Observational constraint on the future projection of temperature in winter over the Tibetan Plateau in CMIP6 models

The Tibetan Plateau (TP) is known as one of the sentinels of global climate change. Substantial winter warming over the TP will likely lead, directly or indirectly, to a series of geological disasters such as snow and glacial avalanches. Hence, for better adaptation to climate change, it is vital to project the future change in winter temperature over the TP. However, the current state-of-the-art climate models involved in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) still produce strong cold biases over most parts of the TP in their historical simulations. On the basis of selecting the optimal models, here we use the statistical downscaling method to constrain the projected winter temperature in CMIP6 models. The results show that the regions with the strongest winter warming over the TP will be near the Himalayas and the densely populated eastern regions. The constrained warming magnitude is much greater than that in the ensemble mean of the original 32 CMIP6 models or six best models over these regions. Therefore, early warning and forecasting services should be strengthened for the future temperature over these regions. Moreover, the long-term spatial warming varies greatly under four different future emission scenarios. Under the most severe scenario, the increase in winter temperature near the Himalayas exceeds 10 degrees C, which will greatly destabilize glaciers in the region, while the increase is only 4 degrees C-6 degrees C under the weakest scenario. Therefore, it is urgent to reduce greenhouse gas emissions to control the future temperature increase at hotspots of climate vulnerability such as the TP.

Automated summary

The Tibetan Plateau is an important region for studying global climate change. This study uses statistical downscaling methods to project future winter temperatures in the region. The results show that the strongest winter warming will occur near the Himalayas and densely populated eastern regions. This has implications for the stability of glaciers and the need for early warning and forecasting services. Additionally, the long-term spatial warming varies greatly depending on future emission scenarios, highlighting the urgency of reducing greenhouse gas emissions to mitigate climate vulnerability.