Evaluation of biases in mid-to-high-latitude surface snowfall and cloud phase in ERA5 and CMIP6 using satellite observations

Hellmuth, Franziska; Carlsen, Tim; Daloz, Anne Sophie; David, Robert Oscar; Che, Haochi; Storelvmo, Trude

doi:https://doi.org/10.5194/acp-25-1353-2025

Articles | Volume 25, issue 2

https://doi.org/10.5194/acp-25-1353-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-25-1353-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 2

Research article

|

31 Jan 2025

Research article |

| 31 Jan 2025

Evaluation of biases in mid-to-high-latitude surface snowfall and cloud phase in ERA5 and CMIP6 using satellite observations

Franziska Hellmuth, Tim Carlsen, Anne Sophie Daloz, Robert Oscar David, Haochi Che, and Trude Storelvmo

Data sets

MIROC MIROC6 model output prepared for CMIP6 CMIP historical H. Tatebe and M. Watanabe https://doi.org/10.22033/ESGF/CMIP6.5603

CCCma CanESM5 model output prepared for CMIP6 CMIP historical N. C. Swart et al. https://doi.org/10.22033/ESGF/CMIP6.3610

AWI AWI-ESM1.1LR model output prepared for CMIP6 CMIP historical C. Danek et al. https://doi.org/10.22033/ESGF/CMIP6.9328

MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 CMIP historical K.-H. Wieners et al. https://doi.org/10.22033/ESGF/CMIP6.6595

MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP historical Y. Tang et al. https://doi.org/10.22033/ESGF/CMIP6.6113

MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP historical J. Ridley et al. https://doi.org/10.22033/ESGF/CMIP6.6109

CMIP6 simulations of the CNRM-CERFACS based on CNRM-CM6-1 model for CMIP experiment historical A. Voldoire https://doi.org/10.22033/ESGF/CMIP6.4066

CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 CMIP historical R. Seferian https://doi.org/10.22033/ESGF/CMIP6.4068

IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP historical O. Boucher et al. https://doi.org/10.22033/ESGF/CMIP6.5195

IPSL IPSL-CM5A2-INCA model output prepared for CMIP6 CMIP historical O. Boucher et al. https://doi.org/10.22033/ESGF/CMIP6.13661

AMSR2 ASI sea ice concentration data, Antarctic, version 5.4 (NetCDF) (July 2012 - December 2019) C. Melsheimer and G. Spreen https://doi.org/10.1594/PANGAEA.898400

AMSR2 ASI sea ice concentration data, Arctic, version 5.4 (NetCDF) (July 2012 - December 2019) C. Melsheimer and G. Spreen https://doi.org/10.1594/PANGAEA.898399

AMSR-E ASI sea ice concentration data, Antarctic, version 5.4 (NetCDF) (June 2002 - September 2011) C. Melsheimer and G. Spreen https://doi.org/10.1594/PANGAEA.919778

AMSR-E ASI sea ice concentration data, Arctic, version 5.4 (NetCDF) (June 2002 - September 2011) C. Melsheimer and G. Spreen https://doi.org/10.1594/PANGAEA.919777

Model code and software

CloudSat, ERA5, and CMIP6 analysis Franziska Hellmuth https://doi.org/10.5281/zenodo.14179823

Short summary

This article compares the occurrence of supercooled liquid-containing clouds (sLCCs) and their link to surface snowfall in CloudSat–CALIPSO, ERA5, and the CMIP6 models. Significant discrepancies were found, with ERA5 and CMIP6 consistently overestimating sLCC and snowfall frequency. This bias is likely due to cloud microphysics parameterization. This conclusion has implications for accurately representing cloud phase and snowfall in future climate projections.