Biological and Environmental Research - Earth and Environmental System Sciences
Earth and Environmental System Modeling

Diagnosing Climate–Carbon Cycle Feedbacks Constrained by ILAMB

Friday, December 13, 2019 - 08:00
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The International Land Model Benchmarking (ILAMB) project is a model–data intercomparison and integration activity designed to inform improvement of land models and the design of new measurement campaigns aimed at reducing uncertainties associated with key land surface processes. Better representation of biogeochemistry–climate feedbacks and ecosystem processes in Earth system models (ESMs) are essential for reducing uncertainties associated with projections of climate change during the remainder of the 21st century and beyond. 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 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. To test the hypothesis that the multi-model range of climate–carbon cycle feedback strengths from more realistic models would diverge less over time, we calculated and compared the ranges of concentration–carbon and climate–carbon sensitivity parameters and the trajectories of land carbon uptake from CMIP5 and CMIP6 models. Since the multi-model means of both the CMIP5 and CMIP6 land models performed better across most variables than any single model that contributed to the means, we also calculated the CMIP5 and CMIP6 multi-model mean feedback sensitivities and uptake trajectories. In an attempt to further reduce uncertainties in carbon cycle projections, we used the ILAMB benchmark performance scores to weight model contributions to the CMIP5 and CMIP6 multi-model means for land carbon uptake and related variables and compared them with observationally constrained estimates for the historical period.

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