Have land surface processes in earth system models improved over time?

Forrest Hoffman, Nathaniel Collier and Zheng Shi, Computational Earth Sciences Group, Oak Ridge National Laboratory, Oak Ridge, TN, Mingquan Mu, ESS, University of California Irvine, Irvine, CA, Chang-En Yang, Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, Gretchen Keppel-Aleks, University of Michigan, David M. Lawrence, NCAR, Boulder, CO, Charles D. Koven, Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, Min Xu, Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, Jiafu Mao, Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, Qing Zhu, Lawrence Berkeley National Laboratory, Berkeley, CA, Hyungjun Kim, University of Tokyo, Tokyo, Japan, William Riley, Earth and Environmental Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, James T. Randerson, Department of Earth System Science, University of California, Irvine, Irvine, CA

Background/Question/Methods
Better representation of biogeochemistry–climate feedbacks and ecosystem processes in Earth system models (ESMs) is essential for reducing uncertainties associated with projections of climate change during the remainder of the 21st century and beyond. Model–data comparison and integration activities are required to inform improvement of land carbon cycle models and the design of new measurement campaigns aimed at reducing uncertainties associated with key land surface processes. The International Land Model Benchmarking (ILAMB) Package was designed to facilitate systematic and comprehensive model–data comparison and improve understanding of factors influencing model fidelity. We used ILAMB to benchmark and intercompare terrestrial carbon cycle models coupled within ESMs used to conduct historical simulations for the Fifth and Sixth Phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6).
Results/Conclusions
Results indicate that the suite of CMIP6 land models exhibits better performance than the suite of CMIP5 land models in comparison with observations for a variety of biogeochemical, hydrological, and energy-related variables. These improvements are partially attributed to reductions of biases in temperature, precipitation, and incoming radiation, suggesting that free-running atmosphere models in these ESMs also improved; however, biases in some regions increased. An analysis of forcing variables, prognostic land variables, and relationships from variable-to-variable comparisons indicate an overall improvement in most CMIP6 models, with relationships for some models exhibiting the greatest improvement in ILAMB scores, suggesting that improved model process representation in some models, and likely increased model complexity, contributed to improved model performance.