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In this paper, we present a characterization of Antarctic sea ice based on the classification of annual sea ice concentration (SIC) data from 1979 to 2018. A clustering algorithm was applied to provide a climatological description of significant annual cycles of SIC and their spatial distribution around the Southern Ocean. Based on these classification results, we investigate the variability of SIC cycles on decadal and inter‐annual time scales. First, we discuss significant spatial shifts of SIC cycles during 1979–1998 and 1999–2018. In the Weddell Sea and in large parts of the Ross Sea, we observed higher SIC during the summer season, and an extension of sea ice cover in winter compared to the long‐term average. Second, we introduce the Climatological Sea Ice Anomaly Index (CSIAI), which is an annual measure for year‐round sea ice anomalies of the Southern Ocean and its regional sub‐sectors. By relating selected years of significant sea ice conditions (1981, 2007 and 2014) with atmospheric influences, we demonstrate that the CSIAI is very useful for assessing inter‐annular Antarctic SIC variability. Positive and negative sea ice anomalies can be qualitatively explained by atmospheric circulation anomalies in the years 1981 and 2007. However, in 2014, the year with the largest observed sea ice extent in our time series, we found that this positive sea ice anomaly was surprisingly not associated with a stationary and inter‐seasonally persistent pattern of circulation anomaly. This suggests that sub‐seasonal to seasonal circulation anomalies and ocean‐related processes favoured the formation of the sea ice maximum in 2014. With this study we provide additional information on the long‐term annual SIC variability around Antarctica. Furthermore, our classification approach and its results have potential for application in the evaluation of sea ice model results., In this paper, we present a characterization of Antarctic sea ice based on the classification of annual sea ice concentration cycles in the period 1979 to 2018. Furthermore, we discuss spatial shifts between 1979–1998 and 1999–2018 and are able to explain significant annual sea ice anomalies by atmospheric circulation anomalies., DLR Management Board: Young Investigator Group Leader Program. F.R. was funded by the Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659

Im Rahmen des bilateralen Forschungsprojekts WETRAX+ (WEather Patterns, Cyclone TRAcks and related precipitation eXtremes) wird unter anderem der Zusammenhang zwischen großräumigen atmosphärischen Zirkulationstypen und Trockenperioden im südlichen Mitteleuropa unter dem Einfluss des Klimawandels untersucht. Das Untersuchungsgebiet umfasst dabei Österreich sowie Teile Deutschlands, der Schweiz und der Tschechischen Republik. Für eine, auf den Niederschlag konditionierte Zirkulationstypklassifikation wurden atmosphärische Variablenfelder aus gegitterten täglichen JRA55-Reanalysedaten (Japanische Meteorologische Agentur 2018) und tägliche Niederschlagsdaten auf Basis von 1756 Wetterstationen im Untersuchungsgebiet (Zentralanstalt für Meteorologie und Geodynamik 2018) für den Beobachtungszeitraum 1961 bis 2017 verwendet. Zur Abschätzung künftiger Veränderungen wurden sechs verschiedene regionale Klimamodellläufe (Treibhausgasszenario RCP 8.5) der Euro-Cordex – Initiative herangezogen. Die großräumigen atmosphärischen Zirkulationstypen werden unter Verwendung einer nicht-hierarchischen Clusteranalyse, die in der COST733 Classification Software bereitgestellt wird, abgeleitet. Den resultierenden Zirkulationstypen können Eigenshaften (trocken, feucht, warm, kalt) zugeordnet werden. Die trockenheitsrelevanten Zirkulationstypen werden hinsichtlich Trends, Persistenzen, Veränderungen der monatlichen Auftrittshäufigkeiten und der typinternen Variabilität untersucht. Bei der Übertragung der Zirkulationstypen auf die Klimamodelldaten wird jeder Einzeltag des Projektions-Zeitraums demjenigen Zirkulationstypen zugeordnet, zu dessen Zentroidfeldern die betreffenden Einzelfelder die geringste euklidische Distanz aufweisen. Im Beobachtungszeitraum zeigen die Analysen, dass das Auftreten trockenheitsrelevanter Zirkulationstypen signifikant häufiger mit höheren Temperaturen und einer geringeren relativen Luftfeuchte einhergeht. Erste Ergebnisse der Analyse für die Klimazukunft ergeben für die Monate April bis September eine Zunahme zentraler Hochdruckgebiete über Mittel- und Osteuropa, während antizyklonale Trogvorderseiten mit resultierender, südwestlicher Anströmungsrichtung für das Untersuchungsgebiet seltener auftreten.

The Southern Annular Mode (SAM) is the predominant atmospheric variability mode in the Southern Hemisphere. In this paper, we present the spatial variability results of the SAM pattern for the period 1979–2018. The SAM‐intrinsic pattern variability analysis is based on the principal component analysis (PCA), which is carried out for the ERA‐Interim 500 hPa geopotential height (GPH) data set. A spatiotemporally resolved data set of SAM pattern maps (PCA loadings) is derived by projecting monthly shifted sub‐sequences of SAM index values (PCA scores) on the corresponding GPH anomalies. The dominant SAM structure within single pattern fields is mapped automatically and can be interpreted as the Southern Hemisphere polar front. This data set allows an analysis of the geographical positions of the characteristic circumpolar SAM structure over four decades and shows considerable variability over space and time. Five different states of SAM patterns, which are associated with characteristic circulation anomalies during different phases of the study period, are identified. Station‐based Antarctic temperature anomalies can be synoptically explained by these circulation anomalies. The overall latitudinal trend of the SAM pattern indicates an intensification of the meridional structure, especially over the East Antarctic Southern Ocean. Furthermore, we show that the SAM pattern variability is significantly correlated with the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation. Composites of 500 hPa GPH anomalies during the positive and negative phases of the respective indices indicate teleconnections with Pacific Decadal Oscillation and Atlantic Multidecadal Oscillation, and this can explain latitudinal trends of the SAM pattern., Key Points: We present a new approach to examine the spatiotemporal Southern Annular Mode pattern variability. Station‐based Antarctic temperature anomalies are related to different structures of the Southern Annular Mode. The trend pattern shows an increasing meridional structure and correlations with Pacific and Atlantic multidecadal oscillations., DLR Management Board: Young Investigator Group Leader Program

The German weather service reports a new record mean June temperature for Germany and intensive heat waves during 2018 and 2019. Between January 2018 and June 2019, three new monthly top extremes were recorded (April 2018, May 2018 and June 2019).In this study the relationships between the persistence and frequency of atmospheric circulation patterns related to drought and surface air temperature anomalies are investigated. The study area is in southern Central Europe, including parts of Germany and Switzerland as well as Austria and Czech Republic.Large-scale atmospheric circulation types (relevant to drought) have been derived by using the COST733 classification software. Atmospheric variables from gridded daily JRA55 reanalysis data (Japanese Meteorological Agency 2018) and gridded precipitation data for the study area (6x6km, based on timeseries of 1756 weather stations from Zentralanstalt für Meteorologie und Geodynamik 2018) were used for the classification. All input variables were specifically weighted in the classification process. Daily maximum temperature data from ECA&D (2019) for different stations within the study area are used to evaluate the relationship between a circulation type and heat (cold) waves.The drought-relevant circulation types are determined according to relative frequencies of circulation type days under a particular percentile of precipitation: If at least 20 percent of the circulation type days are below the 20th percentile of precipitation, the circulation type is defined as drought relevant.For the derived drought-relevant circulation types, the mean seasonal frequencies [in %] (April-September, October-March) and the mean persistence [in days] (1961-2017) are calculated. To evaluate the relationship between a circulation type and heat (cold) waves, an efficiency coefficient is calculated. The efficiency coefficient is defined as ratio between the frequency of the circulation type in heat (cold) waves and its mean seasonal frequency.For the study area, those circulation types relevant to drought with a high proportion of seasonal temperature anomalies could be identified. The circulation type with a dominant Azores high with ridges of high-pressure towards Central/Eastern Europe has the highest proportion of positive temperature anomalies in summer.

In this contribution air temperature differences among Local Climate Zone (LCZ) categories are analysed with special consideration of varying synoptic conditions. Analyses are based upon an LCZ mapping for the urban area of Augsburg (Bavaria, Southern Germany) and hourly air temperature data from a comprehensive logger network. Quality checked air temperature measurements have been stratified according to season, hour of the day and weather situation. For resulting subsamples thermal differences among LCZs have been determined and appropriate statistical tests have been applied. Results confirm that built up LCZs feature higher temperatures than natural LCZs and that most distinct differences among LCZs appear under undisturbed synoptic conditions. With increasing cloudiness and in particular with increasing wind speed differences among LCZs diminish. But, even for strongly disturbed synoptic conditions statistical significance of the influence of LCZs on thermal characteristics could be assured. Thus, our findings provide clear evidence that detectable thermal differences among LCZs are not restricted to „ideal “synoptic conditions but occur as well under disturbed conditions. However, to assure not only the statistical but also the climatological and in particular the bioclimatological and human health related relevance of the documented differences among LCZs further studies incorporating appropriate metrics are intended.

Gridded daily rainfall data for southern central Europe are aggregated to regions of similar precipitation variability by means of S-mode principal component analyses separately for the meteorological seasons. Atmospheric circulation types (CTs) are derived by a particular clustering technique including large-scale fields of SLP, vertical wind and relative humidity at the 700 hPa level as well as the regional rainfall time series. Multiple regression models with monthly CT frequencies as predictors are derived for monthly frequencies and amounts of regional precipitation extremes (beyond the 95 % percentile). Using predictor output from different global climate models (ECHAM6, ECHAM5, EC-EARTH) for different scenarios (RCP4.5, RCP8.5, A1B) and two projection periods (2021–2050, 2071–2100) leads to assessments of future changes in regional precipitation extremes. Most distinctive changes are indicated for the summer season with mainly increasing extremes for the earlier period and widespread decreasing extremes towards the end of the 21st century, mostly for the strong scenario. Considerable uncertainties arise from the predictor use of different global climate models, especially during the winter and spring seasons.

In the context of analyzing temporal varying relationships of heavy precipitation events in the Mediterranean area and associated anomalies of the large-scale circulation, quantile regression models were established. The models were calibrated using different circulation and thermodynamic variables at the 700 hPa and 850 hPa levels as predictors as well as daily precipitation time series at different stations in the Mediterranean area as predictand. Analyses were done for the second half of the 20th century. In the scope of assessing non-stationarities in the predictor-predictand relationships the time series were divided into calibration and validation periods. 100 randomized subsamples were used to calibrate/validate the models under stationary conditions. The highest and lowest skill score of the 100 random samples was used to determine the range of random variability. The model performance under non-stationary conditions was derived from the skill scores of cross-validated running subintervals. If the skill scores of several consecutive years are outside the range of random variability a non-stationarity was declaimed. Particularly the Iberian Peninsula and the Levant region were affected by non-stationarities, the former with significant positive deviations of the skill scores, the latter with significant negative deviations. By means of a case study for the Levant region we determined three possible reasons for non-stationary behavior in the predictor-predictand relationships. The Mediterranean Oscillation as a superordinate system affects the cyclone activity in the Mediterranean basin and the location and intensity of the Cyprus low. Overall, it is demonstrated that non-stationarities have to be taken into account within statistical downscaling model development.

Climate change projections are subject to uncertainty arising from climate model deficiencies, unknown initial conditions and scenario assumptions. In the IPCC reports and many other publications climate changes and uncertainty ranges are usually displayed in terms of multi-model ensemble means and confidence intervals, respectively. In this study, we present a more quantitative assessment and statistical testing of climate change signals in the light of uncertainty. The approach is based on a two-way analysis of variance, referring to 24 climate models from the CMIP3 multi-model ensemble, and extents over the 21st century. The method also distinguishes between different climate variables, time scales and emission scenarios and is combined with a simple bias correction algorithm. The Mediterranean region has been chosen as a case study because it represents an assumed hot spot of future climate change, where temperature is projected to rise substantially and precipitation may decrease dramatically by the end of the 21st century. It is found that future temperature variations are mainly determined by radiative forcing, accounting for up to 60% of total variability, especially in the western Mediterranean Basin. In contrast, future precipitation variability is almost completely attributable to model uncertainty and model internal variability, both being important in more or less equal shares. This general finding is slightly depending on the prescribed emission scenario and strictly sensitive to the considered time scale. In contrast to precipitation, the temperature signal can be enhanced noticeably when bias-correcting the models' climatology during the 20th century: the greenhouse signal then accounts for up to 75% of total temperature variability in the regional mean.

The main purpose of this paper is to contribute to the improvement in the present knowledge concerning regional carbon dioxide (CO2) and methane (CH4) exchange as an essential step towards reducing the uncertainties along with bottom-up estimations of their global budget by identifying the characteristic spatial and temporal scales of the regional greenhouse gas fluxes. To this end, we propose a stepwise statistical top-down methodology for examining the relationship between synoptic-scale atmospheric transport patterns and mole fractions of the climate gases to finally receive a characterisation of the sampling sites with regard to the key processes driving the CO2 or CH4 concentration levels. The results of this study presented in this paper give detailed insights into the emission structures underlying the measurement time series by means of origin-related examinations of the Alpine CO2 and CH4 budgets. The time series of both climate gases from the atmospheric measurements carried out at the four high-alpine observatories Schneefernerhaus, Jungfraujoch, Sonnblick and Plateau Rosa form the basis for the characterisation of the regional CO2 as well as CH4 budget of the Alpine region as the focus area of the Central European study region. For the investigation area so outlined, the project identifies source and relative sink regions with influence on the Alpine climate gas measurements as well as their temporal variations. The therefore required combination of the measurements with the synoptic situation prevailing at the respective measuring time which carries the information about the origin of the analysed air masses is derived by means of a trajectory-based receptor model. The back trajectory receptor model is set up to decipher with high spatial resolution the most relevant source and sink areas, whereby the Alpine region is identified as a significant relative sink for CO2 as well as for CH4 concentrations all year long, whereas major European emitters show their impact during different seasons. The reliable results achieved with this approach in connection with the encouraging model-internal uncertainty assessments and external plausibility checks lend credence to our model and its strength to illustrate dependably spatial–temporal variations of the relevant emitters and absorbers of different climate gases (CO2 and CH4) in high spatial resolution.

Background/Objectives: Meteorological factors seem to influence stroke incidence, however, the complex association between weather and stroke remains unclear. Possible explanations from the literature do not categorize into subdivisions of ischemic strokes, only have small patient numbers, or refer to a selection of isolated weather elements without investigating weather changes and more. Method: In this exploratory trial, almost 18,000 stroke cases from a single stroke center in Southern Germany were analyzed from 2006 to 2015 and classified into the main subgroups of strokes and subdivisions of ischemic stroke etiologies applying the Trial of Org 10172 in Acute Stroke Treatment classification. For each stroke event, the air mass classification was determined from a subset of 7 air mass categories. Relative excess morbidities were derived for the 7 different air mass categories, taking into account the day of the event and up to 2 and 5 days preceding the stroke event. Results: Statistically significant findings (α ≤0.1) reveal that dry tropical air masses were associated with a lower/higher risk for hemorrhagic (HEM)/macroangiopathic strokes (MAS), respectively. Dry polar air masses were associated with a higher risk for intracerebral bleedings and lower risk for ischemic stroke subtypes. Moist air masses were associated with a reduced incidence of MAS. A strong temperature increase 5 days prior to the event was associated with a lower risk of HEM strokes. Temperature increases were associated with lower risks for MAS and cardio-embolic strokes. Significant temperature decreases were associated with a higher risk of MAS. Conclusions: Temperature effects were dependent on both air masses and temperature changes within 5 days prior to the event and were associated with statistically relevant changes in stroke incidence. Decisive factors such as etiology, age, sex, and risk factors were also taken into account.

Projections of seasonal extreme precipitation changes in eight Mediterranean subregions between the end of the twentieth and the end of the twenty-first century are analyzed using weighted multi-model ensembles. Weights are based on the performance of predictor variables in the scope of statistical downscaling. Two indices of precipitation scarcity as well as two indices of heavy precipitation are downscaled from global climate model data of the Coupled Model Intercomparison Project phase 3 and 5 (CMIP3, CMIP5) multi-model ensembles, considering two emission scenarios each. Based on the performance with regard to observations of extreme precipitation as well as inter-model consistency, three weighting metrics are calculated and subsequently applied to each ensemble. While meteorological droughts are projected to increase in most cases, the tendency is less pronounced for heavy precipitation events and mostly points towards reduction. The weighting does not affect the multi-model mean changes, but induces a decrease of ensemble spread (although mostly not significant), implying a decrease of model uncertainty. As the ensemble and scenario considered have minor effect on the findings and also the differences between seasons and subregions are not marked, there is strong evidence for enhanced droughts in the Mediterranean region, implying major socio-economic and ecological consequences.

A statistical downscaling approach allowing for change points in the relationships between atmospheric predictors and local precipitation is introduced. Change point analysis within generalized linear models, and change points in the predictor characteristics were used to develop a change point statistical downscaling approach. The approach is illustrated for station-based winter precipitation in the Mediterranean area. In this study, 94 stations were considered. The change point analysis yielded 37 stations with robust change points in the predictors–precipitation relationships. An added value regarding statistical model performance of the change point approach compared to the use of statistical models without change points was observed for 15 out of the 37 stations. In the projections under enhanced greenhouse gas forcing the application of the change point approach for the 15 stations affects the downscaled precipitation amounts, with significant differences compared to the application of downscaling models without change points for about one third of these stations. Under RCP4.5 and RCP8.5 scenario conditions, mainly increases of winter precipitation are projected until the end of the 21st century for parts of the western and northern Mediterranean area, whereas the north-eastern and eastern Mediterranean areas are affected by decreases.

This paper describes the flood sensitivity of the Bavarian part of the Alpine Foreland of Germany and addresses different questions concerning climate variability and flood frequencies, from the 14th century until today. The focal point of the paper is the flood frequency of the superordinate spatial unit of the Bavarian Foreland. Based on written historical sources, the flood history of the Alpine Foreland of Germany can be reconstructed back to the 14th century. One major result is the occurrence of "flood-rich" and "flood-poor" episodes in almost cyclical sequences. Flood-rich periods, before the 16th century based on limited available data, were recorded in the periods 1300–1335, 1370–1450, 1470–1525, 1555–1590, 1615–1665, 1730–1780, 1820–1870, and 1910–1955 as well as in a ninth period beginning in 1980. The flood-rich periods are characterized by longer flood duration. Most of the flood-rich and flood-poor periods (in particular the beginning and the end of them) can be connected to changes in natural climate variability. These include changing sunspot numbers (as a measure of solar activity), so-called Little Ice Age type events (LIATEs) as well as changes in the North Atlantic Oscillation (NAO). Climate signals from external forcing factors, which could be used to explain the changing flood frequencies in the Bavarian Alpine Foreland, end in 1930. Relationships within the climate system such as the correlation of flood frequencies with the NAO have changed during the transition from the post Little Ice Age period to the Modern Climate Optimum around 1930. Natural climate variability might have been overlaid by anthropogenic climate change.

Trends of Mediterranean extreme temperatures are analysed for the period 1961–1990 based on daily station time series. Increases can be identified in the western Mediterranean area, whereas an opposite trend becomes apparent for the eastern Mediterranean region. Assessments of the 95th percentile of maximum temperatures in summer and of the 5th percentile of minimum temperatures in winter for the 21st century under enhanced greenhouse warming conditions are performed by means of statistical downscaling techniques. Mainly increases of both extreme indices result from these assessments, but considerable differences arise when using different predictors or predictor combinations, respectively. Furthermore, the results give strong indications that changes in temperature extremes do not follow a simple shift of the whole temperature distribution to higher values.

ScaleX is a collaborative measurement campaign, collocated with a long-term environmental observatory of the German Terrestrial Environmental Observatories (TERENO) network in the mountainous terrain of the Bavarian Prealps, Germany. The aims of both TERENO and ScaleX include the measurement and modeling of land surface–atmosphere interactions of energy, water, and greenhouse gases. ScaleX is motivated by the recognition that long-term intensive observational research over years or decades must be based on well-proven, mostly automated measurement systems, concentrated in a small number of locations. In contrast, short-term intensive campaigns offer the opportunity to assess spatial distributions and gradients by concentrated instrument deployments, and by mobile sensors (ground and/or airborne) to obtain transects and three-dimensional patterns of atmospheric, surface, or soil variables and processes. Moreover, intensive campaigns are ideal proving grounds for innovative instruments, methods, and techniques to measure quantities that cannot (yet) be automated or deployed over long time periods. ScaleX is distinctive in its design, which combines the benefits of a long-term environmental-monitoring approach (TERENO) with the versatility and innovative power of a series of intensive campaigns, to bridge across a wide span of spatial and temporal scales. This contribution presents the concept and first data products of ScaleX-2015, which occurred in June–July 2015. The second installment of ScaleX took place in summer 2016 and periodic further ScaleX campaigns are planned throughout the lifetime of TERENO. This paper calls for collaboration in future ScaleX campaigns or to use our data in modelling studies. It is also an invitation to emulate the ScaleX concept at other long-term observatories.

Strong seasonal variability of hygric and thermal soil conditions are a defining environmental feature in northern Australia. However, how such changes affect the soil–atmosphere exchange of nitrous oxide (N2O), nitric oxide (NO) and dinitrogen (N2) is still not well explored. By incubating intact soil cores from four sites (three savanna, one pasture) under controlled soil temperatures (ST) and soil moisture (SM) we investigated the release of the trace gas fluxes of N2O, NO and carbon dioxide (CO2). Furthermore, the release of N2 due to denitrification was measured using the helium gas flow soil core technique. Under dry pre-incubation conditions NO and N2O emissions were very low (

This contribution investigates the relationship between the large-scale atmospheric circulation and interannual variations of the standardized precipitation index (SPI) in Central Europe. To this end, circulation types (CT) have been derived from a variety of circulation type classifications (CTC) applied to daily sea level pressure (SLP) data and mean circulation indices of vorticity (V), zonality (Z) and meridionality (M) have been calculated. Occurrence frequencies of CTs and circulation indices have been utilized as predictors within multiple regression models (MRM) for the estimation of gridded 3-month SPI values over Central Europe, for the period 1950 to 2010. CTC-based MRMs used in the analyses comprise variants concerning the basic method for CT classification, the number of CTs, the size and location of the spatial domain used for CTCs and the exclusive use of CT frequencies or the combined use of CT frequencies and mean circulation indices as predictors. Adequate MRM predictor combinations have been identified by applying stepwise multiple regression analyses within a resampling framework. The performance (robustness) of the resulting MRMs has been quantified based on a leave-one-out cross-validation procedure applying several skill scores. Furthermore, the relative importance of individual predictors has been estimated for each MRM. From these analyses, it can be stated that model skill is improved by (i) the consideration of vorticity characteristics within CTCs, (ii) a relatively small size of the spatial domain to which CTCs are applied and (iii) the inclusion of mean circulation indices. However, model skill exhibits distinct variations between seasons and regions. Whereas promising skill can be stated for the western and northwestern parts of the Central European domain, only unsatisfactory skill is reached in the more continental regions and particularly during summer. Thus, it can be concluded that the presented approaches feature the potential for the downscaling of Central European drought index variations from the large-scale circulation, at least for some regions. Further improvements of CTC-based approaches may be expected from the optimization of CTCs for explaining the SPI, e.g. via the inclusion of additional variables in the classification procedure.

Besides dynamical downscaling by regional climate models, statistical downscaling (SD) is a major tool to derive climate change projections on regional or even local scales. For the Mediterranean area, an increasing number of downscaling studies based on different statistical techniques have been published in the last two decades with a broad range of sometimes differing results relating to different variables and regional domains. This paper gives a short review of these Mediterranean downscaling studies mainly considering the following two aspects: (1) what kind of progress has been realized in this field since the early 1990s? The review addresses the inclusion of extremes in downscaling assessments, the development of probabilistic approaches, the extension of predictor sets, the use of ensembles for both dynamical model simulations and statistical model assessments, the consideration of non-stationarities in the predictor–predictand relationships, and some advances related to synoptic downscaling. (2) What are the main regional climate change signals in the Mediterranean area, considering agreed and controversial points also with respect to dynamical models? Best accordance among future projections can be found in seasonal temperatures with lower rates of warming in winter and spring, and, in most cases, higher ones in summer and autumn. Different results are obtained for the intra-annual range of extreme temperatures, but high-temperature conditions are generally expected to increase. Regarding seasonal precipitation, predominant reductions are indicated for spring, summer, and autumn. For winter, however, projections are distinctly different (GCMs: rainfall decrease; RCMs: increase only in the northernmost parts of the Mediterranean region; SD: widespread increases in the northern and western parts in several studies). Different results are obtained for rainfall extremes, but the entire precipitation distribution tends to shift towards higher and lower values. Apart from some sub-regional deviations, there is a predominant increase in future dry period durations. For near-surface winds, only a few studies are available, and they project some decline mainly for the winter season.

Variability of sea surface temperatures (SSTs) in the tropical southeast Atlantic Ocean has previously been shown to significantly contribute to changes in summer rainfall along the West African as well as the Angolan coast. This study examines links between southeast Atlantic SST and African west coast precipitation variability for an extended 60-year period from 1951 to 2010. In contrast to earlier studies, our analyses cover the whole Atlantic coast from Guinea to South Africa and are not limited to specific seasons. In addition to the analyses of the total variability, pronounced anomalies in terms of warm and cold water events and their impact on African west coast precipitation are analyzed. By using for the first time a new comprehensive classification of Atlantic Nino and Nina events, consistent results are achieved for a larger region of Africa, also considering Atlantic cold water events which have rather been neglected so far. Results show that, depending on the particular region, southeast Atlantic SSTs play an important role for coastal rainfall variability throughout the year. Furthermore, the rainfall response to Atlantic cold and warm water events appears to be asymmetric in season and magnitude. Atlantic cold events can cause a stronger decrease in rainfall along the West African and Gabon coast than the increase is in warm events. In addition, not all seasons show a significant rainfall response to both warm and cold water events.

In order to examine correspondence between different methods for circulation type classification, a dataset of classification catalogs for 12 different European regions has been created using a specially developed software package. Twenty-seven basic automatic classification methods have been applied in several variants to different input datasets describing atmospheric circulation. Together with six manual classifications a total of 33 methods are available for inter-comparison. Pattern correlation, frequency time-series correlation and the adjusted Rand index have been used for comparison. Highly significant correspondence has been detected only for two clustering techniques while the remaining classification methods show surprisingly low similarity. A Monte-Carlo test with 1000 classifications of randomly defined types even shows that most of the methods are not more similar among each other than any arbitrarily chosen types. The predominant dissimilarity between the methods is interpreted to be a result of a lack of inherent structures of the input data. Only simulated annealing clustering and self-organizing maps get nearly identical results because they can optimally fit the partitioning to the outer shape of the data cloud in the phase space. Also methods based on pre-defined types come to very different results because small changes in the definition of thresholds may lead to large differences in the partitioning. It is concluded that because of the missing inner structure of the data there is no clear statistical reason to prefer any of the examined methods. For practice in synoptic climatology this means that finding a suited classification for a certain purpose may require a broad comparison of methods. The software package cost733class for development, comparison and evaluation of classifications which was developed and used in this study is available at http://cost733.geo.uni-augsburg.de to facilitate this task.

Spatial and temporal variability of meteorological variables across urban areas due to differences in land surface characteristics is a common phenomenon. Most pronounced is the effect of land cover on air temperature. In this study, parametric and non-parametric statistical approaches (stepwise multiple linear regression, random forests) were applied in order to model sub-daily and daily spatial patterns of the urban heat island intensity in the major city of Augsburg, Southern Germany, and its rural surroundings. A large number of model setups utilizing variables from different land surface data sets as predictors and taking into account different seasonal, daily and meteorological situations was examined. The results were compared concerning different measures of model performance (mean squared skill score, mean squared error, explained variance). For individual setups and situations considerable skill with a mean squared skill score of up to 0.85 was reached. The best performing models were obtained from multiple linear regression for situations with low wind speeds and cloud cover in the morning and evening. Selected models were utilized to derive continuous spatial distributions of the air temperature deviations from a rural reference station. The resulting maps can be useful for various applications, e.g. in the context of urban planning.

This study investigates whether a regional climate model (RCM) driven by a global general circulation model (GCM) in a nesting approach with observed atmospheric CO2 concentrations shows predictability for temperature and precipitation trends during 1961–1990 in the Mediterranean area, a region strongly influenced by large-scale circulation. Resulting discrepancies between model and observations raise the question whether the model predictability increases after removing impacts of mid-latitude circulation variability. For temperature and precipitation trends we use the RCM REMO and the observational dataset E-OBS, and for atmospheric circulation the driving coupled GCM ECHAM5/MPI-OM and NCEP/NCAR reanalyses. Cross-validated multiple regression analyses between large-scale circulation and regional temperature and precipitation are performed for observed and simulated data. The impact of circulation is removed from the original temperature and precipitation data, and the trends of circulation-related and circulation-unrelated parts are compared. The circulation-related trends of models and observations show discrepancies owing to differing observed and simulated mid-latitude circulation dynamics, i.e. different temporal evolutions of North Atlantic Oscillation and East Atlantic pattern in winter and East Atlantic Jet and a blocking pattern in summer. Such differences can be related to unknown initial conditions of GCM simulations. In fact, we find strong impacts of initial conditions on mid-latitude circulation dynamics of ECHAM5/MPI-OM ensemble members over 30-year periods. The agreement between simulated and observed circulation-unrelated trends is generally higher than for original trends indicating that the predictability of this nesting approach increases by removing impacts of mid-latitude circulation variability. We conclude that initial conditions affect climate variability up to the multi-decadal timescale, at least in parts of the globe which are governed by extratropical circulation modes, and hence, hinder the comparability of simulated and observed climate trends over time periods shorter than the timescale dominated by radiative forcing. In the Mediterranean Basin the latter is definitely beyond 30 years.

Projected changes of extreme precipitation in the Mediterranean area up until the end of the 21st century are analysed by means of statistical downscaling. Generalized linear models are used as downscaling technique to assess different percentile-based indices of extreme precipitation on a fine-scale spatial resolution. In the region under consideration extreme precipitation is related to anomalies of the large-scale circulation as well as to convective conditions. To account for this, predictor selection encompasses variables describing the large-scale circulation (geopotential heights of the 700 hPa and 500 hPa levels, u- and v-wind components of the 850 hPa level) as well as thermo-dynamic parameters (specific humidity of the 850 hPa and 700 hPa levels, Showalter-Index, convective inhibition). In the scope of the statistical downscaling approach a specific statistical ensemble technique is applied in order to allow for non-stationarities in the predictors–predictand relationships. Consequently, the statistical ensembles include a range of possible future evolutions of extreme precipitation. Two different emission scenarios (A1B and B1), multiple runs for each scenario, and output of two different general circulation models (ECHAM5 and HadCM3) are applied to assess extreme precipitation under enhanced greenhouse warming conditions. The results yield mainly decreases over many parts of the Mediterranean area in spring. In summer increases are assessed around the Tyrrhenian Sea, the Ionian Sea, and the Aegean Sea, whereas decreases are projected for most of the western and northern Mediterranean regions. In autumn reductions of heavy rainfall occur over many parts of the western and central areas. In winter distinct increases are widespread in the Mediterranean area. Beyond the assessments using all predictors it is shown in the present contribution that different predictor variables can lead to varying statistical downscaling results. It points to distinct impacts of the change of specific atmospheric conditions on local extreme precipitation.

For the tropical Atlantic Ocean two phenomena showing anomalous sea surface temperature (SST) warming have been described: the Atlantic Ni ˜ no in the equatorial Atlantic and the Benguela Ni ˜ no off the Angolan coast. In this study, both Ni ˜ no types are integrated into a new set of SST regions and the definition of a robust standardized index for the longterm period 1870–2011. Comparisons of these regions and indices show a close connection between anomalous warming and cooling in the equatorial Atlantic and the Benguela region. Therefore, instead of considering equatorial Atlantic and Benguela warm and cold events separately, we propose to classify them into three subtypes of one comprehensive Atlantic Ni ˜ no. Copyright  2013 Royal Meteorological Society

[1] In the present study, nonstationarities in predictor–predictand relationships within the framework of statistical downscaling are investigated. In this context, a novel validation approach is introduced in which nonstationarities are explicitly taken into account. The method is based on results from running calibration periods. The (non)overlaps of the bootstrap confidence interval of the mean model performance (derived by averaging the performances of all calibration/verification periods) and the bootstrap confidence intervals of the individual model errors are used to identify (non)stationary model performance. The specified procedure is demonstrated for mean daily precipitation in the Mediterranean area using the bias to assess model skill. A combined circulation-based and transfer function–based approach is employed as a downscaling technique. In this context, large-scale seasonal atmospheric regimes, synoptic-scale daily circulation patterns, and their within-type characteristics, are related to daily station-based precipitation. Results show that nonstationarities are due to varying predictors–precipitation relationships of specific circulation configurations. In this regard, frequency changes of circulation patterns can damp or increase the effects of nonstationary relationships. Within the scope of assessing future precipitation changes under increased greenhouse warming conditions, the identification and analysis of nonstationarities in the predictors–precipitation relationships leads to a substantiated selection of specific statistical downscaling models for the future assessments. Using RCP4.5 scenario assumptions, strong increases of daily precipitation become apparent over large parts of the western and northern Mediterranean regions in winter. In spring, summer, and autumn, decreases of precipitation until the end of the 21st century clearly dominate over the entire Mediterranean area.

Ex treme precipitation events in theMediterranean area have been defined by different percentile-based indices of extreme precipitation for autumn and winter: the number of events exceeding the 95 th percentile of daily pr ecipitation, percentage, total amount, and mean daily intensity of precipitation from these events. Results from statistical downscaling applying canonical correlation analysis as well as from dynamical downscaling using the regional climate model REMO are mapped for the 1961–1990 baseline period as well as for the magnitude of change for the future time slice 2021–2050 in relation to the former period. Direct output of the coupled global circulation model ECHAM5 is used as an additional source of information. A qualitative comparison of the two different downscaling techniques indicates that under the present climate both the dynamical and the statistical techniques have skill to reproduce extreme precipitation in the Mediterranean area. A good representation of the frequency of extreme precipitation events arises from the statistical downscaling approach, whereas the intensity of such events is adequately modelled by the dynamical downscaling. Concerning the change of extreme precipitation in the Mediterranean area until the mid-21 st century, it is projected that the frequency of extreme precipi tation events will decrease in most parts of the Mediterranean area in autumn and winter. The change of the mean intensity of such events shows a rather heterogeneous pattern with intensity increases in winter most likely at topographical elevations exposed to the West, where the uplift of humid air profits by the increase of atmospheric moisture under climate change conditions. For the precipitation total from events exceeding the 95 th percentile of daily precipitation, wi despread decreases are indicated in autumn, whereas in winter increases occur over the western part of the Iberian Peninsula and southern France, and reductions over southern Turkey, the eastern Mediterranean area, parts of Italy and some North African regions.

Several statistical downscaling techniques are intercompared and evaluated with respect to daily station-based precipitation in the eastern Mediterranean/Middle East region. The study introduces unconditioned and precipitation-conditioned SANDRA (Simulated ANnealing and Diversified RAndomization) cluster analysis (SCA) as new downscaling approaches and additionally uses the two widely used techniques of canonical correlation analysis (CCA) and multiple linear regression analysis (MR). For the precipitation-conditioned SANDRA cluster analysis different weights (percentages of contribution to the clustering) are evaluated. Furthermore, two different predictor combinations are used, a simple one only including mean sea level pressure (SLP), and a more complex one additionally including 500 hPa-geopotential heights, 500 hPa-vorticity and 1000 hPa-moisture flux. Analyses are carried out on a daily basis for the main rainy season from November to March for the period 1961–1990. It is shown that SLP, as single predictor, does not perform sufficiently well, but adding further predictors considerably improves model performance in terms of increased explained variance and model stability as well as reduced root mean square error (RMSE). From all selected techniques MR and CCA show the best performance for the SLP-based models, with comparable results for both techniques, whereas precipitation-conditioned SANDRA cluster analysis performs best when further predictors are included. Performance differences between all techniques are generally smaller than those for a particular technique using different predictor sets. Copyright © 2011 Royal Meteorological Society

The determination of specific sea surface temperature (SST) patterns from large-scale gridded SST-fields has widely been done. Often principal component analysis (PCA) is used to condense the SST-data to major patterns of variability. In the present study SST-fields for the period 1950–2003 from the area 20°S to 60°N are analysed with respect to SST-regimes being defined as large-scale oceanic patterns with a regular and at least seasonal occurrence. This has been done in context of investigations on seasonal predictability of Mediterranean regional climate with large-scale SST-regimes as intended predictors in statistical model relationships. The SST-regimes are derived by means of a particular technique including multiple applications of s-mode PCA. Altogether 17 stationary regimes can be identified, eight for the Pacific Ocean, five for the Atlantic Ocean, two for the Indian Ocean, and two regimes which show a distinct co-variability within different ocean basins. Some regimes exist, with varying strength and spatial extent, throughout the whole year, whereas other regimes are only characteristic for a particular season. Several regimes show dominant variability modes, like the regimes associated with El Nino, with the Pacific Decadal Oscillation or with the North Atlantic Tripole, whereas other regimes describe little-known patterns of large-scale SST variability. The determined SST-regimes are subsequently used as predictors for monthly precipitation and temperature in the Mediterranean area. This subject is addressed in Part II of this paper.

There is increasing concern that precipitation and temperature extremes may be changing in frequency and character as a result of changing climate, and the latter is mostly linked with particular changes in the atmospheric circulation. Therefore the question arises - a key question in the climate change prospective - as to how precipitation and temperature extremes are related to large-scale atmospheric circulation types? To study such relationships over an extended period of more than one and a half centuries, we include daily precipitation and temperature time series compiled during the EU project EMULATE (European and North Atlantic daily to multidecadal climate variability) back to 1850 as well as daily mean SLP reconstructions from the same project for the same period. The latter data set has been used for classifying daily circulation types for each season using a simulated annealing clustering technique. Comparing each of these circulation types with their percentages among extreme days and among non-extreme days (with respect to precipitation or temperature) clearly reveals that in most cases only a few of the seasonal circulation types are conducive to the occurrence of daily extremes. This is shown for heavy precipitation and positive temperature extremes (beyond the 98th percentile in each case), related to the winter (DJF) and summer (JJA) seasons for a central European region. Different circulation patterns proved to be important in this context. Thus, in contrast to positive temperature extremes during winter being linked preferably to zonal circulation patterns (positive mode of the North Atlantic Oscillation, NAO), heavy winter precipitation in central Europe is distinctly associated with less zonal patterns characterized by an eastward or southeastward shift of the subpolar centre of low pressure implying only weak correlations with the NAO. Furthermore, particular indices reveal that changing frequencies of extremes are not only due to corresponding frequency changes of these conducive circulation types, but also to changes of their association to precipitation or temperature extremes (reflected by changes in the percentage of extremes related to the overall occurrence of the corresponding circulation type). These within-type changes of circulation types often govern the low-frequency variations in the overall incidence of extremes.

The combination of a conventional Markov chain model (zero and first order) and a gamma distribution model are found to be applicable to derive meaningful agricultural features from precipitation in the Volta Basin (West Africa). Since the analysis of the monthly or annual precipitation amount does not provide any adequate information on rainfall timing and sufficiency of crop water requirement, rainfall modelling was performed on a daily time scale for 29 rainfall stations. The modelled rainfall features follow distinct spatial patterns, which will be presented as maps of(1) rainfall occurrence probabilities and (2) recommendations of optimal planting dates. In addition, the effective drought index (EDI) working on daily time scales is calculated in order to assess drought properties of five different rainfall regions within the Volta Basin. Apart from the common way of separately modelling the duration and intensity due to their different distributions, a copula approach is chosen in this study to construct a bivariate drought distribution. Application of the measures derived to agricultural decision support will be discussed briefly. Copyright © 2009 Royal Meteorological Society

Statistical downscaling techniques are used to assess Mediterranean precipitation changes for the period 1990–2100 under increased greenhouse warming conditions from climate model output of large-scale predictor changes. Analyses are carried out on a monthly basis for the main rainy season from October to May. Results of two statistical techniques, multiple regression analysis, and canonical correlation analysis, are compared. Furthermore, differences which arise from the use of different combinations of the predictor variables, such as geopotential heights, humidity, and sea surface temperatures are discussed. Predictor output from seven different AOGCM runs (two ECHAM4/OPYC3 runs, four ECHO-G runs including a three-member ensemble, and one HadCM3 run) is used to assess Mediterranean precipitation changes in the 21st century. Five of the AOGCM runs were forced with B2 scenario assumptions according to the special report on emission scenarios (SRES), one with SRES-A2 scenario assumptions, and one with the former IS92a scenario. Using 1000 hPa-/500 hPa-geopotential heights and 1000 hPa-specific humidity as large-scale predictors, a shorter but wetter wet season is evident for the western and northern Mediterranean regions including precipitation increases in winter and decreases in the transitional seasons for the period 2071–2100 compared to 1990–2019. The eastern and southern parts of the Mediterranean area exhibit mainly negative precipitation changes from October to May for an increased greenhouse gas forcing. Copyright © 2007 Royal Meteorological Society

Non-stationarities are inherent characteristics of the climate system and can be observed on different temporal and spatial scales. Non-stationarities in large-scale modes of climate variability of the Northern Hemisphere and the associated changes in the surface climate like modifications of the temperature and precipitation patterns are analysed. As major modes of climate variability the El Nino–Southern Oscillation phenomenon, the Pacific–North American pattern and the North Atlantic Oscillation are selected. The North Atlantic–European area is taken as an example to highlight non-stationarities in the atmospheric circulation and their impact on regional climate. The mechanisms and consequences of circulation-climate non-stationarities are discussed.

The Mediterranean area is strongly vulnerable to future changes in temperature and precipitation, particularly concerning extreme events, and has been identified as a climate change hot spot. This study performs a comprehensive investigation of present-day and future Mediterranean precipitation extremes based on station data, gridded observations and simulations of the regional climate model (REMO) driven by the coupled global general circulation model ECHAM5/MPI-OM. Extreme value estimates from different statistical methods—quantile-based indices, generalized pareto distribution (GPD) based return values and data from a weather generator—are compared and evaluated. Dynamical downscaling reveals improved small-scale topographic structures and more realistic higher rainfall totals and extremes over mountain ranges and in summer. REMO tends to overestimate gridded observational data in winter but is closer to local station information. The dynamical–statistical weather generator provides virtual station rainfall from gridded REMO data that overcomes typical discrepancies between area-averaged model rainfall and local station information, e.g. overestimated numbers of rainy days and underestimated extreme intensities. Concerning future rainfall amount, strong summer and winter drying over the northern and southern Mediterranean, respectively, is confronted with winter wetting over the northern part. In contrast, precipitation extremes tend to increase in even more Mediterranean areas, implying regions with decreasing totals but intensifying extremes, e.g. southern Europe and Turkey in winter and the Balkans in summer. The GPD based return values reveal slightly larger regions of increasing rainfall extremes than quantile-based indices, and the virtual stations from the weather generator show even stronger increases.

This study investigates the teleconnections between the tropical Atlantic and Pacific Oceans in 15 state-of-the-art fully coupled general circulation models and Earth system models without external SST forcing. In contrast to other studies, the teleconnection is considered in both directions—from the Pacific to the Atlantic and from the Atlantic to the Pacific. The model ensemble is generally able to simulate the propagation of atmospheric and oceanic signals to the adjacent ocean basin, generated by warm sea surface temperature (SST) anomalies in the tropical eastern oceans with Atlantic summer events lagging or leading Pacific boreal winter events. This is investigated by means of time-lagged composite analyses of different atmospheric parameters, including sea level pressure, wind, stream function, velocity potential, vertical air movement and divergent wind at several levels. However, the modelled inter-basin teleconnection and its correct frequency of occurrence depend on the strong warm SST biases in the Atlantic Benguela upwelling region and in the Pacific Ocean.

Canonical correlation analysis is used to identify main coupled circulation–rainfall patterns and to relate recent variability and trends of Mediterranean precipitation to large-scale circulation dynamics. Analyses are based on geopotential heights (500 and 1000 hPa levels) for the North Atlantic–European area (National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis) and on highly resolved (0.5° × 0.5° ) monthly rainfall grids (Climatic Research Unit, Norwich) selected for the Mediterranean area during the 1948–98 period. Combining monthly analyses with similar characteristics to seasonal samples yields winter (October–March), spring (April–May) and summer (June–September) types of coupled variability; a particular autumn type for the whole Mediterranean does not occur on the monthly time scale. Coupled patterns specifically linked to one or two seasons include an east Atlantic jet (EA-Jet) related pattern for summer and a Mediterranean meridional circulation (MMC) pattern for winter and spring. The most important pattern recurring with dynamical adjustments throughout the whole year reflects the seasonal cycle of the Mediterranean oscillation (MO), which is linked (with seasonal dependence) to the Northern Hemisphere teleconnection modes of the Arctic oscillation (AO) and North Atlantic oscillation (NAO). Winter rainfall trends of the recent decades marked by widespread decreases in the Mediterranean area and by opposite conditions in the southeastern part are linked to particular changes over time in several of the associated circulation patterns. Thus, different regional rainfall changes are integrated into an overall interrelation between Mediterranean rainfall patterns and large-scale atmospheric circulation dynamics. Copyright © 2003 Royal Meteorological Society

Based on monthly mean sea level pressure grids objectively reconstructed by Luterbacher et al. variations of dynamical modes of the atmospheric circulation for January and July are described by novel indices for running 31-year periods between 1659 and 1999. These indices reflect the continuous evolution of the atmospheric circulation not only with regard to frequency changes of major dynamical modes but also in terms of internal changes within each mode concerning both dynamic (vorticity, intensity) and climatic properties (Central European temperature and precipitation during occurrence of each mode, respectively). Results indicate the great importance of within-mode variations: the zonal circulation mode in January, varying in frequency with long-term cycles, primarily changed its dynamic and climatic properties (towards higher indices) during the transition from the Little Ice Age to modern conditions between 1800 and 1930. Within the Russian High mode of January a change in preference from easterly to westerly patterns above Central Europe occurred around 1850. For July, a striking frequency maximum of the westerly mode at the end of the eighteenth century coincided with a period of marked summer warmth in Central Europe due to negative/positive deviations in vorticity/temperature during occurrence of this mode. The long-term evolution in July indicates a general increase of anticyclonic conditions strengthening during the last 50 years towards a unique phenomenon within the last centuries. The strong increase in the winter-time westerly circulation during the last decades, however, does not appear extraordinary in view of the low-frequency variations of this mode.

Spatially and temporally high-resolution estimates of past natural climate variability are important to assess recent significant climate trends. The mid-latitude atmospheric circulation is the dominant factor for regional changes in temperature, rainfall, and other climatic variables. Here we present reconstructions of gridded monthly sea level pressure (SLP) fields back to 1659 and seasonal reconstructions from 1500-1658 for the eastern North Atlantic-European region (30°W to 40°E; 30°N to 70°N). These were developed using principal component regression analysis based on the combination of early instrumental station series (pressure, temperature and precipitation) and documentary proxy data from Eurasian sites. The relationships were derived over the 1901-1960 calibration period and verified over 1961-1990. Under the assumption of stationarity in the statistical relationships, a transfer function derived over the 1901-1990 period was used to reconstruct the 500-year large-scale SLP fields. Systematic quality testing indicated reliable winter reconstructions throughout the entire period. Lower skill was obtained for the other seasons, although meaningful monthly reconstructions were available from around 1700 onwards, when station pressure series became available. The quality and the reconstructed SLP fields for two exceptionally cold years (1573, 1740) are discussed and climatologically interpreted. An EOF analysis of the 1500-1999 winter SLP revealed, firstly, a zonal flow pattern with pronounced decadal to centenial time scale variations, secondly, a monopole pattern over northwest Europe and thirdly, a pattern modulating the meridional flow component over Europe. These 500-year SLP reconstructions should be useful for modelling studies, particulary for analyses of low-frequency atmospheric variability and for circulation dynamics.

In this chapter, the results of the trend analysis for each index, station or region are presented in the form of maps and time-series plots. The maps, however, are only used for the most recent period (1901–2000) due to the limited number of stations in earlier periods. For each period, seasonal indices starting with spring (MAM) and ending with winter (DJF) are presented. The order of presentation is Tmin/Tmax, followed by Tmean and Precipitation. To make it easier to find an index in a given season and for a given period, a header is put on each page to indicate the period, season, and index group. The circles in the maps indicate station locations for the two longer periods where results for individual stations are presented. All the maps contain a colour bar symmetric around zero and a title. The range is determined by the highest absolute value appearing in the map. An index dependent general colour scheme is used throughout the whole atlas. For temperature indices, the colour scale ranges blue-green-red where red colours indicate warming and blue colours cooling conditions. For example, an increase in Tmean would be shown in red as well as a decreased number of days below the second percentile. Trends in precipitation indices are visualized using a brown-yellow-green colour scale where brownish colour indicate drier and greenish colours wetter conditions. Trend significance is indicated for each symbol marking a station (circles) or regional averages (squares) using significance levels of p < 0.05 and p < 0.01. Analysis period, season, index and the unit of the trend are provided in every figure title.

While the previous chapter can be used to find trends for all individual indices at all stations/regions, this chapter attempts to provide an overview of the trends for the three periods. Since there are many maps and figures in the previous chapter, summary statistics of the results are necessary to obtain an overview. This chapter gives statistics of the trends in terms of the trend estimates, fraction of positive and negative trends and their significance (see chapter 3 for definitions) in the form of tables.