Snow Depth Trends Revealed from CMIP6 Models Conflict with Observations
Seasonal snow cover plays an important role in the interactions between ground and atmosphere, including energy and hydrological fluxes, thus influencing climatological and hydrological processes.
Researchers from the Northwest Institute of Eco-Environment and Resources of the Chinese Academy of Sciences and Lanzhou University evaluated the simulated snow depth from 22 CMIP6 models across high-latitude regions of the Northern Hemisphere over the period 1955–2014 by using a high-quality in situ observational dataset.
Related results were published in Journal of Climate.
The researchers found that the simulated snow depths showed low accuracy and were biased high, exceeding the observed baseline (1976–2005) on average across the study area.
The models reproduced decreasing snow depth trends that contradicted the observations, although they all indicated an increase in precipitation during the cold season.
The study revealed that the simulated snow depths are insensitive to precipitation but too sensitive to air temperature; these inaccurate sensitivities could explain the discrepancies between the observed and simulated snow depth trends.
Based on these findings, they recommend caution when using and interpreting simulated changes in snow depth and associated impacts.
The CMIP6 models may require more detailed and comprehensive treatments of snow physics to more accurately project snow cover.
Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences