Abstract
Abstract. 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 6-hourly ERA-Interim reanalysis data covering the period of 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 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 the 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-level, mid-level and low-level clouds. Furthermore, the cloud-top thermodynamic phase is investigated. All comparisons incorporate knowledge of systematic 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 cloud fraction is generally too low nearly everywhere on the globe except in the polar regions. This underestimation is caused by a lack of mid-level and/or low-level clouds, for which the comparisons only show a minor sensitivity to the cloud optical thickness thresholds applied. The amount of ERA-Interim high-level clouds, being higher than in the observations, agrees with the observations within their estimated uncertainties. Removing the optically very thin clouds (COT <0.15) from the model fields improves the agreement with the observations for high-level cloud fraction locally (e.g. in the tropics), while for the mid-latitude regions, the best agreement for high-level cloud fraction is found when removing all clouds with COT <1.0. Comparisons of the cloud thermodynamic phase at the cloud top reveal 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 occurrence 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.
Highlights
In the last 2 decades major progress has been achieved in improving the representation of clouds in regional and global atmospheric models
Imperfect parametrizations of clouds will have a significant impact on other model variables and, on the modelled climate sensitivity, which contributes to the large spread among present-day climate models in this respect (Dufresne and Bony, 2008)
We evaluate modelled clouds in ERA-Interim by employing a simplified satellite simulator, which can be seen as a light version of a simulator that keeps the modifications to the model fields to a minimum
Summary
In the last 2 decades major progress has been achieved in improving the representation of clouds in regional and global atmospheric models. For most cloud properties such evaluations often remain difficult due to significant differences in the representativeness of modelled clouds compared to clouds obtained from satellite observations These differences are most significant in horizontal and vertical resolution and temporal sampling as well as deviating definitions of some geophysical quantities. Cloud variables that are not standard output in the model fields are determined following parametrizations used in the model if available After undergoing these simplified simulations, ERA-Interim cloud fields are compared to cloud property observations of the Cloud_cci AVHRR-PM v2.0 dataset (Stengel et al, 2017a) with a focus on systematic climatological deviations between the two sources in the period of 1982–2014.
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