Displays

A number of methods exist for the identification of atmospheric circulation regimes. The most commonly-used method is k-means clustering. Often the clustering algorithm is applied to the first several principal components, instead of the full field data. In addition, many studies use a time-filter to get rid of high frequency oscillations before the clustering is executed. We discuss the consequences of these filtering techniques on the identified circulation regimes for the Euro-Atlantic sector in winter. Most studies identify four regimes: the Atlantic Ridge, the Scandinavian Blocking, and the two phases of the North Atlantic Oscillation. However, when k-means clustering is applied to the full field data of a reanalysis dataset, the optimal number of regimes is not found to be four, but six. This optimal number is based on the use of an information criterion, together with consistency arguments. The two additional regimes can be identified as the opposite phases of the Atlantic Ridge and Scandinavian Blocking, since they have a low-pressure area where the original regimes have a high-pressure area. Furthermore, the incorporation of a persistence constraint within the clustering algorithm is found to preserve the occurrence rates of the regimes, and thus maintains the consistency of the results. In contrast, applying a time-filter to enforce persistence of the regimes changes the occurrence rates. We conclude that care must be taken when filtering the data before the clustering algorithm is applied, since this can lead to biases in the identified circulation regimes and their occurrence rates.

How to cite: Falkena, S., de Wiljes, J., Weisheimer, A., and Shepherd, T. G.: Revisiting the Identification of Wintertime Atmospheric Circulation Regimes in the Euro-Atlantic Sector, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-349, https://doi.org/10.5194/egusphere-egu2020-349, 2020.

Abstract. Climate regionalization is crucial for climate studies, especially in the case of heterogeneous regions like East Africa. This paper focuses on categorizing Ethiopia into homogeneous climatic sub-regions by applying a classification of circulation patterns on precipitation. The sub-regions obtained will be applied on the verification of WRF-NOAHMP seasonal simulations performed over the Horn of Africa. We analyzed the occurrence of each circulation type per month and per year over the whole country. Then, trend analysis of temperature and precipitation over the respective sub-regions were performed. Principal Component Analysis (PCA) were applied to group daily mean Sea Level Pressure (SLP) into Circulation Types (CTs). Then, PCA coupled with k-means clustering employed to regionalize precipitation fields (distributed spatially) following CTs into homogeneous climatic sub-regions. Observational data were obtained from the National Center for Environmental Prediction (NCEP) reanalysis, Climate Hazards Group Infrared Precipitation with Stations (CHIRPS version 2), and National Meteorology Agency (NMA) of Ethiopia (gauge 1st and 2nd classes). Five principal components, which explain 98% of the total variance, were maintained using the Scree test technique. Ten CTs were obtained using positive and negative phases of each principal component scores following the extreme score values (> 2 and < −2) procedure. From ten CTs, we found that three (CT1, CT3, and CT8) were characterized by low pressure over the southwest corner of the domain, which consequently brings rainfall over the Ethiopian highlands. The number of days classified under different CTs shows different trends. CTs seasonal distribution agreed with the regional seasons. Long-term monthly mean rainfall ranges from 0-600 mm over the region. Ethiopia is clustered into four homogeneous sub-regions based on the spatial distribution of precipitation following CTs. Rainfall from CHIRPS and gauge did not have any specific trend over the sub-regions, however high standardized anomalies were observed compared to the long term mean. The temperature showed a 2 °C change for the past three decades. There was a negligible difference in the shape, size, and location of regions using data from different sources. The final decision on the optimal number of homogeneous climatic sub-regions depends upon the research objective, geographical domain size, and topographic features of the domain. This study provides an assessment and decision pathway.

Keywords: climatology, regionalization, Ethiopia, precipitation, k-means, circulation types

How to cite: Ware, M., Mori, P., Warrach -Sagi, K., Jury, M., Schiwtalla, T., and Wulfmeyer, V.: Synoptic Circulation Patterns and Climate Regionalization of East Africa, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1454, https://doi.org/10.5194/egusphere-egu2020-1454, 2020.

Under enhanced anthropogenic greenhouse gas forcing heat waves are only one example of climatic risks mankind has to deal with. Especially in urban areas where most of the people will live until the end of the 21^st century heat waves are a serious risk factor since the urban heat island will reinforce such events. For the city of Augsburg, new analog methods are utilized for assessing the development and impacts of heat waves taking into account the varying urban structure.

For model calibration the temperature data from the Augsburg-Mühlhausen weather station operated by the German Weather Service (DWD) and atmospheric circulation variables of the ERA5 reanalysis data set were used to analyze the recent temperature development. For this purpose, the least deviation of the normal vector was used to determine a subsample of analogs corresponding to the day of interest. The normal vector was derived from the regression plane of the prevailing circulation on the respective day. Subsequently, the temperature patterns were used to define the analog day from the subsample. For future periods, the same method was applied to model data for two representative concentration pathways (RCP4.5, RCP8.5) of different general circulation models (GCM: ACCESS1-0, CNRM-CM5, MPI-ESM-LR). Thus, we derive future time series of analogs corresponding to events prevailing in the observational period. To account for projected trends of the GCMs, the trends of all time-series were first removed and, after the analog selection process, added again according to the trends of the GCMs.

Temperature extremes are defined as days with temperatures exceeding the 90^th quantile (Q90) and heat days are defined as days where at least two temperature indices (TMIN, TMEAN, TMAX) exceed Q90. When at least three consecutive days are defined as heat day a heat wave is proclaimed. Analysis have shown that under consideration of RCP8.5 (RCP4.5) and all model runs the number of heat days in the end of the 21^st century will be nine (five) times higher than within the reference period 1970-2000. Furthermore, the mean duration of heatwaves will extend by factor four (two), whereby heat waves of more than 30 (15) consecutive days are possible.

How to cite: Merkenschlager, C., Beck, C., and Hertig, E.: Assessment of future heat events for the city of Augsburg by means of a normal vector based analog approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3438, https://doi.org/10.5194/egusphere-egu2020-3438, 2020.

European climate variability is shaped by atmospheric dynamics and local physical processes over the North Atlantic region. Both have strong seasonal features. So, a better understanding of their future seasonality is essential to anticipate changes in weather conditions for human and natural systems. We revisit the notion of seasons over the North Atlantic region through the concept of seasonal weather regimes (SWRs), by classifying daily fields of geopotential height at 500 hPa (Z500) without a priori separation of seasons. We use data from the ERA-Interim reanalysis, and from 12 climate models of the fifth phase of the Coupled Model Intercomparison Project. The spatial and temporal variability of SWR structures is investigated, as well as associated patterns of surface air temperatures. Although the climate models have biases, they reproduce structures and evolutions of SWRs similar to the reanalysis over 1979-2017: decreasing frequency of winter conditions, which start later and end earlier, and increasing frequency of summer conditions starting earlier and ending later in the year. These changes are stronger over 1979-2100 than over 1979-2017. By the end of the 21^st century, the typical past winter conditions (e.g. 1979-2017) have almost disappeared and correspond to future extreme cold conditions. A new cluster related to summer that was almost absent in 1979-2017 (corresponding to past extreme warm conditions in the past) becomes dominant. To understand whether these changes are linked to uniform Z500 increase or changes in Z500 spatial patterns, we detrend the data (but impose a stationary seasonality) by removing the trend in the seasonal Z500 regional average to define detrended seasonal weather regimes (d-SWRs). The temporal properties of d-SWRs appear almost constant, whereas spatial patterns show evolution. Our results indicate that the evolutions of the SWR temporal features are caused by the regional Z500 trend and that changing spatial patterns in d-SWRs account for the heterogeneity of this trend. Previous research has shown that this large-scale Z500 trend is linked to human influence, suggesting that it drives the changes in seasonality that we find.

How to cite: Breton, F., Vrac, M., Pascal, Y., Vaittinada Ayar, P., and Jézéquel, A.: Seasonal weather regimes in the North Atlantic region: towards new seasonality?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5449, https://doi.org/10.5194/egusphere-egu2020-5449, 2020.

Jets in the upper troposphere constitute a cornerstone of both synoptic meteorology and climate dynamics, thus providing a direct link between weather and mid-latitude climate variability. Conventionally, jet variability is mostly inferred indirectly through the variability of geopotential or sea-level pressure. Here we use a feature-based jet detection and present a global climatology of upper tropospheric jets as well as their variability for ocean sectors in both Hemispheres. The jet streams on both hemispheres are found to spiral poleward, featuring a continuous transition from subtropical to eddy-driven jets. Most intrinsic patterns of jet variability represent a changeover from a meridional shifting type variability to a pulsing-type variability, or vice-versa, across each ocean basin.

For the Southern Hemisphere, we find considerable discrepancies between geopotential and jet-based variability. Specifically, we show that SAM cannot be interpreted in terms of mid-latitude variability, as SAM merely modulates the most poleward part of the cyclone tracks and only marginally influences the distribution of other weather-related features of the storm track (e.g., position of jet axes and Rossby wave breaking). Instead, SAM emerges as the leading pattern of geopotential variability due to strong correlations of sea-level pressure around the Antarctic continent. Considering sector-specific variability pattern, we identify modes of consistent geopotential and jet variability in the South Pacific, and, to a lesser extent, the South Indian Ocean. In the South Pacific the leading mode of variability points towards NAO-like variability.

How to cite: Spensberger, C. and Spengler, T.: Climatology and variability of jets in the upper troposphere, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5965, https://doi.org/10.5194/egusphere-egu2020-5965, 2020.

The Western Mediterranean Oscillation index (WeMOi) presents a statistically significant relationship with the pluviometric totals of the eastern façade of the Iberian Peninsula. Use of the WeMOi at daily resolution has proven to constitute a useful tool for helping to predict torrential rainfall episodes in the east of the peninsula. The present research attempts to determinate which atmospherics circulations defines the WeMOi phases. Also, the WeMOi research has focused on the prediction of it in order to configure itself as a predictive tool, the WeMOTool, for torrential rains associated, especially during the autumn months. The calculation of this index is made using the surface pressure data of the GFS model and is updated with the model outputs at 00h and 12h and up to 144h.

How to cite: Arbiol-Roca, L.: Torrential rainfall prediction: WeMOTool, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8549, https://doi.org/10.5194/egusphere-egu2020-8549, 2020.

In an atmospheric general circulation drought-forming anomaly the nonlinear relationship between soil moisture and evapotranspiration play an important role in transitional (sub-humid and semi-dry) moisture regime. In this study the preceding soil moisture deficit was linked to the following low standardized precipitation index (SPI) indicating atmospheric drought in two major land-atmosphere coupling regions over Eurasia – Northern Eurasian Plains (NEP) and Plains and Uplands of Northeastern China (PUNEC).  Spring season was under consideration as the most significant for crop development failure due to lack of moisture and the most predictable due to prolonged soil memory after major hydrological event of the year – the snowmelt. The Global Energy and Water Exchanges (GEWEX) project deliverables and Climate Prediction Center (CPC) soil moisture data were used after validation with agrometeorological station data. It was shown that May droughts in NEP and PUNEC occur after regional negative soil moisture anomaly in early spring in significantly high proportion of cases for the study period 1985-2019. The soil moisture anomaly is leading to drought when the specific circulation pattern is formed as shown by the composite analysis. Importantly, the circulation pattern is Eurasia-broad with upstream blocking ridge centered in NEP and anticyclone formation in PUNEC. Both ridge and anticyclone are persistent and characterized by low cloudiness, reduced moist static energy (also due to reduction in evapotranspiration by low soil moisture) and low large scale and convective precipitation. That is why low SPI events often co-occur in two study regions. Atmospheric models tend to agree that atmospheric processes do respond to negative anomalies in surface moisture conditions in NEP and PUNEC and positive feedback of soil drought on the atmosphere is largely responsible for enabling atmospheric aridity extremes. The reasons for the simultaneous early spring moisture deficits in two regions are to be searched in the features of winter general circulation which lead to reduced snow accumulation and/or snowmelt regime with lower than average water infiltration to the soil. European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble seasonal forecast skill was also explored. SPI skill scores in April are indicating better forecast in NEP than in PUNEC but skill decreases sharply in May in NEP while remaining high till June in PUNEC. Further prospects for improving meteorological, hydrological and agricultural drought forecasting and forecast post-processing methodology for the regions of study are discussed.

This study was supported by the Russian Federal Targeted Program 1.2. Grant Number RFMEFI60419X0222 “Global climate and agrolandscapes of Russia: development of assessment and risk management system of Russian chernozems degradation” and National Key Research and Development Program of China, Grant Number 2016YFA0600701 “The variation and mechanism of extreme climate in northern China at interannual timescale”

How to cite: Yurova, A., Kozlov, D., and Zhu, Y.: Drought occurrence in key regions of soil moisture-atmosphere interaction in temperate Eurasia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8877, https://doi.org/10.5194/egusphere-egu2020-8877, 2020.

  To know the detailed seasonal cycle in various regions, confined only to the middle and higher latitudes, is the common basis for deeper understanding of the seasonal backgrounds of (1) extreme meteorological or climatological events and (2) cultural generation through the “seasonal feeling” leading to cultural understanding education. For example, our previous studies (e.g., Kato et al. 2017) pointed out that the “seasonal feeling” on the severe winter relating to the traditional event for driving the winter away (“Fasnacht”) around Germany might be due to the intermittent appearance of the extremely low temperature events, although the winter mean temperature there is lower only by about 3~5℃ than in southern Japan. Hamaki et al.(2018) suggested the appearance of such events to be controlled greatly by the intraseasonal behaviors of the Icelandic low. Furthermore, Kuwana et al. (EGU2018 and 2019) pointed out the asymmetric seasonal progression of the behaviors of the Icelandic low including its intraseasonal variation from the autumn to the next spring. However, it has not been clarified yet what kind of seasonal transition of the dominant large-scale daily fields was related to the increase in appearance frequency of such extremely low temperature events after mid-December. Thus the present study will further examine the detailed features on the above processes, mainly for the 2000/2001 winter based on the NCEP/NCAR reanalysis data.

  Appearance frequency of extremely low temperature events (e.g., below -5℃) rapidly increased around mid-December of 2000 with the large amplitude of its intraseasonal variation although the seasonal mean the Icelandic low appeared from mid-October. It is interesting that the daily mean temperature decreased gradually with shorter-period fluctuation until mid-December, even after the seasonal formation of the Icelandic low.

  As for the seasonal mean fields from mid-December to the next March, the northeastern portion of the Icelandic low area extended more closely to the northwestern Europe and the baroclinicity was enhanced especially to the south of ~55°N. Composite analyses suggest that the extremely low temperature events after mid-December around Germany was related not only to the weakening and westward retreat of the Icelandic low but also to the cold air advection by the low-level easterly wind along the southeastern edge of the intraseasonal-scale surface high to the north of Germany.

How to cite: Miyake, C. and Kato, K.: Synoptic climatological analysis on the rather abrupt seasonal transition to mid-winter situation around Germany with intermittent appearance of extremely low temperature events., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13096, https://doi.org/10.5194/egusphere-egu2020-13096, 2020.

The self-organizing maps (SOMs) have become a widespread tool for studying atmospheric circulation and its links to weather elements. The SOMs do not only produce a classification, but also a topology-preserving representation of the input data—a 2D array of circulation types (CTs). Consequently, one can analyse not only CT frequencies, persistence, and their conditioning of weather elements, but also visualise these parameters in a “continuum” of representative patterns. This latter characteristic makes it in theory plausible to define a (considerably) larger number of CTs compared to other classification approaches, and thus better represent extremes of circulation variability, without necessarily compromising the utility of the output by making it unintelligible.

Here, we investigate whether increasing the number of CTs (enlarging the SOM) leads to a classification better suitable to study synoptic forcing of extreme weather, and, in particular, what the effect is of various SOM parameters, which have to be chosen a priori more or less subjectively—such as array shape and size, radius and function of neighbourhood, learning rate, and initialization—on the utility of the resulting classification. Furthermore, we present the Sammon mapping, typically used to evaluate the topological structure of SOMs, as a standalone classification tool that shares some of the advantages with SOMs while potentially circumventing some of their weaknesses.

How to cite: Stryhal, J. and Plavcová, E.: On the use of circulation classifications by self-organizing maps toward studying extreme weather, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17802, https://doi.org/10.5194/egusphere-egu2020-17802, 2020.

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.

How to cite: Thanheiser, S., Homann, M., Philipp, A., Beck, C., and Jacobeit, J.: Persistence and frequency of drought-relevant circulation types during temperature extremes in southern Central Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21274, https://doi.org/10.5194/egusphere-egu2020-21274, 2020.