4-9 September 2022, Bonn, Germany
EMS Annual Meeting Abstracts
Vol. 19, EMS2022-447, 2022, updated on 28 Jun 2022
https://doi.org/10.5194/ems2022-447
EMS Annual Meeting 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Evaluation of Northern Hemisphere snow water equivalent in CMIP6 models during 1982-2014

Kerttu Kouki, Petri Räisänen, Kari Luojus, Anna Luomaranta, and Aku Riihelä
Kerttu Kouki et al.
  • Finnish Meteorological Institute, Helsinki, Finland

Seasonal snow cover of the Northern Hemisphere (NH) is a major factor in the global climate system, which makes snow cover an important variable in climate models. However, climate models have had difficulties in correctly reproducing the seasonal snow and its recent trends. A recent bias-correction method significantly reduces the uncertainty of NH snow water equivalent (SWE) estimation, which enables a more reliable analysis of the climate models’ ability to describe the snow cover. In this study, we have intercompared CMIP6 (Coupled Model Intercomparison Project Phase 6) and observation-based SWE estimates north of 40° N for the period 1982-2014 and analyzed whether temperature (T) and precipitation (P) biases could explain the SWE biases. We analyzed separately SWE in winter and SWE change rate in spring. For SWE reference data, we used bias-corrected SnowCCI data for non-mountainous regions and the mean of Brown, MERRA-2 and Crocus v7 datasets for the mountainous regions. The analysis shows that CMIP6 models tend to overestimate SWE, but large variability exists between models. In winter, the SWE model biases are mainly positive, while in spring, the variability between models increases. In winter, P is the dominant factor causing SWE discrepancies especially in the northern and coastal regions. T contributes to SWE biases mainly in regions, where T is close to 0℃ in winter. In spring, the importance of T in explaining the snowmelt rate discrepancies increases. This is to be expected, because the increase in T is the main factor that causes snow to melt as spring progresses. However, the results also showed that biases in T or P cannot explain all model biases either in SWE in winter or in the snowmelt rate in spring. Other factors, such as observation uncertainty or deficiencies in model parameterizations, also contribute to SWE biases.

How to cite: Kouki, K., Räisänen, P., Luojus, K., Luomaranta, A., and Riihelä, A.: Evaluation of Northern Hemisphere snow water equivalent in CMIP6 models during 1982-2014, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-447, https://doi.org/10.5194/ems2022-447, 2022.

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