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Climate in the near future – results from a simple probabilistic method Jouni Räisänen and Leena Ruokolainen Department of Physical Sciences, Division of Atmospheric Sciences, University of Helsinki, Finland

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What will I show and what is it good for? A resampling ensemble method for deriving probabilistic estimates of climate change –uses existing multi-model ensembles of climate change simulations (IPCC AR4 data set) –first-order representation of both modelling uncertainty and natural variability –related to pattern scaling – but no intention to remove noise Best suited for projections of near-term climate change –sample size –for longer-term projections, the unknown ability of multi-model ensembles to capture the actual modelling uncertainty becomes a larger headache

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Annual mean Temp and Prec changes at (60ºN, 25ºE), from to , as simulated by 21 models under the A1B scenario ~95% probability of warming, ~95% probability of increasing precipitation? However, 21 is a small sample for estimating probabilities

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Resampling ensembles Patterns of simulated climate change remain quasi-constant in time, when the forcing is dominated by increasing GHGs and internal variability is filtered out e.g. by averaging over a large number of models. * Same 21-model mean global warming (0.62 C) in both cases. * Regional differences much smaller than differences between individual simulations (rms difference = 0.11 C)

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Work hypothesis If the multi-model mean global mean temperature change is the same from period P3 to P4 as from P1 to P2, then the probability distribution of regional climate changes should also be approximately the same in the two cases. time multi-model global mean T P1 P2 P3 P

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Resampling ensembles for the climate change from P1 to P2 (e.g., to ) are formed by taking the climate changes in all pairs of periods P3 P4 with the same multi- model mean global warming as plausible realisations of the change from P1 to P2. multi-model global mean T time P1 P2 P3 P4 Cross verification* indicates that the increased sample size (as compared with only using P1 and P2) outweighs eventual biases caused by the methodology, for both T and Precip *Räisänen and Ruokolainen (2006, Tellus 58A, )

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Technical details Data set –IPCC AR4 simulations –21 models for A1B scenario –one transient simulation ( ) per model –also some analysis with constant-forcing control simulations Resampling with 5-year interval in P4 –nominal sample size for forecasts from to = 420 (20 pairs of periods × 21 models) –21 << effective sample size << 420

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Annual mean Temp and Prec changes at (60ºN, 25ºE), from to : the resampling ensemble 95% probability of warming, 80% probability of increasing precipitation? Sample size >> 21 these estimates are likely to be more reliable than the ones (95% and 95%) obtained directly from the and data.

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Annual and seasonal T and P changes at (60ºN, 25ºE), from to Seasonal means have a wider pdf than annual means (for temperature change, particularly in winter), and monthly means even more so. Note: Gaussian shape is used for illustration only (although it seems to be a good approximation)

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Best-guess warming: winter > summer Probability of warming: winter summer Lower signal-to-noise ratio makes forecasts of precipitation change less certain than those of temperature change Temp and Prec changes at (60ºN, 25ºE) from to , A1B scenario Median estimate Prob. of warming Median estimate Prob. of increase DJF 1.2 C 90%5%5%73% JJA 0.6 C 89%3%3%63% Ann 0.9 C 95%4%4%80% Temperature changePrecipitation change

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Annual mean T and P changes at (60ºN,25ºE), from to later decades (A1B scenario) The pdf widens with time, as model differences become increasingly important with increasing forcing

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Best-guess annual mean warming versus probability of warming, as estimated from the models (from to ) %°C°C High probability of warming almost everywhere Particularly high probability of warming in tropical latitudes, where internal variability is small!

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Recent climate changes: vs

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Observed annual mean temperature change from to (Tyndall Centre / CRU) How usual / unusual is this in simulations - with no external forcing - with increasing GHG concentrations? C

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Probability of below-observed temperature change, simulations with no external forcing The same, in (greenhouse gas etc.) forced simulations < 5%: nowhere >95%: 58% of land < 5%: 3% >95%: 5%

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Changes from to Observed temperature changes –in many areas, too large to be reasonably explained by internal climate variability (as estimated from the models) –consistent with a combination of anhtropogenic climate change and internal variability Observed precipitation changes (not shown) –Within the 5-95% range of the model-based distributions in 83% of all land – both for the unforced and the forced simulations Similar conlusions (impact of greenhouse gas forcing clearly detectable in temperature, but not in precipitation) are obtained with more advanced detection-attribution- methods

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Variance correction Resampling ensemble method in its basic form assumes that the magnitude of natural variability is correctly simulated by models If not – the pdfs may become systematically too narrow or too wide (particularly important for short-term forecasts, in which uncertainty is dominated by natural variability) Direct evaluation of interdecadal variability virtually meaningless (small sample sizes) Ruokolainen and Räisänen (2007)* implemented a variance correction scheme based on a comparison of simulated and observed interannual variability Cross verification suggests that the correction makes more good than harm *Tellus 59A,

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Annual mean Temp and Prec changes at (60ºN, 25ºE) – without and with variance correction ( to ) 95% (95%) probability of warming, 75% (80%) probability of increasing precipitation? Models tend to underestimate interannual precipitation variability (at this location) variance correction results in a slightly wider distribution of precipitation changes. In general, the variance correction appears to have only relatively modest effects (but P is affected more than T).

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Strengths and limations of the method Strengths –Simple –Efficient way of extracting probabilistic information from long transient simulations –Applicable to both multi-model and perturbed-parameter ensembles Limitations –Signal assumed to be fully determined by multi-model average global mean warming (not exactly true) –Biases in simulated variability may affect width of the pdfs (although this may be partially corrected in post-processing) –No attempt to use observational constraints to weight or scale model-simulated climate changes (but how much would this change projections of near-term change?)

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Another short story: climatic nowcasting? March 2007 was extremely warm in Helsinki: (T mean = 3.1 C – previous record = 2.0 C) How unusual was this –In the context of the 20th century climate? –In the present AD 2007 climate? Question answered by estimating a pdf for the AD 2007 March temperature –starting point: observations for – -change approach, taking into account (i) observed global mean warming and (ii) AR4-model-simulated changes in March mean temperature and interannual variability –details to be documented…

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Resulting probability distributions Return period estimates C 3.0 C Observed ( ) climate~ 60 yr~ 700 yr?? Present (AD 2007) climate~ 14 yr~ 80 yr?

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Probability of below-observed precipitation change, simulations with no external forcing The same, in (greenhouse gas etc.) forced simulations < 5%: 9% >95%: 8% < 5%: 10% >95%: 7%

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Cross verification – in brief 1.Choose one model simulation as truth, against which forecasts derived from other models are verified 2.Calculate a verification statistics (and average over the global domain) 3.Repeat 1-2 for all choices of the verifying model, and average the verification statistics Cross verification gives no absolute measure of forecast performance in the real world, but it is a useful tool for comparing the potential performance of different forecast methods.

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Cross verification results: annual mean T and P change TemperaturePrecipitation StandardRatioStandardRatio ºC % ºC % ºC %0.982 CRPS = continuous ranked probability score. Perfect deterministic forecast : CRPS = 0. CRPS increases with time: long-term forecasts are less accurate than short-term forecasts Resampling method yields lower CRPS scores than the standard method (in which each simulation is used only once). This suggests that resampling improves the forecasts

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Quantile plots of climate change from to : impact of variance correction Basic resampling method Resampling with variance correction Where and when simulated interannual variability is smaller than the observed variability, variance correction tends to make the derived probability distribution of climate change wider (and vice versa). In most cases, the effect is not dramatic.

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