Martin Stengel

The Community Cloud retrieval for Climate (CC4CL). Part I: A framework applied to multiple satellite imaging sensors

We present here the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time series provided at various resolutions, from 0.5 to 0.02. By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (CloudAerosol lidar with Orthogonal Polarization) observations in the high latitudes and over the Gulf of Guinea–West Africa. The results show that CC4CL provides very realistic estimates of cloud top height and cover for optically thick clouds but, where optically thin clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multiinstrument cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one dataset, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.

The MSG-SEVIRI based cloud property data record CLAAS-2

Clouds play a central role in the Earth’s atmosphere, and satellite observations are crucial for monitoring clouds and understanding their impact on the energy budget and water cycle. Within the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF), a new cloud property data record was derived from geostationary Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) measurements for the time frame 2004–2015. The resulting CLAAS-2 (CLoud property dAtAset using SEVIRI, Edition 2) data record is publicly available via the CM SAF website (https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002). In this paper we present an extensive evaluation of the CLAAS-2 cloud products, which include cloud fractional coverage, thermodynamic phase, cloud top properties, liquid/ice cloud water path and corresponding optical thickness and particle effective radius. Data validation and comparisons were performed on both level 2 (native SEVIRI grid and repeat cycle) and level 3 (daily and monthly averages and histograms) with reference datasets derived from lidar, microwave and passive imager measurements. The evaluation results show very good overall agreement with matching spatial distributions and temporal variability and small biases attributed mainly to differences in sensor characteristics, retrieval approaches, spatial and temporal samplings and viewing geometries. No major discrepancies were found. Underpinned by the good evaluation results, CLAAS-2 demonstrates that it is fit for the envisaged applications, such as process studies of the diurnal cycle of clouds and the evaluation of regional climate models. The data record is planned to be extended and updated in the future.

ESA DUE GlobVapour water vapor products: Validation

The main objective of the European Space Agency (ESA) Data User Element (DUE) GlobVapour project was the development of multi-annual global water vapor data sets. Since water vapour is a key climate variable it is important to have a good understanding of its behavior in the climate system. The ESA DUE GlobVapour project provides water vapor data, including error estimates, based on carefully calibrated and inter-calibrated satellite radiances in response to user requirements for long time series satellite observations. ESA DUE GlobVapour total columnar water vapor (TCWV) products derived from GOME/SCIA/GOME-2 (1996-2008) and SSM/I+MERIS (2003-2008) have been validated for the mentioned period, using satellite-based (AIRS, ATOVS) and ground-based measurements (radiosondes and microwave radiometer). The validation results are discussed in the following. The technical specifications on bias (1 kg/m2 for SSMI+MERIS and 2 kg/m2 for GOME/SCIA/GOME-2) are generally met. For more information, documents and data ...

The Cloudlcci simulator for the ESA Cloudlcci climate data recordand its application to a global and a regional climate model

The Cloud Climate Change Initiative (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 Regional Atmospheric Climate Model (RACMO) Regional Climate Model. We demonstrate the importance of using a satellite simulator that emulates the retrieval process underlying the Climate Data Record (CDR) as op5 posed 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 10 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 modeled and retrieved cloud properties. It is promising to see that for 15 (nearly) all cloud properties the simulator improves the agreement of the model with the satellite data.

Comparing ERA-Interim clouds with satellite observations using a simplified satellite simulator

An evaluation of the ERA-Interim clouds using satellite observations is presented. To facilitate such an evaluation in a proper way, a simplified satellite simulator has been developed and applied to six-hourly ERA-Interim reanalysis data covering the period 1982 to 2014. The simulator converts modelled cloud fields, for example those of the ERA-Interim reanalysis, to simulated cloud fields by accounting for specific characteristics of passive imaging satellite sensors such as the Advanced Very High Resolution Radiometer (AVHRR), which form the basis of many long-term observational datasets of cloud properties. It 5 is attempted to keep the simulated cloud fields close to the original modelled cloud fields to allow a quality assessment of the latter based on comparisons of the simulated clouds fields with the observations. Applying the simulator to ERA-Interim data, this study firstly focuses on spatial distribution and frequency of clouds (total cloud fraction) and on their vertical position, using cloud top pressure to express the cloud fraction of high, mid-level and low clouds. Furthermore, the cloud-top thermodynamic phase is investigated. All comparisons incorporate knowledge of systematic 10 uncertainties in the satellite observations and are further stratified by accounting for the limited sensitivity of the observations to clouds with very low cloud optical thickness (COT). The comparisons show that ERA-Interim has generally too low cloud fraction nearly everywhere on the globe except in the polar regions. This underestimation is caused by a lack of mid-level and/or low clouds for which the comparisons only show a minor sensitivity to cloud optical thickness thresholds applied. The amount of ERA-Interim high clouds, being higher than in 15 the observations, agrees to the observations within their estimated uncertainties. Removing the optically very thin clouds (COT < 0.15) from the model fields improves the agreement to the observations for high cloud fraction locally (e.g. in the Tropics) while for the mid-latitude regions best agreement of high cloud fraction is found when removing all clouds with COT < 1.0. Comparisons of the cloud thermodynamic phase at the cloud top reveals a too high relative ice cloud frequency in ERA-Interim being most pronounced in the higher latitudes. Indications are found that this is due to the suppression of liquid cloud existence 20 for temperatures below -23◦C in ERA-Interim. The application of this simulator facilitates a more effective use of passive satellite observations of clouds in the evaluation of modelled cloudiness, for example in reanalyses. 1 Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-258 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 30 May 2018 c

Satellite observations of aerosols and clouds over southern China from 2006 to 2015: analysis of changes and possible interaction mechanisms

Aerosol and cloud properties over southern China during the 10-year period 2006–2015 are analysed based on observations from passive and active satellite sensors and emission data. The results show a strong decrease in aerosol optical depth (AOD) over the study area, accompanied by an increase in liquid cloud cover and cloud liquid water path (LWP). The most significant changes occurred mainly in late autumn and early winter: AOD decreased by about 35 %, coinciding with an increase in liquid cloud fraction by 40 % and a near doubling of LWP in November and December. Analysis of emissions suggests that decreases in carbonaceous aerosol emissions from biomass burning activities were responsible for part of the AOD decrease, while inventories of other, anthropogenic emissions mainly showed increases. Analysis of precipitation changes suggests that an increase in precipitation also contributed to the overall aerosol reduction. Possible explanatory mechanisms for these changes were examined, including changes in circulation patterns and aerosol–cloud interactions (ACIs). Further analysis of changes in aerosol vertical profiles demonstrates a consistency of the observed aerosol and cloud changes with the aerosol semi-direct effect, which depends on relative heights of the aerosol and cloud layers: fewer absorbing aerosols in the cloud layer would lead to an overall decrease in the evaporation of cloud droplets, thus increasing cloud LWP and cover. While this mechanism cannot be proven based on the present observation-based analysis, these are indeed the signs of the reported changes.