A Satellite Study of the Relationship between Sea Surface Temperature and Column Water Vapor over Tropical and Subtropical Oceans

2013 ◽  
Vol 26 (12) ◽  
pp. 4204-4218 ◽  
Author(s):  
Kaya Kanemaru ◽  
Hirohiko Masunaga
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Tropical Rainfall Measuring Mission ◽  
Scale Height ◽  
Sea Surface ◽  
Systematic Change ◽  
Earth Observing System ◽  
Subtropical Oceans ◽  
The Relationship

Abstract The known characteristics of the relationship between sea surface temperature (SST) and column water vapor (CWV) are reevaluated with recent satellite observations over tropical and subtropical oceans. Satellite data acquired by the Aqua Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), Atmospheric Infrared Sounder (AIRS)/Advanced Microwave Sounder Unit (AMSU) suite, the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR), and the Quick Scatterometer (QuikSCAT) SeaWinds are analyzed together for 7 years from October 2002 to September 2009. CWV is decomposed into surface humidity, presumably coupled closely to SST, and the water vapor scale height as an index of vertical moisture gradient between the boundary layer and the free troposphere. Surface relative humidity is climatologically homogeneous across tropical and subtropical oceans, while the dependence of CWV on SST varies from one region to another. SST mainly accounts for the variation of CWV with the water vapor scale height, which is virtually invariant over subtropical oceans. On the other hand, over tropical oceans, the variability of CWV is explained not only by SST but also by a systematic change of the water vapor scale height. The regional contrast between tropical and subtropical oceans is discussed in the context of the regional moisture budget including vertical moisture transport through convection.

scholarly journals Variability of vertical structure of precipitation with sea surface temperature over the Arabian Sea and the Bay of Bengal as inferred by TRMM PR measurements

2018 ◽  
Author(s):  
Kadiri Saikranthi ◽  
Basivi Radhakrishna ◽  
Thota Narayana Rao ◽  
Sreedharan Krishnakumari Satheesh
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Bay Of Bengal ◽  
Arabian Sea ◽  
Vertical Structure ◽  
Tropical Rainfall Measuring Mission ◽  
Sea Surface ◽  
Cloud Formation ◽  
Freezing Level

Abstract. Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) 2A25 reflectivity profiles data during the period 1998–2013 are used to study the differences in the vertical structure of precipitation and its variation with sea surface temperature (SST) over the Arabian Sea (AS) and the Bay of Bengal (BOB). Even though the AS and the BOB are parts of the Indian Ocean, they exhibit distinct features in vertical structure of precipitation and its variation with SST. The variation of reflectivity and precipitation echo top occurrence with SST is remarkable over the AS but trivial over the BOB. The median reflectivity increases with SST at all heights below 10 km altitude, but the increase is prominent below the freezing level height over the AS. On the other hand, irrespective of altitude, reflectivity profiles are same at all SSTs over the BOB. To understand these differences, variation of aerosols, cloud and water vapor with SST is studied over these seas. At SSTs less than 27 °C, the observed high aerosol optical depth (AOD) and low total column water vapor (TCWV) over the AS results in small Cloud effective radius (CER) values and low reflectivity. As SST increases AOD decreases and TCWV increases, which result in large CER and high reflectivity. Over the BOB the change in AOD, TCWV and CER with SST is marginal. Thus, the observed variations in reflectivity profiles seem to be present from the cloud formation stage itself over both the seas.

Correcting infrared satellite estimates of sea surface temperature for atmospheric water vapor attenuation

1994 ◽  
Vol 99 (C3) ◽  
pp. 5219 ◽  
Author(s):  
William J. Emery ◽  
Yunyue Yu ◽  
Gary A. Wick ◽  
Peter Schluessel ◽  
Richard W. Reynolds
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Atmospheric Water Vapor ◽  
Atmospheric Water

On the Relationships between Subtropical Clouds and Meteorology in Observations and CMIP3 and CMIP5 Models*

2015 ◽  
Vol 28 (8) ◽  
pp. 2945-2967 ◽  
Author(s):  
Timothy A. Myers ◽  
Joel R. Norris
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Cloud Fraction ◽  
Sea Surface ◽  
Radiative Effect ◽  
Low Level ◽  
Temperature Advection ◽  
Cloud Radiative Effect ◽  
High Level ◽  
The Relationship

Abstract Climate models’ simulation of clouds over the eastern subtropical oceans contributes to large uncertainties in projected cloud feedback to global warming. Here, interannual relationships of cloud radiative effect and cloud fraction to meteorological variables are examined in observations and in models participating in phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5, respectively). In observations, cooler sea surface temperature, a stronger estimated temperature inversion, and colder horizontal surface temperature advection are each associated with larger low-level cloud fraction and increased reflected shortwave radiation. A moister free troposphere and weaker subsidence are each associated with larger mid- and high-level cloud fraction and offsetting components of shortwave and longwave cloud radiative effect. It is found that a larger percentage of CMIP5 than CMIP3 models simulate the wrong sign or magnitude of the relationship of shortwave cloud radiative effect to sea surface temperature and estimated inversion strength. Furthermore, most models fail to produce the sign of the relationship between shortwave cloud radiative effect and temperature advection. These deficiencies are mostly, but not exclusively, attributable to errors in the relationship between low-level cloud fraction and meteorology. Poor model performance also arises due to errors in the response of mid- and high-level cloud fraction to variations in meteorology. Models exhibiting relationships closest to observations tend to project less solar reflection by clouds in the late twenty-first century and have higher climate sensitivities than poorer-performing models. Nevertheless, the intermodel spread of climate sensitivity is large even among these realistic models.

Sea Surface Temperature–Precipitation Relationship in Different Reanalyses

2013 ◽  
Vol 141 (3) ◽  
pp. 1118-1123 ◽  
Author(s):  
Arun Kumar ◽  
Li Zhang ◽  
Wanqiu Wang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Time Scales ◽  
Department Of Energy ◽  
Sea Surface ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis ◽  
Temperature And Precipitation ◽  
The Relationship ◽  
Modern Era

Abstract The focus of this investigation is how the relationship at intraseasonal time scales between sea surface temperature and precipitation (SST–P) varies among different reanalyses. The motivation for this work was spurred by a recent report that documented that the SST–P relationship in Climate Forecast System Reanalysis (CFSR) was much closer to that in the observation than it was for the older generation of reanalyses [i.e., NCEP–NCAR reanalysis (R1) and NCEP–Department of Energy (DOE) reanalysis (R2)]. Further, the reason was attributed either to the fact that the CFSR is a partially coupled reanalysis, while R1 and R2 are atmospheric-alone reanalyses, or that R1 and R2 use the observed weekly-averaged SST. The authors repeated the comparison of the SST–P relationship among R1, R2, and CFSR, as well as two recent generations of atmosphere-alone reanalyses, the Modern-Era Retrospective Analysis for Research and Applications (MERRA) and the ECMWF Re-Analysis Interim (ERA-Interim). The results clearly demonstrate that the differences in the SST–P relationship at intraseasonal time scales across different reanalyses are not due to whether the reanalysis system is coupled or atmosphere alone, but are due to the specification of different SSTs. The SST–P relationship in different reanalyses, when computed against a single SST for the benchmark, demonstrates a relationship that is common across all of the reanalyses and observations.

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