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M. D. G. Dukes and J. P. Palutikof

Abstract

Long series of hourly mean wind speeds and the maximum hourly 3-s gust are simulated for four sites in the British Isles in order to investigate methods for the determination of extreme wind speed events. The simulation is performed using a one-step Markov chain model. First, the observations are used to generate series of the same length as the real time series. It is found that the synthetic series reproduce well the means, standard deviations, and maximum and minimum wind speeds of the observed scales. As expected, they are less successful at reproducing the observation autocorrelation properties with the lagged correlation coefficients decaying two rapidly in the simulated series. The method is then used to generate synthetic series of 10, 50, 100, 1000, and 10 000 years in length. In a modified form of Monte Carlo analysis, each run of the Markov model is repeated 1000 times. The maximum wind speed from each run is extracted, and the mean of the 1000 values is taken to be the extreme wind speed for a return period equal to the length of the simulation. The results are compared with extreme wind speed calculated from conventional extreme event analysis. It is hypothesized that over the shorter return period (10 and 50 years) the comparison amounts to a validation since the conventional analysis may be expected to produce a reliable estimate of the true extreme. The authors find relatively close agreement between the extremes predicted by the two techniques at these shorter return periods. At the longer return periods (1000 and 10 000 years in length), the predictions from the synthetic time series are generally lower than those obtained from conventional analysis techniques. It is suggested that the results from the Markov model may be more realistic since one would expect them to be some theoretical maximum to wind speeds at any location imposed by atmospheric circulation characteristics.

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B. B. Brabson and J. P. Palutikof

Abstract

Extreme wind speed predictions are often based on statistical analysis of site measurements of annual maxima, using one of the Generalized Extreme Value (GEV) distributions. An alternative method applies one of the Generalized Pareto Distributions (GPD) to all measurements over a chosen threshold (peaks over threshold). This method increases the number of measurements included in the analysis, and correspondingly reduces the statistical uncertainty of quantile variances, but raises other important questions about, for example, event independence and the choice of threshold. Here an empirical study of the influence of event independence and threshold choice is carried out by performing a GPD analysis of gust speed maxima from five island sites in the north of Scotland. The expected invariance of the GPD shape parameter with choice of threshold is utilized to look for changes of characteristic wind speed behavior with threshold. The impact of decadal variability in wind on GEV and GPD extreme wind speed predictions is also examined, and these predictions are compared with those from the simpler Gumbel and exponential forms.

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J. M. Lough, T. M. L. Wigley, and J. P. Palutikof

Abstract

Scenarios for Europe in a warmer world, such as may result from increased atmospheric carbon dioxide levels, have been constructed using the early 20th century warming as an analogue. Mean temperature, Precipitation and pressure patterns for the period 1934–53 were compared with those for 1901–20. These are the warmest and cooler twenty-year periods this century based on Northern Hemisphere annual mean surface air temperature data, differing by 0.4°C. The climate scenarios show marked subregional scale differences from season to season, and individual season scenarios often show little similarity to the annual scenario. Temperature scenarios show warming for the annual mean and for spring, summer and autumn. The largest positive changes are found in higher latitudes. Winters over a large part of Europe are actually cooler and show greater interannual variability during the warmer period. These changes appear to be associated with a greater frequency of blocking activity. Precipitation changes occur in both directions in all seasons. There is, however, an overall tendency for spring and summer to be drier and autumn and winter to be wetter.

The climate scenarios are used to construct scenarios of the impact of a global warming on energy consumption and agriculture. Cooler winters alone would imply greater energy demand for space heating, but this is largely offset by warmer temperatures in spring and autumn which reduce the length of the heating season. Increased temperature variability combined with a general cooling during winter over north and northwestern Europe suggests a greater frequency of severe winters, and thus larger fluctuations in the demand for heating energy. The impact on agriculture is difficult to assess because of the complexity of crop-climate relationships and because of the importance of nonclimatic factors associated with technological change and, perhaps, with enhanced photosynthesis due to increased carbon dioxide concentrations. In northern latitudes, the increase in the length of the growing season would appear to be favorable for agriculture, but warmer summers drier springs and wetter autumns would be less favorable. A specific study was made of the effect of two different climate scenarios on crop yields in England and Wales with regression models constructed using a principal components regression technique. Most crops showed a decrease in yield for both warm-world scenarios, with largest decreases for hay yield and least effect on wheat yield. A similar regression analysis of French wine quality showed an improvement in the quality of Bordeaux and Champagne in a warmer world.

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J. P. Palutikof, P. M. Kelly, T. D. Davies, and J. A. Halliday

Abstract

Modern applications of wind energy include water pumping and, for supply of electricity, grid-connected wind turbines and wind/direct stand-alone systems. In Britain, wind energy has been found to be particularly suited to isolated communities where the costs of transporting diesel fuel are such that it is prohibitively expensive to provide a constant source of electricity.

The impact of long-term climate variability on wind energy production has been almost totally neglected in wind energy studies. The 1898–1954 Southport windspeed record is analyzed and it is shown that the annual mean varies between 7.3 and 5.2 m s−1. Depending on the turbine characteristics, this can represent a 50% mean reduction in output. A principal components analysis (PCA) was performed on forty-two monthly windspeed records for the years 1962–81 and the scores of the fist component (PC1) were used to analyze temporal variability in the wind field. It was found that the period 1962–81 has three phases alternating high-low-high winds over Britain. The time series of windspeed PCI scores is shown to be highly correlated with the PCI scores of a PCA of the Lamb Catalogue, an index of the atmospheric circulation systems affecting Britain. In meteorological terms, the relative frequency of westerly and anticyclonic conditions is related to the strength of the wind field. By correlation of station wind speeds with an index of westerly/anticyclonic frequency, it is shown that west mast stations are strongly affected by the relative frequency of these two weather types, whereas the relationship at east coast stations is much weaker.

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J. P. Palutikof, J. A. Winkler, C. M. Goodess, and J. A. Andresen

Abstract

For climate change impact analyses, local scenarios of surface variables at the daily scales are frequently required. Empirical transfer functions are a widely used technique to generate scenarios from GCM data at these scales. For successful downscaling, the impact analyst should take into account certain considerations. First, it must be demonstrated that the GCM simulations of the required variable are unrealistic and therefore that downscaling is required. Second, it must be shown that the GCM simulations of the selected predictor variables are realistic. Where errors occur, attempts must be made to compensate for their effect on the transfer function–generated predictions or, where this is not possible, the effect on the transfer function–generated climate series must be understood. Third, the changes in the predictors between the control and perturbed simulation must be examined in the light of the implications for the change in the predicted variable. Finally, the effect of decisions made during the development of the transfer functions on the final result should be explored. This study, presented in two parts, addresses these considerations with respect to the development of local scenarios for daily maximum (TMAX) and minimum (TMIN) temperature for two sites, one in North America (Eau Claire, Michigan) and one in Europe (Alcantarilla, Spain).

Part I confirms for a selected GCM that simulations of daily TMAX and TMIN, whether taken from the nearest land grid point, or obtained by interpolation to the site location, are inadequate. Differences between the GCM 1 × CO2 and observed temperature series arise because of a 0°C threshold in the model data. At both sites, variability is suppressed during periods affected by the threshold. The thresholds persist into the perturbed simulation, affecting not only GCM-predicted 2 × CO2 temperatures but also, because the duration and timing of the threshold effect changes in the perturbed simulation, the magnitude and seasonal distribution of the 2 × CO2 –1 × CO2 GCM differences.

Comparison of modeled and observed 500-hPa geopotential height (Z500) and sea level pressure (SLP) shows that, although systematic errors of the type associated with the 0°C threshold in the temperature data are absent, significant errors do occur in certain seasons at both sites. For example, SLP is poorly modeled at Alcantarilla, where the control and observed means differ significantly in every season. The worst results at both sites are in summer. These results will affect the performance of the transfer functions when initialized with model data. Whereas little change is found to occur in SLP at either site between the 1 × CO2 and 2 × CO2 simulation, there is a noticeable increase in Z500. Other things being equal, therefore, the temperature changes predicted by the transfer functions are likely to be greatest when Z500 contributes the most to the explained variances.

In Part II, a range of transfer functions are developed from the free atmosphere variables and validated, using observations. The performance of these transfer functions when initialized with model data is evaluated in the light of the findings in Part I. The sensitivity of the perturbed climate scenarios to a range of user decisions is explored.

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J. P. Palutikof, C. M. Goodess, S. J. Watkins, and T. Holt

Abstract

A statistical downscaling methodology was implemented to generate daily time series of temperature and rainfall for point locations within a catchment, based on the output from general circulation models. The rainfall scenarios were constructed by a two-stage process. First, for a single station, a conditional first-order Markov chain was used to generate wet and dry day successions. Then, the multisite scenarios were constructed by sampling from a benchmark file containing a daily time series of multiple-site observations, classified by season, circulation weather type, and whether the day is wet or dry at the reference station. The temperature scenarios were constructed using deterministic transfer functions initialized by free atmosphere variables. The relationship between the temperature and rainfall scenarios is established in two ways. First, sea level pressure fields define the circulation weather types underpinning the rainfall scenarios and are used to construct predictor variables in the temperature scenarios. Second, separate temperature transfer functions are developed for wet and dry days.

The methods were evaluated in two Mediterranean catchments. The rainfall scenarios were always too dry, despite the application of Monte Carlo techniques in an attempt to overcome the problem. The temperature scenarios were generally too cool. The scenarios were used to explore the occurrence of extreme events, and the changes predicted in response to climate change, taking the example of temperature. The nonlinear relationship between changes in the mean and changes at the extremes was clearly demonstrated.

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