A process oriented characterization of tropical oceanic clouds for climate model evaluation, based on a statistical analysis of daytime A-train observations

Abstract

International audienceThis paper aims at characterizing how different key cloud properties (cloud fraction, cloud vertical distribution, cloud reflectance, a surrogate of the cloud optical depth) vary as a function of the others over the tropical oceans. The correlations between the different cloud properties are built from 2 years of collocated A-train observations (CALIPSO-GOCCP and MODIS) at a scale close to cloud processes; it results in a characterization of the physical processes in tropical clouds, that can be used to better understand cloud behaviors, and constitute a powerful tool to develop and evaluate cloud parameterizations in climate models. First, we examine a case study of shallow cumulus cloud observed simultaneously by the two sensors (CALIPSO, MODIS), and develop a methodology that allows to build global scale statistics by keeping the separation between clear and cloudy areas at the pixel level (250, 330 m). Then we build statistical instantaneous relationships between the cloud cover, the cloud vertical distribution and the cloud reflectance. The vertical cloud distribution indicates that the optically thin clouds (optical thickness < 1.5) dominate the boundary layer over the trade wind regions. Optically thick clouds (optical thickness > 3.4) are composed of high and mid-level clouds associated with deep convection along the ITCZ and SPCZ and over the warm pool, and by stratocumulus low level clouds located along the East coast of tropical oceans. The cloud properties are analyzed as a function of the large scale circulation regime. Optically thick high clouds are dominant in convective regions (CF > 80 %), while low level clouds with low optical thickness (< 3.5) are present in regimes of subsidence but in convective regimes as well, associated principally to low cloud fractions (CF < 50 %). A focus on low-level clouds allows us to quantify how the cloud optical depth increases with cloud top altitude and with cloud fraction

Use of A-train satellite observations (CALIPSO–PARASOL) to evaluate tropical cloud properties in the LMDZ5 GCM

“…The normalized cloud cover of high-level cloud is defined as NCC_high = CC_high/CC, where CC_high is the cloud cover 366 of high-level clouds and CC is the total cloud cover [Konsta et al, 2012]. In ascent regions, the observed normalized high-367 cloud cover regularly increases with the total cloud cover (except for very small cloud cover) and reaches values close to 368 one in fully overcast situations (Fig.…”

An EarthCARE/ATLID simulator to evaluate cloud description in climate models

“…The maximum relative difference between ATLID and CALIPSO height‐SR histograms is respectively 0.5% and 1% in nighttime and daytime. The differences between ATLID‐like and CALIPSO‐like cloud fraction profiles are much lower than the differences between climate models + COSP/CALIPSO and CALIPSO observations that have been reported in CFMIP experiment [e.g., Konsta et al , ; Nam and Quaas , ; Kay et al , ; Nam et al , ; Bodas‐Salcedo et al , ; Chepfer et al , ], suggesting that ATLID will provide useful observations to evaluate the cloud description in climate models, covering a time period complementary to the CALIPSO one. Moreover, at the time of writing, it is expected that ATLID will provide a better separation between boundary layer optically thin clouds and aerosols than CALIPSO does, thanks to ATLID new HSRL capability.…”

Direct atmosphere opacity observations from CALIPSO provide new constraints on cloud‐radiation interactions

“…Climate feedback analyses reveal that clouds are a large source of uncertainty for the climate sensitivity of climate models and thus for future climate projections [e.g., Taylor et al , ; Webb et al , ; Bony et al , ; Boucher et al , ]. Boosting confidence in climate model projections requires identifying model physics deficiencies via comparison to observations [e.g., Klein and Jakob , ; Bodas‐Salcedo et al , ; Nam et al , ; Cesana and Chepfer , , ; Marchand et al , ; Kay et al , ; English et al , ; Kay et al , ] and then improving the representation of relevant physics in the models [ Konsta et al , ; Kay et al , ; Kay et al , ]. Taylor et al [] suggest that in the analysis of the complex climate system, a useful first step is to consider the processes that are energetically dominant, since processes that weakly affect the energy flow and storage within the system are unlikely to dominate its response to perturbation.…”