A Hierarchical Statistical Framework for Emergent Constraints: Application to Snow‐Albedo Feedback
Abstract Emergent constraints use relationships between future and current climate states to constrain projections of climate response. Here we introduce a statistical, hierarchical emergent constraint (HEC) framework in order to link future and current climates with observations. Under Gaussian ass...
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| Format: | Article |
| Language: | English |
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Wiley
2018-12-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1029/2018GL080082 |
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| _version_ | 1849321822437244928 |
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| author | Kevin W. Bowman Noel Cressie Xin Qu Alex Hall |
| author_facet | Kevin W. Bowman Noel Cressie Xin Qu Alex Hall |
| author_sort | Kevin W. Bowman |
| collection | DOAJ |
| description | Abstract Emergent constraints use relationships between future and current climate states to constrain projections of climate response. Here we introduce a statistical, hierarchical emergent constraint (HEC) framework in order to link future and current climates with observations. Under Gaussian assumptions, the mean and variance of the future state are shown analytically to be a function of the signal‐to‐noise ratio between current climate uncertainty and observation error and the correlation between future and current climate states. We apply the HEC to the climate change, snow‐albedo feedback, which is related to the seasonal cycle in the Northern Hemisphere. We obtain a snow‐albedo feedback prediction interval of (−1.25,−0.58)%/K. The critical dependence on signal‐to‐noise ratio and correlation shows that neglecting these terms can lead to bias and underestimated uncertainty in constrained projections. The flexibility of using HEC under general assumptions throughout the Earth system is discussed. |
| format | Article |
| id | doaj-art-033f8ae20a8a4c50b3384bf0672b4a85 |
| institution | Kabale University |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2018-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-033f8ae20a8a4c50b3384bf0672b4a852025-08-20T03:49:37ZengWileyGeophysical Research Letters0094-82761944-80072018-12-01452313,05013,05910.1029/2018GL080082A Hierarchical Statistical Framework for Emergent Constraints: Application to Snow‐Albedo FeedbackKevin W. Bowman0Noel Cressie1Xin Qu2Alex Hall3Jet Propulsion Laboratory California Institute of Technology Pasadena CA USANational Institute for Applied Statistics Research Australia University of Wollongong Wollongong New South Wales AustraliaDepartment of Atmospheric and Oceanic Sciences University of California Los Angeles CA USADepartment of Atmospheric and Oceanic Sciences University of California Los Angeles CA USAAbstract Emergent constraints use relationships between future and current climate states to constrain projections of climate response. Here we introduce a statistical, hierarchical emergent constraint (HEC) framework in order to link future and current climates with observations. Under Gaussian assumptions, the mean and variance of the future state are shown analytically to be a function of the signal‐to‐noise ratio between current climate uncertainty and observation error and the correlation between future and current climate states. We apply the HEC to the climate change, snow‐albedo feedback, which is related to the seasonal cycle in the Northern Hemisphere. We obtain a snow‐albedo feedback prediction interval of (−1.25,−0.58)%/K. The critical dependence on signal‐to‐noise ratio and correlation shows that neglecting these terms can lead to bias and underestimated uncertainty in constrained projections. The flexibility of using HEC under general assumptions throughout the Earth system is discussed.https://doi.org/10.1029/2018GL080082emergent constraintsclimatesnow‐albedo feedback |
| spellingShingle | Kevin W. Bowman Noel Cressie Xin Qu Alex Hall A Hierarchical Statistical Framework for Emergent Constraints: Application to Snow‐Albedo Feedback Geophysical Research Letters emergent constraints climate snow‐albedo feedback |
| title | A Hierarchical Statistical Framework for Emergent Constraints: Application to Snow‐Albedo Feedback |
| title_full | A Hierarchical Statistical Framework for Emergent Constraints: Application to Snow‐Albedo Feedback |
| title_fullStr | A Hierarchical Statistical Framework for Emergent Constraints: Application to Snow‐Albedo Feedback |
| title_full_unstemmed | A Hierarchical Statistical Framework for Emergent Constraints: Application to Snow‐Albedo Feedback |
| title_short | A Hierarchical Statistical Framework for Emergent Constraints: Application to Snow‐Albedo Feedback |
| title_sort | hierarchical statistical framework for emergent constraints application to snow albedo feedback |
| topic | emergent constraints climate snow‐albedo feedback |
| url | https://doi.org/10.1029/2018GL080082 |
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