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Using Model Output: Uncertainties and Probabilities Comparing model results to observations can only take us so far - how do we evaluate the robustness of future climate change simulations? Two key aspects: 1) What are the uncertainties? How do they manifest and propagate? 2) Can we estimate probabilities of occurrence? How robust are these PDF PDF’s? s? Uncertainty Propagation • How do uncertainties in the large-scale GCM forcing propagate into the much smaller scale RCM? • y Over time RCM solution turns from initial value to boundary value problem; this implies nested models represent an ill-posed mathematical boundary value problem (inconsistencies between large and small scale) • Strong scale dependency - flow regime within RCM may become inconsistent with large-scale g flow • Realism of large-scale flow is extremely important! Dealing with Uncertainty Propagation • Evaluate spread in GCM ensembles: Is the regional response consistent across the ensemble members? Or do they vary in differing ways? What about systematic biases? • Perform multi-decadal, ensemble RCM runs - allows for exploration of uncertainties in mean state and higher-order statistics (but also may constrain spatial resolution; depending on computational resources) • Nudging of the RCM solution can help help, but can also hide biases - can also be problematic when nudging back to GCM solution (as opposed to observations) - this can just accentuate GCM biases Quantifying Uncertainty: Issues • Overall assessment from IPCC: ‘Most sources of uncertainty at regional scales are similar to those at the g global scale’ • Two exceptions: p 1)) inhomogeneities g in land use cover and land use changes; 2) aerosol forcing • Uncertainties are not uniform across climate parameters; e.g., generally larger and more poorly posed for precipitation than for temperature smaller signal to noise ratios • Different parameterizations of sub-grid processes yield different results; this is also scale scale-dependent dependent Quantifying Uncertainty: Techniques • Use of multi-model ensembles to generate quantitative measures of spread across the model simulations (intra(intra and inter-model) Not a very robust approach, approach in particular not a representative sample for generating PDFs Does not provide unbiased sample of the phase space of possible change • One possible modification - weight model results according to biases in control simulations (compared to observed climate) Employ robust observational constraints • Temperature anomalies with respect to 1901 to 1950 for six continental-scale regions for 1906 to 2005 (black line) and as simulated (red envelope) by MMD models incorporating known forcings; and as projected j t d ffor 2001 tto 2100 b by MMD models d l ffor th the A1B scenario i (orange envelope). The bars at the end of the orange envelope represent the range of projected changes for 2091 to 2100 for the B1 scenario ((blue), ), the A1B scenario (orange) ( g ) and the A2 scenario (red). ( ) The black line is dashed where observations are present for less than 50% of the area in the decade concerned. Combined Uncertainties • Another technique uses the concept of ‘combined uncertainties’ • The emission scenarios that estimate future GHG forcing; the GCM that simulates the large-scale response to the GHG forcing; and the regional downscaling techniques each have their own uncertainties that must be combined in some manner • Myriad of possible methodologies; key aspect - are the uncertainties linearly additive, additive or do they have strong non-linear interactions? • A number of projects addressing this approach: PRUDENCE, NARCCAP CREAS NARCCAP, CREAS, CLARIS • Temperature anomalies with respect to 1901 to 1950 for three Central and South American land regions for 1906 to 2005 (black line) and as simulated (red envelope) by MMD models incorporating known forcings; and as projected for 2001 to 2100 by MMD models for the A1B scenario (orange envelope). The bars at the end of the orange envelope represent the range of projected p j changes g for 2091 to 2100 for the B1 scenario ((blue), ), the A1B scenario (orange) and the A2 scenario (red). The black line is dashed where observations are present for less than 50% of the area in the decade concerned. • MMD ensemble annual mean surface air temperatures in South America compared with observations. a) observations from the HadCRUT2v data set (Jones et al., 2001); b) mean of the 21 MMD models; d l c)) diff difference b between t th the multi-model lti d l mean and d th the HadCRUT2v data. Units ーC. • As above, above but for precipitation precipitation. Observations (CMAP) are an update of Xie and Arkin (1997). Units mm/day. Regional Model Comparison Projects PRUDENCE NARCCAP CREAS CLARIS PRUDENCE (Prediction of Regional scenarios and Uncertainties for Defining EuropeaN Climate change risks and Effects) • PRUDENCE will provide high-resolution climate change scenarios for 2071-2100 2071 2100 for Europe using dynamical downscaling methods (i.e., regional climate modelling). • The variability and level of confidence in these scenarios will be characterised as a function of uncertainties in model formulation, natural/internal climate variability, and alternative scenarios sce a os o of future utu e at atmospheric osp e c co composition. pos t o • These scenarios will be used to explore changes in the frequency and magnitude of extreme weather events events. Annual change over Europe in the precipitation amount greater than 20mm/day (2071-2100 vs. 1961-1990) for the A2 scenario simulated with the regional climate model HIRHAM nested in the global ECHAM4/OPYC3 model. Green colours indicate an increase, yellow little change. Orange colours over the globe indicate relative temperature change NARCCAP (North American Regional Climate Change Assessment Program) • NARCCAP is an international program to produce high resolution climate change simulations in order to investigate uncertainties in regional scale projections of future climate and generate climate change scenarios for use in impacts research. • The AOGCMs have been forced with the SRES A2 emissions scenario for the 21st century. Simulations with these models were also produced for the current (historical) period. The RCMs are nested within the AOGCMs for the current period 19712000 and for the future period 2041-2070. • As a preliminary step to evaluate the performance of the RCMs over North America, the RCMS are driven with NCEP Reanalysis II data for the period 1979-2004. All the RCMs are run at a spatial resolution of 50 km. NARCCAP Time Slices • NARCCAP also includes two timeslice experiments at 50 km resolution using the GFDL atmospheric model (AM2.1) and the NCAR CCSM atmospheric model (CAM3). • In a timeslice experiment, the atmospheric component of an AOGCM is run using observed sea surface temperatures and sea ice boundaries for the historical run run, and those same observations combined with perturbations from the future AOGCM for the scenario run. • Omitting the coupled ocean model saves considerable computation and allows the atmospheric model to be run at higher resolution • NEXT FIGURE - - SOME TYPICAL NARCCAP RESULTS NARCCAP 50 KM domain CREAS (Regional Climate Change Scenarios for South America) • High resolution scenarios in South America for raising awareness among government officials and policy-makers • Uses three regional climate models: two from the UK Hadley Center, and a version of the NCEP Eta model • Focus on A2 and B2 emission scenarios • UKMet HADCM3 Global GCM provides large-scale climate change f i forcing • 50 KM resolution for regional models CLARIS A Europe-South p America Network for Climate Change g Assessment and Impact p Studies • The CLARIS project aims at strengthening collaborations between Europe and South America to develop common research strategies on climate change and impact issues in the subtropical region of South America through a multi-scale integrated approach (continentalregional-local). • The CLARIS framework will facilitate the participation of European researchers to IAI (Inter American Institute) projects and the submission of new common research proposals. Moreover, its opening towards stakeholders (e.g. agriculture, reinsurance, hydroelectricity), associated to the project through an expert group, will promote future initiatives on climate impact analysis, thus, contributing to related sustainable development strategies. The Flip-Side of Uncertainty is Probability - Can We Assess and Quantify y Likelihood of Occurrence Constructing and Evaluating Probabilities • No single model is ‘the best’ - a multi-model approach should be used • This leads to the possibility of probabilistic assessments of climate projections from diverse models • Problem of enormous complexity due to high dimensionality of climate model, plus sparse and limited length of observations Common approach to minimize problems - use regional averages g of regional g assessments and But does this constrain the range potential effects? Two Major Issues • Poor convergence (heavy-tailed probability distributions) – tends to minimize probabilities of tails and exaggerate in middle • MODEL BIAS!!! Bias must be minimized or eliminated – one cannot make a robust pdf from a biased dataset – leads to Bayesian approaches Probabilities, cont. Differing approaches can yield very different results and interpretations • Tebali et al. (2004): Bayesian framework; uses model bias and convergence criteria to define the shape and width of pdf’s pdf s for temperature and precipitation • Incorporates expert judgment in the form of how much weight to place on each criteria; assessed over regional averages • Greene et al. (2006): Bayesian framework based on extension of methods used for ensemble seasonal forecasting • Calibrated for the historic period (1902-1998) and then applied to future projections • Two reasons differs from Tebaldi et al.: • 1) Att Attributes ib t large l uncertainty t i t tto models d l th thatt poorly l representt historical hi t i l climate trends • 2) Strong stationarity assumption required to extrapolate from historic record to future trends • Presumed responsible for the smaller warmings and wider PDF’s (i l di a ffew negative (including ti values) l ) • Tends to ‘highlight’ low latitude regions due to weak trends in observations, and/or worse model performance • PDFS NEXT FIGURE - MULTI-MODALITY; LONG TAILS • BIAS AND CONVERGENCE • Map comparing PDFs of change in temperature (2080 to 2099 compared to 1980 to 1999) from Tebaldi et al. (2004a,b) and Greene et al (2006) as well as the raw model projections (represented by shaded al. histograms) for the Giorgi and Francisco (2000) regions. Areas under the curves and areas covered by the histograms have been scaled to equal unity. The scenario is SRES A1B and the season is NH winter ( (DJF). ) Other Techniques for Constructing PDFs • Apply simple pattern scaling to multi-model GCM ensembles • Modulate (normalized) regional patterns of change by global mean temperature changes obtained from a simple simple, probablistic EBM (e (e.g., g MAGICC) • Perturbed physics ensembles - allows for characterization of uncertainties arising from poorly constrained model parameters • Problems worse for climate parameters with nonlinear response (e.g., precipitation) - not so bad for those with linear response (e (e.g., g surface temperature) Techniques, q continued • Use robust observational constraints on model simulations - this plays off observed historic changes over the region of interest versus global changes • Use expert understanding of relevant processes and how they should unfold rather than the probabilistic methods (which are in their infancy and do not yet provide definitive results) • Greater use of multi-variate ‘process-oriented’ analyses based on (presumed) key mechanisms • Robust findings on regional climate change for mean and extreme precipitation, drought, and snow. This regional assessment is based upon AOGCM based studies, Regional Climate Models, statistical downscaling and process understanding. More detail on these findings may be found in the notes below, and their full description, including sources is given in the text. The background map indicates the degree of consistency between AR4 AOGCM simulations (21 simulations used) in the direction of simulated precipitation change. • Results from the perturbed physics ensemble of Harris et al. (2006) showing evolution in the median, and 80%, 90%, and 95% confidence ranges for annual surface temperature change, for a 1% per annum increase in CO2 concentration for 150 years, for all 24 regions described by Giorgi and Francisco (2000). Probabilistic Thresholds • Reliabilityy Ensemble Averaging g g ((REA)) - used byy Giorgi g and Mearns (2002) to calculate the probability of regional climate change exceeding given thresholds based on ensembles of different model simulations. • Probabilities of surface air temperature and precipitation change calculated for 10 regions of subcontinental scale spanning a range of latitudes and climatic settings. • They conclude the REA method can provide a simple and flexible tool to estimate probabilities of regional climate change from ensembles of model simulations for use in risk and cost assessment studies. • Limited sensitivity analysis of regional climate change probabilities for the 21st centuryy • Dessai et al. examined the sensitivityy of regional g climate change g probabilities to various uncertainties. • Used a simple probabilistic energy balance model that sampled uncertainty in greenhouse gas emissions, the climate sensitivity, the carbon cycle, ocean mixing, and aerosol forcing. • Propagated global mean temperature probabilities to General Circulation Models (GCMs) through the pattern-scaling technique. • Devised regional skill scores for each GCM, season (DJF, JJA), and climate variable (surface temperature, and precipitation) in 22 world regions, based on model performance and model convergence. • A range of sensitivity experiments carried out with different skill score schemes, climate sensitivities, and emissions scenarios. • For temperature change probabilities, probabilities emissions scenarios uncertainty tends to dominate the 95th percentile whereas climate sensitivity uncertainty plays a more important role at the 5th percentile. • The sensitivity of precipitation change probabilities to the tested uncertainties are region specific, but some conclusions can be drawn. At the 95th percentile, the uncertainty that tends to dominate is emissions scenarios closely followed by GCM weighting scheme and the climate scenarios, sensitivity. At the 5th percentile, GCM weighting scheme uncertainty tends to dominate for JJA but for DJF all uncertainties have similar proportionate influence JJA, influence. • • Development of probability distributions for regional climate change from uncertain global mean warming and an uncertain scaling relationship B. Hingray, A. Mezghani, T.A. Buishand (KNMI), • To produce probability distributions for regional climate change in surface temperature and precipitation, a probability distribution for global mean temperature increase has been combined with the probability distributions for the appropriate scaling variables, i.e. the changes in regional temperature/precipitation per degree global mean warming. • Each scaling variable is assumed to be normally distributed. • The uncertainty of the scaling relationship arises from systematic differences between the regional changes from global and regional climate model simulations and from natural variability. • The contributions of these sources of uncertainty to the total variance of the scaling variable are estimated from simulated temperature and precipitation data in a suite of regional climate model experiments conducted within the framework of the EU-funded project PRUDENCE, using an Analysis Of Variance (ANOVA). • For the area covered in the 2001–2004 EU-funded project SWURVE, five case study regions (CSRs) are considered: NW England, the Rhine basin, Iberia, Jura lakes (Switzerland) and Mauvoisin dam (Switzerland). • The resulting regional climate changes for 2070–2099 vary quite significantly between CSRs, between seasons and between meteorological variables. • For all CSRs, the expected warming in summer is higher than that expected for the other seasons. This summer warming is accompanied by a large decrease in precipitation. • The uncertainty of the scaling ratios for temperature and precipitation is relatively large in summer because of the differences between regional climate models models. • Differences between the spatial climate-change patterns of global climate model simulations make significant contributions to the uncertainty of the scaling ratio for temperature. • However, no meaningful contribution could be found for the scaling ratio for precipitation due to the small number of global climate models in the PRUDENCE project and natural variability, which is often the largest source of uncertainty. • In contrast, contrast for temperature temperature, the contribution of natural variability to the total variance of the scaling ratio is small, in particular for the annual mean values. • Simulation from the probability distributions of global mean warming and the scaling ratio results in a wider range of regional temperature change than that in the regional climate model experiments. • For the regional change in precipitation, however, a large proportion of the simulations (about 90%) is within the range of the regional climate model simulations.