An estimate of future climate change for western France using a statistical downscaling technique

Timbal, Bertrand; Dufour, Anne; McAvaney, B. J.

doi:10.1007/s00382-002-0298-9

Cited by 90 publications

(99 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…At the daily level, the smallest area-averaged correlations are obtained for precipitation and wind. For precipitation, the value is, nevertheless, comparable to other SD studies (for example Wilby et al, 2004or Timbal et al, 2003. Among the different variables, the best daily correlations are at Courlon-sur-Yonne (gauged area of 10 669 km 2 ) the Marne at Noisiel (12 443 km 2 ), the Oise at Pont-SainteMaxence (13 632 km 2 ), the Seine at Paris (43 509 km 2 ).…”

Section: Statistical Downscaling Of Era40 Reanalysissupporting

confidence: 86%

Statistical and dynamical downscaling of the Seine basin climate for hydro‐meteorological studies

Boé

Terray

Habets

et al. 2007

Intl Journal of Climatology

477

333

View full text Add to dashboard Cite

Abstract:Two downscaling methods designed for the study of the hydrological impact of climate change on the Seine basin in France are tested for present climate. First, a multivariate statistical downscaling (SD) methodology based on weather typing and conditional resampling is described. Then, a bias correction technique for dynamical downscaling based on quantile-quantile mapping is introduced. To evaluate the end-to-end SD methodology, the atmospheric forcing derived from the large-scale circulation (LSC) of the ERA40 reanalysis by SD is used to force a hydrological model. Simulated discharges reproduce historical values reasonably well. Next, the dynamical and statistical approaches are compared using the Météo-France ARPEGE general circulation model in a variable resolution configuration (resolution around 60 km over France). The ARPEGE simulation is downscaled using the two methodologies, and hydrological simulations are performed. Regarding downscaled temperature and precipitation, the statistical approach is more efficient in reproducing the temporal and spatial autocorrelation properties. The simulated river discharges from the two approaches are nevertheless very similar: the two methods reproduce well the seasonal cycle and the daily distribution of streamflows. Finally, the results of the study are discussed from a practical impact study perspective.

show abstract

Section: Statistical Downscaling Of Era40 Reanalysissupporting

confidence: 86%

Statistical and dynamical downscaling of the Seine basin climate for hydro‐meteorological studies

Boé

Terray

Habets

et al. 2007

Intl Journal of Climatology

477

333

View full text Add to dashboard Cite

show abstract

“…Analog models, involving re-sampling of historical values depending on the state of large-scale conditions, have been used to provide realistic scenarios for the 24-h precipitation (Dehn 1999;Fernandez and Saenz 2003;van den Dool 1995;Timbal et al 2003Timbal and Jones 2008;Wilby et al 2004). Zorita and von Storch (1999) argued that more sophisticated non-linear ESD models do not perform better than the simple analog model.…”

Section: Extremes and The Upper Tails Of The Distributionmentioning

confidence: 99%

Downscaling precipitation extremes

Benestad

2009

Theor Appl Climatol

View full text Add to dashboard Cite

A new method for predicting the upper tail of the precipitation distribution, based on empiricalstatistical downscaling, is explored. The proposed downscaling method involves a re-calibration of the results from an analog model to ensure that the results have a realistic statistical distribution. A comparison between new results and those from a traditional analog model suggests that the new method predicts higher probabilities for heavy precipitation events in the future, except for the most extreme percentiles for which sampling fluctuations give rise to high uncertainties. The proposed method is applied to the 24-h precipitation from Oslo, Norway, and validated through a comparison between modelled and observed percentiles. It is shown that the method yields a good approximate description of both the statistical distribution of the wet-day precipitation amount and the chronology of precipitation events. An additional analysis is carried out comparing the use of extended empirical orthogonal functions (EOFs) as input, instead of ordinary EOFs. The results were, in general, similar; however, extended EOFs give greater persistence for 1-day lags. Predictions of the probability distribution function for the Oslo precipitation indicate that future precipitation amounts associated with the upper percentiles increase faster than for the lower percentiles. Substantial random statistical fluctuations in the few observations that make up the extreme upper tail implies that modelling of these is extremely difficult, however. An extrapo-

show abstract

“…Harmonising daily predictor data from different GCMs for use in a multi-model ensemble is a time-consuming process. This might explain why there are so few SD studies based on such ensembles; to our knowledge, only Timbal et al (2003), Timbal & Jones (2008) and Teutschbein et al (2011) have used daily predictor fields from a small multi-model ensemble.…”

Section: Introductionmentioning

confidence: 99%

Statistical downscaling of daily temperatures in the NW Iberian Peninsula from global climate models: validation and future scenarios

Brands¹,

Taboada²,

Cofiño³

et al. 2011

Clim. Res.

View full text Add to dashboard Cite

We used the analogue method to generate ensemble projections of local daily mean, maximum and minimum air temperatures in the NW Iberian Peninsula until the middle of this century. A 3-step method was followed.(1) The error of the analogue method under optimal conditions was estimated, using air temperatures at 850 hPa and mean sea level pressure from reanalysis data as predictor variables. (2) The method's error under suboptimal conditions was assessed by taking these predictors from control runs of a multi-model, multi-initial-conditions ensemble of global climate models. Neither the predictor data nor the downscaled series were corrected. Under these suboptimal conditions, none of the individual downscaled series could robustly reproduce the cumulative distribution function (CDF) of the observations in any season of the year. However, when the single downscaled series were combined into a multi-model series, CDFs were reliably reconstructed for summer and autumn. (3) Temperature series were downscaled from the ensemble's scenario runs and compared to observations in the reference period to detect local climate change. In addition to the mean relative warming, it can be shown that the less frequent the event in the reference period, the higher its frequency increase and the broader its uncertainty interval in the scenario period. This tendency is more pronounced for daytime than for night-time heat/warm events, leading to a tripling to quadrupling of the former in summer. The local projections' uncertainty intervals are dominated by model errors rather than by forcing or initial-conditions uncertainties.

show abstract

An estimate of future climate change for western France using a statistical downscaling technique

Cited by 90 publications

References 31 publications

Statistical and dynamical downscaling of the Seine basin climate for hydro‐meteorological studies

Statistical and dynamical downscaling of the Seine basin climate for hydro‐meteorological studies

Downscaling precipitation extremes

Statistical downscaling of daily temperatures in the NW Iberian Peninsula from global climate models: validation and future scenarios

Contact Info

Product

Resources

About