Understanding the coupling of clouds to large-scale circulation is one of the grand challenges for the global climate research community. In this context, realistically modelling the vertical structure of cloud radiative heating (CRH) and/or cooling in Earth system models is a key premise to understand this coupling. Here, we evaluate CRH in two versions of the European Community Earth System Model (EC-Earth) using retrievals derived from the combined radar and lidar data from the CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellites. One model version is also used with two different horizontal resolutions. Our study evaluates large-scale intraseasonal variability in the vertical structure of CRH and cloud properties and investigates the changes in CRH during different phases of the El Nino-Southern Oscillation (ENSO), a process that dominates the interannual climate variability in the tropics. EC-Earth generally captures both the intraseasonal and meridional pattern of variability in CRH over the convectively active and stratocumulus regions and the CRH during the positive and negative phases of ENSO. However, two key differences between model simulations and satellite retrievals emerge. First, the magnitude of CRH, in the upper troposphere, over the convectively active zones is up to twice as large in the models compared to the satellite data. Further dissection of net CRH into its shortwave and longwave components reveals noticeable differences in their vertical structure. The shortwave component of the radiative heating is overestimated by all model versions in the lower-most troposphere and underestimated in the middle troposphere. These over- and underestimates of shortwave heating are partly compensated by an overestimate of longwave cooling in the lowermost troposphere and heating in the middle troposphere. The biases in CRH can be traced back to disagreement in cloud amount and cloud water content. There is no noticeable improvement of CRH by increasing the horizontal resolution in the model alone. Our findings highlight the importance of evaluating models with satellite observations that resolve the vertical structure of clouds and cloud properties.

Two alternative methods for probabilistic cloud masking of images from the Advanced Very High Resolution Radiometer (AVHRR) sensor have been examined. Both methods are based on Bayesian theory and were trained using data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. Results were evaluated by comparing to independent CALIPSO-CALIOP observations and to a one-year ground-based cloud dataset composed from five different remote sensing systems over the observation site in Cabauw in the Netherlands. In addition, results were compared to two different cloud masks; one derived from the geostationary Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor and one from the Climate Monitoring Satellite Application Facility Clouds (CMSAF), Albedo and Radiation dataset from AVHRR data (CLARA-A1). It was demonstrated that the probabilistic methods compare well with the referenced satellite datasets and for daytime conditions they provide even better performance than the reference methods. Among the two probabilistic approaches, it was found that the formulation based on a Naive Bayesian formulation (denoted PPS-Prob Naive) performed clearly superior to the formulation based on a linear summation of conditional cloud probabilities (denoted PPS-Prob SPARC) for daytime conditions. For the study based on the observations over the Cabauw site, the overall daytime Kuipers Skill Score for PPS-Prob Naive was 0.84, for PPS-Prob SPARC 0.79, for CLARA-A1 0.74 and for SEVIRI 0.66. Corresponding results for night-time conditions were less favourable for the probabilistic formulations (Kuipers Skill Score 0.74 for PPS-Prob Naive, 0.68 for PPS-Prob SPARC, 0.80 for CLARA-A1 and 0.79 for SEVIRI) but still relatively close to the reference dataset The Cabauw distribution of cloudiness occurrences in different octa categories was reproduced very closely by all methods, including the probabilistic formulations. Results based on Cabauw observations were also largely in good agreement with results deduced from comparisons with the CALIPSO-CALIOP cloud mask. The PPS-Prob Naive approach will be implemented in an upcoming version of the Polar Platform System (PPS) cloud software issued by the EUMETSAT Nowcasting Satellite Application Facility (NWC SAF). It will also be used in the second release of the CMSAF CLARA cloud climate data record based on historic AVHRR GAC data (to be denoted CIARA-A2). (C) 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-SA license

Clouds forming during the summer monsoon over the Indian subcontinent affect its evolution through their radiative impact as well as the release of latent heat. While the latter is previously studied to some extent, comparatively little is known about the radiative impact of different cloud types and the vertical structure of their radiative heating/cooling effects. Therefore, the main aim of this study is to partly fill this knowledge gap by investigating and documenting the vertical distributions of the different cloud types associated with the Indian monsoon and their radiative heating/cooling using the active radar and lidar sensors on-board CloudSat and CALIPSO. The intraseasonal evolution of clouds from May to October is also investigated to understand pre-to-post monsoon transitioning of their radiative heating/cooling effects. The vertical structure of cloud radiative heating (CRH) follows the northward migration and retreat of the monsoon from May to October. Throughout this time period, stratiform clouds radiatively warm the middle troposphere and cool the upper troposphere by more than +/- 0.2 K day(-1) (after weighing by cloud fraction), with the largest impacts observed in June, July and August. During these months, the fraction of high thin cloud remains high in the tropical tropopause layer (TTL). Deep convective towers cause considerable radiative warming in the middle and upper troposphere, but strongly cool the base and inside of the TTL. This cooling is stronger during active (-1.23 K day(-1)) monsoon periods compared to break periods (-0.36 K day(-1)). The contrasting radiative warming effect of high clouds in the TTL is twice as largeduring active periods than in break periods. These results highlight the increasing importance of CRH with altitude, especially in the TTL. Stratiform (made up of alto- and nimbostratus clouds) and deep convection clouds radiatively cool the surface by approximately -100 and -400Wm(-2) respectively while warming the atmosphere radiatively by about 40 to 150Wm(-2). While the cooling at the surface induced by deep convection and stratiform clouds is largest during active periods of monsoon, the importance of stratiform clouds further increases during break periods. The contrasting CREs (cloud radiative effects) in the atmosphere and at surface, and during active and break periods, should have direct implications for the monsoonal circulation.

The European Space Agency project for studies of cloud properties in the Climate Change Initiative programme (ESA-CLOUD-CCI) aims at compiling the longest possible time series of cloud products from one single multispectral sensor-The five-channel Advanced Very High Resolution Radiometer (AVHRR) instrument. A particular aspect here is to include corresponding products based on other existing (Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Along-Track Scanning Radiometer (AATSR), MEdium Resolution Imaging Spectrometer (MERIS), Visible and Infrared Radiometer Suite (VIIRS)) and future Sea and Land Surface Temperature Radiometer (SLSTR) sensors measuring in similar (AVHRR-heritage) spectral channels. Initial inter-comparisons of the involved AVHRR-heritage channel radiances over a short demonstration period (2007-2009) were performed. Using Aqua-MODIS as reference, AVHRR (NOAA-18), AATSR, and MERIS channel radiances were evaluated using the simultaneous nadir (SNO) approach. Results show generally agreeing radiances within approximately 3% for channels at 0.6 mu m and 0.8 mu m. Larger deviations (+5%) were found for the corresponding AATSR channel at 0.6 mu m. Excessive deviations but with opposite sign were also indicated for AATSR 1.6 mu m and MERIS 0.8 mu m radiances. Observed differences may largely be attributed to residual temporal and spatial matching differences while excessive AATSR and MERIS deviations are likely partly attributed to incomplete compensation for spectrally varying surface and atmospheric conditions. However, very good agreement was found for all infrared channels among all the studied sensors. Here, deviations were generally less than 0.2% for the measured brightness temperatures with the exception of some remaining non-linear deviations at extreme low and high temperatures.

A method for detailed evaluation of a new satellite-derived global 28 yr cloud and radiation climatology (Climate Monitoring SAF Clouds, Albedo and Radiation from AVHRR data, named CLARA-A1) from polar-orbiting NOAA and Metop satellites is presented. The method combines 1 km and 5 km resolution cloud datasets from the CALIPSO-CALIOP (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite - Observation Cloud-Aerosol Lidar with Orthogonal Polarization) cloud lidar for estimating cloud detection limitations and the accuracy of cloud top height estimations. Cloud detection is shown to work efficiently for clouds with optical thicknesses above 0.30 except for at twilight conditions when this value increases to 0.45. Some misclassifications of cloud-free surfaces during daytime were revealed for semi-arid land areas in the sub-tropical and tropical regions leading to up to 20 % overestimated cloud amounts. In addition, a substantial fraction (at least 20-30 %) of all clouds remains undetected in the polar regions during the polar winter season due to the lack of or an inverted temperature contrast between Earth surfaces and clouds. Subsequent cloud top height evaluation took into account the derived information about the cloud detection limits. It was shown that this has fundamental importance for the achieved results. An overall bias of -274m was achieved compared to a bias of -2762m when no measures were taken to compensate for cloud detection limitations. Despite this improvement it was concluded that high-level clouds still suffer from substantial height underestimations, while the opposite is true for low-level (boundary layer) clouds. The validation method and the specifically collected satellite dataset with optimal matching in time and space are suggested for a wider use in the future for evaluation of other cloud retrieval methods based on passive satellite imagery.