{"id":18318,"date":"2015-04-01T05:08:41","date_gmt":"2015-04-01T10:08:41","guid":{"rendered":"http:\/\/www.realclimate.org\/?p=18318"},"modified":"2015-04-01T07:10:35","modified_gmt":"2015-04-01T12:10:35","slug":"reflections-on-ringberg","status":"publish","type":"post","link":"https:\/\/www.realclimate.org\/index.php\/archives\/2015\/04\/reflections-on-ringberg\/","title":{"rendered":"Reflections on Ringberg"},"content":{"rendered":"
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As previewed last weekend<\/a>, I spent most of last week at a workshop on Climate Sensitivity<\/a> hosted by the Max Planck Institute at Schloss Ringberg. It was undoubtedly one of the better workshops I’ve attended – it was focussed, deep and with much new information to digest (some feel for the discussion can be seen from the #ringberg15<\/a> tweets). I’ll give a brief overview of my impressions below.<\/p>\n

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As we’ve discussed previously<\/a>, there are multiple classes of observational data that could provide some constraints on how warm the planet will get as CO2<\/sub> increases (either on a multi-decadal timescale (TCR) or for the long term equilibrium (ECS)). Principally, there is paleo-climate data from previous quasi-equilibria like the Last Glacial Maximum or Eocene; evidence from the instrumental trends since the 19th Century; and climatological observations that correlate to longer term responses (either in the mean, seasonally or over interannual variations). Each class of data has its own problems, either in terms of observational quality, its relationship to sensitivity, or the level of simplicity of the underlying model.<\/p>\n

The workshop spent a lot of time examining the explicit (and implicit) assumptions that underlie the methods and constructively criticising all of them to see how they can be best reconciled (because we are just looking for one number after all). Andy Dessler recorded his own talk<\/a> on short term constraints and posted it already. Slides from the other talks<\/a> are posted on the MPI website.<\/p>\n

There were two major themes that emerged across a lot of the discussions: the stability of the basic ‘energy balance’ equation (\"$\Delta N = \Delta F - \lambda\Delta T_s$\") that defines the sensitivity, \"$\lambda$\", to zeroth order; and the challenge of estimating cloud feedbacks from process-based understanding. The connection occurs because the clouds are the cause of the biggest variation in sensitivity across GCMs.<\/p>\n

The first topic was triggered by extensive evidence in models that there are structural variations associated with changes of base state, time variations, spatial variations and the different physics of each forcing<\/a> that imply \"$\lambda$\" needs to be thought of as more than constant. For instance, plotting \"$\Delta N$\" against \"$\Delta T_s$\" in an experiment with an abrupt forcing (such as 4xCO2<\/sub>) should give a straight line (red) if \"$\lambda$\" were constant, but instead there is almost always some curvature implying that temperature changes a more for the same forcing change after a century or so than at the start (blue line).<\/p>\n


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That curvature implies that applying a strictly ‘constant \"$\lambda$\"‘ model to a limited set of observations (such as the trends over the last hundred years) is likely to bias any estimate of the sensitivity. Quantification of these issues is ongoing. Without a resolution though (or a set of reasonable corrections), efforts to combine multiple constraints without taking this into account are going to be flawed.<\/p>\n

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The #ringberg15<\/a> blackboard. (Spoiler: cloud feedbacks are complicated) pic.twitter.com\/DZ4WPOJLGl<\/a><\/p>\n

— Gavin Schmidt (@ClimateOfGavin) March 26, 2015<\/a><\/p><\/blockquote>\n