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SUMMARY:EESS talk on "Success stories and challenges in simulating future 
 climate"
DTSTART:20190430T121500
DTEND:20190430T130000
DTSTAMP:20260924T131118Z
UID:3a0c9bdebb7cea8b16e724bbfb75c3e12a2273fe2fc0dfc6f89a3547
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr Reto Knutti\, professor\, Climate Physics Group\, Institute
  for Atmospheric and Climate Science\, Dept. of Environmental Systems Scie
 nce\, ETHZ - is Associate vice president for sustainability of ETH Zurich 
 and the president of ProClim\, the Forum for Climate and Global Change of 
 the Swiss Academies. The activities in research and teaching of the Climat
 e Physics group are directed towards understanding changes in the global c
 limate system caused by the growing emissions of anthropogenic greenhouse 
 gases. The main goal is to understand the 20th century climate change\, to
  quantify the human contribution to it\, and to use that information to im
 prove projections into the future and to quantify the projection uncertain
 ties arising from uncertainties in scenarios\, climate feedbacks and the c
 arbon cycle.\nAbstract:\nAs our understanding improves\, more observations
  become available\, and computational capacity increases\, climate models 
 continue to increase in complexity to synthesize all that knowledge. They 
 have become the standard tool for predicting future climate change\, and t
 he hope is that as more and more processes are considered at greater reali
 sm and higher resolution\, the models will converge to reality. But are th
 ey really\, how do we know\, and indeed should they? What is the purpose o
 f current global climate models? Are they built to understand processes\, 
 to quantify past changes\, or to predict the future\, and do all of those 
 require the same models?\nThere are many success stories in climate modeli
 ng\, but open questions remain. What is the purpose of these models? How d
 o we quantify uncertainty? Climate projections are often summarized as mul
 ti model means\, assuming that the average of models is better than a sing
 le model. Yet averaging models is problematic\, because the models are not
  independent and share biases and code\, and the models may not span the f
 ull uncertainty range. A seemingly obvious step is to select individual mo
 dels based on how well they simulate the past and present climate. But met
 rics of model performance and model weighting is a thorny issue. The lack 
 of verification of the actual climate projections means that we do not kno
 w\, or cannot agree on which metrics are most relevant to identify a good 
 model.\nAn overview of the performance and limitations of current climate 
 models is given\, along with projections to 2100\, with a focus on recent 
 coupled model intercomparisons\, and a discussion of major challenges in i
 nterpreting the results. Model agreement with observations continues to im
 prove\, but uncertainty in climate projections is difficult to quantify\, 
 and has not decreased significantly in the past few years\, partly as a re
 sult of irreducible climate variability. Progress in model evaluation as w
 ell as statistical methods to interpret and combine model projections is u
 rgently needed\, in particular as more models of different quality and hig
 her complexity\, including perturbed physics ensembles and ensembles with 
 structurally different models become available
LOCATION:GR C0 01 https://plan.epfl.ch/?room=GRC001
STATUS:CONFIRMED
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