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

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.

Observed behaviours of precipitable water vapour and precipitation intensity in response to upper air profiles estimated from surface air temperature

“…To determine the typical boundary layer height, we adopt the water vapour scale height 15 (WSH; see Methods) as an index representing how deeply the near-surface atmosphere is mixed by thermal turbulent eddies. In Fig.…”

“…The scale height of water vapour WSH was derived as 15 .where q sfc is the water vapour mixing ratio at the surface and ρ d is the density of dry air (1.2 kg m −3 ) at the surface. For WSH, we used only 321 GPS stations within 20 km of the meteorological stations where observations of relative humidity and air pressure, required for calculating q sfc , are routinely performed with hourly interval (green circles in Fig.…”

The Potential Roles of Background Surface Wind in the SST Variability Associated with Intraseasonal Oscillations

“…(A1a)-(A1c) of Large et al (1994). In this study, T a and q y,s are estimated from a logarithmic vertical profile within the simple surface layer (SL) model by Kanemaru and Masunaga (2013), u s 5 u ML 1 ln(z/z SL ) ln(z u /z SL ) [u( p s , T s ) 2 u ML ] and (8) q y,s 5 q y,ML 1 ln(z/z SL ) ln(z q /z SL ) [q y * (T s ) 2 q y,ML ],…”

“…The AIRX data are interpolated from the neighboring eight grid pixels averaged together to fill data gaps due to clouds and rain. This interpolation decreases sampling bias, although the accuracy of satellite estimates is (Kanemaru and Masunaga 2013). We also use OLR derived by the AIRX product as a proxy of deep convection and QuikSCAT surface wind vector data.…”

Evaluating the effect of climate change on areal reduction factors using regional climate model projections

“…However, the increase of extreme rainfall does not necessarily follow the C-C scaling. Recent studies have found that temperature scaling rate for precipitation extremes can either be above or below the C-C scaling depending on various aspects including storm duration, climate region, temperature range, and the analysis method used (Hardwick Jones et al, 2010;Kanemaru and Masunaga, 2012;Lenderink and van Meijgaard, 2008;Panthou et al, 2014;Shaw et al, 2011;Westra et al, 2012).…”