Abstract This paper analyzes findings from semi-structured interviews and focus groups with 31 farmers in the Willamette Valley in which farmers were asked about their needs for climate data and about the usability of a range of outputs from the Community Earth System Model version 2 (CESM2) for their soil management practices. Findings indicate that climate and soils data generated from CESM and other Earth system models (ESMs), despite their coarse spatial scale resolutions, can inform farmers' long-term decisions, but that the data would be more usable if the outputs were provided in a format that allowed farmers to choose the variables and thresholds relevant to their particular needs and if ESMs incorporated farmer practices including residue removal, cover cropping, and tillage levels into the model operations so that farmers could better understand the impacts of their decisions. Findings also suggest that although there is a significant gap in the spatial resolution at which these global ESMs generate data and the spatial resolution needed by farmers to make most decisions, farmers are adept at making scalar adjustments to apply coarse resolution data to the specifics of their own farm's microclimate. Thus, our findings suggest that, to support agricultural decision-making, development priorities for ESMs should include developing better representations of agricultural management practices within the models and creating interactive data dashboards or platforms.
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. ; National Science Foundation (NSF)National Science Foundation (NSF); National Center for Atmospheric Research - NSF [1852977]; RUBISCO Scientific Focus Area (SFA) - 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; Columbia University Presidential Fellowship; U.S. Department of Agriculture NIFA Award [2015-67003-23485]; NASA Interdisciplinary Science Program Award [NNX17AK19G]; U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Terrestrial Ecosystem Science programUnited States Department of Energy (DOE) [DE-SC0008317, DESC0016188]; National Science FoundationNational Science Foundation (NSF) [DEB-1153401]; NASA's CARBON program; NASA's TE program; National Aeronautics and Space AdministrationNational Aeronautics & Space Administration (NASA) ; 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 DESC0016188; 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).
