Supercooled Liquid-Containing Clouds (sLCCs) play a significant role in Earth's radiative budget and the hydrological cycle, especially through surface snowfall production. Evaluating state-of-the-art climate models with respect to their ability to simulate the frequency of occurrence of sLCCs and the frequency with which they produce snow is, therefore, critically important. Here, we compare these quantities as derived from satellite observations, reanalysis datasets, and Earth System Models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) and find significant discrepancies between the data sets for mid and high latitudes in both hemispheres. Specifically, we find that the ERA5 reanalysis and ten CMIP6 models consistently overestimate the frequency of sLCCs and snowfall frequencies from sLCCs compared to CloudSat-CALIPSO satellite observations, especially over open ocean regions. The biases are very similar for ERA5 and the CMIP6 models, which indicates that the discrepancies in cloud phase and snowfall stem from differences in the representation of cloud microphysics rather than the representation of meteorological conditions. This, in turn, highlights the need for refinements in the models’ parameterizations of cloud microphysics in order for them to represent cloud phase and snowfall accurately. The thermodynamic phase of clouds and precipitation has a strong influence on simulated climate feedbacks and, thus, projections of future climate. Understanding the origin(s) of the biases identified here is, therefore, crucial for improving the overall reliability of climate models.
Connection of Surface Snowfall Bias to Cloud Phase Bias - Satellite Observations, ERA5, and CMIP6
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.
Is there a correlation between the cloud phase and surface snowfall rate in GCMs?
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.
Hellmuth, Franziska, Anne Claire Fouilloux, Robert Oscar David, and Trude Storelvmo. "Connection of Surface Snowfall Bias to Cloud Phase Bias - Satellite Observations, ERA5, and CMIP6." ROHub. Jan 31 ,2022. https://w3id.org/ro-id/5c135a7b-70bf-45f3-9895-3103e0e29c11.
ERA5 data wrangling required to make it comparable
Website: Cloud phase and snowfall analysis
Use ReviewNB to review jupyter notebooks before merging PR.
Notebook Code Review with reviewNb for open PR
Required python packages to run Jupyter Notebooks
Data wrangling which is required to make CloudSat comparable.
University of Oslo, Institute of Geosciences, Department of Meteorology and Oceanography