Henning W. Rust

Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user

Precipitation downscaling improves the coarse resolution and poor representation of precipitation in global climate models and helps end users to assess the likely hydrological impacts of climate change. This paper integrates perspectives from meteorologists, climatologists, statisticians, and hydrologists to identify generic end user (in particular, impact modeler) needs and to discuss downscaling capabilities and gaps. End users need a reliable representation of precipitation intensities and temporal and spatial variability, as well as physical consistency, independent of region and season. In addition to presenting dynamical downscaling, we review perfect prognosis statistical downscaling, model output statistics, and weather generators, focusing on recent developments to improve the representation of space-time variability. Furthermore, evaluation techniques to assess downscaling skill are presented. Downscaling adds considerable value to projections from global climate models. Remaining gaps are uncertainties arising from sparse data; representation of extreme summer precipitation, subdaily precipitation, and full precipitation fields on fine scales; capturing changes in small-scale processes and their feedback on large scales; and errors inherited from the driving global climate model.

Synoptic and meteorological drivers of extreme ozone concentrations over Europe

The present work assesses the relationship between local and synoptic meteorological conditions and surface ozone concentration over Europe in spring and summer months, during the period 1998–2012 using a new interpolated data set of observed surface ozone concentrations over the European domain. Along with local meteorological conditions, the influence of large-scale atmospheric circulation on surface ozone is addressed through a set of airflow indices computed with a novel implementation of a grid-by-grid weather type classification across Europe. Drivers of surface ozone over the full distribution of maximum daily 8 h average values are investigated, along with drivers of the extreme high percentiles and exceedances or air quality guideline thresholds. Three different regression techniques are applied: multiple linear regression to assess the drivers of maximum daily ozone, logistic regression to assess the probability of threshold exceedances and quantile regression to estimate the meteorological influence on extreme values, as represented by the 95th percentile. The relative importance of the input parameters (predictors) is assessed by a backward stepwise regression procedure that allows the identification of the most important predictors in each model. Spatial patterns of model performance exhibit distinct variations between regions. The inclusion of the ozone persistence is particularly relevant over southern Europe. In general, the best model performance is found over central Europe, where the maximum temperature plays an important role as a driver of maximum daily ozone as well as its extreme values, especially during warmer months.

Fewer jumps, less memory: Homogenized temperature records and long memory

[1] Air temperature records are commonly subjected to inhomogeneities, e.g., sudden jumps caused by a relocation of the measurement station or by installing a new type of shelter. We study the effect of these inhomogeneities on the estimation of the Hurst exponent and show that they bias the estimates toward larger values. The Hurst exponent is a parameter to measure long-range dependence (LRD), which is a characteristic frequently used to describe the natural variability of temperature records. Analyzing a set of temperature time series before and after homogenization with respect to LRD, we find that the average Hurst exponent is clearly reduced for the homogenized series. To test whether (1) jumps cause this positive bias and (2) the homogenization does not artificially reduce the Hurst exponent estimates, we perform a simulation study. This test shows that inhomogeneities in the form of jumps bias the Hurst exponent estimation and that the homogenization procedure is able to remove this bias, leaving the Hurst exponent unchanged. This result holds for fractional autoregressive integrated moving average (FARIMA)-based as well as for detrended fluctuation analysis-based estimation. We conclude that the use of homogenized series is necessary to prevent misleading conclusions about the dependence structure and thus about subsequent analysis such as trend tests.

MiKlip - a National Research Project on Decadal Climate Prediction

AbstractMittelfristige Klimaprognose (MiKlip), an 8-yr German national research project on decadal climate prediction, is organized around a global prediction system comprising the Max Planck Institute Earth System 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 target 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 multiyear-mean European summer surface temperatures, extratropical 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 h...

Evaluating decadal predictions of northern hemispheric cyclone frequencies

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 models skill over the North Atlantic with the ability to predict upper ocean temperatures modulating lower troposphere baroclinicity for the respective area and time scales.

Discontinuous Daily Temperatures in the WATCH Forcing Datasets

AbstractThe 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 differe...

IDENTIFICATION OF RATE CONSTANTS AND NONOBSERVABLE ABSORPTION SPECTRA IN NONLINEAR BIOCHEMICAL REACTION DYNAMICS

On analyzing data of biochemical reaction dynamics monitored by time-resolved spectroscopy, one faces the problem that the concentration time courses of the involved components are not directly observed, but the superposition of their absorption spectra. Furthermore the single spectra are often unknown, because the corresponding reagents cannot be isolated. We propose a method based on Bocks multiple shooting algorithm to estimate the rate constants and individual spectra simultaneously. Applying this procedure to a biochemical reaction we identify the specific rate constants characterizing the reaction dynamics as well as the nonobservable absorption spectra. The results lead to a better understanding of the kinetics of a novel modification reaction which was used as trapping reaction in disulfide bond mediated protein folding reactions.

Confidence Intervals for Flood Return Level Estimates Assuming Long-Range Dependence

Standard flood return level estimation is based on extreme value analysis assuming independent extremes, i.e. fitting a model to excesses over a threshold or to annual maximum discharge. The assumption of independence might not be justifiable in many practical applications. The dependence of the daily run-off observations might in some cases be carried forward to the annual maximum discharge. Unfortunately, using the autocorrelation function, this effect is hard to detect in a short maxima series. One consequence of dependent annual maxima is an increasing uncertainty of the return level estimates. This is illustrated using a simulation study. The confidence intervals obtained from the asymptotic distribution of the maximum likelihood estimator (MLE) for the generalized extreme value distribution (GEV) turned out to be too small to capture the resulting variability. In order to obtain more reliable confidence intervals, we compare four bootstrap strategies, out of which one yields promising results. The performance of this semi-parametric bootstrap strategy is studied in more detail. We exemplify this approach with a case study: a confidence limit for a 100-year return level estimate from a run-off series in southern Germany was calculated and compared to the result obtained using the asymptotic distribution of the MLE.

Extreme Value Analysis Considering Trends: Application to Discharge Data of the Danube River Basin

This chapter proposes and applies an extreme value assessment framework, which allows for auto-correlation and non-stationarity in the extremes. This is, e.g., useful to assess the anticipated intensification of the hydrological cycle due to climate change. The costs related to more frequent or more severe floods are enormous. Therefore, an adequate estimation of these hazards and the related uncertainties is of major concern. Exceedances over a threshold are assumed to be distributed according to a generalised Pareto distribution and we use a point process to approximate the data. In order to eliminate auto-correlation, the data are thinned out. Contrary to ordinary extreme value statistics, potential non-stationarity is included by allowing the model parameters to vary with time. By this, changes in frequency and magnitude of the extremes can be tracked. The model which best suits the data is selected out of a set of models which comprises the stationary model and models with a variety of polynomial and exponential trend assumptions. Analysing winter discharge data of about 50 gauges within the Danube River basin, we find trends in the extremes in about one-third of the gauges examined. The spatial pattern of the trends is not immediately interpretable. We observe neighbouring gauges often to display distinct behaviour, possibly due to non-climatic factors such as changes in land use or soil conditions. Importantly, assuming stationary models for non-stationary extremes results in biased assessment measures. The magnitude of the bias depends on the trend strength and we find up to 100% increase for the 100-year return level. The results obtained are a basis for process-oriented, physical interpretation of the trends. Moreover, common practice of water management authorities can be improved by applying the proposed methods, and costs for flood protection buildings can be calculated with higher accuracy.

An NAO Climatology in Reanalysis Data with the Use of Self-organizing Maps

The North Atlantic Oscillation (NAO) is a basic variability mode of the Northern Hemisphere, long recognized to be significantly affecting the weather and climate of Europe. During the last decades, an eastward shift of the NAO centers of action along with a strengthening of the NAO index and a tendency to more positive values, have been noticed. Therefore, further studying and understanding of the changing NAO pattern is crucial. Here, we attempt to examine the different spatial and temporal characteristics of the NAO in NCEP/NCAR reanalysis data for the period 1971–2000, using Self-organizing Maps (SOMs). Winter daily anomalies of 500 hPa geopotential height over the broader European area are used. According to the results, NAO occupies different locations throughout the years, a fact that has to do with displacements of both the Icelandic Low and the Azores High, and is also related to the predominance of NAO positive and negative phases.