CMIP6 ESMs overestimate greening and the photosynthesis trends in Dryland East Asia

Abstract

The Dryland East Asia (DEA) is one of the largest inland arid regions, and vegetation is very sensitive to climate change. The complex environment in DEA with defects of modeling construction make it difficult to simulate and predict changes in vegetation structure and productivity. Here, we use the emergent constraint (EC) method to constrain the future interannual leaf area index (LAI) and gross primary productivity (GPP) trends in DEA, under four scenarios of the latest Sixth Coupled Model Intercomparison Project (CMIP6) model ensemble. LAI and GPP increase in all scenarios in the near term (2015–2050), with continued growth in SSP370 and SSP585 and stasis in SSP126 and SSP245 in the far term (2051–2100). However, after building effective EC relationships, the constrained increasing trends of LAI (GPP) are reduced by 43.5 %–53.9 % (30.5 %–50.0 %) compared with the uncertainties of the original ensemble, which are reduced by 10.0 %–45.7 % (4.6 %–34.3 %). We also extend the EC in moving windows and grid cells, further strengthening the robustness of the constraints, especially by illustrating spatial sources of these emergent relationships. Overestimations of LAI and GPP trends suggest that current CMIP6 models may be insufficient to capture the complex relationships between climate change and vegetation dynamics in DEA; however, these models can be adjusted based on established emergent relationships.