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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-174
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-174
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: methods for assessment of models 07 Aug 2019

Submitted as: methods for assessment of models | 07 Aug 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

A simulator for the CLARA-A2 cloud climate data record and its application to assess EC-Earth polar cloudiness

Salomon Eliasson, Karl-Göran Karlsson, and Ulrika Willén Salomon Eliasson et al.
  • Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden

Abstract. 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.

Salomon Eliasson et al.
Interactive discussion
Status: open (until 02 Oct 2019)
Status: open (until 02 Oct 2019)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Salomon Eliasson et al.
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Short summary
This paper describes a new satellite simulator. Its purpose is to simulate the CLARA-A2 climate data record from a climate model atmosphere. We explain how the simulator takes into account the regionally variable cloud detection skill of the observations. The simulator makes use of the long/lat gridded validation between CLARA-A2 and the CALIOP, satellite-borne lidar dataset. Using the EC Earth climate model, we show a sizable impact on climate model validation, especially at high latitudes.
This paper describes a new satellite simulator. Its purpose is to simulate the CLARA-A2 climate...
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