Refinement of dynamically downscaled precipitation and temperature scenarios

Engen-Skaugen, Torill

doi:10.1007/s10584-007-9251-6

Cited by 64 publications

(54 citation statements)

References 23 publications

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“…Previously used scores include overall measures, such as the root mean square error (Piani et al, 2010b) or the Kolmogorov-Smirnov two sample statistic (Dosio and Paruolo, 2011). Other suggested scores assess specific moments of the distribution including the mean (Engen-Skaugen, 2007;Li et al, 2010;Dosio and Paruolo, 2011;Themeßl et al, 2011;Turco et al, 2011;Teutschbein and Seibert, 2012), the standard deviation (Engen-Skaugen, 2007;Li et al, 2010;Themeßl et al, 2011;Teutschbein and Seibert, 2012) and the skewness (Li et al, 2010). A variety of further scores are based on the comparison of the frequency of days with precipitation (Schmidli et al, 2006(Schmidli et al, , 2007Themeßl et al, 2011) and the magnitude of selected (mostly high) percentiles (Schmidli et al, 2006(Schmidli et al, , 2007Li et al, 2010;Themeßl et al, 2011;Teutschbein and Seibert, 2012).…”

Section: Quantifying Performancementioning

confidence: 99%

“…In recent years a multitude of studies has investigated different post processing techniques, aiming at providing reliable estimators of observed precipitation climatologies given RCM output (e.g. Ines and Hansen, 2006;Engen-Skaugen, 2007;Schmidli et al, 2007;Dosio and Paruolo, 2011;Themeßl et al, 2011;Turco et al, 2011;Chen et al, 2011b;Teutschbein and Seibert, 2012). Among the most popular approaches are statistical transformations that aim to adjust (selected aspects of) the distribution of RCM (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods

Gudmundsson

Bremnes

Haugen

et al. 2012

Hydrol. Earth Syst. Sci.

981

730

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Abstract. The impact of climate change on water resources is usually assessed at the local scale. However, regional climate models (RCMs) are known to exhibit systematic biases in precipitation. Hence, RCM simulations need to be post-processed in order to produce reliable estimates of local scale climate. Popular post-processing approaches are based on statistical transformations, which attempt to adjust the distribution of modelled data such that it closely resembles the observed climatology. However, the diversity of suggested methods renders the selection of optimal techniques difficult and therefore there is a need for clarification. In this paper, statistical transformations for postprocessing RCM output are reviewed and classified into (1) distribution derived transformations, (2) parametric transformations and (3) nonparametric transformations, each differing with respect to their underlying assumptions. A real world application, using observations of 82 precipitation stations in Norway, showed that nonparametric transformations have the highest skill in systematically reducing biases in RCM precipitation.

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Section: Quantifying Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods

Gudmundsson

Bremnes

Haugen

et al. 2012

Hydrol. Earth Syst. Sci.

981

730

View full text Add to dashboard Cite

show abstract

“…Therefore, until advances in climate system modelling improve the representation of localscale climate processes, there is a need to develop methods to better represent local projections of future changes to key climatic variables. One complication with using RCM outputs for impact studies in mountain regions relates to the terrain smoothing within the model; as a result of this, the elevation of specific sites is poorly represented and the observed climate of specific sites is not accurately reproduced (Coll et al 2005, Engen-Skaugen 2007, Beldring et al 2008. Consequently, the approach explored here used locallyresolved temperature lapse rate models (LRMs) variably integrated with temperature change outputs from an RCM and observed station data.…”

Section: Aims and Objectivesmentioning

confidence: 99%

“…Therefore, we suggest that our work represents an advance in terms of deriving locally relevant projections of temperature change with altitude; for example, Moen et al (2004) did not apply such a range of LRVs in their approach. Similarly, the seasonal range of lapse rate adjustments applied here is wider than the adjustment applied to daily temperature outputs from an RCM to correct for altitude biases between RCM grids and station data by Engen-Skaugen (2007). …”

Section: Lapse Rate Modelsmentioning

confidence: 99%

Developing site scale projections of climate change in the Scottish Highlands

Coll¹,

Gibb²,

Price³

et al. 2010

Clim. Res.

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With recent warming trends projected to amplify over the coming century, there are concerns surrounding the impacts on mountain regions. Despite these concerns, global (GCMs) and regional climate models (RCMs) fail to capture local scale-dependent controls on upland climates. A modelling framework combining climate model outputs and station data is presented and used to explore possible future changes to temperature with altitude in the Scottish Highlands. The approach was extended by modelling shifts in seasonal isotherm values associated with existing vegetation zones. To achieve this, temperature lapse rate models (LRMs) were applied to 1961-1990 baseline (BL) observed station data for selected stations in the eastern Highlands using seasonally representative lapse rate values (LRVs) derived from paired station values. Tests against 3 upland station records ranging from 663 to 1245 m indicated a credible model performance for the mean seasonal maximum (T max ) and minimum (T min ) BL values evaluated. Following derivation of seasonal isotherm values for the present upper limit of vegetation zones via the LRMs, selected scenario data outputs from the corresponding Hadley Centre RCM (HadRM3H) 50 × 50 km grid cells were used to project future changes to BL values via the LRMs. The findings suggest substantial shifts in the isotherm associated with each zone for the scenarios selected, with shifts in T min more marked than those for T max , although substantial uncertainties remain. Following an exploration of the results for the region, we suggest that a refinement to the approach linked to a wider modelling effort incorporating other important controlling variables for upland species could inform future management initiatives for mountain areas more generally.

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“…In mountainous areas and regions with complex topography, the RCMs do not provide a representative description of climate variables with sharp spatial gradients, and it is necessary to re-scale the output in order to obtain a realistic statistical distribution (Engen-Skaugen 2004;Skaugen et al 2002). A re-scaling, giving the right mean level or standard deviation, assumes that the RCM reproduces the true shape for the statistical distribution, as well as the correct wet-day frequency in order for other percentiles to also be correct, as all the percentiles are scaled by the same factor.…”

Section: Introductionmentioning

confidence: 99%

Downscaling precipitation extremes

Benestad

2009

Theor Appl Climatol

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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-

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Refinement of dynamically downscaled precipitation and temperature scenarios

Cited by 64 publications

References 23 publications

Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods

Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods

Developing site scale projections of climate change in the Scottish Highlands

Downscaling precipitation extremes

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