Arctic clouds in ECHAM6 and their sensitivity to cloud microphysics and surface fluxes

Abstract

Abstract. Compared to other climate models, the MPI-ESM/ECHAM6 is one of the few models that is able to realistically simulate the typical two-state radiative structure of the Arctic boundary layer and also is able to sustain liquid water at low temperatures as is often observed in high latitudes. To identify processes in the model that are responsible for the abovementioned features, we compare cloud properties from ECHAM6 to observations from CALIPSO-GOCCP using the COSP satellite simulator and perform sensitivity runs. The comparison shows that the model is able to reproduce the spatial distribution and cloud amount in the Arctic to some extent but a positive bias in cloud fraction is found in high latitudes, which is related to an overestimation of low- and high-level clouds. We mainly focus on low-level clouds and show that the overestimated cloud amount is connected to surfaces that are covered with snow or ice and is mainly caused by an overestimation of liquid-containing clouds. The overestimated amount of Arctic low-level liquid clouds can be related to insufficient efficiency of the Wegener–Bergeron–Findeisen (WBF) process but revising this process alone is not sufficient to improve cloud phase on a global scale as it also introduces a negative bias over oceanic regions in high latitudes. Additionally, this measure transformed the positive bias in low-level liquid clouds into a positive bias of low-level ice clouds, keeping the amount of low-level clouds almost unchanged. To avoid this spurious increase in ice clouds, we allowed for supersaturation with respect to ice using a temperature-weighted scheme for saturation vapor pressure but this measure, together with a more effective WBF process, might already be too efficient at removing clouds as it introduces a negative cloud cover bias. We additionally explored the sensitivity of low-level cloud cover to the strength of surface heat fluxes; by increasing surface mixing, the observed cloud cover and cloud phase bias could also be reduced. As ECHAM6 already mixes too strongly in the Arctic regions, it is questionable if one can physically justify it to increase mixing even further.

Employing airborne radiation and cloud microphysics observations to improve cloud representation in ICON at kilometer-scale resolution in the Arctic

“…In this study, we decided to scale to the default CCN profile in ICON to match values representative of the Arctic. The scaling factor is derived from aerosol mass mixing ratios from the reanalysis of atmospheric composition of the Copernicus Atmospheric Monitoring Service (CAMS; Inness et al, 2019), which assimilated Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol retrievals (Levy et al, 2013) into the ECMWF model (Benedetti et al, 2009). We computed the number of activated CCN for various vertical velocities and also supersaturation for a sea-ice-covered domain north of Svalbard during the period from 2 to 5 June following the approach of Block (2018).…”

“…This uncertainty can be related to the general complexity of the Arctic climate system and to misrepresented microphysical processes in global climate models (GCMs) that are used to quantify the cloud feedback. Typical issues associated with the simulation of clouds in the Arctic are incorrectly simulated amount and distribution of clouds (English et al, 2015;Boeke and Taylor, 2016), which often can be linked to an erroneous representation of mixed-phase clouds (Cesana et al, 2012;Pithan et al, 2014;Kretzschmar et al, 2019). This consequently affects the quantification of the effect of Arctic clouds on the (surface) energy budget in GCMs (Karlsson and Svensson, 2013).…”

“…A comparison to satellite-derived quantities can further be made definition aware by using instrument simulators like those provided within the Cloud Feedback Model Intercomparison Project's (CFMIP) Observation Simulator Package (COSP;Bodas-Salcedo et al, 2011). The benefit of using COSP for evaluating clouds in GCMs in the Arctic has been shown in several studies (Barton et al, 2012;Kay et al, 2016;Kretzschmar et al, 2019).…”

Employing airborne radiation and cloud microphysics observations to improve cloud representation in ICON at kilometer-scale resolution in the Arctic

“…This uncertainty can be related to the general complexity of the Arctic climate system and to misrepresented microphysical processes in global climate models (GCMs) that are used to quantify the cloud feedback. Typical issues associated with the simulation of clouds in the Arctic are incorrectly simulated amount and distribution of clouds (English et al, 2015;Boeke and Taylor, 2016), which often can be linked to an erroneous representation of mixed-phase clouds (Cesana et al, 2012;Kretzschmar et al, 2019). This consequently affects the quantification of the effect of Arctic clouds on the (surface) energy budget in GCMs (Karlsson and Svensson, 2013).…”

“…A comparison to satellite derived quantities can further be made definition-aware by using instrument simulators like they are provided within the Cloud Feedback Model Intercomparison Project's (CFMIP) Observation Simulator Package (COSP;Bodas-Salcedo et al, 2011). The benefit of using COSP for evaluating clouds in GCMs in the Arctic has been shown in several studies (Barton et al, 2012;Kay et al, 2016;Kretzschmar et al, 2019).…”