CliMAS colloquia

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Seminar coordinator for Spring 2024 is Professor Deanna Hence: dhence@illinois.edu

Seminar - Forrest Hoffman - ORNL

Event Type
Seminar/Symposium
Sponsor
Department of Atmospheric Sciences
Virtual
wifi event
Date
Feb 16, 2021   3:30 pm  
Views
20

Have Land Surface Processes in Earth System Models Improved Over Time?

Better representation of biophysical–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 the improvement of land surface 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 land surface 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 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.

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