B21E-0513 – Design and application of a community land benchmarking system for Earth system models


Mingquan Mu
University of California Irvine
Forrest Hoffman (forrest at climatemodeling dot org)
Oak Ridge National Laboratory
David Lawrence
National Center for Atmospheric Research
William Riley
Lawrence Berkeley National Laboratory
Gretchen Keppel-Aleks
University of Michigan Ann Arbor
Charles Koven
Lawrence Berkeley National Laboratory
Erik Kluzek
National Center for Atmospheric Research
Jiafu Mao
Oak Ridge National Laboratory
James Randerson
University of California Irvine


New Mechanisms, Feedbacks, and Approaches for Improving Predictions of the Global Carbon Cycle in Earth System Models I Posters
Tuesday, December 15, 2015 08:00–12:20
Moscone South Poster Hall


Benchmarking has been widely used to assess the ability of climate models to capture the spatial and temporal variability of observations during the historical era. For the carbon cycle and terrestrial ecosystems, the design and development of an open-source community platform has been an important goal as part of the International Land Model Benchmarking (ILAMB) project. Here we developed a new benchmarking software system that enables the user to specify the models, benchmarks, and scoring metrics, so that results can be tailored to specific model intercomparison projects. Evaluation data sets included soil and aboveground carbon stocks, fluxes of energy, carbon and water, burned area, leaf area, and climate forcing and response variables. We used this system to evaluate simulations from the 5th Phase of the Coupled Model Intercomparison Project (CMIP5) with prognostic atmospheric carbon dioxide levels over the period from 1850 to 2005 (i.e., esmHistorical simulations archived on the Earth System Grid Federation). We found that the multi-model ensemble had a high bias in incoming solar radiation across Asia, likely as a consequence of incomplete representation of aerosol effects in this region, and in South America, primarily as a consequence of a low bias in mean annual precipitation. The reduced precipitation in South America had a larger influence on gross primary production than the high bias in incoming light, and as a consequence gross primary production had a low bias relative to the observations. Although model to model variations were large, the multi-model mean had a positive bias in atmospheric carbon dioxide that has been attributed in past work to weak ocean uptake of fossil emissions. In mid latitudes of the northern hemisphere, most models overestimate latent heat fluxes in the early part of the growing season, and underestimate these fluxes in mid-summer and early fall, whereas sensible heat fluxes show the opposite trend.

Forrest M. Hoffman (forrest at climatemodeling dot org)