Is there a correlation between the cloud phase and surface snowfall rate in GCMs?
Authors: Hellmuth Franziska , Fouilloux Anne Claire Mireille , Storelvmo Trude , Daloz Anne Sophie 
Published: March 2022

&lt;p&gt;Cloud feedbacks are a major contributor to the spread of climate sensitivity in global climate models (GCMs) [1]. Among the most poorly understood cloud feedbacks is the one associated with the cloud phase, which is expected to be modified with climate change [2]. Cloud phase bias, in addition, has significant implications for the simulation of radiative properties and glacier and ice sheet mass balances in climate models. &amp;#160;&lt;/p&gt;&lt;p&gt;In this context, this work aims to expand our knowledge on how the representation of the cloud phase affects snow formation in GCMs. Better understanding this aspect is necessary to develop climate models further and improve future climate predictions.&amp;#160;&lt;/p&gt;&lt;p&gt;This study will compare surface snowfall, ice, and liquid water content from the Coupled Model Intercomparison Project Phase 6 (CMIP 6) climate models (accessed through Pangeo) to the European Centre for Medium-Range Weather Forecast Re-Analysis 5 (ERA5) data from 1985 to 2014. We conduct statistical analysis at the annual and seasonal timescales to determine the biases in cloud phase and precipitation (liquid and solid) in the CMIP6 models and their potential connection between them.&amp;#160;&lt;/p&gt;&lt;p&gt;For the analysis, we use the Jupyter notebook on the CMIP6 analysis (https://github.com/franzihe/eosc-nordic-climate-demonstrator/blob/master/work/), which guides the user step by step. The use of the Pangeo.io intake package makes it possible to browse the CMIP6 online catalog for the required variables, models, and experiments and stores it in xarray dask datasets. Vertical variables in sigma pressure levels had to be interpolated to standard pressure levels as provided in ERA5. We also interpolated the horizontal and vertical variables to the exact horizontal grid resolution before calculating the climatology.&amp;#160;&lt;/p&gt;&lt;p&gt;A global comparison between the reanalysis (ERA5) and the CMIP6 models shows that models tend to underestimate the ice water path compared to the reanalysis even if most of them can reproduce some of the characteristics of liquid water content and snowfall. To better understand the link between biases in cloud phase and surface snowfall rate, we try to find a relationship between ice water path and surface snowfall in GCMs. Linear regressions within extratropical areas show a positive relationship between ice water content and surface snowfall in the reanalysis data, while CMIP6 models do not have these characteristics.&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&amp;#160;&lt;/p&gt;&lt;p&gt;[1] Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., et al. (2020). Causes of higher climate sensitivity in CMIP6 models. Geophysical Research Letters, 47, e2019GL085782. https://doi-org.ezproxy.uio.no/10.1029/2019GL085782&amp;#160;&lt;/p&gt;&lt;p&gt;[2] Bjordal, J., Storelvmo, T., Alterskj&amp;#230;r, K. et al. Equilibrium climate sensitivity above 5&amp;#8201;&amp;#176;C plausible due to state-dependent cloud feedback. Nat. Geosci. 13, 718&amp;#8211;721 (2020). https://doi-org.ezproxy.uio.no/10.1038/s41561-020-00649-1&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Github: https://github.com/franzihe&amp;#160;&lt;/p&gt;