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Interest in the impacts of climate change is ever increasing. This is particularly true of the water sector where understanding potential changes in the occurrence of both floods and droughts is important for strategic planning. Climate variability has been shown to have a significant impact on UK climate and accounting for this in future climate change projections is essential to fully anticipate potential future impacts. In this paper a new resampling methodology is developed which includes the variability of both baseline and future precipitation. The resampling methodology is applied to 13 CMIP3 climate models for the 2080s, resulting in an ensemble of monthly precipitation change factors. The change factors are applied to the Eden catchment in eastern Scotland with analysis undertaken for the sensitivity of future river flows to the changes in precipitation. Climate variability is shown to influence the magnitude and direction of change of both precipitation and in turn river flow, which are not apparent without the use of the resampling methodology. The transformation of precipitation changes to river flow changes display a degree of non-linearity due to the catchment's role in buffering the response. The resampling methodology developed in this paper provides a new technique for creating climate change scenarios which incorporate the important issue of climate variability. [ABSTRACT FROM AUTHOR]

The first part of this paper demonstrated the existence of bias in GCM-derived precipitation series, downscaled using either a statistical technique (here the Statistical Downscaling Model) or dynamical method (here high resolution Regional Climate Model HadRM3) propagating to river flow estimated by a lumped hydrological model. This paper uses the same models and methods for a future time horizon (2080s) and analyses how significant these projected changes are compared to baseline natural variability in four British catchments. The UKCIP02 scenarios, which are widely used in the UK for climate change impact, are also considered. Results show that GCMs are the largest source of uncertainty in future flows. Uncertainties from downscaling techniques and emission scenarios are of similar magnitude, and generally smaller than GCM uncertainty. For catchments where hydrological modelling uncertainty is smaller than GCM variability for baseline flow, this uncertainty can be ignored for future projections, but might be significant otherwise. Predicted changes are not always significant compared to baseline variability, less than 50% of projections suggesting a significant change in monthly flow. Insignificant changes could occur due to climate variability alone and thus cannot be attributed to climate change, but are often ignored in climate change studies and could lead to misleading conclusions. Existing systematic bias in reproducing current climate does impact future projections and must, therefore, be considered when interpreting results. Changes in river flow variability, important for water management planning, can be easily assessed from simple resampling techniques applied to both baseline and future time horizons. Assessing future climate and its potential implication for river flows is a key challenge facing water resource planners. This two-part paper demonstrates that uncertainty due to hydrological and climate modelling must and can be accounted for to provide sound, scientifically-based advice to decision makers. [ABSTRACT FROM AUTHOR]

This paper compares precipitation, maximum and minimum air temperature and solar radiation estimates from the Hadley Centre's HadRM3 regional climate model (RCM), (50x50 km grid cells), with observed data from 15 meteorological station in the UK, for the period 1960-90. The aim was to investigate how well the HadRM3 is able to represent weather characteristics for a historical period (hindcast) for which validation data exist. The rationale was to determine if the HadRM3 data contain systematic errors and to investigate how suitable the data are for use in climate change impact studies at particular locations. Comparing modelled and observed data helps assess and quantify the uncertainty introduced to climate impact studies. The results show that the model performs very well for some locations and weather variable combinations, but poorly for others. Maximum temperature estimations are generally good, but minimum temperature is overestimated and extreme cold events are not represented well. For precipitation, the model produces too many small events leading to a serious under estimation of the number of dry days (zero precipitation), whilst also over- or underestimating the mean annual total. Estimates represent well the temporal distribution of precipitation events. The model systematically over-estimates solar radiation, but does produce good quality estimates at some locations. It is concluded that the HadRM3 data are unsuitable for detailed (i.e. daily time step simulation model based) site-specific impacts studies in their current form. However, the close similarity between modelled and observed data for the historical case raises the potential for using simple adjustment methods and applying these to future projection data. [ABSTRACT FROM AUTHOR]

A recently developed fluvial erosion model for blanket peatlands, PESERA-PEAT, was applied at ten sites across Great Britain to predict the response of blanket peat erosion to environmental change. Climate change to 2099 was derived from seven UKCP09 future projections and the UK Met Office's historical dataset. Land management scenarios were established based on outputs from earlier published investigations. Modelling results suggested that as climate changes, the response of blanket peat erosion will be spatially very variable across Great Britain. Both relative changes and absolute values of sediment yield were predicted to be higher in southern and eastern areas than in western and northern parts of Great Britain, peaking in the North York Moors of eastern England. Areas with high precipitation and low temperature were predicted to have low relative erosion changes and absolute sediment yield. The model suggested that summer desiccation may become more important for blanket peat erosion under future climate change, and that temperature was more dominant than precipitation in controlling long-term blanket peat erosion change. However, in the North York Moors, precipitation appeared to be more dominant in driving long-term erosion change. Land management measures were shown to provide a means to mitigate against the impacts of climate change on blanket peat erosion. [ABSTRACT FROM AUTHOR]

Although the fundamental traffic diagram provides the characteristics of a typical road traffic speed-flow relationship, little consideration has been given to the impact of adverse weather conditions on the relationship and the subsequent impact on local speed-flow. For the first time, this study uses precipitation radar along with a state-of-the art traffic information system to ascertain the relationship between speed-flow and precipitation on a UK transport corridor at the local (junction to junction) scale. It is evident that precipitation causes a significant reduction in speed and maximum flow on many links of the corridor as well as a downward reduction in the overall speed-flow relationship. With increased instances of heavy precipitation predicted in the UK as a result of climate change, these findings highlight the subsequent impact on journey travel times and associated economic costs. [ABSTRACT FROM AUTHOR]

We present an annually resolved reconstruction of spring-summer precipitation variability in East Anglia, UK (52-53°N, 0-2°E) for the period AD 900-2009. A continuous regional network of 723 living (AD 1590-2009) and historical (AD 781-1790) oak ( Quercus sp.) ring-width series has been constructed and shown to display significant sensitivity to precipitation variability during the March-July season. The existence of a coherent common growth signal is demonstrated in oaks growing across East Anglia, containing evidence of near-decadal aperiodic variability in precipitation throughout the last millennium. Positive correlations are established between oak growth and precipitation variability across a large region of northwest Europe, although climate-growth relationships appear time transgressive with correlations significantly weakening during the early twentieth century. Examination of the relationship between oak growth, precipitation, and the North Atlantic Oscillation (NAO), reveals no evidence that the NAO plays any significant role in the control of precipitation or tree growth in this region. Using Regional Curve Standardisation to preserve evidence of low-frequency growth variability in the East Anglian oak chronology, we produce a millennial length reconstruction that is capable of explaining 32-35% of annual-to-decadal regional-scale precipitation variance during 1901-2009. The full length reconstruction indicates statistically significant anomalous dry conditions during AD 900-1100 and circa-1800. An apparent prolonged wetter phase is estimated for the twelfth and thirteen centuries, whilst precipitation fluctuates between wetter and drier phases at near centennial timescales throughout the fourteenth to seventeenth centuries. Above average precipitation reconstructed for the twenty-first century is comparable with that reproduced for the 1600s. The main estimated wet and dry phases reconstructed here appear largely coherent with an independent tree-ring reconstruction for southern-central England. [ABSTRACT FROM AUTHOR]

We study the influence of synoptic scale atmospheric circulation on extreme daily precipitation across the United Kingdom, using observed time series from 689 rain gauges. To this end we employ a statistical model, that uses airflow strength, direction and vorticity as predictors for the generalised extreme value distribution of monthly precipitation maxima. The inferred relationships are connected with the dominant westerly flow, the orography, and the moisture supply from surrounding seas. We aggregated the results for individual rain gauges to regional scales to investigate the temporal variability of extreme precipitation. Airflow explains a significant fraction of the variability on subannual to decadal time scales. A large fraction of the especially heavy winter precipitation during the 1980s and 1990s in north Scotland can be attributed to a prevailing positive phase of the North Atlantic Oscillation. Our statistical model can be used for statistical downscaling and to validate regional climate model output. [ABSTRACT FROM AUTHOR]

We analyze a set of nine regional climate model simulations for the period 1961–2000 performed at 25 and 50 km horizontal grid spacing over a European domain in order to determine the effects of horizontal resolution on the simulation of precipitation. All of the models represent the seasonal mean spatial patterns and amount of precipitation fairly well. Most models exhibit a tendency to over-predict precipitation, resulting in a domain-average total bias for the ensemble mean of about 20% in winter (DJF) and less than 10% in summer (JJA) at both resolutions, although this bias could be artificially enhanced by the lack of a gauge correction in the observations. A majority of the models show increased precipitation at 25 km relative to 50 km over the oceans and inland seas in DJF, JJA, and ANN (annual average), although the response is strongest during JJA. The ratio of convective precipitation to total precipitation decreases over land for most models at 25 km. In addition, there is an increase in interannual variability in many of the models at 25 km grid spacing. Comparison with gridded observations indicates that a majority of models show improved skill in simulating both the spatial pattern and temporal evolution of precipitation at 25 km compared to 50 km during the summer months, but not in winter or on an annual mean basis. Model skill at higher resolution in simulating the spatial and temporal character of seasonal precipitation is found especially for Great Britain. This geographic dependence of the increased skill suggests that observed data of sufficient density are necessary to capture fine-scale climate signals. As climate models increase their horizontal resolution, it is thus a key priority to produce high quality fine scale observations for model evaluation. [ABSTRACT FROM AUTHOR]

We investigate a question posed by policy makers, namely, “when will changes in extreme precipitation due to climate change be detectable?” To answer this question we use climate prediction.net (CPDN) model simulations from the BBC Climate Change Experiment (CCE) over the UK. These provide us with the unique opportunity to compare 1-day extreme precipitation generated from climate altered by observed forcings (time period 1920–2000) and the SRES A1B emissions scenario (time period 2000–2080) (the Scenario) to extreme precipitation generated by a constant climate for year 1920 (the Control) for the HadCM3L General Circulation Model (GCM). We fit non-stationary Generalized Extreme Value (GEV) models to the Scenario output and compare these to stationary GEV models fit to the parallel Control. We define the time of detectable change as the time at which we would reject a hypothesis at the α = 0.05 significance level that the 20-year return level of the two runs is equal. We find that the time of detectable change depends on the season, with most model runs indicating that change to winter extreme precipitation may be detectable by the year 2010, and that change to summer extreme precipitation is not detectable by 2080. We also investigate which climate model parameters affect the weight of the tail of the precipitation distribution and which affect the time of detectable change for the winter season. We find that two climate model parameters have an important effect on the tail weight, and two others seem to affect the time of detection. Importantly, we find that climate model simulated extreme precipitation has a fundamentally different behavior to observations, perhaps due to the negative estimate of the GEV shape parameter, unlike observations which produce a slightly positive (∼0.0–0.2) estimate. [ABSTRACT FROM AUTHOR]