Key factors governing uncertainty in the response to sunshade geoengineering from a comparison of the GeoMIP ensemble and a perturbed parameter ensemble. Issue 3 (8th July 2014)

• Record Type: Journal Article
• Authors: Irvine, Peter J.; Boucher, Olivier; Kravitz, Ben; Alterskjær, Kari; Cole, Jason N. S.; Ji, Duoying; Jones, Andy; Lunt, Daniel J.; Moore, John C.; Muri, Helene; Niemeier, Ulrike; Robock, Alan; Singh, Balwinder; Tilmes, Simone; Watanabe, Shingo; Yang, Shuting; Yoon, Jin‐Ho
• Is Part Of: Journal of geophysical research. Volume 119:Issue 3(2014:Mar.)
• Page Start: 7946
• Page End: 7962
• Publication Date: 2014-07-08
• Subjects: geoengineering -- climate engineering -- GeoMIP -- perturbed parameter ensemble -- climate -- solar radiation management; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2013JD020716
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Climate model studies of the consequences of solar geoengineering are central to evaluating whether such approaches may help to reduce the harmful impacts of global warming. In this study we compare the sunshade solar geoengineering response of a perturbed parameter ensemble (PPE) of the Hadley Centre Coupled Model version 3 (HadCM3) with a multimodel ensemble (MME) by analyzing the G1 experiment from the Geoengineering Model Intercomparison Project (GeoMIP). The PPE only perturbed a small number of parameters and shares a common structure with the unperturbed HadCM3 model, and so the additional weight the PPE adds to the robustness of the common climate response features in the MME is minor. However, analysis of the PPE indicates some of the factors that drive the spread within the MME. We isolate the role of global mean temperature biases for both ensembles and find that these biases have little effect on the ensemble spread in the hydrological response but do reduce the spread in surface air temperature response, particularly at high latitudes. We investigate the role of the preindustrial climatology and find that biases here are likely a key source of ensemble spread at the zonal and grid cell level. The role of vegetation, and its response to elevated CO2 concentrations through the CO2 physiological effect and changes in plant productivity, is also investigated and proves to have a substantial effect on the terrestrial hydrological response to solar geoengineering and to be a major source of variation within the GeoMIP ensemble. Key Points: Similar solar geoengineering climate response for GeoMIP and a PPE of HadCM3 Undercooling and overcooling do not explain ensemble hydrological differences Key uncertainties identified: piControl biases and CO2 physiological response