Temporal stability of long-term satellite and reanalysis products to monitor snow cover trends
<p>Monitoring snow cover to infer climate change impacts is now feasible using Earth observation data together with reanalysis products derived from Earth system models and data assimilation. Temporal stability becomes essential when these products are used to monitor snow cover changes over t...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-03-01
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| Series: | The Cryosphere |
| Online Access: | https://tc.copernicus.org/articles/17/1023/2023/tc-17-1023-2023.pdf |
| _version_ | 1811161283444080640 |
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| author | R. Urraca N. Gobron |
| author_facet | R. Urraca N. Gobron |
| author_sort | R. Urraca |
| collection | DOAJ |
| description | <p>Monitoring snow cover to infer climate change impacts is now feasible using Earth observation data together with reanalysis products derived from Earth system models and data assimilation. Temporal stability becomes essential when these products are used to monitor snow cover changes over time. While the temporal stability of satellite products can be altered when multiple sensors are combined and due to the degradation and orbital drifts in each sensor, the stability of reanalysis datasets can be compromised when new observations are assimilated into the model. This study evaluates the stability of some of the longest satellite-based and reanalysis products (ERA5, 1950–2020, ERA5-Land, 1950–2020, and the National Oceanic and Atmospheric Administration Climate Data Record (NOAA CDR), 1966–2020) by using 527 ground stations as reference data (1950–2020). Stability is assessed with the time series of the annual bias in snow depth and snow cover duration of the products at the different stations.</p>
<p>Reanalysis datasets face a trade-off between accuracy and stability when assimilating new data to improve their estimations. The assimilation of new observations in ERA5 improved its accuracy significantly during the recent years (2005–2020) but introduced three negative step discontinuities in 1977–1980, 1991–1992, and 2003–2004. By contrast, ERA5-Land is more stable because it does not assimilate snow observations directly, but this leads to worse accuracy despite having a finer spatial resolution. The NOAA CDR showed a positive artificial trend from around 1992 to 2015 during fall and winter that could be related to changes to the availability of satellite data. The magnitude of most of these artificial trends and/or discontinuities is larger than actual snow cover trends and the stability requirements of the Global Climate Observing System (GCOS). The use of these products in seasons and regions where artificial trends and discontinuities appear should be avoided.</p>
<p>The study also updates snow trends (1955–2015) over local sites in the Northern Hemisphere (NH), corroborating the retreat of snow cover, driven mainly by an earlier melt and recently by a later snow onset. In warmer regions such as Europe, snow cover decrease is coincident with a decreasing snow depth due to less snowfall, while in drier regions such as Russia, earlier snowmelt occurs despite increased maximum seasonal snow depth.</p> |
| first_indexed | 2024-04-10T06:12:24Z |
| format | Article |
| id | doaj.art-f80e2b0977484c62a256adc79d39ad97 |
| institution | Directory Open Access Journal |
| issn | 1994-0416 1994-0424 |
| language | English |
| last_indexed | 2024-04-10T06:12:24Z |
| publishDate | 2023-03-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The Cryosphere |
| spelling | doaj.art-f80e2b0977484c62a256adc79d39ad972023-03-02T13:08:26ZengCopernicus PublicationsThe Cryosphere1994-04161994-04242023-03-01171023105210.5194/tc-17-1023-2023Temporal stability of long-term satellite and reanalysis products to monitor snow cover trendsR. UrracaN. Gobron<p>Monitoring snow cover to infer climate change impacts is now feasible using Earth observation data together with reanalysis products derived from Earth system models and data assimilation. Temporal stability becomes essential when these products are used to monitor snow cover changes over time. While the temporal stability of satellite products can be altered when multiple sensors are combined and due to the degradation and orbital drifts in each sensor, the stability of reanalysis datasets can be compromised when new observations are assimilated into the model. This study evaluates the stability of some of the longest satellite-based and reanalysis products (ERA5, 1950–2020, ERA5-Land, 1950–2020, and the National Oceanic and Atmospheric Administration Climate Data Record (NOAA CDR), 1966–2020) by using 527 ground stations as reference data (1950–2020). Stability is assessed with the time series of the annual bias in snow depth and snow cover duration of the products at the different stations.</p> <p>Reanalysis datasets face a trade-off between accuracy and stability when assimilating new data to improve their estimations. The assimilation of new observations in ERA5 improved its accuracy significantly during the recent years (2005–2020) but introduced three negative step discontinuities in 1977–1980, 1991–1992, and 2003–2004. By contrast, ERA5-Land is more stable because it does not assimilate snow observations directly, but this leads to worse accuracy despite having a finer spatial resolution. The NOAA CDR showed a positive artificial trend from around 1992 to 2015 during fall and winter that could be related to changes to the availability of satellite data. The magnitude of most of these artificial trends and/or discontinuities is larger than actual snow cover trends and the stability requirements of the Global Climate Observing System (GCOS). The use of these products in seasons and regions where artificial trends and discontinuities appear should be avoided.</p> <p>The study also updates snow trends (1955–2015) over local sites in the Northern Hemisphere (NH), corroborating the retreat of snow cover, driven mainly by an earlier melt and recently by a later snow onset. In warmer regions such as Europe, snow cover decrease is coincident with a decreasing snow depth due to less snowfall, while in drier regions such as Russia, earlier snowmelt occurs despite increased maximum seasonal snow depth.</p>https://tc.copernicus.org/articles/17/1023/2023/tc-17-1023-2023.pdf |
| spellingShingle | R. Urraca N. Gobron Temporal stability of long-term satellite and reanalysis products to monitor snow cover trends The Cryosphere |
| title | Temporal stability of long-term satellite and reanalysis products to monitor snow cover trends |
| title_full | Temporal stability of long-term satellite and reanalysis products to monitor snow cover trends |
| title_fullStr | Temporal stability of long-term satellite and reanalysis products to monitor snow cover trends |
| title_full_unstemmed | Temporal stability of long-term satellite and reanalysis products to monitor snow cover trends |
| title_short | Temporal stability of long-term satellite and reanalysis products to monitor snow cover trends |
| title_sort | temporal stability of long term satellite and reanalysis products to monitor snow cover trends |
| url | https://tc.copernicus.org/articles/17/1023/2023/tc-17-1023-2023.pdf |
| work_keys_str_mv | AT rurraca temporalstabilityoflongtermsatelliteandreanalysisproductstomonitorsnowcovertrends AT ngobron temporalstabilityoflongtermsatelliteandreanalysisproductstomonitorsnowcovertrends |