Three different resolution (50, 12, and 1.5 km) regional climate model simulations are compared in terms of their ability to simulate moderate and high daily precipitation events over the southern ...United Kingdom. The convection-permitting 1.5-km simulation is carried out without convective parametrisation. As in previous studies, increasing resolution (especially from 50 to 12 km) is found to improve the representation of orographic precipitation. The 50-km simulation underestimates mean precipitation over the mountainous region of Wales, and event intensity tends to be too weak; this bias is reduced in both the 12- and 1.5-km simulations for both summer and winter. In south–east England lowlands where summer extremes are mostly convective, increasing resolution does not necessary lead to an improvement in the simulation. For the 12-km simulation, simulated daily extreme events are overly intense. Even though the average intensity of summer daily extremes is improved in the 1.5-km simulation, this simulation has a poorer mean bias with too many events exceeding high thresholds. Spatial density and clustering of summer extremes in south–east England are poorly simulated in both the 12- and 1.5-km simulations. In general, we have not found any clear evidence to show that the 1.5-km simulation is superior to the 12-km simulation, or vice versa at the daily level.
Improved crop yield forecasts could enable more effective adaptation to climate variability and change. Here, we explore how to combine historical observations of crop yields and weather with climate ...model simulations to produce crop yield projections for decision relevant timescales. Firstly, the effects on historical crop yields of improved technology, precipitation and daily maximum temperatures are modelled empirically, accounting for a nonlinear technology trend and interactions between temperature and precipitation, and applied specifically for a case study of maize in France. The relative importance of precipitation variability for maize yields in France has decreased significantly since the 1960s, likely due to increased irrigation. In addition, heat stress is found to be as important for yield as precipitation since around 2000. A significant reduction in maize yield is found for each day with a maximum temperature above 32 °C, in broad agreement with previous estimates. The recent increase in such hot days has likely contributed to the observed yield stagnation. Furthermore, a general method for producing near‐term crop yield projections, based on climate model simulations, is developed and utilized. We use projections of future daily maximum temperatures to assess the likely change in yields due to variations in climate. Importantly, we calibrate the climate model projections using observed data to ensure both reliable temperature mean and daily variability characteristics, and demonstrate that these methods work using retrospective predictions. We conclude that, to offset the projected increased daily maximum temperatures over France, improved technology will need to increase base level yields by 12% to be confident about maintaining current levels of yield for the period 2016–2035; the current rate of yield technology increase is not sufficient to meet this target.
A simple and coherent framework for partitioning uncertainty in multimodel climate ensembles is presented. The analysis of variance (ANOVA) is used to decompose a measure of total variation ...additively into scenario uncertainty, model uncertainty, and internal variability. This approach requires fewer assumptions than existing methods and can be easily used to quantify uncertainty related to model–scenario interaction—the contribution to model uncertainty arising from the variation across scenarios of model deviations from the ensemble mean. Uncertainty in global mean surface air temperature is quantified as a function of lead time for a subset of the Coupled Model Intercomparison Project phase 3 ensemble and results largely agree with those published by other authors: scenario uncertainty dominates beyond 2050 and internal variability remains approximately constant over the twenty-first century. Both elements of model uncertainty, due to scenario-independent and scenario-dependent deviations from the ensemble mean, are found to increase with time. Estimates of model deviations that arise as by-products of the framework reveal significant differences between models that could lead to a deeper understanding of the sources of uncertainty in multimodel ensembles. For example, three models show a diverging pattern over the twenty-first century, while another model exhibits an unusually large variation among its scenario-dependent deviations.
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Abstract
Verifying forecasts of rare events is challenging, in part because traditional performance measures degenerate to trivial values as events become rarer. The extreme dependency score was ...proposed recently as a nondegenerating measure for the quality of deterministic forecasts of rare binary events. This measure has some undesirable properties, including being both easy to hedge and dependent on the base rate. A symmetric extreme dependency score was also proposed recently, but this too is dependent on the base rate. These two scores and their properties are reviewed and the meanings of several properties, such as base-rate dependence and complement symmetry that have caused confusion are clarified. Two modified versions of the extreme dependency score, the extremal dependence index, and the symmetric extremal dependence index, are then proposed and are shown to overcome all of its shortcomings. The new measures are nondegenerating, base-rate independent, asymptotically equitable, harder to hedge, and have regular isopleths that correspond to symmetric and asymmetric relative operating characteristic curves.
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Future climate change projections are often derived from ensembles of simulations from multiple global circulation models using heuristic weighting schemes. This study provides amore rigorous ...justification for this by introducing a nested family of three simple analysis of variance frameworks. Statistical frameworks are essential in order to quantify the uncertainty associated with the estimate of the mean climate change response.
The most general framework yields the “one model, one vote” weighting scheme often used in climate projection. However, a simpler additive framework is found to be preferable when the climate change response is not strongly model dependent. In such situations, the weighted multimodel mean may be interpreted as an estimate of the actual climate response, even in the presence of shared model biases.
Statistical significance tests are derived to choose the most appropriate framework for specific multimodel ensemble data. The framework assumptions are explicit and can be checked using simple tests and graphical techniques. The frameworks can be used to test for evidence of nonzero climate response and to construct confidence intervals for the size of the response.
The methodology is illustrated by application to North Atlantic storm track data from the Coupled Model Intercomparison Project phase 5 (CMIP5) multimodel ensemble. Despite large variations in the historical storm tracks, the cyclone frequency climate change response is not found to be model dependent over most of the region. This gives high confidence in the response estimates. Statistically significant decreases in cyclone frequency are found on the flanks of the North Atlantic storm track and in the Mediterranean basin.
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Numerical weather prediction (NWP) ensembles often exhibit biases and errors in dispersion, so they need some form of postprocessing to yield sharp and well‐calibrated probabilistic predictions. The ...output of NWP models is usually at a multiplicity of different lead times and, even though information is often required on this range of lead times, many postprocessing methods in the literature are applied either at a fixed lead time or by fitting individual models for each lead time. However, this is (1) computationally expensive because it requires the training of multiple models if users are interested in information at multiple lead times and (2) prohibitive because it restricts the data used for training postprocessing models and the usability of fitted models. This article investigates the lead‐time dependence of postprocessing methods in the idealized Lorenz'96 system as well as temperature and wind‐speed forecast data from the Met Office Global and Regional Ensemble Prediction System (MOGREPS‐G). The results indicate that there is substantial regularity between the models fitted for different lead times and that one can fit models that are lead‐time‐continuous that work for multiple lead times simultaneously by including lead time as a covariate. These models achieve similar and, in small data situations, even improved performance compared with the classical lead‐time‐separated models, whilst saving substantial computation time.
Statistical postprocessing methods for recalibrating forecasts are usually fitted individually for each lead time at which a forecast is available. This, however, is computationally expensive and restricts the usability of models. Here we study the lead‐time dependence of Ensemble Model Output Statistics—a postprocessing method—and develop lead‐time‐continuous postprocessing models that are usable to correct forecasts at different lead times simultaneously. These models save substantially on computation time and show improved performance in small data situations/running‐window training schemes.
This paper presents an overview of changes in the extreme events that are most likely to affect Europe in forthcoming decades. A variety of diagnostic methods are used to determine how heat waves, ...heavy precipitation, drought, wind storms, and storm surges change between present (1961-90) and future (2071-2100) climate on the basis of regional climate model simulations produced by the PRUDENCE project. A summary of the main results follows. Heat waves--Regional surface warming causes the frequency, intensity and duration of heat waves to increase over Europe. By the end of the twenty first century, countries in central Europe will experience the same number of hot days as are currently experienced in southern Europe. The intensity of extreme temperatures increases more rapidly than the intensity of more moderate temperatures over the continental interior due to increases in temperature variability. Precipitation-- Heavy winter precipitation increases in central and northern Europe and decreases in the south; heavy summer precipitation increases in north-eastern Europe and decreases in the south. Mediterranean droughts start earlier in the year and last longer. Winter storms--Extreme wind speeds increase between 45 deg N and 55 deg N, except over and south of the Alps, and become more north-westerly than cuurently. These changes are associated with reductions in mean sea-level pressure, leading to more North Sea storms and a corresponding increase in storm surges along coastal regions of Holland, Germany and Denmark, in particular. These results are found to depend to different degrees on model formulation. While the responses of heat waves are robust to model formulation, the magnitudes of changes in precipitation and wind speed are sensitive to the choice of regional model, and the detailed patterns of these changes are sensitive to the choice of the driving global model. In the case of precipitation, variation between models can exceed both internal variability and variability between different emissions scenarios. PUBLICATION ABSTRACT
Scoring rules condense all information regarding the performance of a probabilistic forecast into a single numerical value, providing a convenient framework with which to rank and compare competing ...prediction schemes objectively. Although scoring rules provide only a single measure of forecast accuracy, the expected score can be decomposed into components that each assess a distinct aspect of the forecast, such as its calibration or information content. Since these components could depend on several factors, it is useful to evaluate forecast performance under different circumstances; if a forecaster were able to identify situations in which their forecasts perform particularly poorly, then they could more easily develop their forecast strategy to account for these deficiencies. To help forecasters identify such situations, a novel decomposition of scores is introduced that quantifies conditional forecast biases, allowing for a more detailed examination of the sources of information in the forecast. From this, we claim that decompositions of proper scores provide a broad generalisation of the well‐known analysis of variance (ANOVA) framework. The new decomposition is applied to the Brier score, which is then used to evaluate forecasts that the daily maximum temperature will exceed a range of thresholds, issued by the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss). We demonstrate how the additional information provided by this decomposition can be used to improve the performance of these forecasts, by identifying appropriate auxiliary information to include within statistical postprocessing methods.
A novel decomposition of scores is introduced that allows conditional forecast biases to be quantified and the sources of information in the forecast to be examined. This suggests that score decompositions provide a broad generalisation of the well‐known analysis of variance framework. The decomposition is applied to the Brier score, and we show how the additional information provided by this decomposition can be used to improve operational weather forecasts, by identifying appropriate auxiliary information to include within statistical postprocessing methods.
Calibration Strategies Ho, Chun Kit; Stephenson, David B.; Collins, Matthew ...
Bulletin of the American Meteorological Society,
01/2012, Letnik:
93, Številka:
1
Journal Article
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... HadRM3 simulated present-day temperatures are more positively skewed compared to observations in parts of this region, a feature observed in simulations of a number of other regional climate ...models. ... HadRM3 projects temperatures to become even more positively skewed with time over northern continental Europe.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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