Clouds – their coverage, nature, and interactions with the energy budget of the planet – represent one of the primary sources of uncertainty in future…Clouds – their coverage, nature, and interactions with the energy budget of the planet – represent one of the primary sources of uncertainty in future climate prediction. Despite the crucial role of desert clouds in the distribution of water and energy budgets, their climatology is still largely incomplete. With arid regions projected to become dryer under global warming conditions, understanding the characteristics of their cloud cover can provide critical insights. In this work, cloud coverage was investigated over one of the Earth's most arid regions – the Arabian Peninsula. Four total cloud cover (TCC) products, namely the International Satellite Cloud Climatology Project H (ISCCP), the CM SAF Cloud, Albedo and Surface Radiation AVHRR 2 (CLARA) satellite datasets, the National Centers for Environmental Prediction – National Center for Atmospheric Research (NCEP–NCAR) Reanalysis (R-2) (NCEP) and the ECMWF Interim Re-Analysis (ERA) reanalyses, were used to construct a climatology of desert clouds over the peninsula between 1984 and 2009, accounting for the different products' uncertainties and limitations. Satellite retrievals and reanalysis fields were first validated against ground observations from the United Arab Emirates, for which homogeneity assessments were conducted. The validation was done using statistical indicators, including Normalized Root Mean Square Errors (nRMSE), relative biases (rBIAS), and correlation coefficients, on monthly and seasonal scales. The ISCCP dataset resulted in the highest correlations with the ground observations (overall 0.38) and the lowest nRMSE (overall 0.54), while CLARA had the lowest rBIAS (overall 0.02). All products showed discrepancies when compared to the ground observations, both annually and on a seasonal basis. When extended to the entire Arabian Peninsula, the satellite and reanalysis products showed decreasing spring and increasing summer (except for ISCCP) TCC evolution across the region. At inter-annual scales, the TCC over the peninsula showed a significant discontinuity in 1998. This shift could be linked to the forcing of the El Niño Southern Oscillation on water vapor transport over the region, as well as to documented artifacts in satellite retrievals and model outputs. These discrepancies are indicative of a need for detailed assessments to be made of the uncertainties existing in TCC data for the Arabian Peninsula.
Plain Language Summary
Clouds represent a primary source of uncertainty in future climate predictions. This is particularly pronounced in dryland clouds, given their sporadic and intermittent nature. With global warming predicted to enhance aridification trends in terrestrial arid zones, historical cloudiness trends over arid and hyper arid regions are very important – yet their climatology to date is still largely incomplete. The quality of observations and/or model outputs are an important consideration, making validation and intercomparison assessments imperative. This work investigated cloud cover over the Arabian Peninsula, one of the most arid regions on the Earth. Four cloud cover datasets were used, derived from satellite measurements and atmospheric reanalysis model outputs. The selected study period was between 1984 and 2009. The four datasets were initially compared with observations taken at ground stations in the United Arab Emirates. The four products were then studied over the entire Arabian Peninsula. Results showed that cloud cover displayed marked seasonal characteristics and large inter-annual temporal evolution. An abrupt change in regional cloud cover time series was found to occur in 1998. This could be attributed to the forcing of the El Niño Southern Oscillation, although significant documented uncertainties in satellite products over this time span call for deeper investigations of this causal relation.more
Can we build stable Climate Data Records (CDRs) spanning several satellite generations? This study outlines how the ClOud Fractional Cover dataset fro…Can we build stable Climate Data Records (CDRs) spanning several satellite generations? This study outlines how the ClOud Fractional Cover dataset from METeosat First and Second Generation (COMET) of the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) was created for the 25-year period 1991–2015. Modern multi-spectral cloud detection algorithms cannot be used for historical Geostationary (GEO) sensors due to their limited spectral resolution. We document the innovation needed to create a retrieval algorithm from scratch to provide the required accuracy and stability over several decades. It builds on inter-calibrated radiances now available for historical GEO sensors. It uses spatio-temporal information and a robust clear-sky retrieval. The real strength of GEO observations—the diurnal cycle of reflectance and brightness temperature—is fully exploited instead of just accounting for single “imagery”. The commonly-used naive Bayesian classifier is extended with covariance information of cloud state and variability. The resulting cloud fractional cover CDR has a bias of 1% Mean Bias Error (MBE), a precision of 7% bias-corrected Root-Mean-Squared-Error (bcRMSE) for monthly means, and a decadal stability of 1%. Our experience can serve as motivation for CDR developers to explore novel concepts to exploit historical sensor data.more
In West Africa (WA), interest in solar energy development has risen in recent years with many planned and ongoing projects currently in the region. Ho…In West Africa (WA), interest in solar energy development has risen in recent years with many planned and ongoing projects currently in the region. However, a major drawback to this development in the region is the intense cloud cover that reduces the incoming solar radiation when present and causes fluctuations in solar power production. Therefore, understanding the occurrence of clouds and their link to the surface solar radiation in the region is important for making plans to manage future solar energy production. In this study, we use the state-of-the-art European Centre for Medium-range Weather Forecasts ReAnalysis (ERA5) dataset to examine the occurrence and persistence of cloudy and clear-sky conditions in the region. Then, we investigate the effects of cloud cover on the quantity and variability of the incoming solar radiation. The cloud shortwave radiation attenuation (CRA↓SW) is used to quantify the amount of incoming solar radiation that is lost due to clouds. The results showed that the attenuation of incoming solar radiation is stronger in all months over the southern part of WA near the Guinea Coast. Across the whole region, the maximum attenuation occurs in August, with a mean CRA↓SW of about 55% over southern WA and between 20% and 35% in the Sahelian region. Southern WA is characterized by a higher occurrence of persistent cloudy conditions, while the Sahel region and northern WA are associated with frequent clear-sky conditions. Nonetheless, continuous periods with extremely low surface solar radiation were found to be few over the whole region. The analysis also showed that the surface solar radiation received from November to April only varies marginally from one year to the other. However, there is a higher uncertainty during the core of the monsoon season (June to October) with regard to the quantity of incoming solar radiation. The results obtained show the need for robust management plans to ensure the long-term success of solar energy projects in the regionmore
Clouds affected by solar eclipses could influence the reflection of sunlight back into space and might change local precipitation patterns. Satellite …Clouds affected by solar eclipses could influence the reflection of sunlight back into space and might change local precipitation patterns. Satellite cloud retrievals have so far not taken into account the lunar shadow, hindering a reliable spaceborne assessment of the eclipse-induced cloud evolution. Here we use satellite cloud measurements during three solar eclipses between 2005 and 2016 that have been corrected for the partial lunar shadow together with large-eddy simulations to analyze the eclipse-induced cloud evolution. Our corrected data reveal that, over cooling land surfaces, shallow cumulus clouds start to disappear at very small solar obscurations (~15%). Our simulations explain that the cloud response was delayed and was initiated at even smaller solar obscurations. We demonstrate that neglecting the disappearance of clouds during a solar eclipse could lead to a considerable overestimation of the eclipse-related reduction of net incoming solar radiation. These findings should spur cloud model simulations of the direct consequences of sunlight-intercepting geoengineering proposals, for which our results serve as a unique benchmark.more
In the framework of the Satellite Application Facility on Climate Monitoring (CM-SAF) an algorithm was developed to retrieve Cloud Physical Properties…In the framework of the Satellite Application Facility on Climate Monitoring (CM-SAF) an algorithm was developed to retrieve Cloud Physical Properties (CPP) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation (METEOSAT−8) and the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) satellites. This paper presents the CPP algorithm and determines if SEVIRI can be used together with AVHRR to build a consistent and accurate data set of cloud optical thickness (COT) and cloud liquid water path (CLWP) over Europe for climate research purposes. After quantifying the differences in 0.6 and 1.6 μm operational calibrated reflectances of SEVIRI and AVHRR, a recalibration procedure is proposed to normalize and absolutely calibrate these reflectances. The effects of recalibration, spatial resolution, and viewing geometry differences on the SEVIRI and AVHRR cloud property retrievals are evaluated. The intercomparison of 0.6 and 1.6 μm operationally calibrated reflectances indicates ∼6 and ∼26% higher reflectances for SEVIRI than for AVHRR. These discrepancies result in retrieval differences between AVHRR and SEVIRI of ∼8% for COT and ∼60% for CLWP. Owing to recalibration these differences reduce to ∼5%, while the magnitude of the median COT and CLWP values of AVHRR decrease ∼2 and ∼60% and the SEVIRI values increase ∼10 and ∼55%, respectively. The differences in spatial resolution and viewing geometry slightly influence the retrieval precision. Thus the CPP algorithm can be used to build a consistent and high-quality data set of SEVIRI and AVHRR retrieved cloud properties for climate research purposes, provided the instrument reflectances are recalibrated, preferably guided by the satellite operators.more
This paper describes a new method for cloud-correcting observations of black-sky surface albedo derived using the Advanced Very High Resolution Radiom…This paper describes a new method for cloud-correcting observations of black-sky surface albedo derived using the Advanced Very High Resolution Radiometer (AVHRR). Cloud cover constitutes a major challenge for surface albedo estimation using AVHRR data for all possible conditions of cloud fraction and cloud type with any land cover type and solar zenith angle. This study shows how the new cloud probability (CP) data to be provided as part of edition A3 of the CLARA (CM SAF cLoud, Albedo and surface Radiation dataset from AVHRR data) record from the Satellite Application Facility on Climate Monitoring (CM SAF) project of EUMETSAT can be used instead of traditional binary cloud masking to derive cloud-free monthly mean surface albedo estimates. Cloudy broadband albedo distributions were simulated first for theoretical cloud distributions and then using global cloud probability (CP) data for 1 month. A weighted mean approach based on the CP values was shown to produce very-high-accuracy black-sky surface albedo estimates for simulated data. The 90 % quantile for the error was 1.1 % (in absolute albedo percentage) and that for the relative error was 2.2 %. AVHRR-based and in situ albedo distributions were in line with each other and the monthly mean values were also consistent. Comparison with binary cloud masking indicated that the developed method improves cloud contamination removal.more
The second Advanced Technology Microwave Sounder (ATMS) was onboard the National Oceanic and Atmospheric Administration (NOAA)-20 satellite when launc…The second Advanced Technology Microwave Sounder (ATMS) was onboard the National Oceanic and Atmospheric Administration (NOAA)-20 satellite when launched on 18 November 2017. Using nearly six months of the earliest NOAA-20 observations, the biases of the ATMS instrument were compared between NOAA-20 and the Suomi National Polar-Orbiting Partnership (S-NPP) satellite. The biases of ATMS channels 8 to 13 were estimated from the differences between antenna temperature observations and model simulations generated from Meteorological Operational (MetOp)-A and MetOp-B satellites’ Global Positioning System (GPS) radio occultation (RO) temperature and water vapor profiles. It was found that the ATMS onboard the NOAA-20 satellite has generally larger cold biases in the brightness temperature measurements at channels 8 to 13 and small standard deviations. The observations from ATMS on both S-NPP and NOAA-20 are shown to demonstrate an ability to capture a less than 1-h temporal evolution of Hurricane Florence (2018) due to the fact that the S-NPP orbits closely follow those of NOAA-20.more