Michael Evans, UMD Department of Geology and ESSIC Professor, is cited in a new paper Climate of the Past: Discussions titled, “Climate Change Detection and Attribution using observed and simulated Tree-Ring Width”. Evans’ collaborators are Jörg Franke from the University of Bern as well as Andrew Schurer and Gabriele Clarissa Hegerl from The University of Edinburgh.
In the study, the researchers perform a detection and attribution (D&A) via regression of tree ring width (TRW) observations on TRW simulations which are forward modeled from climate simulations. Temperature and moisture-sensitive TRW simulations show distinct patterns in time and space. Previous studies using the paleoclimate record have use globally or hemispherically averaged reconstructed temperature; this study explores the extent to which the multivariate and nonlinear nature of the way in which both temperature and soil moisture variations are encoded in tree rings expands the potential for detection and attribution of forced climate change in both these variables.
These results suggest that use of nonlinear and multivariate proxy system models in paleoclimatic detection and attribution studies may permit more realistic, spatially resolved and multivariate fingerprint detection studies, and evaluation of the climate sensitivity to external radiative forcing, than has previously been possible.
Evans is particularly interested in placing climate variability and change within the context of the past. His most recent work with PAGES includes editing its 30th Anniversary Magazine issue and cheering on the first PAGES Horizons magazine for teens. Evans first had the idea for this research on his sabbatical visit at Universitat Bern in July-September 2015, and was inspired by the PAGES/DAPS workshop hosted by ESSIC in May 2019.
This idea arose out of a sabbatical visit by me at Universitat Bern in July-Sept 2015, and was re-activated by the PAGES/DAPS workshop that was graciously hosted at ESSIC in May 2019.
To access the article, click here: “Climate Change Detection and Attribution using observed and simulated Tree-Ring Width”.