Assessing future changes in extreme precipitation over Britain using regional climate model integrations

Jones, Phil; Reid, P

doi:10.1002/joc.677

Cited by 80 publications

(74 citation statements)

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“…It is clear that the gridded data have biases that need to be considered in such evaluations, as in some cases mismatches between climate model output and observations may be partly due to inaccuracies in the observational data. It may be more prudent to evaluate RCMs against only those observational grid boxes that satisfy certain ''station density'' criteria, as for example Beniston et al (2007), Buonomo et al (2007), Huntingford et al (2003), Jones and Reid (2001) and Semmler and Jacob (2004) have done in the past. More remains to be done to ensure that over-smoothed gridded data do not result in an over-smoothing RCM being incorrectly selected as the best performing one in an evaluation of climate models.…”

Section: Discussionmentioning

confidence: 99%

“…In some areas the station density is so high that only stations within each grid box are used to estimate the grid-box area-average. Some studies use these dense areas for the evaluation of RCMs (e.g., Beniston et al 2007;Buonomo et al 2007;Huntingford et al 2003;Jones and Reid 2001;Semmler and Jacob 2004), while for other areas, RCMs have been evaluated using gridded data developed with much sparser station networks. An example of climate model evaluation with gridded data developed with sparse station networks is the study by Christidis et al (2005), who use the gridded global daily temperature dataset developed by Caesar et al (2006) for the evaluation of their general circulation model (GCM).…”

Section: Introductionmentioning

confidence: 99%

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The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data

2009

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We study the influence of station network density on the distributions and trends in indices of areaaverage daily precipitation and temperature in the E-OBS high resolution gridded dataset of daily climate over Europe, which was produced with the primary purpose of Regional Climate Model evaluation. Area averages can only be determined with reasonable accuracy from a sufficiently large number of stations within a grid-box. However, the station network on which E-OBS is based comprises only 2,316 stations, spread unevenly across approximately 18,000 0.22°grid-boxes. Consequently, grid-box data in E-OBS are derived through interpolation of stations up to 500 km distant, with the distance of stations that contribute significantly to any grid-box value increasing in areas with lower station density. Since more dispersed stations have less shared variance, the resultant interpolated values are likely to be over-smoothed, and extreme daily values even more so. We perform an experiment over five E-OBS grid boxes for precipitation and temperature that have a sufficiently dense local station network to enable a reasonable estimate of the area-average. We then create a series of randomly selected station subnetworks ranging in size from four to all stations within the E-OBS interpolation search radii. For each sub-network realisation, we estimate the grid-box average applying the same interpolation methodology as used for E-OBS, and then evaluate the effect of network density on the distribution of daily values, as well as trends in extremes indices. The results show that when fewer stations have been used for the interpolation, both precipitation and temperature are over-smoothed, leading to a strong tendency for interpolated daily values to be reduced relative to the ''true'' areaaverage. The smoothing is greatest for higher percentiles, and therefore has a disproportionate effect on extremes and any derived extremes indices. For many regions of the E-OBS dataset, the station density is sufficiently low to expect this smoothing effect to be significant and this should be borne in mind by any users of the E-OBS dataset.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data

2009

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“…Hence, limiting the geographical domain of these atmospheric models reduces the total number of grid points and allows one to perform simulations at high resolutions with an affordable computational cost. Because of the ability of these high-resolution LAMs or RCMs to reproduce meaningful small-scale features over a limited region (Denis et al 2002;Giorgi et al 2004), they have become a popular tool in both the NWP and the climate community for studying extreme events at regional and local scales (e.g., Jones and Reid 2001;Buonomo et al 2007;Deque and Somot 2008;Duliere et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Multiscale Performance of the ALARO-0 Model for Simulating Extreme Summer Precipitation Climatology in Belgium

et al. 2013

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Daily summer precipitation over Belgium from the Aire Limitee Adaptation Dynamique Developpement International (ALADIN) model and a version of the model that has been updated with physical parameterizations, the so-called ALARO-0 model [ALADIN and AROME (Application de la Recherche a l'Operationnel a Meso-Echelle) combined model, first baseline version released in 1998], are compared with respect to station observations for the period 1961-90. The 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) is dynamically downscaled using both models on a horizontal resolution of 40 km, followed by a one-way nesting on high spatial resolutions of 10 and 4 km. This setup allows us to explore the relative importance of spatial resolution versus parameterization formulation on the model skill to correctly simulate extreme daily precipitation. Model performances are assessed through standard statistical errors and density, frequency, and quantile distributions as well as extreme value analysis, using the peak-over-threshold method and generalized Pareto distribution. The 40-km simulations of ALADIN and ALARO-0 show similar results, both reproducing the observations reasonably well. For the high-resolution simulations, ALARO-0 at both 10 and 4 km is in better agreement with the observations than ALADIN. The ALADIN model consistently produces too high precipitation rates. The findings demonstrate that the new parameterizations within the ALARO-0 model are responsible for a correct simulation of extreme summer precipitation at various horizontal resolutions. Moreover, this study shows that ALARO-0 is a good candidate model for regional climate modeling.

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“…Most recently, a few studies have used output from regional climate models (RCMs) to construct scenarios of extremes [97][98][99][100][101][102]. Durman et al [100], for example, focus on the occurrence of intense precipitation events over Europe and the UK defined using two different thresholds (15 mm per day and the upper 1% percentile calculated from the model control run).…”

Section: Recent Work On Scenario Development Methods For Extremesmentioning

confidence: 99%

“…A comparison is made of future scenarios (for 2080-2100) constructed from Hadley Centre HadCM2 GCM and RCM output in order to determine the added value of using high-resolution model output [100]. Jones and Reid [101] also use the HadCM2 RCM to construct future scenarios of extreme precipitation for the UK, in this case focusing on the occurrence of the top 10% quantile events (calculated using the method of Osborn et al [103]) and 5, 10, 20 and 50 year return period events. This RCM was also used by Booij [102] to construct scenarios of return period precipitation events for the Meuse catchment in western Europe and to compare these with HIRHAM4 RCM and three GCM-based scenarios.…”

Section: Recent Work On Scenario Development Methods For Extremesmentioning

confidence: 99%

Representing Climate and Extreme Weather Events in Integrated Assessment Models: A Review of Existing Methods and Options for Development

Goodess

Hanson

Hulme

et al. 2003

Integrated Assessment

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The lack of information about future changes in extreme weather is a major constraint of Integrated Assessment Models (IAMs) of climate change. The generation of descriptions of future climate in current IAMs is assessed. We also review recent work on scenario development methods for weather extremes, focusing on those issues which are most relevant to the needs of IAMs. Finally, some options for implementing scenarios of weather extremes in IAMs are considered.Keywords: Integrated Assessment Models, climate change, scenarios, extreme weather events, general circulation models, regional climate models. INTEGRATED ASSESSMENT AND CLIMATE CHANGEOne of the essential characteristics of integrated assessment is the simultaneous consideration of the multiple dimensions of environmental problems such as climate change. A number of formal integrated assessment models (IAMs) for climate change have been developed over the last decade, starting with the models IMAGE 1.0 and ESCAPE in the early 1990s [1,2]. These models were constructed using modules that are reduced-form versions of more complex models, for example of the climate system, the economy and ecosystems. Most climate modules in IAMs generate zero (i.e., globally-averaged; e.g., PAGE) or one (i.e., zonallyaveraged; e.g., IMAGE) dimensional descriptions of future climate, usually at a mean (e.g., 30-year average) seasonal or annual resolution [3][4][5]. Some IAMs (e.g., IMAGE 2.2 and AIM) then generate spatially explicit, i.e., two-dimensional, descriptions of future climate, usually by accessing stored patterns of climate change derived from more complex General Circulation Model (GCM) experiments [6][7][8].These approaches to generating future climate descriptions in IAMs, which are reviewed in Section 2, are computationally efficient and allow multiple experiments to be easily conducted in an integrated framework. The climate output is then input into an ecosystem, agriculture or health impacts module (e.g., AIM), or used directly to estimate the economic cost of climate damage from a lookup climate damage function (e.g., DICE). In either case, the current lack of any information about changes in daily or extreme weather (the focus of this review) is a major weakness. Agriculture, for example, is likely to be as, or more, sensitive to changes in daily weather sequences and the occurrence of extreme weather events than to changes in mean monthly or seasonal climate [9][10][11]. Potential changes in extremes and the ensuing changes in risk are also important for sectors such as water resources and insurance [12,13]. Climate damage functions that express the economic impact of climate change as a function of global-(or regional-) mean climate alone are likely to underestimate the economic damage associated with extreme events such as flooding and storms (e.g., in the UK, the October 1987 windstorm event is estimated to have cost insurers 3.1 billion US dollars, with further economic losses of 2.7 billion US dollars [14]). The lack of information about chan...

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Assessing future changes in extreme precipitation over Britain using regional climate model integrations

Cited by 80 publications

References 15 publications

The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data

The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data

Multiscale Performance of the ALARO-0 Model for Simulating Extreme Summer Precipitation Climatology in Belgium

Representing Climate and Extreme Weather Events in Integrated Assessment Models: A Review of Existing Methods and Options for Development

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