Resampling of regional climate model output for the simulation of extreme river flows

Leander, Robert; Buishand, T. A.

doi:10.1016/j.jhydrol.2006.08.006

Cited by 419 publications

(326 citation statements)

References 14 publications

(15 reference statements)

Supporting

Mentioning

304

Contrasting

Unclassified

Order By: Relevance

“…However, if the spatial correlation in the observations is different from the correlation in the model result, this need not be the case. For instance, Leander & Buishand (2007) found larger spatial correlations in the output of an RCM integration than in the observational data set for sub-basins of the Meuse River. Also, the spatial correlation in the E-OBS data set could be affected by the low station density used in E-OBS in some areas (see Section 6).…”

Section: Methodsmentioning

confidence: 96%

Exploring metrics of extreme daily precipitation in a large ensemble of regional climate model simulations

Lenderink¹

2010

Clim. Res.

View full text Add to dashboard Cite

The ability of a large ensemble of 15 state-of-the-art regional climate models (RCMs) to simulate precipitation extremes was investigated. The 99th, 99.9th and 99.99th percentiles of daily precipitation in the models were compared with those in the recently released E-OBS observational database for winter, spring, summer and autumn. The majority of the models overestimated the values of the precipitation extremes compared with E-OBS, on average by approximately 38%, but some models exceeded 50%. To measure model performance, a simple metric is proposed that averages a nonlinear function of the seasonal biases over the European area. The sensitivity of the metric to different assumptions in the construction and the quality of the observational data was explored. Generally, low sensitivities of the metric to spatial and seasonal averaging were found. However, large sensitivities to potential biases in the observational database were found. An alternative metric that measures the spatial pattern of the extremes (which is not sensitive to a potential constant offset in the observational data) was further explored. With this metric, the ranking between the models changed substantially. However, the 2 models with the worst score in the standard metric also displayed the worst scores with this alternative metric. Finally, the regional climate models displayed the largest biases compared with E-OBS in areas where the underlying station density used in E-OBS is low, thus suggesting that data quality is indeed an important issue. In summary, the results show that: (1) there is no metric that guarantees an objective and precise ranking or weighting of the models, (2) by exploring different metrics it nevertheless appears possible to indentify models that perform consistently worse than other models, and (3) the observational data quality should be considered when designing and interpreting metrics.

show abstract

Section: Methodsmentioning

confidence: 96%

Exploring metrics of extreme daily precipitation in a large ensemble of regional climate model simulations

Lenderink¹

2010

Clim. Res.

View full text Add to dashboard Cite

show abstract

“…MOS removes much of the model error in the precipitation, making it more useful in impact studies, which is considered by many to allow confidence in the examination of future changes in flow regimes in catchments from Europe and the wider world (e.g. Bell et al, 2007, Fowler et al, 2007bLeander and Buishand, 2007;Akhtar et al, 2009;Linde et al, 2010;Marke et al, 2011;Rojas et al, 2011;Turco et al, 2011). However, MOS can potentially also remove much of the spread in the driving variables, which could disrupt signals of climate change.…”

Section: Introductionmentioning

confidence: 99%

Modelling climate impact on floods with ensemble climate projections

Cloke

Wetterhall

et al. 2012

Quart J Royal Meteoro Soc

108

104

View full text Add to dashboard Cite

The evidence provided by modelled assessments of future climate impact on flooding is fundamental to water resources and flood risk decision making. Impact models usually rely on climate projections from global and regional climate models (GCM/RCMs). However, challenges in representing precipitation events at catchment-scale resolution mean that decisions must be made on how to appropriately pre-process the meteorological variables from GCM/RCMs. Here the impacts on projected high flows of differing ensemble approaches and application of Model Output Statistics to RCM precipitation are evaluated while assessing climate change impact on flood hazard in the Upper Severn catchment in the UK. Various ensemble projections are used together with the HBV hydrological model with direct forcing and also compared to a response surface technique. We consider an ensemble of single-model RCM projections from the current UK Climate Projections (UKCP09); multi-model ensemble RCM projections from the European Union's FP6 'ENSEMBLES' project; and a joint probability distribution of precipitation and temperature from a GCM-based perturbed physics ensemble.The ensemble distribution of results show that flood hazard in the Upper Severn is likely to increase compared to present conditions, but the study highlights the differences between the results from different ensemble methods and the strong assumptions made in using Model Output Statistics to produce the estimates of future river discharge. The results underline the challenges in using the current generation of RCMs for local climate impact studies on flooding.

show abstract

“…Tmean mean daily temperature; Twet mean daily temperature for days with precipitation; Tdry mean daily temperature for dry days (Yang et al 2010) representation of the annual cycle (e.g. Boé et al 2007;Leander and Buishand 2007).…”

Section: Skill Of Downscaling Methodsmentioning

confidence: 99%

Projected Change—Models and Methodology

Wibig

Maraun

Benestad

et al. 2015

Regional Climate Studies

View full text Add to dashboard Cite

General (global) circulation models (GCMs) are a useful tool for studying how climate may change in the future. Although GCMs have high temporal resolution, their spatial resolution is low. To simulate the future climate of the Baltic Sea region, it is necessary to downscale GCM data. This chapter describes the two conceptually different ways of downscaling: regional climate models (RCMs) nested in GCMs and using empirical and/or statistical relations between large-scale variables from GCMs and small-scale variables. There are many uncertainties in climate models, including uncertainty related to future land use and atmospheric greenhouse gas concentrations, limits on the amount of input data and their accuracy, and the chaotic nature of weather. The skill of methods for describing regional climate futures is also limited by natural climate variability. For the Baltic Sea area, the lack of an oceanic component in RCMs and poor representation of forcing by aerosols and changes in land use are major limitations. IntroductionThe development of general circulation models (GCMs) has created a useful tool for projecting how climate may change in the future. Such models describe the climate at a set of grid points, regularly distributed in space and time and with the same density over land and ocean. Their temporal resolution is relatively high, but their spatial resolution is limited by computing power. Many important processes, such as cloud formation, convection, and precipitation, occur at spatial scales much smaller than the distance between grid points. This means that these so-called sub-grid processes are not explicitly simulated by the models, but must be approximated with simplifying algorithms referred to as parameterisations. The low spatial resolution also means that the topography, coastline, and processes at the land-air, ocean-air, and land-ocean boundaries are coarsely represented in GCMs.

show abstract

Resampling of regional climate model output for the simulation of extreme river flows

Cited by 419 publications

References 14 publications

Exploring metrics of extreme daily precipitation in a large ensemble of regional climate model simulations

Exploring metrics of extreme daily precipitation in a large ensemble of regional climate model simulations

Modelling climate impact on floods with ensemble climate projections

Projected Change—Models and Methodology

Contact Info

Product

Resources

About