RIVE / ECCC CRD seminar: From ORCHIDEE to CLASSIC: improving the simulated snow cover heterogeneity and its impact on the climate

Abstract

Two studies carried out during my PhD and my current 2-year postdoc project will be presented. The first study investigates climate change in High Mountain Asia (HMA) from 1979 to 2014 and future projections using CMIP6 global climate models (GCMs). It reveals biases in these models, including a cold bias and snow cover overestimation. However, model bias alone shouldn’t rule out models in trend analysis. HMA is projected to warm by 1.9 to 6.5 °C by 2100, emphasizing the need for a better understanding of climate processes in this complex region. The second study assesses five snow cover fraction (SCF) parameterizations for GCMs, including two novel ones, with a focus on their performance in mountainous regions. They are assessed against the High Mountain Asia Snow Reanalysis (HMASR), and three of them are implemented into the ORCHIDEE land surface model (LSM) to quantify their performances in global land-atmosphere coupled simulations. Parameterizations that consider topographic variations significantly improve SCF accuracy and reduce surface cold bias in HMA. My current postdoc project aims to enhance the snow model within CLASSIC, addressing limitations like single-layer snow scheme, lack of explicit SCF parameterization, and the non-consideration of blowing snow sublimation losses. The project will develop, implement, and test these new processes to improve the simulated snowpack in the Arctic regions and assess their impact on Snow CCI variables, surface energy, and carbon fluxes.

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