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This paper describes a new satellite simulator for the CLARA-A2 climate data record (CDR). This simulator takes into account the variable skill in cloud detection in the CLARA-A2 CDR by using a different approach to other similar satellite simulators to emulate the ability to detect clouds. In particular, the paper describes three methods to filter out clouds from climate models undetectable by observations. The first method is comparable to the current simulators in the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP), since it relies on a single visible cloud optical depth at 550 nm (τc) threshold applied globally to delineate cloudy and cloud-free conditions. Methods two and three apply long/lat-gridded values separated by daytime and nighttime conditions. Method two uses gridded varying τc as opposed to method one, which uses just a τc threshold, and method three uses a cloud probability of detection (POD) depending on the model τc. The gridded POD values are from the CLARA-A2 validation study by. Methods two and three replicate the relative ease or difficulty for cloud retrievals depending on the region and illumination. They increase the cloud sensitivity where the cloud retrievals are relatively straightforward, such as over midlatitude oceans, and they decrease the sensitivity where cloud retrievals are notoriously tricky, such as where thick clouds may be inseparable from cold snow-covered surfaces, as well as in areas with an abundance of broken and small-scale cumulus clouds such as the atmospheric subsidence regions over the ocean. The simulator, together with the International Satellite Cloud Climatology Project (ISCCP) simulator of the COSP, is used to assess Arctic clouds in the EC-Earth climate model compared to the CLARA-A2 and ISCCP H-Series (ISCCP-H) CDRs. Compared to CLARA-A2, EC-Earth generally underestimates cloudiness in the Arctic. However, compared to ISCCP and its simulator, the opposite conclusion is reached. Based on EC-Earth, this paper shows that the simulated cloud mask of CLARA-A2, using method three, is more representative of the CDR than method one used for the ISCCP simulator. The simulator substantially improves the simulation of the CLARA-A2-detected clouds, especially in the polar regions, by accounting for the variable cloud detection skill over the year. The approach to cloud simulation based on the POD of clouds depending on their τc , location, and illumination is the preferred one as it reduces cloudiness over a range of cloud optical depths. Climate model comparisons with satellite-derived information can be significantly improved by this approach, mainly by reducing the risk of misinterpreting problems with satellite retrievals as cloudiness features. Since previous studies found that the CLARA-A2 CDR performs well in the Arctic during the summer months, and that method three is more representative than method one, the conclusion is that EC-Earth likely underestimates clouds in the Arctic summer. [ABSTRACT FROM AUTHOR]

This paper describes a new satellite simulator for the Satellite Application Facility on Climate Monitoring (CM SAF) cLoud, Albedo and RAdiation dataset (CLARA), Advanced Very High Resolution Radiometer (AVHRR)-based, version 2 (CLARA-A2) Climate Data Record (CDR). This simulator takes into account the variable skill in cloud detection in the CLARA-A2 CDR by using a different approach to other similar satellite simulators to emulate the ability to detect clouds. In particular, the paper describes three methods to filter out clouds from climate models undetectable by observations. The first method, compared to the simulators in Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP), relies on one global visible cloud optical depth at 550nm (τc) threshold to delineate cloudy and cloud-free conditions. Method two and three apply long/lat -gridded values separated by day and nighttime conditions. Method two uses gridded varying τc as opposed to method one that uses just a single τc threshold, and method three uses a cloud Probability of Detection (POD) depending on the model τc. Method two and three replicate the relative ease or difficulty for cloud retrievals depending on the region and illumination by increasing the cloud sensitivity where the cloud retrievals are relatively straightforward, such as over mid-latitude oceans, and by decreasing the sensitivity where cloud retrievals are notoriously tricky, such as over the Arctic region during the polar night. Method three has the added advantage that it indirectly takes into account that cloud retrievals in some areas are more likely than others to miss some clouds. This situation is common in cold regions where even thick clouds may be inseparable from cold, snow-covered surfaces and also in areas with an abundance of broken and small scale cumulus clouds such as the atmospheric subsidence regions over the ocean. The simulator, together with the International Satellite Cloud Climatology Project (ISCCP) simulator of COSP, is used to assess Arctic clouds in the EC-Earth climate model compared to the CLARA-A2 and ISCCP-H CDRs. Compared to CLARA-A2, EC-Earth is shown to underestimate cloudiness in the Arctic generally. However, compared to ISCCP and its simulator, the opposite conclusion is reached. Previous studies have found that the CLARA-A2 CDR performs well in the Arctic during the summer months, and this paper shows that the simulated cloud mask of CLARA-A2 using method three is more representative of the CDR than method one used in COSP, using a global τc threshold to simulate clouds. Therefore, the conclusion that EC-Earth underpredicts clouds in the Arctic is the more likely one. The simulator substantially improves the simulation of the CLARA-A2 detected clouds, especially in the polar regions, by accounting for the variable cloud detection skill over the year. The approach to cloud simulation based on the POD of clouds depending on their cloud optical depth, location, and illumination is the preferred one as it reduces cloudiness over a range of cloud optical depths. Climate model comparisons with satellite-derived information can be significantly improved by this approach, mainly by reducing the risk of misinterpreting problems with satellite retrievals as cloudiness features. [ABSTRACT FROM AUTHOR]

The Cloud_cci satellite simulator has been developed to enable comparisons between the Cloud_cci Climate Data Record (CDR) and climate models. The Cloud_cci simulator is applied here to the EC-Earth Global Climate Model as well as the RACMO Regional Climate Model. We demonstrate the importance of using a satellite simulator that emulates the retrieval process underlying the CDR as opposed to taking the model output directly. The impact of not sampling the model at the local overpass time of the polar-orbiting satellites used to make the dataset was shown to be large, yielding up to 100% error in Liquid Water Path (LWP) simulations in certain regions. The simulator removes all clouds with optical thickness smaller than 0.2 to emulate the Cloud_cci CDR's lack of sensitivity to very thin clouds. This reduces Total Cloud Fraction (TCF) globally by about 10% for EC-Earth and by a few percent for RACMO over Europe. Globally, compared to the Cloud_cci CDR, EC-Earth is shown to be mostly in agreement on the distribution of clouds and their height, but it generally underestimates the high cloud fraction associated with tropical convection regions, and overestimates the occurrence and height of clouds over the Sahara and the Arabian sub-continent. In RACMO, TCF is higher than retrieved over the northern Atlantic Ocean, but lower than retrieved over the European continent, where in addition the Cloud Top Pressure (CTP) is underestimated. The results shown here demonstrate again that a simulator is needed to make meaningful comparisons between modelled and retrieved cloud properties. It is promising to see that for (nearly) all cloud properties the simulator improves the agreement of the model with the satellite data. [ABSTRACT FROM AUTHOR]

The Microwave Radiometers (MWR) on-board ERS-1, ERS-2, and Envisat provide a continuous time series of brightness temperature observations between 1991 and 2012. Here we report on a new Total Column Water Vapour (TCWV) and Wet Tropospheric Correction (WTC) dataset that builds on this time series. We use a one-dimensional variational approach to derive TCWV from MWR observations and ERA-Interim background information. A particular focus of this study lies on the intercalibration of the three different instruments, which is performed using constraints on liquid water path (LWP) and TCWV. Comparing our MWR-derived time series of TCWV against TCWV derived from Global Navigation Satellite System (GNSS) we find that the MWR-derived TCWV time series is stable over time. However, observations potentially affected by precipitation show a degraded performance compared to precipitation-free observations in terms of the accuracy of retrieved TCWV. An analysis of WTC shows further that the retrieved WTC is superior to purely model-derived WTC for all satellites and for the entire time series. Even compared to operational WTC retrievals, which incorporate additional observational data, the here-described dataset shows improvements in particular for the mid-latitudes and for the two earlier satellites ERS-1 and ERS-2. The dataset is publicly available under doi:10.5676/DWD_EMIR/V001. [ABSTRACT FROM AUTHOR]

Abstract: A pressing task in numerical weather prediction and climate modelling is the evaluation of modelled cloud fields. Recent progress in spatial and temporal resolution of satellite remote sensing increases the potential of such evaluation efforts. This paper presents new methodologies to compare satellite remote sensing observations of clouds and output of atmospheric models and demonstrates their usefulness for evaluation. The comparison is carried out for two MODerate resolution Imaging Spectrometer (MODIS) scenes from the BALTEX Bridge Campaigns. Both scenes are characterised by low-level clouds with a substantial amount of liquid water. Cloud cover and cloud optical thickness of five different models, LM, Méso-NH, MM5 (non-hydrostatic models), RACMO2, and RCA (regional climate models) as well as corresponding retrievals from high resolution remote sensing observations of MODIS onboard the Terra satellite form the basis of a statistical analysis to compare the data sets. With the newly introduced patchiness parameters it is possible to separate differences between the two scenes on the one hand and between the models and the satellite on the other hand. We further introduce a new approach to spatially aggregate cloud optical thickness. Generally the models overestimate cloud optical thickness which can in part be ascribed to the lack of subgrid-scale variability. However, LM underestimates the frequency of occurrence of cloud optical thickness at values around 25. Furthermore, we compare the standard operational output of the non-hydrostatic models to simulations of the same models including parameterised shallow convection. However, clear improvements in the representation of low-level clouds are not found for these models. A change of the coefficients for autoconversion in RCA shows that LWP and precipitation strongly depend on this parameter. Refined vertical resolution, implemented in RACMO2, leads to a better agreement between model and satellite but still leaves room for further improvements. In general, this study reveals deficiencies of the models in representing low-level clouds, in particular for a stratiform cloud. [Copyright &y& Elsevier]