Abstract
Results from four global cloud climate data records (ISCCP-HGM, ESA Cloud CCI V3, CLARA-A2 and PATMOS-x) have been inter-compared in global time series plots, in global maps and in zonal region plots covering the period in common, 1984–2009. The investigated cloud parameters were total cloud fraction and cloud top pressure. Averaged seasonal cycles of cloud cover, as observed by the CALIPSO-CALIOP sensor over the 2007–2015 period, were also used as an additional independent and high-quality reference for the study of global cloud cover. All CDRs show good agreement on global cloud amounts (~65%) and also a weak negative trend (0.5–1.9% per decade) over the period of investigation. Deviations between the CDRs are seen especially over the southern mid-latitude region and over the poles. Particularly good results are shown by PATMOS-x and by ESA Cloud CCI V3 when compared to the CALIPSO-CALIOP reference. Results for cloud top pressure show large differences (~60 hPa) between ISCCP-HGM and the other CDRs for the global mean. The two CDR groups show also opposite signs in the trend over the period.
Highlights
Monitoring the global distribution of clouds and their optical and thermal properties is a key task for space-based Earth observation systems, taking into account that cloud descriptions and cloud feedback processes in climate models are still considered to be major sources of uncertainties in most recent climate predictions [1,2,3,4,5]
Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation (CALIPSO)-Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP) data at 5 km horizontal resolution are shown as an independent reference. Since these data do not overlap more than 3.5 years with the other climate data records (CDRs), climatological monthly cloud fractions are prepared from 9 years of CALIPSO-CALIOP data (2007 through 2015) and this same 12-month time series is repeatedly plotted for all years from 1984 through 2009, mainly as a reference for the expected seasonal variability in the total cloud fraction
An interesting aspect here is that the artificial neural networks (ANNs)-based method used in European Space Agency (ESA) Cloud Climate Change Initiative (CCI) as well as the naïve Bayesian classifier used in PATMOS-x is relying on CALIPSO-CALIOP data for the training, yet still the results diverge remarkably, especially over the polar regions
Summary
Monitoring the global distribution of clouds and their optical and thermal properties is a key task for space-based Earth observation systems, taking into account that cloud descriptions and cloud feedback processes in climate models are still considered to be major sources of uncertainties in most recent climate predictions [1,2,3,4,5]. Satellite-based observations with global coverage and with a quality permitting cloud detection as well as analysis of fundamental cloud properties were introduced by the end of the 1970s These imaging sensors were multispectral, meaning that they measured in both visible and infrared spectral bands. As a consequence of the increasing length of this measurement record (currently 40+ years), the value of these observations for climate monitoring applications and for climate change studies are steadily increasing This has led to the compilation of several global cloud climate data records (CDRs) throughout the years where various cloud properties and their changes over time have been examined.
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