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A Hierarchical Statistical Framework for Emergent Constraints: Application to Snow-Albedo Feedback

Journal Article


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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.

Authors


  •   Bowman, K W. (external author)
  •   Cressie, Noel A.
  •   Qu, Xin (external author)
  •   Hall, Alex (external author)

Publication Date


  • 2018

Citation


  • Bowman, K. W., Cressie, N., Qu, X. & Hall, A. (2018). A Hierarchical Statistical Framework for Emergent Constraints: Application to Snow-Albedo Feedback. Geophysical Research Letters, 45 (23), 13050-13059.

Scopus Eid


  • 2-s2.0-85058495656

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=3227&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/2221

Number Of Pages


  • 9

Start Page


  • 13050

End Page


  • 13059

Volume


  • 45

Issue


  • 23

Place Of Publication


  • United States

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.

Authors


  •   Bowman, K W. (external author)
  •   Cressie, Noel A.
  •   Qu, Xin (external author)
  •   Hall, Alex (external author)

Publication Date


  • 2018

Citation


  • Bowman, K. W., Cressie, N., Qu, X. & Hall, A. (2018). A Hierarchical Statistical Framework for Emergent Constraints: Application to Snow-Albedo Feedback. Geophysical Research Letters, 45 (23), 13050-13059.

Scopus Eid


  • 2-s2.0-85058495656

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=3227&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/2221

Number Of Pages


  • 9

Start Page


  • 13050

End Page


  • 13059

Volume


  • 45

Issue


  • 23

Place Of Publication


  • United States