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Increasing Risk of Great Floods in a Changing Climate

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Increasing Risk of Great Floods in a Changing Climate letters to nature 10 1 uncertainty in climate-prediction ensembles . In addition, such a extreme-event risk analysis would bene®t from an increase in the 0.8 resolution of present-day climate models (with grid sizes of hun- dreds of kilometres). For example, it would clearly be of more direct 0.6 relevance for the decision problem discussed above if E was de®ned 0.4 by some speci®c river bursting its banks during a particular season. However, estimating the future risk of this type of event would 0.2 require feeding climate model output into a basin-speci®c hydro- 0 logical model. In general, climate model grid sizes of the order of ten 0.1 0.2 0.5 1 2 5 10 20 50 100 kilometres would be necessary to simulate precipitation statistics 1 adequately over a typical catchment area (though for rivers with Value b large catchment basins, such as the Brahmaputra, coarser climate 0.8 model grids may be adequate). An ability to estimate reliably the 0.6 probability of extreme climate events is an important factor in de®ning future computational requirements for the climate change 0.4 problem. M 0.2 Received 27 September; accepted 18 December 2001. 0 1. Houghton, J. T. et al. (eds) Climate Change 2001: The Scienti®c Basis (Cambridge Univ. Press, 0.1 0.2 0.5 1 2 5 10 20 50 100 Cambridge, 2001). C/L (%) 2. Palmer, T. N. Predicting uncertainty in forecasts of weather and climate. Rep. Prog. Phys. 63, 71±116 (2000). Figure 3 The potential economic value of probabilistic projections of climate change 3. Richardson, D. S. Skill and relative economic value of the ECMWF ensemble prediction system. Q. J. R. based on a generic binary decision model (see text for details). C and L are possible Meteorol. Soc. 126, 649±668 (2000). ®nancial losses, relative to some reference index, associated with two alternative 4. Palmer, T. N., Brankovic, C. & Richardson, D. S. A probability and decision-model analysis of PROVOST seasonal multi-model ensemble integrations. Q. J. R. Meteorol. Soc. 126, 2013±2034 investments. Solid lines, value based on the CMIP2 greenhouse ensemble probability of En (2000). over the next 80 years. Dashed lines, value based on an estimate of En from a single 5. Marsh, T. J. The 2000/01 ¯oods in the UKÐa brief overview. Weather 56, 343±345 (2001). member of the CMIP2 greenhouse ensemble. When value is zero or less, the 6. RaÈisaÈnen, J. & Palmer, T. N. A probability and decision-model analysis of a multi-model ensemble of climate change simulations. J. Clim. 14, 3212±3226 (2001). corresponding estimate of the probability of En is no better than an estimate based on the 7. Meehl, G. A., Boer, G. J., Covey, C., Latif, M. & Stouffer, R. J. The coupled model intercomparison control ensemble. A value of one would correspond to a perfect decision strategy. project. Bull. Am. Meteorol. Soc. 81, 313±318 (2000). a, n 2; b, n 3. 8. Palmer, T. N. A nonlinear dynamical perspective on model error: A proposal for non-local stochastic- dynamic parametrisation in weather and climate prediction models. Q. J. R. Meteorol. Soc. 127, 279± 304 (2001). 9. Kharin, V. V. & Zwiers, F. W. Changes in the extremes in an ensemble of transient climate simulations globe. This value is illustrated in terms of the non-dimensional with a coupled atmosphere-ocean GCM. J. Clim. 13, 3760±3788 (2000). 10. Allen, M. R. Do-it-yourself climate prediction. Nature 401, 642 (1999). quantity V i Mi 2 Ml= Mu 2 Ml, where Ml and Mu are the lower and upper reference values respectively. Hence if V i 0, then the estimate of pÅ of subset i is no better than an estimate which Acknowledgements assumes that the climate of the twenty-®rst century will be the same We thank M. Blackburn and P. J. Webster for comments on an earlier draft of this as that of the twentieth century. On the other hand, perfect Letter. investment decisions have a value V i 1. In calculating Vi we average over all choices of verifying model. For V2 we also average over all choices of forecast model. Figure 3 shows V1 and V2 for n 2 and n 3, as a function of C/L. We do not show V3 because it is never greater than zero. The ................................................................. reason for this is straightforward: the number of occurrences of extreme climate events is severely underestimated in the consensus Increasing risk of great ¯oods projection. This arises because ensemble averaging tends to produce smooth ®elds which are unrealistically biased towards in a changing climate the climate-mean state. If C/L is either suf®ciently small, or suf®ciently large, then P. C. D. Milly*, R. T. Wetherald², K. A. Dunne* & T. L. Delworth² accurate ensemble forecasts are clearly not needed to make good investment decisions (buy B or A, respectively). Hence, the green- * US Geological Survey, GFDL/NOAA; and ² Geophysical Fluid Dynamics house ensemble only has positive economic value (compared with Laboratory/NOAA, P.O. Box 308, Princeton, New Jersey 08542, USA the baseline value Ml) for a subset of possible C/L. This ensemble .............................................................................................................................................. tends to be most valuable (compared with Ml) when C/L is Radiative effects of anthropogenic changes in atmospheric com- comparable with the control ensemble estimate of pÅ.AspÅ is smaller position are expected to cause climate changes, in particular an 1 for E3 than for E2, then the greenhouse ensemble provides value for intensi®cation of the global water cycle with a consequent 2 smaller C/L, when decisions are based on the occurrences of E3 increase in ¯ood risk . But the detection of anthropogenically rather than E2. It can be seen that the value of the greenhouse forced changes in ¯ooding is dif®cult because of the substantial ensemble is never less than the value of a single deterministic natural variability3; the dependence of stream¯ow trends on ¯ow projection, and that, overall, the difference in value between the regime4,5 further complicates the issue. Here we investigate the ensemble and the single deterministic projection is larger for E3 than changes in risk of great ¯oodsÐthat is, ¯oods with discharges 2 for E2. This is because the relative unreliability of the single exceeding 100-year levels from basins larger than 200,000 km Ð deterministic projection is larger for E3 than for E2. using both stream¯ow measurements and numerical simulations In fact, much larger ensembles are needed to provide reliable of the anthropogenic climate change associated with greenhouse 6 estimates of the probability of E3 on a regional basis (for example, gases and direct radiative effects of sulphate aerosols . We ®nd speci®c to the UK or for Bangladesh). Moreover, more research is that the frequency of great ¯oods increased substantially during needed to develop sound methodologies for representing model the twentieth century. The recent emergence of a statistically 514 © 2002 Macmillan Magazines Ltd NATURE | VOL 415 | 31 JANUARY 2002 | www.nature.com letters to nature signi®cant positive trend in risk of great ¯oods is consistent with results from the climate model, and the model suggests that the 60 trend will continue. A focus on large basins facilitates the initiation of quantitative 30 analyses of ¯ood risk with numerical climate models; basins commonly used in trend analyses4,5 are too small to be resolved by 0 current models. Estimation of extreme-event risk requires multi- Latitude (deg.) decadal observational records. Here, we consider 29 basins larger –30 2 than 200,000 km in area for which discharge observations span at –120 –60 0 60 120 least 30 yr. We analyse annual maximum monthly-mean ¯ows, Longitude (deg.) rather than annual maximum instantaneous ¯ows; these two are 2-5 10-20 30-40 80-120 strongly correlated in large basins. In contrast with earlier studies4,5, this investigation has a global scope and focuses on extreme events; 5-10 20-30 40-80 1,000-10,000 we analyse the 100-yr ¯ood (that is, the river discharge that has a Years probability of 0.01 of being exceeded in any given year), which is Figure 2 Map showing the gauged drainage areas and ¯ood-risk sensitivities of the 29 commonly used in ¯ood-risk assessment for river-basin planning river basins in this study. Colour indicates the modelled return period, under idealized and design of major structures. Choosing such a large-magnitude quadrupling of atmospheric CO2 concentrations, of the ¯ood magnitude associated with threshold probably reduces any distortion of our analysis by non- a 100-yr return period in the control experiment. Although results for low-latitude climatic factors such as land-use changes and river development. basins are provided, the poor performance of the model in low latitudes should be kept For each basin, we ®tted observed annual maximum monthly- in mind. mean discharges to Pearson's type III distribution7 by the method of moments, and determined the 100-yr ¯ood magnitude from the ®tted distribution. The 100-yr ¯ood was exceeded 21 times in our observational record of 2066 station-years. Flood events were were made after 1953, and 16 of the ¯ood events occurred after concentrated in the latter half of the record; half of the observations 1953. Under the assumption that ¯ood events were independent outcomes of a stationary process, we used binomial probability theory to determine a probability of 1.3% of having 16 or more of 21 events during the second part of the record. For observations from an extratropical subset of the basins (see below), the corresponding 101 probability is 3.5%, for 7 out of 8 ¯ood events in the second half of the record.
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