Abstract
Model simulations of the next few decades are widely used in assessments of climate change impacts and as guidance for adaptation. Their non-linear nature reveals a level of irreducible uncertainty which it is important to understand and quantify, especially for projections of near-term regional climate. Here we use large idealised initial condition ensembles of the FAMOUS global climate model with a 1 %/ year compound increase in CO2 levels to quantify the range of future temperatures in model-based projections. These simulations explore the role of both atmospheric and oceanic initial conditions and are the largest such ensembles to date. Short-term simulated trends in global temperature are diverse, and cooling periods are more likely to be followed by larger warming rates. The spatial pattern of near-term temperature change varies considerably, but the proportion of the surface showing a warming is more consistent. In addition, ensemble spread in inter-annual temperature declines as the climate warms, especially in the North Atlantic. Over Europe, atmospheric initial condition uncertainty can, for certain ocean initial conditions, lead to 20 year trends in winter and summer in which every location can exhibit either strong cooling or rapid warming. However, the details of the distribution are highly sensitive to the ocean initial condition chosen and particularly the state of the Atlantic meridional overturning circulation. On longer timescales, the warming signal becomes more clear and consistent amongst different initial condition ensembles. An ensemble using a range of different oceanic initial conditions produces a larger spread in temperature trends than ensembles using a single ocean initial condition for all lead times. This highlights the potential benefits from initialising climate predictions from ocean states informed by observations. These results suggest that climate projections need to be performed with many more ensemble members than at present, using a range of ocean initial conditions, if the uncertainty in nearterm regional climate is to be adequately quantified.
Highlights
Electronic supplementary material The online version of this article contains supplementary material, which is available to authorized users.NCAS‐Climate, Department of Meteorology, University of Reading, Reading, UKMet Office Hadley Centre, Exeter, UK 3 Grantham Institute, London School of Economics, London, UK 4 Department of Physics, University of Warwick, Coventry, UK 5 Environmental Change Institute, University of Oxford, Oxford, UKPredictions of regional climatic changes during the few decades are sought by decision makers
Multiple projections are simulated from a single ocean initial condition with many different atmospheric states to examine the magnitude of uncertainty associated with the non-linear nature of the atmosphere
Interannual temperature variability in the FAMOUS control simulation shows a similar geographical pattern to, but with a much larger amplitude than, an observational estimate from ERA-40 (Fig. 1, for 1958–2001 after linear detrending) (Uppala et al 2005). This has large implications for any comparison of these simulations with the real world, and is a caveat on the results, the speed of FAMOUS makes it a good test-bed to explore the role of variability and how to design ensembles to sample initial condition uncertainty
Summary
Electronic supplementary material The online version of this article (doi:10.1007/s00382-015-2806-8) contains supplementary material, which is available to authorized users. A key question is determining the size of the internal variability when compared to other sources of uncertainty and the magnitude of the expected change from causes other than internal climate fluctuations This question has previously been addressed by considering large ensembles of climate projections with a single climate model and a single future emissions trajectory (Selten et al 2004; Sterl et al 2008; Deser et al 2012a, b; Kay et al 2015). In these ensembles, multiple projections are simulated from a single ocean initial condition with many different atmospheric states to examine the magnitude of uncertainty associated with the non-linear nature of the atmosphere. To examine the role of internal variability in near-term climate projections we analyse a 1200-year pre-industrial control simulation and four large ensembles with the FAMOUS AOGCM
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