A study on combining global and regional climate model results for generating climate scenarios of temperature and precipitation for the Netherlands

Lenderink, Geert; Ulden, A. van; Hurk, Bart van den; Keller, Franziska

doi:10.1007/s00382-007-0227-z

Cited by 79 publications

(60 citation statements)

References 28 publications

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“…There is widespread evidence from models and observations that precipitation extremes increase with higher temperatures (e.g. Frei et al 2006, Fowler et al 2007, Pall et al 2007, Lenderink et al 2007, Lenderink & van Meijgaard 2008, 2010, O'Gorman & Schneider 2009). …”

Section: Introductionmentioning

confidence: 99%

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

Lenderink¹

2010

Clim. Res.

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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.

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Section: Introductionmentioning

confidence: 99%

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

Lenderink¹

2010

Clim. Res.

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“…Hay and Clark 2003;Lenderink et al 2007b;Piani et al 2010). MOS is particularly suitable to correct orographic effects on precipitation intensity in regions where the topography is misrepresented by the coarse model grid.…”

Section: Skill Of Downscaling Methodsmentioning

confidence: 99%

“…RCMs can resolve mesoscale atmospheric features explicitly and they add small-scale structures to the large-scale circulation provided by the driving model (Feser 2006). Local climate is influenced by large-scale dynamics, regional physiographic features such as local orography, land-sea contrasts land use, and soil type, as well as by small-scale atmospheric features such as frontal systems or convective cells (Lenderink et al 2007b;Feser et al 2011). This is particularly the case for the simulation of precipitation.…”

Section: Added Value Of Dynamical Downscalingmentioning

confidence: 99%

Projected Change—Models and Methodology

Wibig

Maraun

Benestad

et al. 2015

Regional Climate Studies

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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.

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“…The increase is probably caused by higher sea surface temperatures and changes in circulation, but could also be influenced by land use change and urbanization (Daniëls 2016). Global climate models especially indicate an increase in winter precipitation in the Netherlands related to the global mean temperature change; the projected change in mean summer precipitation is less conclusive (Lenderink et al 2007). The model results indicate that the increases in winter precipitation are the main cause of the increased groundwater levels in the Veluwe area.…”

Section: Climate Change Scenariosmentioning

confidence: 97%

“…The Royal Netherlands Meteorological Institute (KNMI) constructed climate scenarios for the Netherlands based on global and regional climate models (Lenderink et al 2007; Van den Hurk et al 2007). In 2014 the KNMI scenarios for 2050 and 2085 were updated (Van den Hurk et al 2014).…”

Section: Climate Scenarios In the Netherlandsmentioning

confidence: 99%

Impact of Changes in Groundwater Extractions and Climate Change on Groundwater-Dependent Ecosystems in a Complex Hydrogeological Setting

Engelenburg

Hueting

Rijpkema³

et al. 2017

Water Resour Manage

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Climate change and other future developments can influence the availability of groundwater resources for drinking water. The uncertainty of the projected impact is a challenge given the urgency to decide on adaptation measures to secure the drinking water supply. Improved understanding on how climate change affects the groundwater system is necessary to develop adaptation strategies. AZURE is used, a detailed, well-calibrated hydrological model to study the projected impact of climate change scenarios on the large Veluwe aquifer in the Netherlands. The Veluwe area is an important source of drinking water. However, some existing groundwater extractions in the area affect nearby groundwaterdependent ecosystems. Redistribution of the licensed extraction volumes of these sites is considered to reduce the impact on these ecosystems. The projected impact of climate change and redistribution to groundwater levels is studied. The research shows that in a slowly responding large aquifer the projected climate change may cause rising groundwater levels despite the projected increase in summer dryness. The results indicate that this impact may exceed the impact of redistribution of extraction volumes. In addition, it is shown that the combined effect strongly depends on local conditions, thus highlighting the need for highresolution modelling.

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A study on combining global and regional climate model results for generating climate scenarios of temperature and precipitation for the Netherlands

Cited by 79 publications

References 28 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

Projected Change—Models and Methodology

Impact of Changes in Groundwater Extractions and Climate Change on Groundwater-Dependent Ecosystems in a Complex Hydrogeological Setting

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