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.
The concept of weather regimes represents a process‐oriented method of organizing the varying states of the atmospheric circulation. We define weather regimes as preferred, or recurrent, circulation ...patterns. We use a suite of reanalysis products and general circulation model (GCM) simulations to assess the reproducibility and variability of the regimes. We find distinct variability of the regimes in observational periods as well as in future projections. Most notable is the high variability of the North Atlantic Oscillation (NAO) regime anomaly patterns in the GCM simulations which is not evident in reanalyses, and the substantial increase of variability regarding the frequency of occurrence of the Atlantic ridge regime and the NAO+ regime.
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.
ABSTRACT
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.
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.
Extreme precipitation events in the Mediterranean area have been defined by different percentile-based indices of extreme precipitation for autumn and winter: the number of events exceeding the 95th ...percentile of daily precipitation, 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-21st century, it is projected that the frequency of extreme precipitation 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 95th percentile of daily precipitation, widespread 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.
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 700hPa and 850hPa 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.
•Precipitation extremes of the Mediterranean area are affected by non-stationarities.•Non-stationarities are the consequences of shifts within the large scale circulation.•A skill-score depending method is introduced to determine non-stationarities.•An ensemble of regression models is available for assessing precipitation extremes.•Well-fitted regression models are less transferable to other periods.
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.
Winter precipitation in the Mediterranean area for the twenty-first century was statistically downscaled under the explicit consideration of nonstationarities. Nonstationarities arise from ...substantial modifications of the atmospheric circulation, which lead to significant changes of regional precipitation characteristics. For the detection of nonstationarities in the relationships of the large-scale circulation and regional precipitation in the observational period, statistical model performance under potentially nonstationary conditions was compared to model performance under stationarity. To account for nonstationarity in the future projections, circulation characteristics in general circulation model (GCM) output used to downscale precipitation were also analysed. The correspondence of GCM and observed circulation characteristics was used to specifically select appropriate downscaling models. Statistical model performance was affected by nonstationarities, which was most pronounced not only in the north-eastern Mediterranean regions but also in western Mediterranean North Africa. Furthermore, it was found that variability in the GCM data used for the projections is at least as large as seen in the observational period. This finding underlines the need to explicitly take nonstationarities in statistical downscaling into account. As downscaling result we obtain mainly a reduction of the probability of rain and a rather indifferent pattern regarding the change of the 75 % up to the 95 % quantiles for most regions of the Mediterranean area until the end of the twenty-first century were mainly obtained. However, due to the nonstationarities, results depend strongly on the specific time periods under consideration.
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.
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