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The role of clouds remains the largest uncertainty in climate projections. They influence solar and thermal radiative transfer and the earth's water cycle. Therefore, there is an urgent need for accurate cloud observations to validate climate models and to monitor climate change. Passive satellite imagers measuring radiation at visible to thermal infrared (IR) wavelengths provide a wealth of information on cloud properties. Among others, the cloud top height (CTH) - a crucial parameter to estimate the thermal cloud radiative forcing - can be retrieved. In this paper we investigate the skill of ten current retrieval algorithms to estimate the CTH using observations from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG). In the first part we compare ten SEVIRI cloud top pressure (CTP) data sets with each other. The SEVIRI algorithms catch the latitudinal variation of the CTP in a similar way. The agreement is better in the extratropics than in the tropics. In the tropics multi-layer clouds and thin cirrus layers complicate the CTP retrieval, whereas a good agreement among the algorithms is found for trade wind cumulus, marine stratocumulus and the optically thick cores of the deep convective system. In the second part of the paper the SEVIRI retrievals are compared to CTH observations from the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) instruments. It is important to note that the different measurement techniques cause differences in the retrieved CTH data. SEVIRI measures a radiatively effective CTH, while the CTH of the active instruments is derived from the return time of the emitted radar or lidar signal. Therefore, some systematic differences are expected. On average the CTHs detected by the SEVIRI algorithms are 1.0 to 2.5 km lower than CALIOP observations, and the correlation coefficients between the SEVIRI and the CALIOP data sets range between 0.77 and 0.90. The a

A new satellite-derived climate dataset - denoted CLARA-A1 ("The CM SAF cLoud, Albedo and RAdiation dataset from AVHRR data") - is described. The dataset covers the 28 yr period from 1982 until 2009 and consists of cloud, surface albedo, and radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar-orbiting operational meteorological satellites. Its content, anticipated accuracies, limitations, and potential applications are described. The dataset is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project. The dataset has its strengths in the long duration, its foundation upon a homogenized AVHRR radiance data record, and in some unique features, e. g. the availability of 28 yr of summer surface albedo and cloudiness parameters over the polar regions. Quality characteristics are also well investigated and particularly useful results can be found over the tropics, mid to high latitudes and over nearly all oceanic areas. Being the first CM SAF dataset of its kind, an intensive evaluation of the quality of the datasets was performed and major findings with regard to merits and shortcomings of the datasets are reported. However, the CM SAF's long-term commitment to perform two additional reprocessing events within the time frame 2013-2018 will allow proper handling of limitations as well as upgrading the dataset with new features (e. g. uncertainty estimates) and extension of the temporal coverage.

Column-averaged volume mixing ratios of CH4 were retrieved with a precision of better than 0.5% from infrared solar absorption spectra obtained at Ny-Alesund (Spitsbergen, 79°N) between 1997 and 2004 and during two ship cruises (54°N34°S) on the Atlantic in 2003. The retrieval has been performed in a spectral region available to all operational FTIR (Fourier Transform InfraRed) spectrometers performing solar absorption measurements. The seasonality and the long-term increase of the tropospheric volume-mixing ratio, derived from the infrared measurements agree well with data from surface sampling at this site. The latitudinal variation of ship-borne measurements between 54°N and 34°S is in agreement with inverse model simulations which are optimized vs. the global NOAA/ESRL measurements.

The UV/Vis/near infrared spectrometer SCIAMACHY on board the European ENVISAT satellite enables total column retrieval of atmospheric methane with high sensitivity to the lower troposphere. The vertical column density of methane is converted to column averaged mixing ratio by using carbon dioxide retrievals as proxy for the probed atmospheric column. For this purpose, we apply concurrent total column measurements of CO_2 in combination with modeled column-averaged CO_2 mixing ratios. Possible systematic errors are discussed in detail while the precision error is 1.8% on average. This paper focuses on methane retrievals from January 2003 through December 2004. The measurements with global coverage over continents are compared with model results from the chemistry–transport model TM4. In the retrievals, the north-south gradient as well as regions with enhanced methane levels can be clearly identified. The highest abundances are found in the Red Basin of China, followed by northern South America, the Gangetic plains of India and central parts of Africa. Especially the abundances in northern South America and the Red Basin are generally higher than modeled. Further, we present the seasonal variations within the investigated time period. Peak values in Asia due to rice emissions are observed from August through October. We expand earlier investigations that suggest underestimated emissions in the tropics. It is shown that these underestimations show a seasonal behavior that peaks from August through December. The global measurements may be used for inverse modeling and are thus an important step towards better quantification of the methane budget.