Global Cloud Amount Research Articles

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

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.

Inter-Comparison and Evaluation of the Four Longest Satellite-Derived Cloud Climate Data Records: CLARA-A2, ESA Cloud CCI V3, ISCCP-HGM, and PATMOS-x

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.

Cloud climatologies from the infrared sounders AIRS and IASI: strengths and applications

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.

Evaluation of ISCCP cloud amount with MODIS observations

The goal of the International Satellite Cloud Climatology Project (ISCCP) is to provide global cloud amount statistics for atmospheric radiation flux modeling, which is a key element of climate change studies. However, ISCCP estimates rely on two spectral channels only, while the most advanced satellite sensors offer over 20 spectral bands, and thus a higher probability of correct cloud detection. We validated the accuracy of ISCCP mean monthly cloud amount statistics using the state-of-the-art, 36-spectral channel Moderate-resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra and Aqua satellites. Based on the MODIS Level 2 Cloud Mask we developed a dedicated Level 3 product for Central Europe (2004–2009). For the first time, MODIS swath data were projected onto an ISCCP equal-area grid, which guaranteed an exact geometrical agreement between both climatologies. Results showed that there was a close correlation between ISCCP and MODIS data (ρ=0.872, α=0.99), especially at warmer part of the year (ρ≥0.940, α>0.99). However, ISCCP estimations were found to be unreliable in wintertime when surface was covered with snow. The presence of snow resulted in a significant underestimate of cloud amount by 0.45 for individual ISCCP grid boxes. Our results suggest that MODIS cloud climatology is more reliable when estimates of mean monthly cloud amount are required.

The recent state of the climate: Driving components of cloud-type variability

[1] To reduce the Earth's radiation budget uncertainty related to cloud types' changes, and better understand the climate constraints resulting from long-term clouds' variability, frequent and finer (than actually existing) observations are necessary. This is one of the aims of future satellite programs such as the Global Change Observation Mission-Climate (GCOM-C) satellite, to be launched by the Japan Aerospace Exploration Agency (JAXA). To facilitate the transition from past to future observations, the actual state of climate variables (e.g., cloud types) needs to be evaluated. This evaluation is attempted in the present work with the analysis of long-term cloud types' distribution and amounts. The data set used for this study is 25 years (1982–2006) of global daytime cloud properties observed by the National Oceanic and Atmospheric Administration-Advanced Very-High-Resolution Radiometer (NOAA-AVHRR) satellites sensors. Though various calibrations have been applied on NOAA-AVHRR data, the effects of the orbit drift experienced by these satellites need to be corrected. A signal processing decomposition method allowing the filtering of the cloud types' amount trend affected by the orbit drift is used to perform the necessary corrections. The results obtained show a quantifiable improvement of the cloud amount estimation and trends of the individual NOAA satellites initial observations, at the global and regional scales. The corrected global cloud amount shows a slight decrease in its linear trend. The driving factors of this trend are the decrease in mid and low clouds overwhelming the increase in high clouds (+0.04% cloud amount/yr). A comparison with other cloud climatology studies such as the International Cloud Satellite Climatology Project (ISCCP) data set shows that the global cloud decrease noticed in NOAA-AVHRR's data is smaller. And, contrary to the NOAA-AVHRR's data, the driving force of the ISCCP linear trend is a sharp decrease in low clouds (−0.20% cloud amount/yr).

Estimation of fractional cloud cover for Moderate Resolution Imaging Spectroradiometer/Terra cloud mask classes with high‐resolution over ocean ASTER observations

Cloud masks are the final product of cloud detection and the starting point for determining the cloud amount. To calculate the cloud amount within thematic classes of cloud masks, the cloud fraction for each class must be assigned. This is usually done subjectively, assuming that clear pixels indicate a cloud fraction value of 0.0 and cloudy pixels indicate a cloud fraction value of 1.0. An analogous interpretation can also be applied using the Moderate Resolution Imaging Spectroradiometer (MODIS) level 3 approach (L3), which calculates global cloud amounts from instantaneous cloud masks. In this paper, the cloud fractions for each of the four MODIS cloud mask classes were derived in a more objective manner. They were based on high‐resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) observations that were collocated in space and time with MODIS data. This study was limited to cumulus and stratocumulus clouds observed during the daytime over the ocean surface with a MODIS nadir viewing geometry. Cloud fraction values were averaged and used to calculate the cloud amounts for all analyzed ASTER scenes, which were then compared to statistics that assumed an L3 approach. Our results show that the L3 approach tended to overestimate the cloud amount in general by 0.087, and this value ranged between 0.007 and 0.100, depending on the observation conditions. We found that cloud fraction values were influenced by the average cloud amount in the analyzed ASTER scenes, especially in the case of “cloudy” and “uncertain” pixels. The MODIS 250 m cloud mask was also evaluated and found to be a reasonable source of cloud fraction information for cloud mask classes during the daytime over the ocean, reducing L3 overestimation by approximately 30%.

A 6-year global cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat

Abstract. We present a six-year global climatology of cloud properties, obtained from observations of the Atmospheric Infrared Sounder (AIRS) onboard the NASA Aqua satellite. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) combined with CloudSat observations, both missions launched as part of the A-Train in 2006, provide a unique opportunity to evaluate the retrieved AIRS cloud properties such as cloud amount and height. In addition, they permit to explore the vertical structure of different cloud types. AIRS-LMD cloud detection agrees with CALIPSO about 85% over ocean and about 75% over land. Global cloud amount has been estimated from 66% to 74%, depending on the weighting of not cloudy AIRS footprints by partial cloud cover from 0 to 0.3. 42% of all clouds are high clouds, and about 42% of all clouds are single layer low-level clouds. The "radiative" cloud height determined by the AIRS-LMD retrieval corresponds well to the height of the maximum backscatter signal and of the "apparent middle" of the cloud. Whereas the real cloud thickness of high opaque clouds often fills the whole troposphere, their "apparent" cloud thickness (at which optical depth reaches about 5) is on average only 2.5 km. The real geometrical thickness of optically thin cirrus as identified by AIRS-LMD is identical to the "apparent" cloud thickness with an average of about 2.5 km in the tropics and midlatitudes. High clouds in the tropics have slightly more diffusive cloud tops than at higher latitudes. In general, the depth of the maximum backscatter signal increases nearly linearly with increasing "apparent" cloud thickness. For the same "apparent" cloud thickness optically thin cirrus show a maximum backscatter about 10% deeper inside the cloud than optically thicker clouds. We also show that only the geometrically thickest opaque clouds and (the probably surrounding anvil) cirrus penetrate the stratosphere in the tropics.

宇宙線と雲形成―フォーブッシュ現象で雲は減るか？―

Possible influences of cosmic rays on terrestrial climate have been studied by many researchers since a good correlation between neutron monitor counts and global cloud amount was reported by Svensmark and Friis-Christensen in 1997. The cosmic ray-cloud relationship may be best tested during Forbush decrease events, in which cosmic rays largely decrease for several days associated with coronal mass ejections. Some cloud parameters are likely to respond to the transient decrease of cosmic rays with a typical time delay of several days, although we do not know the physics behind the cosmic-ray cloud relationship.

Cloud Properties and Their Seasonal and Diurnal Variability from TOVS Path-B

Abstract Eight years of cloud properties retrieved from Television Infrared Observation Satellite-N (TIROS-N) Observational Vertical Sounder (TOVS) observations aboard the NOAA polar orbiting satellites are presented. The relatively high spectral resolution of these instruments in the infrared allows especially reliable cirrus identification day and night. This dataset therefore provides complementary information to the International Satellite Cloud Climatology Project (ISCCP). According to this dataset, cirrus clouds cover about 27% of the earth and 45% of the Tropics, whereas ISCCP reports 19% and 25%, respectively. Both global datasets agree within 5% on the amount of single-layer low clouds, at 30%. From 1987 to 1995, global cloud amounts remained stable to within 2%. The seasonal cycle of cloud amount is in general stronger than its diurnal cycle and it is stronger than the one of effective cloud amount, the latter the relevant variable for radiative transfer. Maximum effective low cloud amount over ocean occurs in winter in SH subtropics in the early morning hours and in NH midlatitudes without diurnal cycle. Over land in winter the maximum is in the early afternoon, accompanied in the midlatitudes by thin cirrus. Over tropical land and in the other regions in summer, the maximum of mesoscale high opaque clouds occurs in the evening. Cirrus also increases during the afternoon and persists during night and early morning. The maximum of thin cirrus is in the early afternoon, then decreases slowly while cirrus and high opaque clouds increase. TOVS extends information of ISCCP during night, indicating that high cloudiness, increasing during the afternoon, persists longer during night in the Tropics and subtropics than in midlatitudes. A comparison of seasonal and diurnal cycle of high cloud amount between South America, Africa, and Indonesia during boreal winter has shown strong similarities between the two land regions, whereas the Indonesian islands show a seasonal and diurnal behavior strongly influenced by the surrounding ocean. Deeper precipitation systems over Africa than over South America do not seem to be directly reflected in the horizontal coverage and mesoscale effective emissivity of high clouds.

Recent Trends in Cloudiness over the United States: A Tale of Monitoring Inadequacies

Automated Surface Observation Systems (ASOS) were widely introduced to replace manned weather stations around the mid- 1990s over North America and other parts of the world. While laser beam ceilometers of the ASOS in North America measure overhead clouds within the lower 3.6 km of the atmosphere, they do not contain cloud-type and opacity information and are not comparable with previous cloud records. However, a network of 124 U.S. military weather stations with continuous human observations provides useful information of total cloud cover over the contiguous United States, thus lessening the disruption caused by the ASOS. Analyses of the military cloud data suggest an increasing trend (~1.4% of the sky cover per decade) in U.S. total cloud cover from 1976 to 2004, with increases over most of the country except the Northwest, although large uncertainties exist because of sparse spatial sampling. Thus, inadequacies exist in surface observations of global cloud amounts and types, especially over the oceans, Canada, and the United States since the mid- 1990s. The problem is compounded by inhomogeneities in satellite cloud data. Reprocessing of satellite data has the potential for improvements if priority is given to the improved continuity of records.

Comparison of NOAA's Operational AVHRR-Derived Cloud Amount to Other Satellite-Derived Cloud Climatologies

Abstract A comparison is made between a new operational NOAA Advanced Very High Resolution Radiometer (AVHRR) global cloud amount product to those from established satellite-derived cloud climatologies. The new operational NOAA AVHRR cloud amount is derived using the cloud detection scheme in the extended Clouds from AVHRR (CLAVR-x) system. The cloud mask within CLAVR-x is a replacement for the Clouds from AVHRR phase 1 (CLAVR-1) cloud mask. Previous analysis of the CLAVR-1 cloud climatologies reveals that its utility for climate studies is reduced by poor high-latitude performance and the inability to include data from the morning orbiting satellites. This study demonstrates, through comparison with established satellite-derived cloud climatologies, the ability of CLAVR-x to overcome the two main shortcomings of the CLAVR-1-derived cloud climatologies. While systematic differences remain in the cloud amounts from CLAVR-x and other climatologies, no evidence is seen that these differences represent a failure of the CLAVR-x cloud detection scheme. Comparisons for July 1995 and January 1996 indicate that for most latitude zones, CLAVR-x produces less cloud than the International Satellite Cloud Climatology Project (ISCCP) and the University of Wisconsin High Resolution Infrared Radiation Sounder (UW HIRS). Comparisons to the Moderate Resolution Imaging Spectroradiometer (MODIS) for 1–8 April 2003 also reveal that CLAVR-x tends to produce less cloud. Comparison of the seasonal cycle (July–January) of cloud difference with ISCCP, however, indicates close agreement. It is argued that these differences may be due to the methodology used to construct a cloud amount from the individual pixel-level cloud detection results. Overall, the global cloud amounts from CLAVR-x appear to be an improvement over those from CLAVR-1 and compare well to those from established satellite cloud climatologies. The CLAVR-x cloud detection results have been operational since late 2003 and are available in real time from NOAA.

Comparison of cloud fields from atmospheric general circulation model, in situ and satellite measurements

This paper focuses on the comparison of cloud amounts derived from an atmospheric general circulation model (AGCM), Satellite‐observed clouds, and Ground‐based cloud observations. This is distinctly different from Earth Radiation Budget Experiment (ERBE)‐type comparisons because it does not mix potential errors in the cloud amount with those in the radiation code embedded in the model. Long term cloud climatologies were used to compare global cloud amounts and regional seasonal cycles. The results obtained were surprising in many respects. The AGCM successfully reproduced the signatures of the warm pool and North Pacific seasonal cycle cloudiness but failed in the low stratus region off the coast of South America, a known problem for AGCMs. The data sets also reproduced the anomaly signature associated with El Niño in the warm pool region, but the model amounts were lower. Global results had a similar success rate, with the model generally producing lower total cloud amounts compared to the satellite and in situ measurements. Also, an attempt was made to compare cloud vertical distributions between the data sets. Because of the inherent differences in the measuring processes among the three data sets, the cloud height may need to be validated using the corresponding radiation fields. Unfortunately there were also some large discrepancies between the two observed cloud data sets. We conclude that the character of the observed cloud data sets, while tremendously improved over the last decade, must be substantially enhanced before they will be useful in validating AGCMs by any but the crudest levels of comparison.

AVHRR clear-sky radiation data sets at NOAA/NESDIS

Currently, global cloud amount estimates from the National Environmental Satellite, Data, and Information Service (NESDIS) experimental cloud remote sensing algorithm, CLAVR ( Cl ouds from AV HR R ) - Phase I, are biased towards 50% cloud cover. This is caused by assigning 50% cloud amount to 2×2 global area coverage (GAC) pixel arrays which have been classified as “mixed”. The classification identifies pixel arrays that are spatially non-uniform in albedo or temperature. To remove this source of bias, a clear-sky radiation data set (CRDS) is produced from “clear” pixels located in half degree latitude/longitude grids. Statistics of each channel's radiation measurements are computed over the 9-day repeat cycle of the polar orbiting satellite. The previous 8-day CRDS record is then used to predict the clear-sky radiation values for the current day (9th day) being processed. These are used to reclassify mixed pixels into clear or cloudy categories.