Influence of snowmelt on soil moisture and on near surface air temperature during winter–spring transition season
This study examines relationships between snowmelt and soil moisture (SM), in particular, the influence of snowmelt on soil moisture memory (SMM) and on near surface air temperature (T2M) over the extra-tropical northern hemisphere (ENH) using four state-of-the-art reanalysis products: ERA-Interim,...
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description | This study examines relationships between snowmelt and soil moisture (SM), in particular, the influence of snowmelt on soil moisture memory (SMM) and on near surface air temperature (T2M) over the extra-tropical northern hemisphere (ENH) using four state-of-the-art reanalysis products: ERA-Interim, ERA-Interim Land, MERRA-Land, and GLDAS, as well as using Canadian Seasonal and Interannual Prediction System (CanSIPS) seasonal hindcast data, over a 20 year period (1986–2005). We use correlation-based metrics along with a simple classification-based on when the top layer soil temperature (
T
g
) rises above freezing point during the annual freeze–thaw season, to evaluate the influence of snowmelt on SM. Our results show considerable differences across reanalyses as well as CanSIPS hindcasts regarding timing of maximum SWE (
SWE
max
) occurrences as well as the onset of thawing of the frozen soil. Correlation statistics indicate that
SWE
max
strongly influences SM. As a measure of the persistence of this relationship, a decay time is defined by lag in days over which the correlation of SM with lagged
SWE
max
decays to 1/
e
of its peak value. For a majority of grid cells over ENH this decay time is less than 45 days, which suggests
SWE
max
does not strongly influence the SM beyond subseasonal time scales. The interannual autocorrelation of SM indicates strong persistence over subseasonal time scales, consistently across reanalyses as well as CanSIPS hindcasts. However, intra-seasonal autocorrelations of ERA-Interim and MERRA-Land SM over North America show anomalous sudden decline of SMM compared to the other products, likely due to the offline forcing of atmospheric variables which blocks the atmosphere’s response to land feedbacks. One of the models used in CanSIPS, the Canadian Climate Model version 4 (CanCM4), also shows a sudden decline of intra-seasonal autocorrelation over Central Asia which is most likely due to weak land–atmosphere coupling over the region. Lag–lead correlation statistics between SM and T2M during the soil thaw period suggests that SM anomalies have measurable lagged influence on T2M with varying decay time over different regions and across different datasets. |
doi_str_mv | 10.1007/s00382-017-3955-8 |
format | Article |
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T
g
) rises above freezing point during the annual freeze–thaw season, to evaluate the influence of snowmelt on SM. Our results show considerable differences across reanalyses as well as CanSIPS hindcasts regarding timing of maximum SWE (
SWE
max
) occurrences as well as the onset of thawing of the frozen soil. Correlation statistics indicate that
SWE
max
strongly influences SM. As a measure of the persistence of this relationship, a decay time is defined by lag in days over which the correlation of SM with lagged
SWE
max
decays to 1/
e
of its peak value. For a majority of grid cells over ENH this decay time is less than 45 days, which suggests
SWE
max
does not strongly influence the SM beyond subseasonal time scales. The interannual autocorrelation of SM indicates strong persistence over subseasonal time scales, consistently across reanalyses as well as CanSIPS hindcasts. However, intra-seasonal autocorrelations of ERA-Interim and MERRA-Land SM over North America show anomalous sudden decline of SMM compared to the other products, likely due to the offline forcing of atmospheric variables which blocks the atmosphere’s response to land feedbacks. One of the models used in CanSIPS, the Canadian Climate Model version 4 (CanCM4), also shows a sudden decline of intra-seasonal autocorrelation over Central Asia which is most likely due to weak land–atmosphere coupling over the region. Lag–lead correlation statistics between SM and T2M during the soil thaw period suggests that SM anomalies have measurable lagged influence on T2M with varying decay time over different regions and across different datasets.</description><identifier>ISSN: 0930-7575</identifier><identifier>EISSN: 1432-0894</identifier><identifier>DOI: 10.1007/s00382-017-3955-8</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Air temperature ; Anomalies ; Atmosphere ; Atmospheric models ; Atmospheric temperature ; Autocorrelation ; Climate models ; Climatology ; Correlation ; Data processing ; Decay ; Earth and Environmental Science ; Earth Sciences ; Environmental aspects ; Freezing ; Freezing point ; Frozen ground ; Geophysics/Geodesy ; Influence ; Melting points ; Northern Hemisphere ; Oceanography ; Seasons ; Snow ; Snowmelt ; Soil ; Soil layers ; Soil moisture ; Soil temperature ; Spring ; Statistical methods ; Statistics ; Surface temperature ; Surface-air temperature relationships ; Temperature ; Temperature effects ; Thawing ; Time ; Tropical climate ; Winter</subject><ispartof>Climate dynamics, 2018-08, Vol.51 (4), p.1295-1309</ispartof><rights>Springer-Verlag GmbH Germany 2017</rights><rights>COPYRIGHT 2018 Springer</rights><rights>Climate Dynamics is a copyright of Springer, (2017). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c420t-2a1a399f05a5c95fb835cec906bb40eb292a4dccb4e86e2fc6be6ef988f9abe93</citedby><cites>FETCH-LOGICAL-c420t-2a1a399f05a5c95fb835cec906bb40eb292a4dccb4e86e2fc6be6ef988f9abe93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00382-017-3955-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00382-017-3955-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids></links><search><creatorcontrib>Ambadan, Jaison Thomas</creatorcontrib><creatorcontrib>Berg, Aaron A.</creatorcontrib><creatorcontrib>Merryfield, William J.</creatorcontrib><creatorcontrib>Lee, Woo-Sung</creatorcontrib><title>Influence of snowmelt on soil moisture and on near surface air temperature during winter–spring transition season</title><title>Climate dynamics</title><addtitle>Clim Dyn</addtitle><description>This study examines relationships between snowmelt and soil moisture (SM), in particular, the influence of snowmelt on soil moisture memory (SMM) and on near surface air temperature (T2M) over the extra-tropical northern hemisphere (ENH) using four state-of-the-art reanalysis products: ERA-Interim, ERA-Interim Land, MERRA-Land, and GLDAS, as well as using Canadian Seasonal and Interannual Prediction System (CanSIPS) seasonal hindcast data, over a 20 year period (1986–2005). We use correlation-based metrics along with a simple classification-based on when the top layer soil temperature (
T
g
) rises above freezing point during the annual freeze–thaw season, to evaluate the influence of snowmelt on SM. Our results show considerable differences across reanalyses as well as CanSIPS hindcasts regarding timing of maximum SWE (
SWE
max
) occurrences as well as the onset of thawing of the frozen soil. Correlation statistics indicate that
SWE
max
strongly influences SM. As a measure of the persistence of this relationship, a decay time is defined by lag in days over which the correlation of SM with lagged
SWE
max
decays to 1/
e
of its peak value. For a majority of grid cells over ENH this decay time is less than 45 days, which suggests
SWE
max
does not strongly influence the SM beyond subseasonal time scales. The interannual autocorrelation of SM indicates strong persistence over subseasonal time scales, consistently across reanalyses as well as CanSIPS hindcasts. However, intra-seasonal autocorrelations of ERA-Interim and MERRA-Land SM over North America show anomalous sudden decline of SMM compared to the other products, likely due to the offline forcing of atmospheric variables which blocks the atmosphere’s response to land feedbacks. One of the models used in CanSIPS, the Canadian Climate Model version 4 (CanCM4), also shows a sudden decline of intra-seasonal autocorrelation over Central Asia which is most likely due to weak land–atmosphere coupling over the region. Lag–lead correlation statistics between SM and T2M during the soil thaw period suggests that SM anomalies have measurable lagged influence on T2M with varying decay time over different regions and across different datasets.</description><subject>Air temperature</subject><subject>Anomalies</subject><subject>Atmosphere</subject><subject>Atmospheric models</subject><subject>Atmospheric temperature</subject><subject>Autocorrelation</subject><subject>Climate models</subject><subject>Climatology</subject><subject>Correlation</subject><subject>Data processing</subject><subject>Decay</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Environmental aspects</subject><subject>Freezing</subject><subject>Freezing point</subject><subject>Frozen ground</subject><subject>Geophysics/Geodesy</subject><subject>Influence</subject><subject>Melting points</subject><subject>Northern Hemisphere</subject><subject>Oceanography</subject><subject>Seasons</subject><subject>Snow</subject><subject>Snowmelt</subject><subject>Soil</subject><subject>Soil layers</subject><subject>Soil moisture</subject><subject>Soil temperature</subject><subject>Spring</subject><subject>Statistical methods</subject><subject>Statistics</subject><subject>Surface temperature</subject><subject>Surface-air temperature relationships</subject><subject>Temperature</subject><subject>Temperature effects</subject><subject>Thawing</subject><subject>Time</subject><subject>Tropical climate</subject><subject>Winter</subject><issn>0930-7575</issn><issn>1432-0894</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kc1q3DAUhU1poNOkD9CdoVDowumVbNnWMoSmHQgU-rMWsuZqomBLU12ZtLu8Q98wTxI5LrSzKFoIHb5zJZ1TFK8ZnDOA7j0B1D2vgHVVLYWo-mfFhjV1VnrZPC82IGuoOtGJF8VLolsA1rQd3xS09Xac0Rssgy3Jh7sJx1QGX1JwYzkFR2mOWGq_W0SPOpY0R6uzQbtYJpwOGPUTs5uj8_vyzvmE8eH-Nx2ezilqTy65ZSZqCv6sOLF6JHz1Zz8tvl99-Hb5qbr-_HF7eXFdmYZDqrhmupbSgtDCSGGHvhYGjYR2GBrAgUuum50xQ4N9i9yadsAWrex7K_WAsj4t3qxzDzH8mJGSug1z9PlKxaHjwDvZNZk6X6m9HlE5b0N-sMlrh5MzwaN1Wb8QTQcAfcuy4d2RITMJf6a9nonU9uuXY_btP-wN6jHdUBjnJQ06BtkKmhiIIlqVw5t0_KUYqKVhtTascsNqaVj12cNXzxo0xr__-7_pEf4Uqw0</recordid><startdate>20180801</startdate><enddate>20180801</enddate><creator>Ambadan, Jaison Thomas</creator><creator>Berg, Aaron A.</creator><creator>Merryfield, William J.</creator><creator>Lee, Woo-Sung</creator><general>Springer Berlin 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Thomas ; Berg, Aaron A. ; Merryfield, William J. ; Lee, Woo-Sung</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c420t-2a1a399f05a5c95fb835cec906bb40eb292a4dccb4e86e2fc6be6ef988f9abe93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Air temperature</topic><topic>Anomalies</topic><topic>Atmosphere</topic><topic>Atmospheric models</topic><topic>Atmospheric temperature</topic><topic>Autocorrelation</topic><topic>Climate models</topic><topic>Climatology</topic><topic>Correlation</topic><topic>Data processing</topic><topic>Decay</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Environmental aspects</topic><topic>Freezing</topic><topic>Freezing point</topic><topic>Frozen ground</topic><topic>Geophysics/Geodesy</topic><topic>Influence</topic><topic>Melting points</topic><topic>Northern Hemisphere</topic><topic>Oceanography</topic><topic>Seasons</topic><topic>Snow</topic><topic>Snowmelt</topic><topic>Soil</topic><topic>Soil layers</topic><topic>Soil moisture</topic><topic>Soil temperature</topic><topic>Spring</topic><topic>Statistical methods</topic><topic>Statistics</topic><topic>Surface temperature</topic><topic>Surface-air temperature relationships</topic><topic>Temperature</topic><topic>Temperature effects</topic><topic>Thawing</topic><topic>Time</topic><topic>Tropical climate</topic><topic>Winter</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ambadan, Jaison Thomas</creatorcontrib><creatorcontrib>Berg, Aaron A.</creatorcontrib><creatorcontrib>Merryfield, William J.</creatorcontrib><creatorcontrib>Lee, Woo-Sung</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Meteorological & Geoastrophysical 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dynamics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ambadan, Jaison Thomas</au><au>Berg, Aaron A.</au><au>Merryfield, William J.</au><au>Lee, Woo-Sung</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Influence of snowmelt on soil moisture and on near surface air temperature during winter–spring transition season</atitle><jtitle>Climate dynamics</jtitle><stitle>Clim Dyn</stitle><date>2018-08-01</date><risdate>2018</risdate><volume>51</volume><issue>4</issue><spage>1295</spage><epage>1309</epage><pages>1295-1309</pages><issn>0930-7575</issn><eissn>1432-0894</eissn><abstract>This study examines relationships between snowmelt and soil moisture (SM), in particular, the influence of snowmelt on soil moisture memory (SMM) and on near surface air temperature (T2M) over the extra-tropical northern hemisphere (ENH) using four state-of-the-art reanalysis products: ERA-Interim, ERA-Interim Land, MERRA-Land, and GLDAS, as well as using Canadian Seasonal and Interannual Prediction System (CanSIPS) seasonal hindcast data, over a 20 year period (1986–2005). We use correlation-based metrics along with a simple classification-based on when the top layer soil temperature (
T
g
) rises above freezing point during the annual freeze–thaw season, to evaluate the influence of snowmelt on SM. Our results show considerable differences across reanalyses as well as CanSIPS hindcasts regarding timing of maximum SWE (
SWE
max
) occurrences as well as the onset of thawing of the frozen soil. Correlation statistics indicate that
SWE
max
strongly influences SM. As a measure of the persistence of this relationship, a decay time is defined by lag in days over which the correlation of SM with lagged
SWE
max
decays to 1/
e
of its peak value. For a majority of grid cells over ENH this decay time is less than 45 days, which suggests
SWE
max
does not strongly influence the SM beyond subseasonal time scales. The interannual autocorrelation of SM indicates strong persistence over subseasonal time scales, consistently across reanalyses as well as CanSIPS hindcasts. However, intra-seasonal autocorrelations of ERA-Interim and MERRA-Land SM over North America show anomalous sudden decline of SMM compared to the other products, likely due to the offline forcing of atmospheric variables which blocks the atmosphere’s response to land feedbacks. One of the models used in CanSIPS, the Canadian Climate Model version 4 (CanCM4), also shows a sudden decline of intra-seasonal autocorrelation over Central Asia which is most likely due to weak land–atmosphere coupling over the region. Lag–lead correlation statistics between SM and T2M during the soil thaw period suggests that SM anomalies have measurable lagged influence on T2M with varying decay time over different regions and across different datasets.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00382-017-3955-8</doi><tpages>15</tpages></addata></record> |
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subjects | Air temperature Anomalies Atmosphere Atmospheric models Atmospheric temperature Autocorrelation Climate models Climatology Correlation Data processing Decay Earth and Environmental Science Earth Sciences Environmental aspects Freezing Freezing point Frozen ground Geophysics/Geodesy Influence Melting points Northern Hemisphere Oceanography Seasons Snow Snowmelt Soil Soil layers Soil moisture Soil temperature Spring Statistical methods Statistics Surface temperature Surface-air temperature relationships Temperature Temperature effects Thawing Time Tropical climate Winter |
title | Influence of snowmelt on soil moisture and on near surface air temperature during winter–spring transition season |
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