis tool for CMIP6 and future model-data intercomparison experiments. This team has developed initial prototype benchmarking systems for ILAMB, which will be improved and extended to include ocean model metrics and diagnostics.
  • As a contribution to International Land Model Benchmarking (ILAMB) Project, we are providing new analysis approaches, benchmarking tools, and science leadership. The goal of ILAMB is to assess and improve the performance of land models through international cooperation and to inform the design of new measurement campaigns and field studies to reduce uncertainties associated with key biogeochemical processes and feedbacks. ILAMB is expected to be a primary analysis tool for CMIP6 and future model-data intercomparison experiments. This team has developed initial prototype benchmarking systems for ILAMB, which will be improved and extended to include ocean model metrics and diagnostics.
  • Operations at the Savannah River Site (SRS) result in releases of small amounts of radioactive materials to the atmosphere and to the Savannah River. For regulatory compliance purposes, potential offsite radiological doses are estimated annually using computer models that follow U.S. Nuclear Regulatory Commission (NRC) regulatory guides. Within the regulatory guides, default values are provided for many of the dose model parameters, but the use of applicant site-specific values is encouraged. Detailed surveys of land-use and water-use parameters were conducted in 1991 and 2010. They are being updated in this report. These parameters include local characteristics of meat, milk andmore » vegetable production; river recreational activities; and meat, milk and vegetable consumption rates, as well as other human usage parameters required in the SRS dosimetry models. In addition, the preferred elemental bioaccumulation factors and transfer factors (to be used in human health exposure calculations at SRS) are documented. The intent of this report is to establish a standardized source for these parameters that is up to date with existing data, and that is maintained via review of future-issued national references (to evaluate the need for changes as new information is released). These reviews will continue to be added to this document by revision.« less