Arctic cloud annual cycle biases in climate models

Abstract. Arctic clouds exhibit a robust annual cycle with maximum cloudiness in fall and minimum cloudiness in winter. These variations affect energy flows in the Arctic with a large influence on the surface radiative fluxes. Contemporary climate models struggle to reproduce the observed Arctic cloud amount annual cycle and significantly disagree with each other. The goal of this analysis is to quantify the cloud-influencing factors that contribute to winter–summer cloud amount differences, as these seasons are primarily responsible for the model discrepancies with observations. We find that differences in the total cloud amount annual cycle are primarily caused by differences in low, rather than high, clouds; the largest differences occur between the surface and 950 hPa. Grouping models based on their seasonal cycles of cloud amount and stratifying cloud amount by cloud-influencing factors, we find that model groups disagree most under strong lower tropospheric stability, weak to moderate mid-tropospheric subsidence, and cold lower tropospheric air temperatures. Intergroup differences in low cloud amount are found to be a function of lower tropospheric thermodynamic characteristics. Further, we find that models with a larger low cloud amount in winter have a larger ice condensate fraction, whereas models with a larger low cloud amount in summer have a smaller ice condensate fraction. Stratifying model output by the specifics of the cloud microphysical scheme reveals that models treating cloud ice and liquid condensate as separate prognostic variables simulate a larger ice condensate fraction than those that treat total cloud condensate as a prognostic variable and use a temperature-dependent phase partitioning. Thus, the cloud microphysical parameterization is the primary cause of inter-model differences in the Arctic cloud annual cycle, providing further evidence of the important role that cloud ice microphysical processes play in the evolution and modeling of the Arctic climate system.

[1]  J. Mülmenstädt,et al.  Arctic cloud cover bias in ECHAM6 and its sensitivity to cloud microphysics and surface fluxes , 2018 .

[2]  Stephen G. Warren,et al.  The effect of moonlight on observation of cloud cover at night, and application to cloud climatology , 1995 .

[3]  C. Frantzidis,et al.  Response to Reviewers Reviewer #1 , 2010 .

[4]  P. Taylor.  Does a relationship between Arctic low clouds and sea ice matter , 2017 .

[5]  Mengistu Wolde,et al.  The dependence of ice microphysics on aerosol concentration in arctic mixed‐phase stratus clouds during ISDAC and M‐PACE , 2012 .

[6]  Julien Boé,et al.  Atmospheric inversion strength over polar oceans in winter regulated by sea ice , 2009 .

[7]  H. Treut,et al.  THE CALIPSO MISSION: A Global 3D View of Aerosols and Clouds , 2010 .

[8]  Andrew Gettelman,et al.  Cloud influence on and response to seasonal Arctic sea ice loss , 2009 .

[9]  J. Key,et al.  Assessment of Arctic Cloud Cover Anomalies in Atmospheric Reanalysis Products Using Satellite Data , 2016 .

[10]  Simone Tanelli,et al.  CloudSat mission: Performance and early science after the first year of operation , 2008 .

[11]  Judith A. Curry,et al.  Overview of Arctic Cloud and Radiation Characteristics , 1996 .

[12]  Andrew Gettelman,et al.  Contributions of Clouds, Surface Albedos, and Mixed-Phase Ice Nucleation Schemes to Arctic Radiation Biases in CAM5 , 2014 .

[13]  Graeme L. Stephens,et al.  A global survey of the instantaneous linkages between cloud vertical structure and large‐scale climate , 2014 .

[14]  J. Riedi,et al.  High Sensitivity of Arctic Liquid Clouds to Long‐Range Anthropogenic Aerosol Transport , 2018 .

[15]  Andrew Gettelman,et al.  The contribution of cloud and radiation anomalies to the 2007 Arctic sea ice extent minimum , 2008 .

[16]  J. Karlsson,et al.  The simulation of Arctic clouds and their influence on the winter surface temperature in present-day climate in the CMIP3 multi-model dataset , 2011 .

[17]  P. Guest,et al.  Measurements near the Atmospheric Surface Flux Group tower at SHEBA: Near‐surface conditions and surface energy budget , 2002 .

[18]  D. Thompson,et al.  Observed linkages between the northern annular mode/North Atlantic Oscillation, cloud incidence, and cloud radiative forcing , 2014 .

[19]  R. E. Moritz,et al.  Toward an Explanation of the Annual Cycle of Cloudiness over the Arctic Ocean , 1999 .

[20]  M. Shupe,et al.  Resilience of persistent Arctic mixed-phase clouds , 2012 .

[21]  M. Shupe,et al.  Cloud Radiative Forcing of the Arctic Surface: The Influence of Cloud Properties, Surface Albedo, and Solar Zenith Angle , 2004 .

[22]  Ivy Tan,et al.  Sensitivity Study on the Influence of Cloud Microphysical Parameters on Mixed-Phase Cloud Thermodynamic Phase Partitioning in CAM5 , 2016 .

[23]  J. M. English,et al.  Arctic Radiative Fluxes: Present-Day Biases and Future Projections in CMIP5 Models , 2015 .

[24]  Stephen A. Klein,et al.  Arctic synoptic regimes: Comparing domain‐wide Arctic cloud observations with CAM4 and CAM5 during similar dynamics , 2012 .

[25]  Patrick C. Taylor,et al.  Evaluation of the Arctic surface radiation budget in CMIP5 models , 2015 .

[26]  G. Cesana,et al.  Ubiquitous low‐level liquid‐containing Arctic clouds: New observations and climate model constraints from CALIPSO‐GOCCP , 2012 .

[27]  H. Chepfer,et al.  Isolating the Liquid Cloud Response to Recent Arctic Sea Ice Variability Using Spaceborne Lidar Observations , 2018 .

[28]  Tristan S. L'Ecuyer,et al.  Observational constraints on Arctic Ocean clouds and radiative fluxes during the early 21st century , 2013 .

[29]  M. Shupe,et al.  Evidence of liquid dependent ice nucleation in high‐latitude stratiform clouds from surface remote sensors , 2011 .

[30]  Patrick C. Taylor,et al.  Covariance between Arctic sea ice and clouds within atmospheric state regimes at the satellite footprint level , 2015, Journal of geophysical research. Atmospheres : JGR.

[31]  Axel Schweiger,et al.  Synoptic Conditions, Clouds, and Sea Ice Melt Onset in the Beaufort and Chukchi Seasonal Ice Zone , 2017 .

[32]  H. Chepfer,et al.  Recent Advances in Arctic Cloud and Climate Research , 2016, Current Climate Change Reports.

[33]  T. Storelvmo,et al.  On the relationships among cloud cover, mixed‐phase partitioning, and planetary albedo in GCMs , 2016 .

[34]  Karl E. Taylor,et al.  An overview of CMIP5 and the experiment design , 2012 .

[35]  Amy Solomon,et al.  Linking atmospheric synoptic transport, cloud phase, surface energy fluxes, and sea-ice growth: observations of midwinter SHEBA conditions , 2017, Climate Dynamics.

[36]  J. Penner,et al.  Intercomparison of the cloud water phase among global climate models , 2014 .

[37]  B. Medeiros,et al.  Mixed-phase clouds cause climate model biases in Arctic wintertime temperature inversions , 2014, Climate Dynamics.

[38]  M. Shupe,et al.  Cloud vertical distribution from combined surface and space radar–lidar observations at two Arctic atmospheric observatories , 2017 .

[39]  J. Karlsson,et al.  Consequences of poor representation of Arctic sea‐ice albedo and cloud‐radiation interactions in the CMIP5 model ensemble , 2013 .

[40]  Sunny Sun-Mack,et al.  CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data—Part I: Algorithms , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[41]  G. Mace,et al.  Arctic cloud macrophysical characteristics from CloudSat and CALIPSO , 2012 .

[42]  Patrick Minnis,et al.  Relationships among cloud occurrence frequency, overlap, and effective thickness derived from CALIPSO and CloudSat merged cloud vertical profiles , 2010 .

[43]  B. M. Hegyi,et al.  On the Increasing Importance of Air-Sea Exchanges in a Thawing Arctic: A Review , 2018 .