Search

This paper introduces the Distribution-Independent Storm Severity Index (DI-SSI). The DI-SSI represents an approach to quantify the severity of exceptional surface wind speeds of large scale windstorms that is complementary to the SSI introduced by Leckebusch et al. While the SSI approaches the extremeness of a storm from a meteorological and potential loss (impact) perspective, the DI-SSI defines the severity in a more climatological perspective. The idea is to assign equal index values to wind speeds of the same singularity (e.g. the 99th percentile) under consideration of the shape of the tail of the local wind speed climatology. Especially in regions at the edge of the classical storm track, the DI-SSI shows more equitable severity estimates, e.g. for the extra-tropical cyclone Klaus. In order to compare the indices, their relation with the North Atlantic Oscillation is studied, which is one of the main large scale drivers for the intensity of European windstorms.

This paper introduces the Distribution-Independent Storm Severity Index (DI-SSI). The DI-SSI represents an approach to quantify the severity of exceptional surface wind speeds of large scale windstorms that is complementary to the SSI introduced by Leckebusch et al. While the SSI approaches the extremeness of a storm from a meteorological and potential loss (impact) perspective, the DI-SSI defines the severity in a more climatological perspective. The idea is to assign equal index values to wind speeds of the same singularity (e.g. the 99th percentile) under consideration of the shape of the tail of the local wind speed climatology. Especially in regions at the edge of the classical storm track, the DI-SSI shows more equitable severity estimates, e.g. for the extra-tropical cyclone Klaus. In order to compare the indices, their relation with the North Atlantic Oscillation is studied, which is one of the main large scale drivers for the intensity of European windstorms.

This paper introduces the Distribution-Independent Storm Severity Index (DI-SSI). The DI-SSI represents an approach to quantify the severity of exceptional surface wind speeds of large scale windstorms that is complementary to the SSI introduced by Leckebusch et al. While the SSI approaches the extremeness of a storm from a meteorological and potential loss (impact) perspective, the DI-SSI defines the severity in a more climatological perspective. The idea is to assign equal index values to wind speeds of the same singularity (e.g. the 99th percentile) under consideration of the shape of the tail of the local wind speed climatology. Especially in regions at the edge of the classical storm track, the DI-SSI shows more equitable severity estimates, e.g. for the extra-tropical cyclone Klaus. In order to compare the indices, their relation with the North Atlantic Oscillation is studied, which is one of the main large scale drivers for the intensity of European windstorms.

A German national project coordinates research on improving a global decadal climate prediction system for future operational use. MiKlip, an eight-year German national research project on decadal climate prediction, is organized around a global prediction system comprising the climate model MPI-ESM together with an initialization procedure and a model evaluation system. This paper summarizes the lessons learned from MiKlip so far; some are purely scientific, others concern strategies and structures of research that targets future operational use. Three prediction-system generations have been constructed, characterized by alternative initialization strategies; the later generations show a marked improvement in hindcast skill for surface temperature. Hindcast skill is also identified for multi-year-mean European summer surface temperatures, extra-tropical cyclone tracks, the Quasi-Biennial Oscillation, and ocean carbon uptake, among others. Regionalization maintains or slightly enhances the skill in European surface temperature inherited from the global model and also displays hindcast skill for wind-energy output. A new volcano code package permits rapid modification of the predictions in response to a future eruption. MiKlip has demonstrated the efficacy of subjecting a single global prediction system to a major research effort. The benefits of this strategy include the rapid cycling through the prediction-system generations, the development of a sophisticated evaluation package usable by all MiKlip researchers, and regional applications of the global predictions. Open research questions include the optimal balance between model resolution and ensemble size, the appropriate method for constructing a prediction ensemble, and the decision between full-field and anomaly initialization. Operational use of the MiKlip system is targeted for the end of the current decade, with a recommended generational cycle of two to three years.

Winter wind storms related to intense extra-tropical cyclones are meteorological extreme events, often with major impacts on economy and human life, especially for Europe and the mid-latitudes. Hence, skillful decadal predictions regarding the frequency of their occurrence would be of great socio-economic value. The present paper extends the study of Kruschke et al. (2014) in several aspects. First, this study is situated in a more impact oriented context by analyzing the frequency of potentially damaging wind storm events instead of targeting at cyclones as general meteorological features which was done by Kruschke et al. (2014). Second, this study incorporates more data sets by analyzing five decadal hindcast experiments – 41 annual (1961–2001) initializations integrated for ten years each – set up with different initialization strategies. However, all experiments are based on the Max-Planck-Institute Earth System Model in a low-resolution configuration (MPI-ESM-LR). Differing combinations of these five experiments allow for more robust estimates of predictive skill (due to considerably larger ensemble size) and systematic comparisons of the underlying initialization strategies. Third, the hindcast experiments are corrected for model bias and potential drifts over lead time by means of a novel parametric approach, accounting for non-stationary model drifts. We analyze whether skillful probabilistic three-category forecasts (enhanced, normal or decreased) can be provided regarding winter (ONDJFM) wind storm frequencies over the Northern Hemisphere (NH). Skill is assessed by using climatological probabilities and uninitialized transient simulations as reference forecasts. It is shown that forecasts of average winter wind storm frequencies for winters 2–5 and winters 2–9 are skillful over large parts of the NH. However, most of this skill is associated with external forcing from transient greenhouse gas and aerosol concentrations, already included in the uninitialize

Winter wind storms related to intense extra-tropical cyclones are meteorological extreme events, often with major impacts on economy and human life, especially for Europe and the mid-latitudes. Hence, skillful decadal predictions regarding the frequency of their occurrence would be of great socio-economic value. The present paper extends the study of Kruschke et al. (2014) in several aspects. First, this study is situated in a more impact oriented context by analyzing the frequency of potentially damaging wind storm events instead of targeting at cyclones as general meteorological features which was done by Kruschke et al. (2014). Second, this study incorporates more data sets by analyzing five decadal hindcast experiments – 41 annual (1961–2001) initializations integrated for ten years each – set up with different initialization strategies. However, all experiments are based on the Max-Planck-Institute Earth System Model in a low-resolution configuration (MPI-ESM-LR). Differing combinations of these five experiments allow for more robust estimates of predictive skill (due to considerably larger ensemble size) and systematic comparisons of the underlying initialization strategies. Third, the hindcast experiments are corrected for model bias and potential drifts over lead time by means of a novel parametric approach, accounting for non-stationary model drifts. We analyze whether skillful probabilistic three-category forecasts (enhanced, normal or decreased) can be provided regarding winter (ONDJFM) wind storm frequencies over the Northern Hemisphere (NH). Skill is assessed by using climatological probabilities and uninitialized transient simulations as reference forecasts. It is shown that forecasts of average winter wind storm frequencies for winters 2–5 and winters 2–9 are skillful over large parts of the NH. However, most of this skill is associated with external forcing from transient greenhouse gas and aerosol concentrations, already included in the uninitialize

A German national project coordinates research on improving a global decadal climate prediction system for future operational use. MiKlip, an eight-year German national research project on decadal climate prediction, is organized around a global prediction system comprising the climate model MPI-ESM together with an initialization procedure and a model evaluation system. This paper summarizes the lessons learned from MiKlip so far; some are purely scientific, others concern strategies and structures of research that targets future operational use. Three prediction-system generations have been constructed, characterized by alternative initialization strategies; the later generations show a marked improvement in hindcast skill for surface temperature. Hindcast skill is also identified for multi-year-mean European summer surface temperatures, extra-tropical cyclone tracks, the Quasi-Biennial Oscillation, and ocean carbon uptake, among others. Regionalization maintains or slightly enhances the skill in European surface temperature inherited from the global model and also displays hindcast skill for wind-energy output. A new volcano code package permits rapid modification of the predictions in response to a future eruption. MiKlip has demonstrated the efficacy of subjecting a single global prediction system to a major research effort. The benefits of this strategy include the rapid cycling through the prediction-system generations, the development of a sophisticated evaluation package usable by all MiKlip researchers, and regional applications of the global predictions. Open research questions include the optimal balance between model resolution and ensemble size, the appropriate method for constructing a prediction ensemble, and the decision between full-field and anomaly initialization. Operational use of the MiKlip system is targeted for the end of the current decade, with a recommended generational cycle of two to three years.

A German national project coordinates research on improving a global decadal climate prediction system for future operational use. MiKlip, an eight-year German national research project on decadal climate prediction, is organized around a global prediction system comprising the climate model MPI-ESM together with an initialization procedure and a model evaluation system. This paper summarizes the lessons learned from MiKlip so far; some are purely scientific, others concern strategies and structures of research that targets future operational use. Three prediction-system generations have been constructed, characterized by alternative initialization strategies; the later generations show a marked improvement in hindcast skill for surface temperature. Hindcast skill is also identified for multi-year-mean European summer surface temperatures, extra-tropical cyclone tracks, the Quasi-Biennial Oscillation, and ocean carbon uptake, among others. Regionalization maintains or slightly enhances the skill in European surface temperature inherited from the global model and also displays hindcast skill for wind-energy output. A new volcano code package permits rapid modification of the predictions in response to a future eruption. MiKlip has demonstrated the efficacy of subjecting a single global prediction system to a major research effort. The benefits of this strategy include the rapid cycling through the prediction-system generations, the development of a sophisticated evaluation package usable by all MiKlip researchers, and regional applications of the global predictions. Open research questions include the optimal balance between model resolution and ensemble size, the appropriate method for constructing a prediction ensemble, and the decision between full-field and anomaly initialization. Operational use of the MiKlip system is targeted for the end of the current decade, with a recommended generational cycle of two to three years.

Winter wind storms related to intense extra-tropical cyclones are meteorological extreme events, often with major impacts on economy and human life, especially for Europe and the mid-latitudes. Hence, skillful decadal predictions regarding the frequency of their occurrence would be of great socio-economic value. The present paper extends the study of Kruschke et al. (2014) in several aspects. First, this study is situated in a more impact oriented context by analyzing the frequency of potentially damaging wind storm events instead of targeting at cyclones as general meteorological features which was done by Kruschke et al. (2014). Second, this study incorporates more data sets by analyzing five decadal hindcast experiments – 41 annual (1961–2001) initializations integrated for ten years each – set up with different initialization strategies. However, all experiments are based on the Max-Planck-Institute Earth System Model in a low-resolution configuration (MPI-ESM-LR). Differing combinations of these five experiments allow for more robust estimates of predictive skill (due to considerably larger ensemble size) and systematic comparisons of the underlying initialization strategies. Third, the hindcast experiments are corrected for model bias and potential drifts over lead time by means of a novel parametric approach, accounting for non-stationary model drifts. We analyze whether skillful probabilistic three-category forecasts (enhanced, normal or decreased) can be provided regarding winter (ONDJFM) wind storm frequencies over the Northern Hemisphere (NH). Skill is assessed by using climatological probabilities and uninitialized transient simulations as reference forecasts. It is shown that forecasts of average winter wind storm frequencies for winters 2–5 and winters 2–9 are skillful over large parts of the NH. However, most of this skill is associated with external forcing from transient greenhouse gas and aerosol concentrations, already included in the uninitialize

The Water and Global Change (WATCH) forcing datasets have been created to support the use of hydrological and land surface models for the assessment of the water cycle within climate change studies. They are based on 40-yr ECMWF Re-Analysis (ERA-40) or ECMWF interim reanalysis (ERA-Interim) with temperatures (among other variables) adjusted such that their monthly means match the monthly temperature dataset from the Climatic Research Unit. To this end, daily minimum, maximum, and mean temperatures within one calendar month have been subjected to a correction involving monthly means of the respective month. As these corrections can be largely different for adjacent months, this procedure potentially leads to implausible differences in daily temperatures across the boundaries of calendar months. We analyze day-to-day temperature fluctuations within and across months and find that across-months differences are significantly larger, mostly in the tropics and frigid zones. Average across-months differences in daily mean temperature are typically between 10% and 40% larger than their corresponding within-months average temperature differences. However, regions with differences up to 200% can be found in tropical Africa. Particularly in regions where snowmelt is a relevant player for hydrology, a few degrees Celsius difference can be decisive for triggering this process. Daily maximum and minimum temperatures are affected in the same regions, but in a less severe way.

The Water and Global Change (WATCH) forcing datasets have been created to support the use of hydrological and land surface models for the assessment of the water cycle within climate change studies. They are based on 40-yr ECMWF Re-Analysis (ERA-40) or ECMWF interim reanalysis (ERA-Interim) with temperatures (among other variables) adjusted such that their monthly means match the monthly temperature dataset from the Climatic Research Unit. To this end, daily minimum, maximum, and mean temperatures within one calendar month have been subjected to a correction involving monthly means of the respective month. As these corrections can be largely different for adjacent months, this procedure potentially leads to implausible differences in daily temperatures across the boundaries of calendar months. We analyze day-to-day temperature fluctuations within and across months and find that across-months differences are significantly larger, mostly in the tropics and frigid zones. Average across-months differences in daily mean temperature are typically between 10% and 40% larger than their corresponding within-months average temperature differences. However, regions with differences up to 200% can be found in tropical Africa. Particularly in regions where snowmelt is a relevant player for hydrology, a few degrees Celsius difference can be decisive for triggering this process. Daily maximum and minimum temperatures are affected in the same regions, but in a less severe way.

The Water and Global Change (WATCH) forcing datasets have been created to support the use of hydrological and land surface models for the assessment of the water cycle within climate change studies. They are based on 40-yr ECMWF Re-Analysis (ERA-40) or ECMWF interim reanalysis (ERA-Interim) with temperatures (among other variables) adjusted such that their monthly means match the monthly temperature dataset from the Climatic Research Unit. To this end, daily minimum, maximum, and mean temperatures within one calendar month have been subjected to a correction involving monthly means of the respective month. As these corrections can be largely different for adjacent months, this procedure potentially leads to implausible differences in daily temperatures across the boundaries of calendar months. We analyze day-to-day temperature fluctuations within and across months and find that across-months differences are significantly larger, mostly in the tropics and frigid zones. Average across-months differences in daily mean temperature are typically between 10% and 40% larger than their corresponding within-months average temperature differences. However, regions with differences up to 200% can be found in tropical Africa. Particularly in regions where snowmelt is a relevant player for hydrology, a few degrees Celsius difference can be decisive for triggering this process. Daily maximum and minimum temperatures are affected in the same regions, but in a less severe way.

Mid-latitudinal cyclones are a key factor for understanding regional anomalies in primary meteorological parameters such as temperature or precipitation. Extreme cyclones can produce notable impacts on human society and economy, for example, by causing enormous economic losses through wind damage. Based on 41 annually initialised (1961–2001) hindcast ensembles, this study evaluates the ability of a single-model decadal forecast system (MPI-ESM-LR) to provide skilful probabilistic three-category forecasts (enhanced, normal or decreased) of winter (ONDJFM) extra-tropical cyclone frequency over the Northern Hemisphere with lead times from 1 yr up to a decade. It is shown that these predictions exhibit some significant skill, mainly for lead times of 2–5 yr, especially over the North Atlantic and Pacific. Skill for intense cyclones is generally higher than for all detected systems. A comparison of decadal hindcasts from two different initialisation techniques indicates that initialising from reanalysis fields yields slightly better results for the first forecast winter (month 10–15), while initialisation based on an assimilation experiment provides better skill for lead times between 2 and 5 yr. The reasons and mechanisms behind this predictive skill are subject to future work. Preliminary analyses suggest a strong relationship of the model’s skill over the North Atlantic with the ability to predict upper ocean temperatures modulating lower troposphere baroclinicity for the respective area and time scales.

Mid-latitudinal cyclones are a key factor for understanding regional anomalies in primary meteorological parameters such as temperature or precipitation. Extreme cyclones can produce notable impacts on human society and economy, for example, by causing enormous economic losses through wind damage. Based on 41 annually initialised (1961–2001) hindcast ensembles, this study evaluates the ability of a single-model decadal forecast system (MPI-ESM-LR) to provide skilful probabilistic three-category forecasts (enhanced, normal or decreased) of winter (ONDJFM) extra-tropical cyclone frequency over the Northern Hemisphere with lead times from 1 yr up to a decade. It is shown that these predictions exhibit some significant skill, mainly for lead times of 2–5 yr, especially over the North Atlantic and Pacific. Skill for intense cyclones is generally higher than for all detected systems. A comparison of decadal hindcasts from two different initialisation techniques indicates that initialising from reanalysis fields yields slightly better results for the first forecast winter (month 10–15), while initialisation based on an assimilation experiment provides better skill for lead times between 2 and 5 yr. The reasons and mechanisms behind this predictive skill are subject to future work. Preliminary analyses suggest a strong relationship of the model’s skill over the North Atlantic with the ability to predict upper ocean temperatures modulating lower troposphere baroclinicity for the respective area and time scales.

Mid-latitudinal cyclones are a key factor for understanding regional anomalies in primary meteorological parameters such as temperature or precipitation. Extreme cyclones can produce notable impacts on human society and economy, for example, by causing enormous economic losses through wind damage. Based on 41 annually initialised (1961–2001) hindcast ensembles, this study evaluates the ability of a single-model decadal forecast system (MPI-ESM-LR) to provide skilful probabilistic three-category forecasts (enhanced, normal or decreased) of winter (ONDJFM) extra-tropical cyclone frequency over the Northern Hemisphere with lead times from 1 yr up to a decade. It is shown that these predictions exhibit some significant skill, mainly for lead times of 2–5 yr, especially over the North Atlantic and Pacific. Skill for intense cyclones is generally higher than for all detected systems. A comparison of decadal hindcasts from two different initialisation techniques indicates that initialising from reanalysis fields yields slightly better results for the first forecast winter (month 10–15), while initialisation based on an assimilation experiment provides better skill for lead times between 2 and 5 yr. The reasons and mechanisms behind this predictive skill are subject to future work. Preliminary analyses suggest a strong relationship of the model’s skill over the North Atlantic with the ability to predict upper ocean temperatures modulating lower troposphere baroclinicity for the respective area and time scales.

We investigate how well the variability of extreme daily precipitation events across the United Kingdom is represented in a set of regional climate models and the E-OBS gridded data set. Instead of simply evaluating the climatologies of extreme precipitation measures, we develop an approach to validate the representation of physical mechanisms controlling extreme precipitation variability. In part I of this study we applied a statistical model to investigate the influence of the synoptic scale atmospheric circulation on extreme precipitation using observational rain gauge data. More specifically, airflow strength, direction and vorticity are used as predictors for the parameters of the generalised extreme value (GEV) distribution of local precipitation extremes. Here we employ this statistical model for our validation study. In a first step, the statistical model is calibrated against a gridded precipitation data set provided by the UK Met Office. In a second step, the same statistical model is calibrated against 14 ERA40 driven 25 km resolution RCMs from the ENSEMBLES project and the E-OBS gridded data set. Validation indices describing relevant physical mechanisms are derived from the statistical models for observations and RCMs and are compared using pattern standard deviation, pattern correlation and centered pattern root mean squared error as validation measures. The results for the different RCMs and E-OBS are visualised using Taylor diagrams. We show that the RCMs adequately simulate moderately extreme precipitation and the influence of airflow strength and vorticity on precipitation extremes, but show deficits in representing the influence of airflow direction. Also very rare extremes are misrepresented, but this result is afflicted with a high uncertainty. E-OBS shows considerable biases, in particular in regions of sparse data. The proposed approach might be used to validate other physical relationships in regional as well as global climate models.

We investigate how well the variability of extreme daily precipitation events across the United Kingdom is represented in a set of regional climate models and the E-OBS gridded data set. Instead of simply evaluating the climatologies of extreme precipitation measures, we develop an approach to validate the representation of physical mechanisms controlling extreme precipitation variability. In part I of this study we applied a statistical model to investigate the influence of the synoptic scale atmospheric circulation on extreme precipitation using observational rain gauge data. More specifically, airflow strength, direction and vorticity are used as predictors for the parameters of the generalised extreme value (GEV) distribution of local precipitation extremes. Here we employ this statistical model for our validation study. In a first step, the statistical model is calibrated against a gridded precipitation data set provided by the UK Met Office. In a second step, the same statistical model is calibrated against 14 ERA40 driven 25 km resolution RCMs from the ENSEMBLES project and the E-OBS gridded data set. Validation indices describing relevant physical mechanisms are derived from the statistical models for observations and RCMs and are compared using pattern standard deviation, pattern correlation and centered pattern root mean squared error as validation measures. The results for the different RCMs and E-OBS are visualised using Taylor diagrams. We show that the RCMs adequately simulate moderately extreme precipitation and the influence of airflow strength and vorticity on precipitation extremes, but show deficits in representing the influence of airflow direction. Also very rare extremes are misrepresented, but this result is afflicted with a high uncertainty. E-OBS shows considerable biases, in particular in regions of sparse data. The proposed approach might be used to validate other physical relationships in regional as well as global climate models.

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.

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.

We develop a vector generalised linear model to describe the influence of the atmospheric circulation on extreme daily precipitation across the UK. The atmospheric circulation is represented by three covariates, namely synoptic scale airflow strength, direction and vorticity; the extremes are represented by the monthly maxima of daily precipitation, modelled by the generalised extreme value distribution (GEV). The model parameters for data from 689 rain gauges across the UK are estimated using a maximum likelihood estimator. Within the framework of vector generalised linear models, various plausible models exist to describe the influence of the individual covariates, possible nonlinearities in the covariates and seasonality. We selected the final model based on the Akaike information criterion (AIC), and evaluated the predictive power of individual covariates by means of quantile verification scores and leave-one-out cross validation. The final model conditions the location and scale parameter of the GEV on all three covariates; the shape parameter is modelled as a constant. The relationships between strength and vorticity on the one hand, and the GEV location and scale parameters on the other hand are modelled as natural cubic splines with two degrees of freedom. The influence of direction is parameterised as a sine with amplitude and phase. The final model has a common parameterisation for the whole year. Seasonality is partly captured by the covariates themselves, but mostly by an additional annual cycle that is parameterised as a phase-shifted sine and accounts for physical influences that we have not attempted to explicitly model, such as humidity.

We develop a vector generalised linear model to describe the influence of the atmospheric circulation on extreme daily precipitation across the UK. The atmospheric circulation is represented by three covariates, namely synoptic scale airflow strength, direction and vorticity; the extremes are represented by the monthly maxima of daily precipitation, modelled by the generalised extreme value distribution (GEV). The model parameters for data from 689 rain gauges across the UK are estimated using a maximum likelihood estimator. Within the framework of vector generalised linear models, various plausible models exist to describe the influence of the individual covariates, possible nonlinearities in the covariates and seasonality. We selected the final model based on the Akaike information criterion (AIC), and evaluated the predictive power of individual covariates by means of quantile verification scores and leave-one-out cross validation. The final model conditions the location and scale parameter of the GEV on all three covariates; the shape parameter is modelled as a constant. The relationships between strength and vorticity on the one hand, and the GEV location and scale parameters on the other hand are modelled as natural cubic splines with two degrees of freedom. The influence of direction is parameterised as a sine with amplitude and phase. The final model has a common parameterisation for the whole year. Seasonality is partly captured by the covariates themselves, but mostly by an additional annual cycle that is parameterised as a phase-shifted sine and accounts for physical influences that we have not attempted to explicitly model, such as humidity.