Consistency of Estimated Global Water Cycle Variations over the Satellite Era
Motivated by the question of whether recent interannual to decadal climate variability and a possible “climate shift” may have affected the global water balance, we examine precipitation minus evaporation (P – E) variability integrated over the global oceans and global land for the period 1979–2010...
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description | Motivated by the question of whether recent interannual to decadal climate variability and a possible “climate shift” may have affected the global water balance, we examine precipitation minus evaporation (P – E) variability integrated over the global oceans and global land for the period 1979–2010 from three points of view—remotely sensed retrievals and syntheses over the oceans, reanalysis vertically integrated moisture flux convergence (VMFC) over land, and land surface models (LSMs) forced with observations-based precipitation, radiation, and near-surface meteorology.
Over land, reanalysis VMFC andP– evapotranspiration (ET) from observationally forced LSMs agree on interannual variations (e.g., El Niño/La Niña events); however, reanalyses exhibit upward VMFC trends 3–4 times larger thanP– ET trends of the LSMs. Experiments with other reanalyses using reduced observations show that upward VMFC trends in the full reanalyses are due largely to observing system changes interacting with assimilation model physics. The much smallerP– ET trend in the LSMs appears due to changes in frequency and amplitude of warm events after the 1997/98 El Niño, a result consistent with coolness in the eastern tropical Pacific sea surface temperature (SST) after that date.
When integrated over the global oceans,Eand especiallyPvariations show consistent signals of El Niño/La Niña events. However, at scales longer than interannual there is considerable uncertainty especially inE. This results from differences among datasets in near-surface atmospheric specific humidity and wind speed used in bulk aerodynamic retrievals. ThePvariations, all relying substantially on passive microwave retrievals over ocean, also have uncertainties in decadal variability, but to a smaller degree. |
doi_str_mv | 10.1175/jcli-d-13-00384.1 |
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Over land, reanalysis VMFC andP– evapotranspiration (ET) from observationally forced LSMs agree on interannual variations (e.g., El Niño/La Niña events); however, reanalyses exhibit upward VMFC trends 3–4 times larger thanP– ET trends of the LSMs. Experiments with other reanalyses using reduced observations show that upward VMFC trends in the full reanalyses are due largely to observing system changes interacting with assimilation model physics. The much smallerP– ET trend in the LSMs appears due to changes in frequency and amplitude of warm events after the 1997/98 El Niño, a result consistent with coolness in the eastern tropical Pacific sea surface temperature (SST) after that date.
When integrated over the global oceans,Eand especiallyPvariations show consistent signals of El Niño/La Niña events. However, at scales longer than interannual there is considerable uncertainty especially inE. This results from differences among datasets in near-surface atmospheric specific humidity and wind speed used in bulk aerodynamic retrievals. ThePvariations, all relying substantially on passive microwave retrievals over ocean, also have uncertainties in decadal variability, but to a smaller degree.</description><identifier>ISSN: 0894-8755</identifier><identifier>EISSN: 1520-0442</identifier><identifier>DOI: 10.1175/jcli-d-13-00384.1</identifier><language>eng</language><publisher>Boston, MA: American Meteorological Society</publisher><subject>Accuracy ; Algorithms ; Annual variations ; Atmospheric models ; Atmospheric moisture ; Biogeochemistry ; Budgets ; Climate ; Climate change ; Climate variability ; Climatology. Bioclimatology. Climate change ; Consistency ; Convergence ; Datasets ; Earth, ocean, space ; El Nino ; El Nino phenomena ; Evaporation ; Evapotranspiration ; Evapotranspiration trends ; Exact sciences and technology ; External geophysics ; Flux ; Global climate models ; Global water balance ; Hydrologic cycle ; Hydrological cycle ; Interannual variations ; La Nina ; La Nina events ; Land ; Land area ; Land surface models ; Marine ; Meteorological satellites ; Meteorology ; Moisture effects ; Moisture flux ; Oceans ; Physics ; Precipitation ; Radiation ; Remote sensing ; Remote sensing systems ; Sea surface ; Sea surface temperature ; Sensors ; Specific humidity ; Surface temperature ; Trends ; Uncertainty ; Variability ; Variation ; Water balance ; Water cycle ; Water in the atmosphere (humidity, clouds, evaporation, precipitation) ; Wind speed ; Wind velocity</subject><ispartof>Journal of climate, 2014-08, Vol.27 (16), p.6135-6154</ispartof><rights>2014 American Meteorological Society</rights><rights>2015 INIST-CNRS</rights><rights>Copyright American Meteorological Society Aug 15, 2014</rights><rights>Copyright American Meteorological Society 2014</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c528t-bba6c559748e5f4735fe80e65bcc11c5c86837af0561d5748cac9c769db1b3103</citedby><cites>FETCH-LOGICAL-c528t-bba6c559748e5f4735fe80e65bcc11c5c86837af0561d5748cac9c769db1b3103</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26193763$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26193763$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,799,3668,27901,27902,57992,58225</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28733884$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Robertson, F. R.</creatorcontrib><creatorcontrib>Bosilovich, M. G.</creatorcontrib><creatorcontrib>Roberts, J. B.</creatorcontrib><creatorcontrib>Reichle, R. H.</creatorcontrib><creatorcontrib>Adler, R.</creatorcontrib><creatorcontrib>Ricciardulli, L.</creatorcontrib><creatorcontrib>Berg, W.</creatorcontrib><creatorcontrib>Huffman, G. J.</creatorcontrib><title>Consistency of Estimated Global Water Cycle Variations over the Satellite Era</title><title>Journal of climate</title><description>Motivated by the question of whether recent interannual to decadal climate variability and a possible “climate shift” may have affected the global water balance, we examine precipitation minus evaporation (P – E) variability integrated over the global oceans and global land for the period 1979–2010 from three points of view—remotely sensed retrievals and syntheses over the oceans, reanalysis vertically integrated moisture flux convergence (VMFC) over land, and land surface models (LSMs) forced with observations-based precipitation, radiation, and near-surface meteorology.
Over land, reanalysis VMFC andP– evapotranspiration (ET) from observationally forced LSMs agree on interannual variations (e.g., El Niño/La Niña events); however, reanalyses exhibit upward VMFC trends 3–4 times larger thanP– ET trends of the LSMs. Experiments with other reanalyses using reduced observations show that upward VMFC trends in the full reanalyses are due largely to observing system changes interacting with assimilation model physics. The much smallerP– ET trend in the LSMs appears due to changes in frequency and amplitude of warm events after the 1997/98 El Niño, a result consistent with coolness in the eastern tropical Pacific sea surface temperature (SST) after that date.
When integrated over the global oceans,Eand especiallyPvariations show consistent signals of El Niño/La Niña events. However, at scales longer than interannual there is considerable uncertainty especially inE. This results from differences among datasets in near-surface atmospheric specific humidity and wind speed used in bulk aerodynamic retrievals. ThePvariations, all relying substantially on passive microwave retrievals over ocean, also have uncertainties in decadal variability, but to a smaller degree.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Annual variations</subject><subject>Atmospheric models</subject><subject>Atmospheric moisture</subject><subject>Biogeochemistry</subject><subject>Budgets</subject><subject>Climate</subject><subject>Climate change</subject><subject>Climate variability</subject><subject>Climatology. Bioclimatology. Climate change</subject><subject>Consistency</subject><subject>Convergence</subject><subject>Datasets</subject><subject>Earth, ocean, space</subject><subject>El Nino</subject><subject>El Nino phenomena</subject><subject>Evaporation</subject><subject>Evapotranspiration</subject><subject>Evapotranspiration trends</subject><subject>Exact sciences and technology</subject><subject>External geophysics</subject><subject>Flux</subject><subject>Global climate models</subject><subject>Global water balance</subject><subject>Hydrologic cycle</subject><subject>Hydrological cycle</subject><subject>Interannual variations</subject><subject>La Nina</subject><subject>La Nina events</subject><subject>Land</subject><subject>Land area</subject><subject>Land surface models</subject><subject>Marine</subject><subject>Meteorological satellites</subject><subject>Meteorology</subject><subject>Moisture effects</subject><subject>Moisture flux</subject><subject>Oceans</subject><subject>Physics</subject><subject>Precipitation</subject><subject>Radiation</subject><subject>Remote sensing</subject><subject>Remote sensing systems</subject><subject>Sea surface</subject><subject>Sea surface temperature</subject><subject>Sensors</subject><subject>Specific humidity</subject><subject>Surface temperature</subject><subject>Trends</subject><subject>Uncertainty</subject><subject>Variability</subject><subject>Variation</subject><subject>Water balance</subject><subject>Water cycle</subject><subject>Water in the atmosphere (humidity, clouds, evaporation, precipitation)</subject><subject>Wind speed</subject><subject>Wind velocity</subject><issn>0894-8755</issn><issn>1520-0442</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>BEC</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkUtrGzEQx0Vooa7bD9BDQFAKuWyqkTSS9lg2zguXHvo6Cq2spWs2q1SSA_72UeqQQg_NaV6_-TMPQt4BOwXQ-HHrp7HZNCAaxoSRp3BEFoCcNUxK_oIsmGllYzTiK_I65y1jwBVjC_K5i3Mecwmz39M40FUu440rYUMvpti7if6sQaLd3k-B_nBpdGWsHTTe1Wz5FejXWp-msQS6Su4NeTm4KYe3j3ZJvp-vvnWXzfrLxVX3ad145KY0fe-UR2y1NAEHqQUOwbCgsPcewKM3ygjtBoYKNlgp73zrtWo3PfQCmFiSk4PubYq_dyEXezNmX-dwc4i7bEFJLoRg0D6PomIAiktZ0ff_oNu4S3NdxHIDApXhXP-PAkShEYzESsGB8inmnMJgb1M9bNpbYPbhY_a6W1_ZMwvC_vlYdZbkw6Oyy95NQ3KzH_NTIzdaCGMe5jw-cNtcYvpbV3VdrYS4B26xnYc</recordid><startdate>20140815</startdate><enddate>20140815</enddate><creator>Robertson, F. 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R.</au><au>Bosilovich, M. G.</au><au>Roberts, J. B.</au><au>Reichle, R. H.</au><au>Adler, R.</au><au>Ricciardulli, L.</au><au>Berg, W.</au><au>Huffman, G. J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Consistency of Estimated Global Water Cycle Variations over the Satellite Era</atitle><jtitle>Journal of climate</jtitle><date>2014-08-15</date><risdate>2014</risdate><volume>27</volume><issue>16</issue><spage>6135</spage><epage>6154</epage><pages>6135-6154</pages><issn>0894-8755</issn><eissn>1520-0442</eissn><abstract>Motivated by the question of whether recent interannual to decadal climate variability and a possible “climate shift” may have affected the global water balance, we examine precipitation minus evaporation (P – E) variability integrated over the global oceans and global land for the period 1979–2010 from three points of view—remotely sensed retrievals and syntheses over the oceans, reanalysis vertically integrated moisture flux convergence (VMFC) over land, and land surface models (LSMs) forced with observations-based precipitation, radiation, and near-surface meteorology.
Over land, reanalysis VMFC andP– evapotranspiration (ET) from observationally forced LSMs agree on interannual variations (e.g., El Niño/La Niña events); however, reanalyses exhibit upward VMFC trends 3–4 times larger thanP– ET trends of the LSMs. Experiments with other reanalyses using reduced observations show that upward VMFC trends in the full reanalyses are due largely to observing system changes interacting with assimilation model physics. The much smallerP– ET trend in the LSMs appears due to changes in frequency and amplitude of warm events after the 1997/98 El Niño, a result consistent with coolness in the eastern tropical Pacific sea surface temperature (SST) after that date.
When integrated over the global oceans,Eand especiallyPvariations show consistent signals of El Niño/La Niña events. However, at scales longer than interannual there is considerable uncertainty especially inE. This results from differences among datasets in near-surface atmospheric specific humidity and wind speed used in bulk aerodynamic retrievals. ThePvariations, all relying substantially on passive microwave retrievals over ocean, also have uncertainties in decadal variability, but to a smaller degree.</abstract><cop>Boston, MA</cop><pub>American Meteorological Society</pub><doi>10.1175/jcli-d-13-00384.1</doi><tpages>20</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Accuracy Algorithms Annual variations Atmospheric models Atmospheric moisture Biogeochemistry Budgets Climate Climate change Climate variability Climatology. Bioclimatology. Climate change Consistency Convergence Datasets Earth, ocean, space El Nino El Nino phenomena Evaporation Evapotranspiration Evapotranspiration trends Exact sciences and technology External geophysics Flux Global climate models Global water balance Hydrologic cycle Hydrological cycle Interannual variations La Nina La Nina events Land Land area Land surface models Marine Meteorological satellites Meteorology Moisture effects Moisture flux Oceans Physics Precipitation Radiation Remote sensing Remote sensing systems Sea surface Sea surface temperature Sensors Specific humidity Surface temperature Trends Uncertainty Variability Variation Water balance Water cycle Water in the atmosphere (humidity, clouds, evaporation, precipitation) Wind speed Wind velocity |
title | Consistency of Estimated Global Water Cycle Variations over the Satellite Era |
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