Snow Reconciles Observed and Simulated Phase Partitioning and Increases Cloud Feedback
Abstract 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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-10-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1029/2021GL094876 |
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| _version_ | 1850115839701811200 |
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| author | Grégory V. Cesana Andrew S. Ackerman Ann M. Fridlind Israel Silber Maxwell Kelley |
| author_facet | Grégory V. Cesana Andrew S. Ackerman Ann M. Fridlind Israel Silber Maxwell Kelley |
| author_sort | Grégory V. Cesana |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-69f8e663051b4f12b658ece52c3c4ecf |
| institution | OA Journals |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2021-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-69f8e663051b4f12b658ece52c3c4ecf2025-08-20T02:36:28ZengWileyGeophysical Research Letters0094-82761944-80072021-10-014820n/an/a10.1029/2021GL094876Snow Reconciles Observed and Simulated Phase Partitioning and Increases Cloud FeedbackGrégory V. Cesana0Andrew S. Ackerman1Ann M. Fridlind2Israel Silber3Maxwell Kelley4Center for Climate Systems Research Columbia University New York NY USANASA Goddard Institute for Space Studies New York NY USANASA Goddard Institute for Space Studies New York NY USAPennsylvania State University University Park PA USANASA Goddard Institute for Space Studies New York NY USAAbstract 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.https://doi.org/10.1029/2021GL094876cloud phasesnowcloud feedbackCALIPSOlidar simulatorclimate models |
| spellingShingle | Grégory V. Cesana Andrew S. Ackerman Ann M. Fridlind Israel Silber Maxwell Kelley Snow Reconciles Observed and Simulated Phase Partitioning and Increases Cloud Feedback Geophysical Research Letters cloud phase snow cloud feedback CALIPSO lidar simulator climate models |
| title | Snow Reconciles Observed and Simulated Phase Partitioning and Increases Cloud Feedback |
| title_full | Snow Reconciles Observed and Simulated Phase Partitioning and Increases Cloud Feedback |
| title_fullStr | Snow Reconciles Observed and Simulated Phase Partitioning and Increases Cloud Feedback |
| title_full_unstemmed | Snow Reconciles Observed and Simulated Phase Partitioning and Increases Cloud Feedback |
| title_short | Snow Reconciles Observed and Simulated Phase Partitioning and Increases Cloud Feedback |
| title_sort | snow reconciles observed and simulated phase partitioning and increases cloud feedback |
| topic | cloud phase snow cloud feedback CALIPSO lidar simulator climate models |
| url | https://doi.org/10.1029/2021GL094876 |
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