On the robustness of changes in extreme precipitation over Europe from two high resolution climate change simulations

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1 QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY Q. J. R. Meteorol. Soc. 133: (27) Published online in Wiley InterScience ( On the robustness of changes in extreme precipitation over Europe from two high resolution climate change simulations E. Buonomo, 1 * R. Jones, 2 C. Huntingford 3 and J. Hannaford 3 1 Met Office Hadley Centre, Exeter, UK 2 Met Office Hadley Centre (Reading Unit), Meteorology Building, University of Reading, UK 3 Centre for Ecology and Hydrology, Wallingford, UK ABSTRACT: Two Regional Climate Model (RCM) projections of changes in extreme precipitation over Europe are assessed and compared. This provides insight into the importance of RCM formulation in representing changes in climate extremes at high spatial resolution. The models concerned are two recent Hadley Centre RCMs, HadRM2 and HadRM3, and are applied at a horizontal resolution of approximately 5 km over Europe, nested within the Hadley Centre coupled Atmosphere Ocean General Circulation Model (AOGCM), HadCM2. The simulation periods are thirty years with fixed concentrations of greenhouse gases representing the climate of and twenty years representing transient climate change for 281. The use of common boundary conditions to drive the two RCMs allows us to determine whether their different formulations significantly alter the downscaled projections. The RCM simulations of precipitation extremes are compared with observations from a dense rain-gauge network over Great Britain, aggregated to the grid used by the RCMs. Both RCMs simulate realistically extreme precipitation occurring over timescales of one to thirty days and for return periods of two to twenty years. In particular, relative errors in the magnitude of extreme precipitation are generally no larger than those in the mean. The two regional models show different patterns of errors for daily precipitation extremes, with the main difference in the western and upland areas of Great Britain where they are underestimated in HadRM2 and overestimated in HadRM3. Change in extremes over all land areas in the domain show increases in intensity everywhere (except for the Iberian peninsula and Mediterranean coast) with most of these significant at the 5% level. Projected increases are greatest for those extremes which are the rarest and shortest duration (i.e. the most intense), both in relative and thus absolute terms. The large-scale patterns of these changes are very similar in the two RCMs implying they are generally robust to the RCM formulation changes. Given the demonstrated quality of the models this enhances our confidence in the projected changes and suggests that they are mainly conditioned by the large-scale response in the driving GCM. Crown Copyright 27. Reproduced with the permission of the Controller of HMSO. Published by John Wiley & Sons, Ltd KEY WORDS Climate change; Extreme events; Precipitation; Regional climate models Received 22 July 26; Revised 2 November 26; Accepted 7 November Introduction In a future warmer climate, it is likely to be variation in extremes that have the most impact on human welfare. Specifically, in the case of precipitation any increase in the frequency or intensity of heavy rainfall will generally imply enhanced flood risk and attendant problems of loss of life and property. Many relevant processes occur at spatial scales smaller than that of typical General Circulation Model (GCM) grid boxes and a reasonable description of, for example, cyclones, fronts, convective events and interaction with topography is required. To take this further and predict extreme precipitation behaviour is even more complex as this requires the numerical models to give a reliable description of very particular events, requiring fine-scale representation both * Correspondence to: E. Buonomo, Met Office Hadley Centre, Fitzroy Road, Exeter, EX1 3PB, UK. e.buonomo@metoffice.gov.uk temporally and spatially. To address this, a major scientific thrust at the Hadley Centre has been the development of a nested regional climate modelling system whereby for any given region of the world, output from a GCM is used as boundary conditions to drive an RCM (Jones et al., 24). The latter model has high resolution grid boxes, typically of the order of 25 5 km, enabling it to improve the description of these important processes. The importance of the increased horizontal resolution has been demonstrated by comparing the precipitation distributions simulated by GCMs and RCMs (Jones et al., 1997; Durman et al., 21). They show that, on a daily timescale, the RCMs produce better estimates of intense precipitation and can lead to very different conclusions about its response to climate change. Furthermore, RCMs can reproduce many features of precipitation distributions over regions with complex orography such as the Alps (Frei et al., 23, 26). Crown Copyright 27. Reproduced with the permission of the Controller of HMSO.

2 66 E. BUONOMO ET AL. The regional climate modelling system, with nesting for the European area, has been used in three numerical experiments to provide detailed guidance on possible future changes in surface climate, (Jones et al., 1997; Murphy, 2; Hulme et al., 22). In the second of these the Hadley Centre s second generation RCM, HadRM2 (Murphy, 2), was used to provide a detailed projection of climate change for Europe driven by boundary conditions from HadCM2. Climate change experiments using HadCM2 were released to United Kingdom impacts researchers by the United Kingdom Climate Impacts Programme (UKCIP) in 1998 (the UKCIP98 scenarios, Hulme and Jenkins, 1998). The HadRM2 experiment (Murphy, 2) was then used to provide a high resolution climate change scenario consistent with the medium high emissions scenario of UKCIP98. These were subsequently released to impacts researchers via the Climate Impacts LINK (LINK) and analysed in some detail over Scotland (Hulme et al., 21) and other areas of the United Kingdom, both for present and future climate (Jones and Reid, 21). Soon after the release of these data, severe flooding occurred in many regions of the United Kingdom during the autumn of 2 (DEFRA, 21; Marsh, 21). This was as a consequence of repeated large rainfall amounts between mid-october 2 and mid-november 2, and not as a result of one major precipitation event (DEFRA, 21; Marsh, 21). Possible links of these events with climate change were investigated in a report published by the UK Department of Environment, Food and Rural Affairs (DEFRA, 21). The main rainfall event was estimated to have a return period larger than 2 years for many areas in the United Kingdom. One question posed but unanswered in the report was whether these major events can be attributed to climate change, as opposed to natural variability. Results from the HadRM2 climate change experiment were used to investigate the issue. They showed that in the projected future climate significant reductions in the return period for such events were seen. However, in the absence of sufficient records to establish clear trends both in the river flows extreme series and the observed rainfall annual maxima it was only possible to say that the observed events were consistent with a warming climate. A detailed study was then undertaken of the HadRM2 simulations for some of the affected areas (Huntingford et al., 23). A statistical analysis (utilising Generalised Extreme Value functions, or GEVs) was performed to characterise probabilities of extreme rainfall events occurring over a set of duration periods. These included the 3-day timescale over which the rainfall totals were particularly large during autumn 2 (DEFRA 21; Marsh 21). The study focused on three RCM grid boxes which included the catchments near to cities that were particularly affected by the flooding. Dense raingauge data were aggregated over the RCM grid boxes and then used to validate the model. There was found to be good agreement between the GEV extreme indices estimated from the model and observed data for the contemporary climate. A projection of the climate of under the old IPCC IS92a emission scenario (Legget et al., 1992) found large reductions in the return periods of extreme precipitation (significant at the 5% level) over all durations for the three grid-boxes analysed. There have been two recent and important advances that allow an analysis of changes in extreme precipitation, extending the work above. First, a more comprehensive validation of the models is possible as high spatial resolution daily rainfall data have now been collated for all of Great Britain and then aggregated to provide daily rainfall series for each RCM grid box. Second, the Hadley Centre developed a new suite of models to provide high resolution climate change information. Based on the coupled model HadCM3 (Gordon et al., 2), this was used to provide the most recent set of climate change scenarios for the United Kingdom (UKCIP2, Hulme et al., 22). The regional model involved in this study, HadRM3, was developed from an improved version of the atmospheric component of HadCM3 (see model description for details). This model has been used to provide another high resolution version of the original HadCM2 scenario over Europe, i.e. repeating the original HadRM2 experiment but with the new RCM. This new experiment will be used in this study to isolate and investigate the influence of changes in RCM formulation on the simulation of changes in extreme precipitation. Results similar to those by Huntingford et al. (23) were obtained in other studies from Hadley Centre models for the United Kingdom (Jones and Reid, 21 from HadCM2/HadRM2; Fowler et al., 25; Ekström et al., 24 from HadCM3/HadRM3). Increases in extreme precipitation have also been found from other RCMs (Räisänen and Joelsson, 21; Christensen et al., 21, 23 and 24; Sánchez et al., 24; Semmler and Jacob, 24), over the European continent. An intercomparison of six RCMs driven by the same HadAM3H GCM simulations has been carried out recently for Europe (Frei et al., 26). The result shows a widespread increase of extreme precipitation for winter, robust with respect to the RCM formulation; in summer, instead, there is a reduction in the Southern part of Europe which is common to all models but the subcontinental signals are dependent on the choice of the RCM. The aim of this paper is to use the advances mentioned above to further enhance our confidence in the projection of extreme precipitation. Detailed validation data for the whole of the United Kingdom allow a more comprehensive assessment of the quality of the RCM simulations of the current climate. Comparison between the two RCM projections (i.e. HadRM2 and HadRM3 both driven by HadCM2) allows us to assess their robustness with respect to RCM formulation and we extend this assessment to the whole of the European domain covered by the RCMs. Then combining this information allows us to assess whether it provides for more confident

3 CHANGES IN EXTREME PRECIPITATION OVER EUROPE 67 statements about future precipitation extremes. The next section describes the observed data, the models and the experiments performed. This is followed by their analysis with the final two sections providing a summary of the results and relevant conclusions. 2. Description of the models, observations and analysis methods 2.1. Summary description of the global and regional climate models The AOGCM providing the boundary conditions for the two RCM experiments analysed in this paper is HadCM2 (Johns et al., 1997). It has a horizontal resolution of (corresponding to a grid scale of 3 km at mid latitudes) for both ocean and atmosphere, 19 hybrid vertical levels in the atmosphere and 2 terrain-following vertical levels for the ocean. Standard parametrisation schemes are included, the details of which have been described by Johns et al. (1997); this paper also describes the ocean atmosphere coupling procedure which includes flux adjustment. The RCM HadRM2 (Murphy, 2) is a limited area model built from the atmospheric part of HadCM2. It has a horizontal resolution of in a coordinate system where the poles are rotated so the area modelled lies across the equator in the rotated system thus giving a quasi-uniform grid box length of about 5 km. No dependence on the increased resolution in the parametrisation schemes has been included in the regional model, while diffusion has been modified to take account of the higher resolution and the filtering normally required for stability at high latitudes removed due to the almost uniform resolution of the domain. The RCM is driven by 6-hourly values of winds, temperature and humidity imposed at the boundaries of the domain and monthly values of sea surface temperature and sea ice variables. Both sets of variables can be provided by global model or derived from observations (see e.g. Noguer et al., 1998). A detailed description of the nesting technique can be found in Jones et al. (1995). The regional climate model HadRM3 is based on the atmospheric component, HadAM3 (Pope et al., 2), of the global coupled model HadCM3 (Gordon et al., 2). HadCM3 has been changed compared to HadCM2 in many respects, including the physics parametrisation schemes for both ocean and atmosphere. It also has increased horizontal resolution for the ocean ( ), while the atmospheric horizontal resolution and the vertical levels for both atmosphere and the ocean are the same. The improved description of atmosphere and ocean lead to the removal of the flux adjustments applied at the air sea interface. In order to overcome some deficiencies in the atmospheric general circulation simulated by HadCM3, a higher resolution version of its atmospheric component, i.e. HadAM3, with improved physics parametrisatrion was developed to drive RCMs. These changes with respect to HadAM3 were the introduction of a parametrisation for the critical relative humidity (Rh crit ) for cloud formation (Cusack et al., 1999), an improved scheme to calculate the radiative properties of anvils (Gregory et al., 1999), a modified parametrisation of the cloud fraction as a function of the specific humidity, the inclusion of a radiative coupling between the vegetation and the land surface and a more frequent update of the long wave radiative cooling of the surface. In addition, changes were made to various parameters and thresholds relating to the formation and precipitation of cloud water and ice. The RCM, HadRM3, was then configured from this AGCM (denoted HadAM3H) with an identical formulation except for an implicit dependence on the horizontal resolution within the Rh crit scheme and explicit dependencies introduced into the assumed fractional grid box coverage of precipitation (see Annex 1 of Jones et al., 24 for details). All the modifications just described, together with the change in atmospheric physics formulation introduced in HadCM3 (the parametrisation schemes involved are listed in Gordon et al., 2) with respect to HadCM2, constitute the difference between HadRM3 and HadRM2. However, for convenience we list in brief those directly related to precipitation. First, an important change has been made in the diffusion of moisture whereby in HadRM3 this is switched off over steep orography in order to avoid the excessive diffusion of moisture in these regions. This has the effect of removing a spurious moisture source which lead to excessive precipitation over high orography (rather than on its windward upslopes). Second, several changes have been made in the large-scale precipitation scheme. The condensation of water vapour in HadRM3 depends on a diagnosed Rh crit rather than model vertical level dependent but horizontally uniform values. Conversion of cloud water and ice to precipitation occurs at lower thresholds in HadRM3. Finally, the rate of conversion of cloud water to precipitation has been increased as has the fall speed of frozen precipitation. The first of these changes is a significant improvement in realism with the others acting to locally decrease cloud lifetimes/increase precipitation formation rates. Third, in the case of the convection scheme, though significant changes have been made these mainly involve initiation of convection and radiative effects of convective cloud. No changes to the formation of convective precipitation have been introduced Details of the climate simulations In order to assess the uncertainty in changes in precipitation due to the regional climate model formulation, the two RCMs, HadRM2 and HadRM3, have been driven by the same lateral and sea-surface boundary conditions produced by two HadCM2 simulations. These were: i) a 3 year control simulation with fixed CO 2 equal to the observed value in 1975, i.e. the midpoint of the recent past climatological period of (more details in Huntingford et al., 23); ii) a transient climate simulation with observed increases in CO 2 from

4 68 E. BUONOMO ET AL. and then a compound 1% increase of CO 2 concentration from 199. From the latter, boundary conditions were saved for the period 281 (Murphy, 2). To further ensure compatibility between the two RCM experiments the region used by both was identical, covering an area that includes Europe, part of the eastern Atlantic and western Asia, the Mediterranean and north Africa (see Figure 1) Observations For the purposes of validation of the RCM data, a daily time-series of areal rainfall data was derived for Great Britain as represented by the regional models grid. The raw daily rainfall data used in the study was taken from the United Kingdom rain-gauge network from the UK Met Office daily rainfall archive, as held on the National Water Archive held at CEH Wallingford. The methodology adopted for up-scaling rainfall measurements to the grid box scale closely follows that used in a previous RCM study (Huntingford et al., 23). Areal averages were calculated from the point rain-gauge measurements following the triangle method (Jones, 1983). Firstly, a mesh was constructed to overlay the cell, the resolution of the mesh being determined by the number of rain-gauges in the cell using an empirical relationship derived by Jones (1983). For each mesh point, a search algorithm was used to find three rain-gauges which formed the vertices of a triangle enclosing the mesh point. An estimate of rainfall depth at the mesh point could then be established using weights which were assigned the gauges using an inverse-square distance method. The average rainfall for each RCM cell was then calculated as the average of the mesh point estimates, CRU (mm/day) Diff HanRM3 HadRM2 (%) Bias HadRM2 (%) Bias HadRM3 (%) Figure 1. Average annual precipitation for the present climate ( ) obtained from the CRU observations (top left panel), differences between the two models (percentage compared to HadRM2, top right panel) and model biases with respect to CRU observations (HadRM2, bottom left; HadRM3 bottom right panel) of the average precipitation for the same period. The white contour line in the difference and biases plots is for the zero of the scale.

5 CHANGES IN EXTREME PRECIPITATION OVER EUROPE 69 normalised by the Standard Average Annual Rainfall (SAAR) value for the grid box for the period. Figure 2 illustrates the relative density of rain-gauges used to create the areal measurements for each grid box. Whereas the number of gauges operating on any one day will vary, for practical reasons this plot uses the total number of gauges available in each cell to give an indication of the spatial variability in gauge density. The varying density of the gauging network between cells partly reflects the limited land coverage in some grid boxes but also shows the typical pattern of a relatively low number of gauges in some upland areas, particularly in the north of Scotland, compared to the more densely populated lowland areas, especially south-east England. The variability in gauge density must be considered in appraising the extent to which modelled rainfall amounts fit the observed areal rainfall data. Whilst the triangle method is designed to be applicable in areas with relatively low gauge density (Jones, 1983), areal estimates will as with any estimation method be more subject to error where rain-gauges are particularly sparse. In addition, uncertainty will also be greater in mountainous areas where the majority of the network tends to be concentrated in lower-lying areas and so higher elevations are under-represented. Finally, the inherent uncertainties in precipitation measurement, such as systematic rain-gauge under-catchment (Rodda, 1967; Frei et al., 23), must also be considered Characterisation of Extremes: Theory Time series are generated of annual maximum values (for given duration periods and each grid box) derived from both the two RCMs and measurements. Generalised Extreme-Value (GEV) functions are fitted to derive functional forms relating annual maxima to return period. These functions contain three parameters (a location parameter, a scale parameter and a shape parameter) which are estimated from the available data. The functions are then used to estimate the return levels, i.e. the extreme precipitation amounts corresponding to the given return periods. It is important to quantify uncertainty in these parameters, both to characterise confidence in projections based on only limited datasets and for intercomparison studies between models or between models and data. In general terms, for a fixed return period, if a return level estimated from a model is outside the (given) confidence interval estimated from observed data, then the model is considered not to be representative (at the given statistical level). This procedure is repeated for all the land grid boxes in the RCM domain (see Figure 1) for the model simulations and all the available Great Britain grid boxes (see Figure 2) for the observed data. The confidence level has been fixed at 95%. In the analysis in the following sections, the three GEV parameters have been obtained by utilising the Maximum Likelihood Estimation (MLE) (Coles, 21) for both Number of gauges Figure 2. Population of rain-gauges for the grid boxes of Great Britain as described in the land-mask of the RCMs. simulated and observed data. The applicability of the MLE requires the validity of the asymptotic properties (for large sample size) of the estimator (Coles, 21): this condition limits the values of the acceptable shape parameters to a particular range of values. The validity of such conditions can be used as a diagnostic tool to investigate the applicability of the MLE to the problem. A more basic requirement for the use of the extreme value theory is that the annual maxima are indeed distributed according to the GEV distribution. Therefore, an assessment of the quality of the fit to a GEV curve is required to validate this approach. Goodnessof-fit tests provide a measure of the quality of the fit, by assessing whether the given sample is compatible with the estimated distribution. In the present study, the Kolmogorov Smirnov goodness-of-fit test has been applied to all the precipitation time-series under investigation. The test has been carried out as in Kharin and Zwiers (2), where 1 samples of size 3 (2 for greenhouse-gas forced runs) has been generated from each of the fitted GEV, in order to determine the critical value for the rejection of the null hypothesis that the initial sample is drawn from the estimated GEV distribution. Once the applicability of the extreme value theory has been assessed then it is possible to infer that the original sample is composed of a sufficiently large number of independent data to include the tail of the original sample distribution. This aspect is particularly

6 7 E. BUONOMO ET AL. 6N 2 HadCM2, DJF 6N HadCM2, JJA 45N 2 45N 3N 3N 3E HadRM2, DJF 3E HadRM2, JJA 6N 2 6N 45N 3N 2 45N 3N 3E 3E HadRM3, DJF HadRM3, JJA 6N 2 6N 45N 3N 2 45N 3N 3E 3E Figure 3. Model biases of mean-sea-level pressure with respect to ERA-4, for the driving GCM (HadCM2, top panels) and the two RCMs (HadRM2, middle panels; HadRM3 bottom panels), for winter and summer. Contour lines spacing is 1 hpa, dotted areas indicate negative differences. important when studying annual maxima for large aggregation periods, since they are usually evaluated from the mean value for an n-day sequence, for all days of the year. The sample data, therefore, comprise values with non-negligible correlation. Thus, the effective size of the sample is strongly reduced, raising the issue of the applicability of the extreme value theory, which is valid for block maxima taken for a large sample of independent data. As mentioned above, the return levels for a range of return periods are calculated from the GEV curves: for each return level value, the 95% confidence interval has been estimate by using the profile likelihood method (Coles, 21). From this choice of confidence interval, it is expected that the 5% of pairs of estimated return levels will be found statistically different purely by chance, even if they come from the same distribution. This is a zeroth-order estimate of the significant differences which will be found between the datasets which will be analysed. Since the aim of this analysis is to get a simple estimate of the uncertainty due to the sample sizes, additional factors will not be taken into account. 3. Validation of the models and extreme precipitation change over Great Britain 3.1. Summary of model performance As a background for the rest of the paper we compare the main features of the present climate simulated by HadCM2 and the two RCMs with observations for the period Table I reports bias and spatial

7 CHANGES IN EXTREME PRECIPITATION OVER EUROPE 71 Table I. Domain-averaged model biases and correlations (land points only). BIAS CORR HadCM2 HadRM2 HadRM3 HadCM2 HadRM2 HadRM3 mslp (DJF) mslp (JJA) temp (DJF) temp (JJA) precip (DJF) precip (JJA) The results in the table are for the global model (HadCM2) and the two regional models considered in this study (HadRM2 and HadRM3), for the European domain shown in Figure 1. Biases are in hpa for mslp, K for temperature and mm/day for precipitation. See text for the observational datasets. correlations for mean-sea-level pressure (mslp), temperature and precipitation (land grid boxes only) for winter and summer. The simulations have been compared with a 3-year average of mslp taken from ECMWF reanalysis project, ERA-4 (Simmons and Gibson, 2), while observed temperature and precipitation are from the 1 latitude/longitude dataset constructed at the Climate Research Unit (CRU), University of East Anglia by New et al. (22) (labelled as the CRU dataset in the rest of the paper). In general terms, the regional models produce larger negative biases for mslp (though only slightly for HadRM3, Figure 3) and the temperatures are lower by.5 1 K with respect to the global model. This corrects the overall GCM warm bias in winter while exacerbating the cold bias in summer. The RCMs precipitation is also overestimated with respect to the CRU observations; the following section will discuss the precipitation result more in detail. In terms of the patterns of differences in mslp, in winter the global model has a southward shift of the storm track which also extends to north-east continental Europe. The winter low pressure system in the Central Mediterranean is not reproduced by HadCM2. The RCMs generally follow the GCM closely, HadRM3 more so. The only differences between the two models are due to the high elevation areas where HadRM3 gives a stronger response to the flow and in continental Russia where HadRM2 further reduces the extension of the Siberian anticyclone. The spatial patterns of temperature biases (figure not shown) are consistent with these differences in pressure. In summer the models simulate a contraction of the observed Azores high which does not extend to the United Kingdom and central Europe. HadCM2 has a general negative pressure bias which is larger in northern Europe (Figure 3). In this season, the RCMs add mesoscale patterns to the GCM circulation, the most notable of which is a low pressure anomaly centred on Scandinavia. The RCMs also produce a series of low pressure systems over the Mediterranean region. This improves the circulation pattern with respect to observations (an example of RCMs ability to generate realistic detail absent in GCMs) although it adds a small negative pressure bias with respect to the GCM. The RCMs eliminate the GCM warm bias in the eastern side of the domain whilst the cold bias over Scandinavia is kept together with some other colder areas in the western side of the domain (not shown). For both seasons, HadRM3 follows more closely the circulation patterns of the driving global model with respect to HadRM2; the same is not seen for temperatures where the two RCMs results are more similar Mean precipitation: background Precipitation is one of the most sensitive quantities to the different parametrisation schemes of the climate models and to their interplay with the dynamics of the atmosphere represented in the models. For this variable it has been shown that the RCMs are able to contribute significant additional information to the driving global simulations, both in space and time (e.g. Jones et al., 24; Jones et al., 1995; Durman et al., 21). In general terms, the RCMs produce an intensification of precipitation (Jones et al., 1995; Durman et al., 21) with respect to the driving GCM, related to the intensification of the hydrological cycle (Jones et al., 1995). The increase in average precipitation for the domain under investigation is around.5 mm/day (Table I). The correlation results (Table I) indicate that all the models simulate realistic seasonal patterns with correlations worse in winter due to the impact of larger circulation biases. In winter, the GCM pattern mostly relates to the resolution of the orography and coasts, with correct orders of magnitude, together with increased precipitation over areas in north-eastern Europe where the storm track erroneously extends. In summer, there is an excessive reduction over Ukraine and Russia in the GCM where it has also the warmest bias, suggesting a possible impact of soil moisture deficiencies in determining the local climate. In the RCMs, precipitation is significantly influenced in both seasons by the more realistic orography with enhancements upstream of and over high elevation grid boxes and rain-shadow effects downstream giving realistic detail not resolved in the GCM. In winter the RCM simulations have similar patterns whereas in summer there

8 72 E. BUONOMO ET AL. are more marked differences. HadRM3 simulates more precipitation over western and central Europe, respectively reversing and partially correcting dry biases seen in HadRM2. Given the aim of the present study, it is interesting to discuss how the RCMs simulate the precipitation on an annual timescale. HadRM2 overestimates the precipitation almost everywhere, except for the Mediterranean coast for which there is difference smaller than 1% (Figure 1). The sign of the difference is reversed over central and western Europe, with the bias increasing eastward to 5% reaching values larger than 1% around the Caspian Sea (though these correspond to absolute differences smaller than 1 mm/day). The largest differences are found for the high elevation areas. These areas are also those for which the uncertainties of the observations are also the highest, both due to the measurement of precipitation (rain-gauge under-catchment) and to the distribution of the stations from which the results are aggregated (Frei et al., 23), in which high-elevation stations are usually underrepresented. For the measurements only, estimates of the bias can be as large as 4% at height greater than 15 m (Frei et al., 23). However, even though part of the discrepancy can be attributed to the under-catchment problem, there is still a rather large fraction of the bias that can be attributed to model errors. An assessment of possible CRU biases, over Great Britain, can be obtained by comparing the mean with the same quantity estimated from the precipitation dataset presented in this work: the results show substantial agreement (within 1%) over this domain, once the difference in horizontal resolution between the two datasets has been taken into account. Figure 2 also reports the comparison of the mean precipitation from HadRM3. The wet bias over the eastern parts of the domain is reduced with respect to HadRM2 while the negative difference on the Mediterranean coast is similar between the two RCMs. The enhanced wet bias in HadRM2 is due to its use of fixed thresholds of Rh for condensation and these being the values used in the driving GCM (Jones et al., 1995). Also, the use of the Rh crit parameterisation in HadRM3 which simulates condensation more realistically will contribute to these differences. At high elevation grid boxes there are large differences with respect to the observations though they are not as large as in HadRM2. The regions where the RCMs differ most are the western coasts, for which HadRM3 gives higher precipitation than HadRM2, and the high elevation areas, where HadRM2 precipitation is greater (Figure 1). These are consistent with the change in formulation of moisture diffusion over significant orography giving enhanced precipitation on up-slopes in HadRM3 rather than over orography in HadRM2. The first effect dominates over the second: precipitation for the Norwegian and Scottish mountains is larger in HadRM3. These changes in formulation in HadRM3 all contribute to the improved seasonal correlations (Table I) Validation of precipitation extremes The precipitation extremes have been studied by estimating the return levels for 2, 5 and 2 years return periods, from annual maxima obtained from two different accumulation periods, 1 and 3 days. The estimated GEV curves from the observational dataset, composed of a 3- year daily precipitation time series for each of the 94 grid boxes of the RCM grid describing Great Britain, have been used to evaluate the return levels. The comparison with the RCM simulated data representing the present climate has been done by estimating the return levels for the modelled data. The statistical significance of the difference between models and observation has been assessed by obtaining a confidence interval for the GEV fit of the observed data: modelled return levels not included in this interval are considered statistically different from the same quantity estimated from observations (see section 2.4). All the GEV curves obtained have shape parameters within the range for which the MLE asymptotic properties are valid. The quality of fits for the Great Britain grid boxes were established by the Kolmogorov Smirnov goodness-of-fit test (Kharin and Zwiers, 2). The null hypothesis that the annual maxima are drawn from the fitted GEV distribution is rejected, at the 1% significance level, for 8 points of the observational estimates, 1 for HadRM2 and 9 for HadRM3 for the 1-day accumulation period out of a total of 94 grid boxes describing Great Britain. For the 3-day accumulation periods, the null hypothesis is rejected for 11 grid boxes for the observations and HadRM2 and 9 for HadRM3. In all these cases, the number of rejections is comparable with the expected 1% of the total number of grid boxes for Great Britain. Therefore, the estimated GEV curves should give a good description of the extreme distributions considered in this study. We first compare the analyses of the observation and the two regional models control simulations for 1-day accumulation period with results for the 5-years return level (Figure 4). Even for a quite small domain such as Great Britain, the range of return levels is large, with a difference larger than 5 mm/day between the minimum and the maximum value. The largest values are found on the western coast of the domain, which is under the direct influence of the mean westerly airflow, and on high elevation grid boxes. In the eastern part of the country there is a marked reduction of the return levels for this return period, essentially a rain-shadow effect. For the 5-years return levels estimated from the observations there is a difference between the west and the east coasts of 4 mm/day which is almost constant for the range of return periods studied. The results from the HadRM2 simulation give a quite accurate picture of the distribution of the 5-years return levels. However, the model tends to underestimate extreme precipitation mainly in Wales and northern Scotland where the largest errors are found. The map of differences shows large areas with significant negative biases mainly on the western coast, suggesting that

9 CHANGES IN EXTREME PRECIPITATION OVER EUROPE 73 Obs Diff HadRM2 Obs HadRM Climate Change HadRM2 Diff HadRM3 Obs Climate Change HadRM Figure 4. Return levels for the 5-years return period, for the 1-day accumulation period, estimated from the observations (top left panel) and from HadRM2 (top right panel), in mm/day. The HadRM2 and HadRM3 biases (%) with respect to the observations are reported in the middle left and right panels, respectively, with the non-significant statistical differences plotted in gray (dark gray, positive bias; light gray, negative bias). The climate responses evaluated from HadRM2 and HadRM3 are shown in the bottom left and right panels (gray shading also indicates non-statistical significant changes) as the difference between the (218) and present climate simulations. this bias seen in the mean also extends to the tail of the distribution for these areas. Over the rest of the domain there appears to be a tendency to underestimate return levels but the differences are not large enough to be statistically significant at the 5% level. The results for other return periods show a trend toward negative bias with increasing return periods: for the 2-years return levels, all the significant differences are negative. However, the largest relative error is quite constant from the 2-years to the 2-years return levels, with biases less than 3% almost everywhere. Validation over the United Kingdom of the extreme precipitation simulated by HadRM2 has been done previously at CRU (Jones and Reid, 21), using the Gumbel distribution to fit the time-series of annual maxima for the 3-years control simulation and a dataset of 11 rain-gauges, interpolated over the RCM grid. These results, from both the observations and the HadRM2 simulation, are in good agreement with the results presented in this study. In the case of HadRM3, the spatial pattern of estimated 5-years return levels for the 1-day accumulation period is again quite well reproduced. The model overestimates the precipitation on the western side of the domain and on high elevation grid boxes with a bias with respect to the observations of larger than 1%. However, as we have discussed above, the observational data are likely to be negatively biased (with the problem probably worse for extreme precipitation) which strongly suggests that in these areas HadRM3 represents an improvement over HadRM2. In contrast, the general tendency to the south and east of negative bias remains in HadRM3 despite the improvements in simulated average precipitation. In common with the HadRM2 return levels, the WE coastal difference does not change too much when the return period is varied (from 2 to 2 years) though at around 5 mm/day it is larger (by about 25%) due to the different sign of the bias in the east and the west. For the 3-days accumulations at the 5-years return level the distribution estimated from the observed data also shows a trend toward a reduction of extreme precipitation eastward, with a difference of about 8 mm/day between the two coasts (Figure 5). Results from the other analysed return periods, 2 and 2-years, show similar trends but with the westward coastal difference increasing with return period (from 6 mm/day for 2-years return levels to 9 mm/day for 2 years return levels). The estimates from the observations are smoother than the HadRM2 results; however, the distribution and the range of return levels are reasonably well reproduced. The pattern of the difference gives some hints about the different aspects that contributed to precipitation extremes for long accumulation periods, where periods of high (but not extreme) precipitation are combined with events from the main part of the precipitation distribution (which can be described by its average). The difference pattern shows two regions of significant biases, one being the west where they are generally negative and the other being the south-east where they are positive. The main contribution to the bias in the west comes from the tail of the distribution, while the lighter precipitation events that strongly influence the average precipitation are less important. The

10 74 E. BUONOMO ET AL. obs HadRM Diff HadRM2-Obs Climate change HadRM Diff HadRM2-Obs Climate change HadRM Figure 5. Same as Figure 4 but for 3-days accumulation period. strong positive bias to the east is mainly due to overestimation of the average precipitation (see Figure 1), here predominating over the negative bias shown by the tail of the 1-day precipitation distribution. Similar results are also obtained for the 2-years and 2-years return levels; the variation of the westward coastal difference of the return levels with the return period is reproduced correctly by the model. For the 5-year return levels estimated from the 3-day accumulations, HadRM3 reproduces the observed pattern with good skill. As with the 1-day events, the model appears to have a (strong) positive bias in western upland areas where HadRM2 has generally negative (or small positive) biases (Figure 5). This is due to the increase in precipitation both in the tail of the distribution and for the less intense events with respect to HadRM2. The latter events are still underestimated with respect to the observation but to a much reduced extent when compared to HadRM2. Therefore, the bias in the west has a similar order of magnitude for the 3-days accumulation period as for the 1-day accumulation period. In the lee of these areas in Scotland there is a clear negative bias (also apparent in HadRM2) indicating an excessive rain-shadow effect. In other eastern areas there is a similar but reducedtendency towards positive biases as in HadRM2. Again this (reduced) bias can be attributed to (a reduced) overestimation of lighter precipitation events (see Figure 1). Since the positive bias is much stronger in the west, the resulting coastal difference is increased with respect to the observed values Analysis of return periods and changes over Great Britain The daily precipitation from the HadRM2 simulations were used by Huntingford et al., (23) to investigate the flood events during the year 2 in the United Kingdom. They analysed extreme precipitation from HadRM2 since the spatial resolution of the regional climate model (5 km 5 km) is consistent with medium-size river catchment areas. Three grid boxes, corresponding to three areas where the flood events occurred, were investigated to understand the possible role of human-induced greenhouse-gas emissions in increasing the flood probabilities for the present and the future climate with respect to that of the pre-industrial period (their location is show in figure 1 in Huntingford et al., 21, The present day results were validated using the same approach as in this paper, i.e. by comparing rain-gauge data aggregated over a spatial scale corresponding to the RCM grid. In this study, we extend the validation and the assessment of the impact of climate change on precipitation extremes to the whole of Great Britain. Also, in Huntingford et al. (23) the analysis was done by comparing return periods (i.e. the expected frequency of exceedance of a given precipitation amount) rather than return levels (i.e. the amount of precipitation that will be exceeded with a given expected frequency) which we consider in all other sections of this paper. In order to estimate the changes in return period, we have applied a method similar to the one described in their section 6.3: for a given return period, the return level from the present climate simulation is estimated from the GEV curve. The fitted GEV curve for the observational dataset and the future climate simulation were then used to estimate the return period corresponding to a precipitation amount with the estimated return period from the present climate simulation. The significance of change has been assessed with the same method described (again in their section 6.3), by evaluating the 95% confidence interval for the present climate return period (though, in Huntingford et al., 21, this was determined using a bootstrapping approach). In order to compare with this previous study, Figure 6 reports the ratio of return periods between the present

11 CHANGES IN EXTREME PRECIPITATION OVER EUROPE 75 Bias ctrl/obs 5 years Response chg/ctrl 5 years Bias ctrl/obs 1 years Response chg/ctrl 1 years Bias ctrl/obs 2 years Response chg/ctrl 2 years Figure 6. Biases (left column) and climate-change responses (right column) from HadRM2 for the 5-years, 1-years and 2-years return periods, expressed as the ratio between the estimated period for the present climate (left column) and future climate (right column), corresponding to the return level for the given return period of the reference data (observation, left column, present climate, right column) and the given return period. The results have been obtained from the 3-days accumulation. Gray grid boxes indicate non-significant statistical differences (dark, gray, ratio smaller than one; light gray, ratio larger than one). climate simulations and the observations (left column) and the future climate precipitation (right column). It considers the 3-day precipitation accumulations and three return periods, 5, 1 and 2 years. These show that the present climate simulated by HadRM2 is significantly different (at the 5% level) from the observations for large areas such as Scotland, the western coasts of Great Britain in general (longer return periods than observed) and some of eastern and central England (which simulate shorter return periods). For the western coast, the model underestimation is significant for all the return periods included in this study (generally consistent with the picture from the return level analysis in Figure 5). In the case of the significantly shorter return periods simulated in some of the other areas (again generally consistent with the return level analysis), this tendency reduces at the higher return periods. The future climate gives a reduction of return periods in all cases and over the whole domain, with a stronger reduction on the western coasts. A similar picture (not shown) is obtained from the HadRM3 results driven by the same GCM (HadCM2). The significant reduction in return periods for the three grid boxes investigated by Huntingford et al. (23) have been confirmed for both models by the present analysis. Also confirmed are the smaller, non-significant differences, in comparison with the climate change results, between the HadRM2 present climate and the observational dataset. However, HadRM3 simulates return periods much smaller than those estimated from the observations, with differences which are statistically significant in two of the three grid boxes under investigation. This finding, together with the widespread differences (at the grid box scale) found from the validation for both models, emphasises the need to estimate all sources of uncertainties, including those coming from regional climate model formulations, when local data (i.e. at grid box level) are needed. However, this study also shows that the quality of the control simulation does not have a major impact on their responses, at least in this case of two models that have relatively small biases (with respect to the climate change signal) in their simulations of extreme precipitation. The climate change results from the return level analysis (Figure 5) are, as expected, also consistent with the return period analysis. The broad patterns are very similar though again there are local differences in the magnitude and significance of the changes. 4. Response of HadRM2 and HadRM3 precipitation over Europe to increased greenhouse-gas concentrations 4.1. Mean changes Before discussing the results from the analysis of extremes, it is useful to understand the mean changes in the precipitation on an annual timescale for both of the regional models (Figure 7). The response shows a marked reduction of precipitation in southern Europe and Mediterranean Africa and a small increase in Scandinavia and NE Europe. A zonal pattern of small changes (less

12 76 E. BUONOMO ET AL. HadRM2 HadRM Figure 7. Climate change responses ([218] present) for annual average precipitation, in percentage with respect to present climate: left panel, HadRM2 changes; right panel, HadRM3 results. The dotted patterns indicate negative differences, the contour lines show the middle value for a given colour interval. than 15%) separate the two regions. The two models predict a similar pattern of response, despite the fact that HadRM2 simulates more precipitation compared to HadRM3. This difference is slightly greater in the future (8%) than for the present (6%) which causes a shift of the zonal band of small changes towards the north-east from Central Europe. Many of the responses can be explained in terms of changes (not shown) of the wet days fraction (defined with respect to a 1 mm/day threshold) which are highly correlated with changes in the mean (.84 for HadRM2,.79 for HadRM3, land points only). The seasonal changes (not shown) follow the same broad pattern and have a similar structure in both models. The winter season shows a large increase in precipitation over the Atlantic Ocean off Spain and around the Baltic Sea, related to the south-east shift of the storm track and the receding of the Siberian anticyclone in the future climate, with consequent extension of the storm track over Scandinavia. There is a large decrease in the area including the east Mediterranean and the Black Sea, due to the shift of the Mediterranean high pressure system to the east. Precipitation responses in spring show a zonal pattern of changes, with increased precipitation in the north due to the northward shift of the storm track and the addition of a low pressure system in northeast Europe. In summer, changes are still zonal with the areas of reduced precipitation extending to central Europe, due to the general warming, reductions in soil moisture, the extension of the Azores anticyclone and local feedbacks (Rowell and Jones, 26), while there is a slight increase of precipitation in northern Europe. An almost zonal pattern of the precipitation response in autumn is due to the intensification of the storm track for this season to the north, with the extension of the Azores anticyclone to the Mediterranean, and probably the other mechanisms relevant to the summer changes, causing the zonal decrease of precipitation to the south Changes in extremes Changes in precipitation extremes have been analysed for the land points of the whole RCM domain, starting from the annual maxima of the 1-day and 3-day precipitation series. Return levels corresponding to three return periods (2, 5 and 2 years) have been estimated by the procedure described in section 2.4. For the 3-year control experiments, the number of grid boxes for which the asymptotic properties of the MLE are not valid is less than 2%, while for the 2-years greenhouse gas (ghg) forced experiment the largest fraction amounts to 4.5% of the domain. Since these grid boxes are scattered over the whole domain (i.e. there are no areas of clustering of grid boxes with problems in the GEV parameter estimates), these grid boxes have been included in the analysed data. As the discussion will be focused on large areas of significant changes, possible problems with these grid boxes can be neglected. The Kolmogorov Smirnov goodness-of-fit test has again been applied to verify the accuracy of the GEV fits. At the 1% significance level, the rejection occurs at between 8.8% and 9.8% of points for the 1-day accumulation period and 8.4% and 1.1% for the 3- day accumulation periods for the two sets of RCM simulations. The total number of grid boxes, which includes all the land-points in the domain in Figure 1 is There are no regions where the rejections are located preferentially thus the results are valid for all the different climatic regions included in the domain. The results of the test justify the use of the GEV distribution as a model for the precipitation extremes for both accumulation periods included in this study. As discussed in section 2.4, these findings also indirectly confirm the applicability of the Extreme Value Theory to the 3- days accumulation period; in this case, annual maxima are taken from a daily time series of 3-days averaged

13 CHANGES IN EXTREME PRECIPITATION OVER EUROPE 77 precipitation, for which data are strongly correlated. However, since the resulting annual maxima samples are distributed as GEV curves, we can infer that the daily series includes a sufficient number of independent points to adequately represent the extremes. The significance of changes of return levels has been estimated by comparing return levels from the ghg-forced simulations with a 95% confidence interval estimated from the present climate simulations. The estimated changes for the 2, 5 and 2 years return levels calculated from the 1-day precipitation accumulations from HadRM2 are shown in Figure 8. This shows increases of precipitation extremes over most of Europe which are significant at the 5% level, with the exception of the Pyrenees and the southern and eastern coasts of the Mediterranean Sea. The changes are larger than 2%: it is worth noting that the predicted increase in extremes is taking place also in regions where there is a marked reduction in the average precipitation response. The pattern of extreme precipitation changes with increasing return periods shows an increase in the percentage change of precipitation, ranging from an average of 13% for 2-years to 18% for the 2-years return period and a marked reduction of the areas with statistically significant responses. The 3-day accumulation period changes for HadRM2 have the same pattern and percentage change with the exception of a large area around the Mediterranean Sea (from southern Italy to Turkey) where a larger area of significant decrease is predicted (Figure 8). However, the pattern of significant responses of both signs are much broader for the 3-day accumulation period with respect to the 1-day events. Even in this case, clearly the reduction of average precipitation is not the major factor in explaining the changes in the extremes. Since the 3-day accumulation period is strongly influenced by statistics of lighter precipitation, these results can be partly explained from the changes in the bulk of the precipitation distribution. As we have already discussed, the decrease in average precipitation can be largely explained by the reduction of the fraction of wet days. For precipitation days with more than 1 mm/day, there is a positive shift of the median and a similar increase of the inter-quartile range (IQR) (not shown). This implies a future climate with a substantial reduction of light events and a change to a distribution with more intense events. The pattern of the median and IQR changes are similar to the responses of the return levels (for the 2- and 5- years return level, the pattern correlation with the median and IQR larger than 7% and 75% respectively). When analysed on a continental scale, HadRM3 produces similar results (Figure 9). This demonstrates the primary role of the change in the boundary forcing 1 day accum., 2 years return level 1 day accum., 5 years return level 1 day accum., 2 years return level day accum., 2 years return level day accum., 5 years return level day accum., 2 years return level Figure 8. Climate change responses for return levels corresponding to 2-years (left), 5-years (middle) and 2-years (right) return periods, estimated from the HadRM2 annual distribution of 1-day (top) and 3-days accumulation (bottom) for the (218) with respect to the present climate ( ). Gray shaded areas represent non-significant changes (dark gray, positive change; light gray, negative change). The results are shown as percentage difference with respect to the present climate estimates.

14 78 E. BUONOMO ET AL. 1 day accum., 2 years return level 1 day accum., 5 years return level 1 day accum., 2 years return level day accum., 2 years return level 3 day accum., 5 years return level 3 day accum., 2 years return level Figure 9. Same results as in Figure 8, estimated from HadRM3 simulations. derived from the global climate model even on the higher moments of the distribution of precipitation at local and regional scales. Differences in the patterns of changes (which can been seen most clearly for the lower return periods due to lack of significance at 2-years) for the 1-day accumulation period are mostly located in central and eastern Europe. The estimated changes in return levels for the 3-day return period have their largest differences in central Europe (from the Balkans to northern Germany). In these regions, HadRM3 predicts a stronger reduction of average precipitation compared to HadRM2 which clearly affect the changes in these longer period events Changes in the seasonality of extremes We now consider possible changes in the seasonality of the extremes. Seasonal distribution of annual maxima for the two regional models show similar features. The changes in the distributions are summarised in Table II where the comparison of the distributions for the present and future climate from the two models is estimated from the seasonal fraction of events and their intensity. There are some differences in the seasonal fraction between the two models, for example the HadRM3 control has more events in winter compared to HadRM2 which are compensated by the fraction in summer while for the 3-day event in the future climate HadRM3 has a larger fraction for spring. However, the spatial patterns of frequency are in good correlation for both present and future climates. It is worth noting that the reported correlations for the seasonal fraction are substantially higher than those between present and future seasonal mean precipitation obtained from the same experiments (which are around 1% less than the values reported in Table I). The intensities have similar patterns for both models for the present and future climate simulations. This high spatial correlation between the two models of the seasonal distribution of extremes again demonstrates the primary role of the boundary forcing in the behaviour of higher moments of the distribution. That these correlations are higher than for mean precipitation also implies the models are more consistently downscaling the GCM for the higher moments than for the mean. Despite the consistent picture of the seasonal distribution of extremes reported in Table II, the responses (not shown) are giving a noisy signal that is difficult to explain in term of simple physical mechanisms. For the 3-day events, the structure of the signal is more spatially coherent; however, only for winter is there a good correlation of the changes in intensities with the seasonal change in precipitation (.77 for HadRM2,.75 for HadRM3). 5. Summary of results and discussion In the previous sections we have analysed the precipitation for the present and a future, greenhouse-gas forced, climate simulated by two regional climate models,