Articles | Volume 16, issue 3
https://doi.org/10.5194/tc-16-1007-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-1007-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Mar 2022
Research article |
| 15 Mar 2022
| 15 Mar 2022
Evaluation of Northern Hemisphere snow water equivalent in CMIP6 models during 1982–2014
Finnish Meteorological Institute, Helsinki, P.O. Box 503, 00101,
Finland
Petri Räisänen
Finnish Meteorological Institute, Helsinki, P.O. Box 503, 00101,
Finland
Kari Luojus
Finnish Meteorological Institute, Helsinki, P.O. Box 503, 00101,
Finland
Anna Luomaranta
Finnish Meteorological Institute, Helsinki, P.O. Box 503, 00101,
Finland
Aku Riihelä
Finnish Meteorological Institute, Helsinki, P.O. Box 503, 00101,
Finland
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Cited
24 citations as recorded by crossref.
- Snow Cover in the Three Stable Snow Cover Areas of China and Spatio-Temporal Patterns of the Future Y. Zou et al. 10.3390/rs14133098
- Reducing Snow Amount Uncertainty in CMIP6 PanCanadian Climate Projections D. Matte et al. 10.1080/07055900.2025.2478835
- Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research? B. Poschlod & A. Daloz 10.5194/tc-18-1959-2024
- Assessing the Snow Water Equivalent Dynamics of the Brahmaputra River Basin S. Rani et al. 10.17491/jgsi/2025/174080
- Interannual responses of Arctic temperatures to Eurasian snow cover variations in early spring L. Ma et al. 10.1016/j.atmosres.2024.107866
- Pervasive alterations to snow-dominated ecosystem functions under climate change W. Wieder et al. 10.1073/pnas.2202393119
- Quantifying sources of subseasonal prediction skill in CESM2 J. Richter et al. 10.1038/s41612-024-00595-4
- Snow cover duration delays spring green-up in the northern hemisphere the most for grasslands X. Wang et al. 10.1016/j.agrformet.2024.110130
- Constrained Earth system models show a stronger reduction in future Northern Hemisphere snowmelt water Y. Chai et al. 10.1038/s41558-025-02308-y
- Changes in March mean snow water equivalent since the mid-20th century and the contributing factors in reanalyses and CMIP6 climate models J. Räisänen 10.5194/tc-17-1913-2023
- Divergent responses of historic rain-on-snow flood extremes to a warmer climate D. Hao et al. 10.1038/s43247-025-02354-6
- Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications S. Schilling et al. 10.3390/rs16061085
- Winter snowpack loss increases warm-season compound hot-dry extremes H. Liu et al. 10.1038/s43247-024-01734-8
- Spatial patterns of snow distribution in the sub-Arctic K. Bennett et al. 10.5194/tc-16-3269-2022
- Snowpack Dynamics Influence Tree Growth and Signals in Tree Rings of Tianshan Mountain, Central Asia Y. Fan et al. 10.3390/rs15112849
- Detecting snowfall events over the Arctic using optical and microwave satellite measurements E. Jääskeläinen et al. 10.5194/hess-28-3855-2024
- Retrieving snow water equivalent from GRACE/GRACE-FO terrestrial water storage anomalies using modified spectral combination theory F. Fatolazadeh et al. 10.1016/j.jhydrol.2025.133754
- Evolution of global snow drought characteristics from 1850 to 2100 M. Cowherd et al. 10.1088/1748-9326/acd804
- Evidence of human influence on Northern Hemisphere snow loss A. Gottlieb & J. Mankin 10.1038/s41586-023-06794-y
- Validation of key Arctic energy and water budget components in CMIP6 S. Winkelbauer et al. 10.1007/s00382-024-07105-5
- Pacific decadal oscillation modulates the decadal variations of spring Eurasian snow cover T. Zhang & H. Chen 10.1016/j.atmosres.2025.108102
- Customizing large-scale hydrological models: Harnessing the open data realm for impactful local applications I. Pechlivanidis & J. Musuuza 10.1016/j.ejrh.2025.102390
- Evaluation of Earth Observations and In Situ Data Assimilation for Seasonal Hydrological Forecasting J. Musuuza et al. 10.1029/2022WR033655
- Insight into historical and future spring snow cover from satellite observation and model simulations over the Northern Hemisphere H. Guo et al. 10.1002/joc.8117
22 citations as recorded by crossref.
- Snow Cover in the Three Stable Snow Cover Areas of China and Spatio-Temporal Patterns of the Future Y. Zou et al. 10.3390/rs14133098
- Reducing Snow Amount Uncertainty in CMIP6 PanCanadian Climate Projections D. Matte et al. 10.1080/07055900.2025.2478835
- Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research? B. Poschlod & A. Daloz 10.5194/tc-18-1959-2024
- Assessing the Snow Water Equivalent Dynamics of the Brahmaputra River Basin S. Rani et al. 10.17491/jgsi/2025/174080
- Interannual responses of Arctic temperatures to Eurasian snow cover variations in early spring L. Ma et al. 10.1016/j.atmosres.2024.107866
- Pervasive alterations to snow-dominated ecosystem functions under climate change W. Wieder et al. 10.1073/pnas.2202393119
- Quantifying sources of subseasonal prediction skill in CESM2 J. Richter et al. 10.1038/s41612-024-00595-4
- Snow cover duration delays spring green-up in the northern hemisphere the most for grasslands X. Wang et al. 10.1016/j.agrformet.2024.110130
- Constrained Earth system models show a stronger reduction in future Northern Hemisphere snowmelt water Y. Chai et al. 10.1038/s41558-025-02308-y
- Changes in March mean snow water equivalent since the mid-20th century and the contributing factors in reanalyses and CMIP6 climate models J. Räisänen 10.5194/tc-17-1913-2023
- Divergent responses of historic rain-on-snow flood extremes to a warmer climate D. Hao et al. 10.1038/s43247-025-02354-6
- Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications S. Schilling et al. 10.3390/rs16061085
- Winter snowpack loss increases warm-season compound hot-dry extremes H. Liu et al. 10.1038/s43247-024-01734-8
- Spatial patterns of snow distribution in the sub-Arctic K. Bennett et al. 10.5194/tc-16-3269-2022
- Snowpack Dynamics Influence Tree Growth and Signals in Tree Rings of Tianshan Mountain, Central Asia Y. Fan et al. 10.3390/rs15112849
- Detecting snowfall events over the Arctic using optical and microwave satellite measurements E. Jääskeläinen et al. 10.5194/hess-28-3855-2024
- Retrieving snow water equivalent from GRACE/GRACE-FO terrestrial water storage anomalies using modified spectral combination theory F. Fatolazadeh et al. 10.1016/j.jhydrol.2025.133754
- Evolution of global snow drought characteristics from 1850 to 2100 M. Cowherd et al. 10.1088/1748-9326/acd804
- Evidence of human influence on Northern Hemisphere snow loss A. Gottlieb & J. Mankin 10.1038/s41586-023-06794-y
- Validation of key Arctic energy and water budget components in CMIP6 S. Winkelbauer et al. 10.1007/s00382-024-07105-5
- Pacific decadal oscillation modulates the decadal variations of spring Eurasian snow cover T. Zhang & H. Chen 10.1016/j.atmosres.2025.108102
- Customizing large-scale hydrological models: Harnessing the open data realm for impactful local applications I. Pechlivanidis & J. Musuuza 10.1016/j.ejrh.2025.102390
2 citations as recorded by crossref.
- Evaluation of Earth Observations and In Situ Data Assimilation for Seasonal Hydrological Forecasting J. Musuuza et al. 10.1029/2022WR033655
- Insight into historical and future spring snow cover from satellite observation and model simulations over the Northern Hemisphere H. Guo et al. 10.1002/joc.8117
Latest update: 05 Jul 2025
Short summary
We analyze state-of-the-art climate models’ ability to describe snow mass and whether biases in modeled temperature or precipitation can explain the discrepancies in snow mass. In winter, biases in precipitation are the main factor affecting snow mass, while in spring, biases in temperature becomes more important, which is an expected result. However, temperature or precipitation cannot explain all snow mass discrepancies. Other factors, such as models’ structural errors, are also significant.
We analyze state-of-the-art climate models’ ability to describe snow mass and whether biases...
