Decadal Stability and Trends in the Global Cloud Amount and Cloud Top Temperature in the Satellite-Based Climate Data Records

Abstract

Forty years of cloud observations are available globally from satellites, allowing derivation of climate data records (CDRs) for climate change studies. The aim of this study is to investigate how stable these cloud CDRs are and whether they qualify stability requirements recommended by the WMO’s Global Climate Observing System (GCOS). We also investigate robust trends in global total cloud amount (CA) and cloud top temperature (CTT) that are significant and common across all CDRs. The latest versions of four global cloud CDRs, namely CLARA-A3, ESA Cloud CCI, PATMOS-x, and ISCCP-HGM are analysed. This assessment finds that all three AVHRR-based cloud CDRs (i.e., CLARA-A3, ESA Cloud CCI and PATMOS-x) satisfy even the strictest GCOS stability requirements for CA and CTT when averaged globally. While CLARA-A3 is most stable in global averages when tested against MODIS-Aqua, PATMOS-x offers the most stable CDR spatially. While we find these results highly encouraging, there remain, however, large spatial differences in the stability of and across the CDRs. All four CDRs continue to agree on the statistically significant decrease in global cloud amount over the last four decades, although this decrease is now weaker compared to the previous assessments. This decreasing trend has been stabilizing or even reversing in the last two decades; the latter is seen also in MODIS-Aqua and CALIPSO GEWEX datasets. Statistically significant trends in CTT are observed in global averages in the AVHRR-based CDRs, but the spatial agreement in the sign and the magnitude of the trends is weaker compared to those in CA. We also present maps of Common Stability Coverage and Common Trend Coverage that could provide a valuable metric to carry out an ensemble-based analysis of the CDRs.

Difference between WMO Climate Normal and Climatology: Insights from a Satellite-Based Global Cloud and Radiation Climate Data Record

“…The differences between CN_WMO and Clim40 shown in Figures 1 and 3 for the total and low cloud fraction, respectively, are noteworthy. Previous studies have shown that the global cloud cover is generally decreasing when the trends are computed over the last forty years [21,23,25,26,48]. The decrease has mainly occurred over the sub-tropical to mid-latitudes in both hemispheres.…”

“…It employs cloud probabilistic detection based on the Naïve Bayesian theory, while the cloud top property algorithms employ artificial neural networks. A number of recent studies have documented the theoretical basis, validations, and improvements in the CLARA-A3 climate data record [21,26,[47][48][49].…”

“…The satellite sensors now provide information on more than two-thirds of all ECVs, and many of them do cover the entire 1991-2020 period [14,15]. Furthermore, there have been stratospheric improvements in the retrievals of ECVs in the recent decades, uplifting their quality and maturity to a level where climate data records of ECVs are being provided [16][17][18][19][20][21][22][23][24][25][26][27].…”