Connection of Surface Snowfall Bias to Cloud Phase Bias - Satellite Observations, ERA5, and CMIP6
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Meteorology
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Applied sciences
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Earth sciences
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Climatology
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Earth observation
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European Research Council
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Trude Storelvmo
affiliation: UiO
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University of Oslo
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ERC Startup Grant
title: MC2 Mixed Phase clouds and climate
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Connection of Surface Snowfall Bias to Cloud Phase Bias - Satellite Observations, ERA5, and CMIP6
about: Meteorology
about: Applied sciences
about: Earth sciences
about: Climatology
about: Earth observation
content size: 26566788
contributor: Trude Storelvmo
copyright holder: University of Oslo
date created: 2022-01-31 11:20:52.183760+00:00
date modified: 2025-03-05 00:47:03.286051+00:00
date published: 2022-01-31 11:20:52.183760+00:00
description: 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.
encoding format: application/ld+json
funding: ERC Startup Grant
license: no-permission
publisher: University of Oslo
creation mode: MANUAL
concept: bias
concept: climate model
concept: climate sensitivity
concept: cloud
concept: feedback
concept: phase
concept: rate
concept: snow
concept: water content
field of research: earth sciences
IPTC: Climate change
IPTC: Environment
IPTC: IT-computer sciences
IPTC: Meteorology
IPTC: Weather
lemma: CMIP phase 6
lemma: climate model
lemma: cloud
lemma: feedback
lemma: phase
lemma: rate
lemma: snowfall
NASA: geosciences
phrase: cloud feedback
phrase: cloud phase
phrase: global climate models
phrase: phase bias
phrase: snow formation
phrase: surface snowfall rate
sentence: 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.
sentence: Is there a correlation between the cloud phase and surface snowfall rate in GCMs?
sentence: 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.
time reference: 2020
time reference: from 1985 to 2014
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domain: meteorology
cite-as: 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.

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ERA5 analysis
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description: ERA5 data wrangling required to make it comparable
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Website: Cloud phase and snowfall analysis
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References
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Notebook Code Review with reviewNb for open PR
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CloudSat analysis
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Anne Claire Fouilloux
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Anne Fouilloux
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Franziska Hellmuth
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Robert Oscar David
affiliation: University of Oslo, Institute of Geosciences, Department of Meteorology and Oceanography
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