The surprising increase of Earth's climate sensitivity in the most recent coupled model intercomparison project (CMIP) models has been largely attributed to extratropical cloud feedback, which is thought to be driven by greater supercooled water in present-day cloud phase partitioning (CPP). Here, we report that accounting for precipitation in the Goddard Institute for Space Studies ModelE3 radiation scheme, neglected in more than 60% of CMIP6 and 90% of CMIP5 models, systematically changes its apparent CPP and substantially increases its cloud feedback, consistent with results using CMIP models. Including precipitation in the comparison with cloud–aerosol lidar and infrared pathfinder satellite observations (CALIPSO) measurements and in model radiation schemes is essential to faithfully constrain cloud amount and phase partitioning, and simulate cloud feedbacks. Our findings suggest that making radiation schemes precipitation-aware (missing in most CMIP6 models) should strengthen their positive cloud feedback and further increase their already high mean climate sensitivity.
Cesana, Grégory V., Ackerman, Andrew S., Fridlind, Ann M., et al., "Snow Reconciles Observed and Simulated Phase Partitioning and Increases Cloud Feedback," Geophysical Research Letters 48, no. 20 (2021), https://doi.org/10.1029/2021gl094876
@article{osti_1828775,
author = {Cesana, Grégory V. and Ackerman, Andrew S. and Fridlind, Ann M. and Silber, Israel and Kelley, Maxwell},
title = {Snow Reconciles Observed and Simulated Phase Partitioning and Increases Cloud Feedback},
annote = {The surprising increase of Earth's climate sensitivity in the most recent coupled model intercomparison project (CMIP) models has been largely attributed to extratropical cloud feedback, which is thought to be driven by greater supercooled water in present-day cloud phase partitioning (CPP). Here, we report that accounting for precipitation in the Goddard Institute for Space Studies ModelE3 radiation scheme, neglected in more than 60% of CMIP6 and 90% of CMIP5 models, systematically changes its apparent CPP and substantially increases its cloud feedback, consistent with results using CMIP models. Including precipitation in the comparison with cloud–aerosol lidar and infrared pathfinder satellite observations (CALIPSO) measurements and in model radiation schemes is essential to faithfully constrain cloud amount and phase partitioning, and simulate cloud feedbacks. Our findings suggest that making radiation schemes precipitation-aware (missing in most CMIP6 models) should strengthen their positive cloud feedback and further increase their already high mean climate sensitivity.},
doi = {10.1029/2021gl094876},
url = {https://www.osti.gov/biblio/1828775},
journal = {Geophysical Research Letters},
issn = {ISSN 0094-8276},
number = {20},
volume = {48},
place = {United States},
publisher = {American Geophysical Union},
year = {2021},
month = {09}}
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center; NASA Goddard Inst. for Space Studies (GISS), New York, NY (United States); Pennsylvania State Univ., University Park, PA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
Grant/Contract Number:
SC0021004; SC0018046
OSTI ID:
1828775
Alternate ID(s):
OSTI ID: 2571346
Journal Information:
Geophysical Research Letters, Journal Name: Geophysical Research Letters Journal Issue: 20 Vol. 48; ISSN 0094-8276