Abstract

Abstract. Global cloud climatologies have been built from 13 years of Atmospheric Infrared Sounder (AIRS) and 8 years of Infrared Atmospheric Sounding Interferometer (IASI) observations, using an updated Clouds from Infrared Sounders (CIRS) retrieval. The CIRS software can handle any infrared (IR) sounder data. Compared to the original retrieval, it uses improved radiative transfer modelling, accounts for atmospheric spectral transmissivity changes associated with CO2 concentration and incorporates the latest ancillary data (atmospheric profiles, surface temperature and emissivities). The global cloud amount is estimated to be 0.67–0.70, for clouds with IR optical depth larger than about 0.1. The spread of 0.03 is associated with ancillary data. Cloud amount is partitioned into about 40 % high-level clouds, 40 % low-level clouds and 20 % mid-level clouds. The latter two categories are only detected in the absence of upper clouds. The A-Train active instruments, lidar and radar of the CALIPSO and CloudSat missions, provide a unique opportunity to evaluate the retrieved AIRS cloud properties. CIRS cloud height can be approximated either by the mean layer height (for optically thin clouds) or by the mean between cloud top and the height at which the cloud reaches opacity. This is valid for high-level as well as for low-level clouds identified by CIRS. IR sounders are particularly advantageous to retrieve upper-tropospheric cloud properties, with a reliable cirrus identification, day and night. These clouds are most abundant in the tropics, where high opaque clouds make up 7.5 %, thick cirrus 27.5 % and thin cirrus about 21.5 % of all clouds. The 5 % annual mean excess in high-level cloud amount in the Northern compared to the Southern Hemisphere has a pronounced seasonal cycle with a maximum of 25 % in boreal summer, in accordance with the moving of the ITCZ peak latitude, with annual mean of 4° N, to a maximum of 12° N. This suggests that this excess is mainly determined by the position of the ITCZ. Considering interannual variability, tropical cirrus are more frequent relative to all clouds when the global (or tropical) mean surface gets warmer. Changes in relative amount of tropical high opaque and thin cirrus with respect to mean surface temperature show different geographical patterns, suggesting that their response to climate change might differ.

Highlights

  • Clouds cover about 70 % of the Earth’s surface and play a key role in the energy and water cycle of our planet

  • For the evaluation of cloud height, we identify the geometrical profiling (GEOPROF) cloud layer which is closest to zcld from Atmospheric Infrared Sounder (AIRS) and estimate the height at which the cloud reaches a Cloud optical depth (COD) of 0.5, zCOD0.5, from CALIPSO. zCOD0.5 is required to be located within the corresponding GEOPROF cloud layer. zCOD0.5 is deduced from the CALIPSO L2 COD, assuming a constant increase of COD from cloud top towards cloud base, except for high-level clouds, for which the shape of the ice water content profile as a function of cloud emissivity is taken into account (Feofilov et al, 2015b)

  • Compared to the AIRS-LMD cloud retrieval presented in Stubenrauch et al (2010), the agreement with CALIPSO–CloudSat has improved both over ocean and land but slightly decreased over sea ice

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Summary

Introduction

Clouds cover about 70 % of the Earth’s surface and play a key role in the energy and water cycle of our planet. The Global Energy and Water Exchanges (GEWEX) Cloud Assessment (Stubenrauch et al, 2013) has highlighted the value of cloud properties derived from space observations for climate studies and model evaluation and has identified reasons for discrepancies in the retrieval of specific scenes, in particular thin cirrus, alone or with underlying low-level clouds. The cloud property retrieval employs radiative transfer modelling and atmospheric and surface ancillary data (atmospheric temperature and water vapour profiles, surface temperature and surface emissivity, identification of snow and ice). We present the results of (i) an updated and extended 13-year AIRS cloud climatology (2003–2015), using two different sets of the latest ancillary data (originating from retrievals and from meteorological reanalyses), and (ii) a new 8-year IASI cloud climatology (2008–2015).

AIRS data
IASI data
ERA-Interim meteorological reanalyses
Collocated AIRS–CALIPSO–CloudSat data
CIRS cloud property retrieval
Preparation and comparison of atmospheric and surface ancillary data
Accounting for changes in atmospheric CO2 concentration
Multi-spectral a posteriori cloud detection
Evaluation of cloud properties using the A-Train synergy
Cloud detection
Cloud height
Average cloud properties and variability
Applications
Hemispheric differences in UT clouds
Relating surface temperature anomalies to changes in UT clouds
Conclusions
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