Search

We model monthly precipitation maxima at 132 stations in Germany for a wide range of durations from 1 min to about 6 d using a duration-dependent generalized extreme value (d-GEV) distribution with monthly varying parameters. This allows for the estimation of both monthly and annual intensity–duration–frequency (IDF) curves: (1) the monthly IDF curves of the summer months exhibit a more rapid decrease of intensity with duration, as well as higher intensities for short durations than the IDF curves for the remaining months of the year. Thus, when short convective extreme events occur, they are very likely to occur in summer everywhere in Germany. In contrast, extreme events with a duration of several hours up to about 1 d are conditionally more likely to occur within a longer period or even spread throughout the whole year, depending on the station. There are major differences within Germany with respect to the months in which long-lasting stratiform extreme events are more likely to occur. At some stations the IDF curves (for a given quantile) for different months intersect. The meteorological interpretation of this intersection is that the season in which a certain extreme event is most likely to occur shifts from summer towards autumn or winter for longer durations. (2) We compare the annual IDF curves resulting from the monthly model with those estimated conventionally, that is, based on modeling annual maxima. We find that adding information in the form of smooth variations during the year leads to a considerable reduction of uncertainties. We additionally observe that at some stations, the annual IDF curves obtained by modeling monthly maxima deviate from the assumption of scale invariance, resulting in a flattening in the slope of the IDF curves for long durations.

Due to the strong temperature dependence of surface ozone concentrations (O3), future warmer conditions may worsen ozone pollution levels despite continued efforts on emission controls of ozone precursors. Using long-term measurements of hourly O3 concentrations co-located with NOx concentrations in stations distributed throughout Germany, we assess changes in the climate penalty, defined as the slope of ozone-temperature relationship during the period 1999–2018. We find a stronger temperature sensitivity in the urban stations over the southwestern regions, especially in the first period of the study (1999–2008).We show a decrease in the climate penalty in most of stations during the second period of the study (2009–2018), with some exceptions (e.g. Berlin) where the climate penalty did not show significant changes. To examine the impacts of NOx reductions on the O3 sensitivity to temperature, we propose a statistical approach based on generalized additive models (GAMs) to describe ozone production rates, inferred from hourly observations, as a function of NOx and temperature, among other variables relevant during the O3 production. We find lower O3 production rates during the second period (2009–2018) at most stations and a decreasing sensitivity to temperature, pointing out that lowering NOx concentrations resulted in decreasing O3 production rates. However, we also observe changes in the shape of the function representing the O3-temperature relationship, which indicate that NOx reductions alone can not explain the changes in the temperature dependence of O3. Our analysis would suggest that decreasing NOx concentrations are not the only factor causing the observed changes in the climate penalty factor.

To explicitly account for asymptotic dependence between rainfall intensity maxima of different accumulation duration, a recent development for estimating Intensity-Duration-Frequency (IDF) curves involves the use of a max-stable process. In our study, we aimed to estimate the impact on the performance of the return levels resulting from an IDF model that accounts for such asymptotical dependence. To investigate this impact, we compared the performance of the return level estimates of two IDF models using the quantile skill index (QSI). One IDF model is based on a max-stable process assuming asymptotic dependence, the other is a simplified (or reduced) duration-dependent GEV model assuming asymptotic independence. The resulting QSI shows that the overall performance of the two models is very similar, with the max-stable model slightly outperforming the other model for short durations (d&le, 10h). From a simulation study, we conclude that max-stable processes are worth considering for IDF curve estimation when focusing on short durations if the model&rsquo, s asymptotic dependence can be assumed to be properly captured.

The Antarctic Oscillation (AAO) is the dominant mode of atmospheric variability in the southern hemisphere. It is obtained via a principal component analysis (PCA) for geopotential height anomalies. Being the southern hemisphere's dominant mode, an adequate representation in earth system models is desirable. This paper evaluates to what extent the AAO and related precipitation is represented in the Max Planck Institute's Earth System Model (MPI-ESM). To this end we compare AAO spatial patterns (empirical orthogonal functions, EOFs), spectral properties of the associated principal components (PCs) and AAO-related precipitation patterns of MPI-ESM to three reanalyses: the ECMWF's ERA-40 and ERA-Interim, and the NCEP/NCAR 40-year reanalysis project. Differences between MPI-ESM and ERA-Interim leading EOFs reveal that the three typical centres of action are less pronounced and slightly shifted in the model. Spectral density estimates of the associated PCs show reduced variability in the MPI-ESM for periods between 4 to 5 months. The relation between AAO and southern hemispheric precipitation is assessed via composites and correlation analysis. In both, model and reanalyses, a negative AAO index leads to a general increase of precipitation between 30° S and 50° S and a decrease south of 50° S. Differences between maps of correlation for AAO and precipitation are most prominent near Indonesia and Antarctica probably due to a lack of pressure around Antarctica in the model. Altogether the MPI-ESM underestimates the relation of AAO and southern hemispheric precipitation but gives the correct sign and spatial distribution of correlation values.

The implementation of European emission abatement strategies has led to a significant reduction in the emissions of ozone precursors during the last decade. Ground-level ozone is also influenced by meteorological factors such as temperature, which exhibit interannual variability and are expected to change in the future. The impacts of climate change on air quality are usually investigated through air-quality models that simulate interactions between emissions, meteorology and chemistry. Within a multi-model assessment, this study aims to better understand how air-quality models represent the relationship between meteorological variables and surface ozone concentrations over Europe. A multiple linear regression (MLR) approach is applied to observed and modelled time series across 10 European regions in springtime and summertime for the period of 2000-2010 for both models and observations. Overall, the air-quality models are in better agreement with observations in summertime than in springtime and particularly in certain regions, such as France, central Europe or eastern Europe, where local meteorological variables show a strong influence on surface ozone concentrations. Larger discrepancies are found for the southern regions, such as the Balkans, the Iberian Peninsula and the Mediterranean basin, especially in springtime. We show that the air-quality models do not properly reproduce the sensitivity of surface ozone to some of the main meteorological drivers, such as maximum temperature, relative humidity and surface solar radiation. Specifically, all air-quality models show more limitations in capturing the strength of the ozone-relative-humidity relationship detected in the observed time series in most of the regions, for both seasons. Here, we speculate that dry-deposition schemes in the air-quality models might play an essential role in capturing this relationship. We further quantify the relationship between ozone and maximum temperature (mo3−T, climate penalty) in observations and air-quality models. In summertime, most of the air-quality models are able to reproduce the observed climate penalty reasonably well in certain regions such as France, central Europe and northern Italy. However, larger discrepancies are found in springtime, where air-quality models tend to overestimate the magnitude of the observed climate penalty.

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.

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 uninitialized simulations. Only over East Asia and the Northwest Pacific, the Northwest Atlantic as well as the Eastern Mediterranean the initialized hindcasts perform significantly better than the uninitialized simulations. While no significant differences are evident between anomaly- and full-field-initialization, initializing the model's ocean component from GECCO2-ocean-reanalysis yields slightly better results than from ORA-S4, especially over the Northeast Pacific. Additionally, it is shown that the novel parametric drift-correction approach – estimating potential cubic drifts with parameters linearly changing in time – is more appropriate than the standard procedure – estimating constant model drifts via the lead-time-dependent bias – and, hence, yields higher skill estimates.