Daniel S. Wilks

Statistical downscaling of general circulation model output: A comparison of methods

A range of different statistical downscaling models was calibrated using both observed and general circulation model (GCM) generated daily precipitation time series and intercompared. The GCM used was the U.K. Meteorological Office, Hadley Centres coupled ocean/atmosphere model (HadCM2) forced by combined CO2 and sulfate aerosol changes. Climate model results for 1980–1999 (present) and 2080–2099 (future) were used, for six regions across the United States. The downscaling methods compared were different weather generator techniques (the standard “WGEN” method, and a method based on spell-length durations), two different methods using grid point vorticity data as an atmospheric predictor variable (B-Circ and C-Circ), and two variations of an artificial neural network (ANN) transfer function technique using circulation data and circulation plus temperature data as predictor variables. Comparisons of results were facilitated by using standard sets of observed and GCM-derived predictor variables and by using a standard suite of diagnostic statistics. Significant differences in the level of skill were found among the downscaling methods. The weather generation techniques, which are able to fit a number of daily precipitation statistics exactly, yielded the smallest differences between observed and simulated daily precipitation. The ANN methods performed poorly because of a failure to simulate wet-day occurrence statistics adequately. Changes in precipitation between the present and future scenarios produced by the statistical downscaling methods were generally smaller than those produced directly by the GCM. Changes in daily precipitation produced by the GCM between 1980–1999 and 2080–2099 were therefore judged not to be due primarily to changes in atmospheric circulation. In the light of these results and detailed model comparisons, suggestions for future research and model refinements are presented.

The weather generation game: a review of stochastic weather models

This article reviews the historical development of statistical weather models, from simple analyses of runs of consecutive rainy and dry days at single sites, through to multisite models of daily precipitation. Weather generators have been used extensively in water engineering design and in agricultural, ecosystem and hydrological impact studies as a means of in-filling missing data or for producing indefinitely long synthetic weather series from finite station records. We begin by describing the statistical properties of the rainfall occurrence and amount processes which are necessary precursors to the simulation of other (dependent) meteorological variables. The relationship between these daily weather models and lower-frequency variations in climate statistics is considered next, noting that conventional weather generator techniques often fail to capture wholly interannual variability. Possible solutions to this deficiency - such as the use of mixtures of slowly and rapidly varying conditioning variables - are discussed. Common applications of weather generators are then described. These include the modelling of climate-sensitive systems, the simulation of missing weather data and statistical downscaling of regional climate change scenarios. Finally, we conclude by considering ongoing advances in the simulation of spatially correlated weather series at multiple sites, the downscaling of interannual climate variability and the scope for using nonparametric techniques to synthesize weather series.

ADAPTING STOCHASTIC WEATHER GENERATION ALGORITHMS FOR CLIMATE CHANGE STUDIES

While large-scale climate models (GCMs) are in principle the most appropriate tools for predicting climate changes, at present little confidence can be placed in the details of their projections. Use of tools such as crop simulation models for investigation of potential impacts of climatic change requires daily data pertaining to small spatial scales, not the monthly-averaged and large-scale information typically available from the GCMs. A method is presented to adapt stochastic weather generation models, describing daily weather variations in the present-day climate at particular locations, to generate synthetic daily time series consistent with assumed future climates. These assumed climates are specified in terms of the commonly available monthly means and variances of temperature and precipitation, including time-dependent (so-called ‘transient’) climate changes. Unlike the usual practice of applying assumed changes in mean values to historically observed data, simulation of meteorological time series also exhibiting changes in variability is possible. Considerable freedom in climate change ‘scenario’ construction is allowed. The results are suitable for investigating agricultural and other impacts of a variety of hypothetical climate changes specified in terms of monthly-averaged statistics.

Impact of temperature and precipitation variability on crop model predictions

Future climate changes, as well as differences in climates from one location to another, may involve changes in climatic variability as well as changes in means. In this study, a synthetic weather generator is used to systematically change the within-year variability of temperature and precipitation (and therefore also the interannual variability), without altering long-term mean values. For precipitation, both the magnitude and the qualitative nature of the variability are manipulated. The synthetic daily weather series serve as input to four crop simulation models. Crop growth is simulated for two locations and three soil types. Results indicate that average predicted yield decreases with increasing temperature variability where growing-season temperatures are below the optimum specified in the crop model for photosynethsis or biomass accumulation. However, increasing within-year variability of temperature has little impact on year-to-year variability of yield. The influence of changed precipitation variability on yield was mediated by the nature of the soil. The response on a droughtier soil was greatest when precipitation amounts were altered while keeping occurrence patterns unchanged. When increasing variability of precipitation was achieved through fewer but larger rain events, average yield on a soil with a large plant-available water capacity was more affected. This second difference is attributed to the manner in which plant water uptake is simulated. Failure to account for within-season changes in temperature and precipitation variability may cause serious errors in predicting crop-yield responses to future climate change when air temperatures deviate from crop optima and when soil water is likely to be depleted at depth.

A Farm-Level Analysis of Economic and Agronomic Impacts of Gradual Climate Warming

The potential economic and agronomic impacts of gradual climate warming are examined at the farm level. Three models of the relevant climatic, agronomic, and economic processes are developed and linked to address climate change impacts and agricultural adaptability. Several climate warming scenarios are analyzed, which vary in severity. The results indicate that grain farmers in southern Minnesota can effectively adapt to a gradually changing climate (warmer and either wetter or drier) by adopting later maturing cultivars, changing crop mix, and altering the timing of field operations to take advantage of a longer growing season resulting from climate warming.

Comparison of Ensemble-MOS Methods Using GFS Reforecasts

Abstract Three recently proposed and promising methods for postprocessing ensemble forecasts based on their historical error characteristics (i.e., ensemble-model output statistics methods) are compared using a multidecadal reforecast dataset. Logistic regressions and nonhomogeneous Gaussian regressions are generally preferred for daily temperature, and for medium-range (6–10 and 8–14 day) temperature and precipitation forecasts. However, the better sharpness of medium-range ensemble-dressing forecasts sometimes yields the best Brier scores even though their calibration is somewhat worse. Using the long (15 or 25 yr) training samples that are available with these reforecasts improves the accuracy and skill of these probabilistic forecasts to levels that are approximately equivalent to gains of 1 day of lead time, relative to using short (1 or 2 yr) training samples.

Maximum Likelihood Estimation for the Gamma Distribution Using Data Containing Zeros

Abstract A method for fitting parameters of the gamma distribution to data containing some zero values using maximum likelihood methods is presented. The procedure is based on a conceptual model of the data having resulted from a censoring process so that the number, but not the numerical values of observations failing below a detection limit are known. For the case of precipitation data, this detection limit is related to the threshold value for reporting occurrence or nonoccurrence. The procedure is shown to provide parameter estimates that are more efficient (i.e., precise) than those obtained using the method of moments.

A skill score based on economic value for probability forecasts

An approach to evaluating probability forecasts for dichotomous events, based on their economic value over all possible cost/loss ratio decision problems, is proposed. The resulting Value Score (VS) curve shows non-dimensionalised relative economic value as a function of the cost/loss ratios for different decision-makers, over their full meaningful range. The VS curve is similar in terms of computational mechanics and graphical display to the Relative Operating Characteristic (ROC) curve, but the ROC curve is shown to be insensitive to either conditional or unconditional biases and thus to reflect potential rather than actual skill. The possibility of collapsing the VS curve into a single scalar score is addressed, and it is shown that the results can depend very strongly on the assumed distribution of cost/loss ratios in the community of forecast users. Copyright

Comparison of three‐parameter probability distributions for representing annual extreme and partial duration precipitation series

Performance of 8 three-parameter probability distributions for representing annual extreme and partial duration precipitation data at stations in the northeastern and southeastern United States is investigated. Particular attention is paid to fidelity on the right tail, through use of a bootstrap procedure simulating extrapolation on the right tail beyond the data. It is found that the beta-κ distribution best describes the extreme right tail of annual extreme series, and the beta-P distribution is best for the partial duration data. The conventionally employed two-parameter Gumbel distribution is found to substantially underestimate probabilities associated with the larger precipitation amounts for both annual extreme and partial duration data. Fitting the distributions using left-censored data did not result in improved fits to the right tail.

Impacts of Climate Change on Irrigated Agriculture in the Maipo Basin, Chile: Reliability of Water Rights and Changes in the Demand for Irrigation

AbstractAgricultural regions located in snowmelt-dominated Mediterranean climate basins have been identified as being highly vulnerable to the impacts of climate change. The Maipo basin in central Chile is one such region. Projections of future climate conditions suggest major challenges for this basin. Precipitation levels are projected to decrease by the end of the century, and temperature levels in the mountains are expected to increase by around 3–4°C. Such changes would affect both river discharge and irrigation water demand. This paper illustrates potential climate change impacts on the hydroclimatological regime of the Maipo basin, focusing on irrigated agriculture and its demands on water use rights. The impact assessment was carried out by combining a multisite stochastic weather generator with a disaggregation technique for historical monthly flows of the Maipo river at El Manzano. Demand for irrigation was simulated with a daily water budget model. Data showed that water demands from irrigated ...