NH1.7

Heavy precipitation events (HPEs) in the densely populated eastern Mediterranean trigger natural hazards, such as flash floods and urban flooding. However, they also supply critical amounts of fresh water to this desert-bounded region. The impact of global warming on such events is thus vital to the inhabitants of the region. HPEs are poorly represented in global climate models, leading to large uncertainty in their sensitivity to climate change. Is total rainfall in HPEs decreasing, as projected for the mean annual rainfall? Are short duration rain rates decreasing, or rather increasing as expected from the higher atmospheric moisture content? Where are the changes more pronounced, near the sea or farther inland towards the desert? To answer these questions, we have identified 41 historical HPEs from a long weather radar record (1990-2014) and simulated them in the same resolution (1 km²) using the convection-permitting weather research and forecasting (WRF) model. Results were validated versus the radar data, and served as a control group to simulations of the same events under ‘pseudo global warming’ (PGW) conditions. The PGW methodology we use imposes results from the ensemble mean of 29 Coupled Model Intercomparison Project Phase 5 (CMIP5) models for the end of the century on the initial and boundary conditions of each event simulated. The results indicate that HPEs in the future may become more temporally focused: they are 6% shorter and exhibit maximum local short-duration rain rates which are ~20% higher on average, with larger values over the sea and the wetter part of the region, and smaller over the desert. However, they are also much drier; total precipitation during the future-simulated HPEs decreases substantially (~-20%) throughout the eastern Mediterranean. The meteorological factors leading to this decrease include shallower cyclones and the projected differential land-sea warming, which causes reduced relative humidity over land. These changing rainfall patterns are expected to amplify water scarcity – a known nexus of conflict and strife in the region – highlighting the urgent need for deeper knowledge, and the implementation of adaptation and mitigation strategies.

How to cite: Armon, M., Marra, F., Garfinkel, C., Rostkier-Edelstein, D., Adam, O., Dayan, U., Enzel, Y., and Morin, E.: Global warming decreases rainfall but increases short-duration rain-rates during heavy precipitation events in the eastern Mediterranean, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1573, https://doi.org/10.5194/egusphere-egu21-1573, 2021.

Extreme precipitation is considered to be one of the natural disasters with greatest impact on human society, leading to floods and debris flows. To better understand the spatio-temporal effects on extreme precipitation, and to predict the intensity of extreme precipitation ahead in different return periods, this study focus on quantifying both climate and spatial effects on the intensity of extreme precipitation in coastal areas of southeast China, considering different weather system. A hierarchical Bayesian model with generalized extreme value distribution (GEV) is applied to maximum daily precipitation through 94 stations in study area from 1964 to 2013 in JAS. Tropical cyclone (TC) and non-TC influenced extreme precipitation are analyzed separately. Climate and spatial effects are introduced through regression models associating parameter values in GEV with different covariates, such as climate indices and distance to coastline. It was observed that SST anomaly in North Pacific, SLP anomaly above North India Ocean are found to be the main climate indices that influence extreme precipitation in coastal areas of southeast China. Using SST, we can predict the intensity of extreme precipitation in different return period at 6-month lag. Extreme precipitation was found to decrease as distance to coastline increase. In addition, different performances of extreme precipitation along with distance to coastline were found among various subregions and weather systems.

How to cite: Qian, W. and Sun, X.: Spatio-temporal effects on extreme precipitation in the coastal areas of southeast China, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2043, https://doi.org/10.5194/egusphere-egu21-2043, 2021.

Climate change is a phenomenon that is claimed to be responsible for a significant alteration of the precipitation regime in different regions worldwide and for the induced potential changes on related hydrological hazards. In particular, some consensus has raised about the fact that climate changes can induce a shift to shorter but more intense rainfall events, causing an intensification of urban and flash flooding hazards.  Regional climate models (RCMs) are a useful tool for trying to predict the impacts of climate change on hydrological events, although their application may lead to significant differences when different models are adopted. For this reason, it is of key importance to ascertain the quality of regional climate models (RCMs), especially with reference to their ability to reproduce the main climatological regimes with respect to an historical period. To this end, several studies have focused on the analysis of annual or monthly data, while few studies do exist that analyze the sub-daily data that are made available by the regional climate projection initiatives. In this study, with reference to specific locations in eastern Sicily (Italy), we first evaluate historical simulations of precipitation data from selected RCMs belonging to the Euro-CORDEX (Coordinated Regional Climate Downscaling Experiment for the Euro-Mediterranean area) with high temporal resolution (three-hourly), in order to understand how they compare to fine-resolution observations. In particular, we investigate the ability to reproduce rainfall event characteristics, as well as annual maxima precipitation at different durations. With reference to rainfall event characteristics, we specifically focus on duration, intensity, and inter-arrival time between events. Annual maxima are analyzed at sub-daily durations. We then analyze the future simulations according to different Representative concentration scenarios. The proposed analysis highlights the differences between the different RCMs, supporting the selection of the most suitable climate model for assessing the impacts in the considered locations, and to understand what trends for intense precipitation are to be expected in the future.

How to cite: Nanni, P., Peres, D. J., Musumeci, R. E., and Cancelliere, A.: Analysis of EURO-CORDEX sub-daily rainfall simulations and derived event characteristics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2119, https://doi.org/10.5194/egusphere-egu21-2119, 2021.

Extreme precipitation and winds can have a severe impact on society, particularly when they occur at the same place and time. Studies have investigated the frequency of co-occurring extreme precipitation and wind using observational data. However, due to the rarity of very extreme events, these results are limited, since a large number of samples is needed to get robust estimates. Additionally, it is very difficult for estimates based on observations alone to help us understand the risk of future unprecedented events. Using the UNSEEN method (UNprecedented Simulated Extremes using ENsembles) this risk can be estimated from large ensembles of climate simulations. The Met Office's Global Seasonal forecast system version 5 (GloSea5) model ensembles are evaluated against ERA5 reanalysis data to find out how well they represent extreme precipitation, extreme wind and extreme co-occurring events over Europe. This model has not been evaluated in such a way before and this is needed before the model can be used to estimate the likelihood of unprecedented events using the UNSEEN method. We find that although the intensity of precipitation and wind extremes differ between the model and observations (by up to 12 mm and 9 m/s), the frequency of co-occurring events is well represented. The extremal dependency measure, χ, which measures co-occurrence, compares well spatially over Europe between GloSea5 and ERA5. However, significant differences in χ are found over areas of high topography, over the North Atlantic, Western Europe and the Norwegian Sea. Generally, GloSea5 underestimates χ over the ocean, and performs better over land. Mean sea level pressure anomaly composites for co-occurring extreme events show that at a number of selected locations, the co-occurring extremes are produced by very similar synoptic situations in the model and reanalysis. This gives increased confidence in the model. The model ensembles can then be used to assess the present day likelihood of unprecedented 3 hourly compound precipitation and wind extremes for winter over Europe, and to find out how the NAO index influences the frequency of co-occurring events over Europe.

How to cite: Owen, L., Catto, J., Stephenson, D., and Dunstone, N.: Model evaluation of compound precipitation and wind extremes over Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2168, https://doi.org/10.5194/egusphere-egu21-2168, 2021.

Supercell thunderstorms are often associated with severe weather conditions, such as tornadoes, hail, strong wind gusts, heavy rainfall, and flash-floods, producing damage to populations and assets. The goal of the study is to analyze and improve our understanding of pre-convective environments conducive for supercell development in the different regions of Spain. We use 2014-2020 data from the Spanish Supercell Database (Martin et al., 2020), ERA-5 reanalysis, and a dynamical downscaling with WRF-ARW model to a 9 km spatial resolution to be able to generate sounding-derived parameters at the moment of formation of each supercell. Results indicate that supercells are more common in high values of CAPE and Shear 0-6 Km, but in the south-western of Spain predominates supercells of HSLC (High Shear-Low CAPE) in the cold season.

How to cite: Calvo-Sancho, C. and Martín, Y.: Supercell Pre-convective Environments in Spain: a dynamic downscaling of ERA-5 Reanalysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2967, https://doi.org/10.5194/egusphere-egu21-2967, 2021.

Between 7-10 January 2020, severe snowfall and precipitation event swept over south, center and eastern Spain, with a total amount of precipitation of more than 200 mm on the south, snowfall accumulations of 50 cm or more on widespread areas of center Spain and 25 cm on Zaragoza and Ebro valley.

The low, called Filomena, was an unusual event with excessive social impact. In this study we will present the synoptic framework, characterized by the presence of three different air masses: cold air mass on low levels, more humid Mediterranean air mass on low-mid levels, at around 2-3 kilometres from surface; and a wet and warm subtropical air mass from the south. The interaction of these three air masses lead to the exceptional precipitation and snow accumulations. For this end, ERA-5 reanalysis and satellite images will be used. For mesoscale analysis, WRF-ARW will be used with both GFS and ERA-5 reanalysis. This extreme event, although it was generally predictable, had key points of low predictability in some parts with high social impact, including very populated areas.

How to cite: Rodríguez-Sánchez, A., Granda-Maestre, R., Calvo-Sancho, C., and Oliver-García, Á.: An approach to Storm Filomena severe snowfall and precipitation in Spain: preliminary results, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10406, https://doi.org/10.5194/egusphere-egu21-10406, 2021.

Precipitation extremes are widely thought to intensify with the global warming due to exponential growth, following the Clausius-Clapeyron (C-C) equation of atmosphere water holding capacity with rising temperatures. However, a number of recent studies based on station and reanalysis data for the contemporary period showed that scaling rates between extreme precipitation and temperature are strongly dependent on temperature range, region and moisture availability. Here, we examine the scaling between daily precipitation extremes and surface air temperature over Russian territory for the last four decades using meteorological stations data and ERA-Interim reanalysis. The precipitation-temperature relation is examined for total precipitation amount and, separately, for convective and large-scale precipitation types. In winter, a general increase of extreme precipitation of all types according to C-C relation is revealed. For the Russian Far East region, the stratiform precipitation extremes scale with surface air temperature following even super C-C rates, about two times as fast as C-C. However, in summer we find a peak-like structure of the precipitation-temperature scaling, especially for the convective precipitation in the southern regions of the country. Being consistent with the C-C relationship, extreme precipitation peaks at the temperature range between 15 °C and 20 °C. For the higher temperatures, the negative scaling prevails. Furthermore, it was shown that relative humidity in general decreases with growing temperature in summer. Notably, there appears to be a temperature threshold in the 15-20 °C range, beyond that relative humidity begins to decline more rapidly. This indicates that moisture availability can be the major factor for the peak-shaped relationship between extreme precipitation and temperature revealed by our analysis.

How to cite: Aleshina, M., Semenov, V., and Chernokulsky, A.: A relation between extreme precipitation and surface air temperature over Russia in the last four decades, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9166, https://doi.org/10.5194/egusphere-egu21-9166, 2021.

In the field of hydrogeological risk, rainfalls represent the most important triggering factor for superficial terrain failures such as shallow landslides, soil slips and debris flow. The availability of local rain gauges measurements is fundamental for defining the cause-effect relationship for predicting failure scenarios. Unfortunately, these hydrogeological phenomena are typical triggered over mountains regions where the density of the ground-based meteorological network is poor, and the local effects caused by mountains topography can change dramatically the spatial and temporal distribution of rainfall. Therefore, trying to reconstruct a representative rainfall field across mountain areas is a challenge but is a mandatory task for the interpretation of triggering causes. We present a reanalysis of an ensemble of extreme rainfall events happened across central Alps and Pre-Alps, in the northern part of Lombardy Region, Italy. We have investigated around some critical aspects such as their intensity and persistency also proposing a modelling of their meteorological evolution, using the Linear Upslope-Rainfall Model (LUM). We have considered this model because it is designed for describing the mechanism of orographic precipitation intensification that was identified as the main cause of that extreme events. To test and calibrate the LUM model we have considered local rain gauges data because they represent the effective rainfall poured on the ground. These punctual data are generally considered for landslide assessment, in particular for rainfall induced phenomena such as shallow landslides and debris flows. Considering our test cases, the results obtained have shown that the LUM has been able to reproduce accurately the rainfall field. In this regard, LUM model can help to address further information around those ungauged area where rainfall estimation could be critical for evaluating the hazard. We are conscious that our and other studies around this topic would be propaedeutic in the next future for the adoption of an integrated framework among the real-time meteorological modelling and the hydrogeological induced risk assessment and prevision.

How to cite: Abbate, A., Longoni, L., and Papini, M.: Reconstruction of a realistic rainfall field for extreme events happened in mountain area, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9176, https://doi.org/10.5194/egusphere-egu21-9176, 2021.

It is difficult to predict the occurrence and rain volume of torrential rainfalls, such as guerrilla rain, rain band with typhoon and linear precipitation zone. As heavy rain area is spatially localized and the parent thunderstorm tends to develop within a short time, it makes difficult to accurately predict the occurrence location/time and rain volume. Recently, the machine learning technique is remarkably developed with the improved processing speed of computers and with a huge amount of the data. In addition to this, the application of the machine learning methods to the meteorological fields is intensively tried in the world. Since 2017, we started installing the automatic weather observation system (AWS) named as P-POTEKA in Metro Manila, the Philippines, which is one of the cities suffering from the torrential rainfall and related flood. So far, we installed 35-P-POTEKAs in Metro Manila and continue the weather observations (rain volume, temperature, air pressure, humidity, wind speed, wind direction and solar radiation) with the time resolution of 1 min. In this study, we used both P-POTEKA rain volume data and machine learning model (ConvLSTM: Convolutional Long-Short Term Memory) in order to predict the near future (< 1hour) rain volume and distribution. At the presentation, we will show the results derived from the machine learning prediction of the rain volume and distribution more in detail.

How to cite: Noda, A.: Machine Learning Prediction of Precipitation in Metro Manila, Philippines, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11927, https://doi.org/10.5194/egusphere-egu21-11927, 2021.

Record-breaking amount of Mei-yu rainfall around the Yangtze River has been observed in the 2020 Mei-yu season.  This shows the necessity and urgency of accurate prediction of extreme Mei-yu precipitation over China for the current and future climate.  Such information could further improve the decision and policy making in the region.  Many studies in the past have shown that large-scale modes, e.g. western north Pacific subtropical high and the south Asia high, play a role in controlling extreme Mei-yu precipitation over China. Although the spatial resolution of typical climate models might be too coarse to simulate extreme precipitation accurately, they are likely to simulate large-scale modes reasonably well.  One might be possible to construct a causally guided statistical model based on those known large-scale modes to predict extreme Mei-yu precipitation. 

In this presentation, we show preliminary results of the relationship between known large-scale atmospheric and oceanic modes and extreme Mei-yu precipitation in the two regions of China, i.e. Yangtze River Valley and Southern China, using the causal network discovery approach.  The relationships between large-scale modes and extreme Mei-yu precipitation on different time scale are explored.  Implication of relationships in constructing statistical predictive model is also discussed.

How to cite: Ng, K., Leckebusch, G., and Hodges, K.: An evaluation of the effectiveness of known large-scale modes for predicting extreme Mei-yu precipitation over China using causality driven approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10568, https://doi.org/10.5194/egusphere-egu21-10568, 2021.

Thunderstorms can have a wide range of impacts on modern societies and their assets. Severe thunderstorms associated with thunder squall, hail, tornado, and lightning cause extensive damage and losses to lives, especially in the densely populated sub-tropical countries like Bangladesh. In this study the future changes in thunderstorm conducive environments, in terms convective available potential energy (CAPE), have been assessed under the RCP 8.5 scenario for the selected major cities of Bangladesh. Results show an increase in CAPE for all the selected cities and in the range of 44%–106%. Later, a statistical thunderstorm frequency prediction model has been developed based on CAPE and convective precipitation and the probable scenario of thunderstorm frequency in the 21st century under future climate has been projected. The simulations were carried out for three different time slices (Early, Mid and Late 21^st century) with CMCC-CM (Centro Euro-Mediterraneo per Cambiamenti Climatici Climate Model) model data. The future projection of thunderstorm shows an increase in thunderstorm frequency for all the season in a warmer future climate. But pre-monsoon and monsoon are found to be the most thunderstorm frequent season. Given the substantial damage from severe thunderstorms in the current climate, such increases imply an increasing risk of thunderstorm-related damage in this disaster-prone region of the world.

How to cite: Esha, S. A. and Jahan, N.: Change in thunderstorm activity in a projected warmer future climate over Bangladesh, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8518, https://doi.org/10.5194/egusphere-egu21-8518, 2021.

The effects of climate change on precipitation patterns can be observed on global scale, however, global climate change affects different regions more or less severely. Because of the high variability of precipitation in particular, future changes related to precipitation can be very different, even opposite on continental/regional scale. Even within Europe, the detected trends in precipitation patterns and extremes differ across the continent. According to climate model simulations for the future, Northern Europe is projected to become wetter, while the southern parts of the continent will tend to become drier by the end of the 21st century. The frequency and intensity of extreme precipitation will also increase in the whole continent. The possible shifts in precipitation patterns from wetter to drier conditions with fewer but increased extreme precipitation events can cause severe natural hazards, such as extended drought periods, water scarcity, floods and flash floods, therefore appropriate risk management is essential. For this purpose the analysis of possible hazards associated to specific precipitation-related weather phenomena is necessary and serves as key input.

Since plain regions play an important role in agricultural economy and are more exposed to floods because of their geographic features and the gravitational movement of surface water, our primary goal was to examine temporal and spatial changes in extreme precipitation events and dry spells in three European lowlands, located in the southern part of the continent. We selected the following regions: the Po-Valley located in Italy with humid subtropical climate; the Romanian Plain in Romania, and the Pannonian Plain covering different parts of Hungary, Serbia, Slovakia, Croatia, Romania and Ukraine with humid continental climatic conditions.

Precipitation time series were used from the E-OBS v.22 dataset on a 0.1° regular grid. The dataset is based on station measurements from Europe and are available from 1950 onward with daily temporal resolution. For the analysis of main precipitation patterns, dry spells and extreme events, we use 17 climate indices (most of them are defined by the Expert Team on Climate Change Detection and Indices, ECCDI). The analysis focuses on annual and seasonal changes in the three regions. The selected indices are capable to represent the differences and similarities between and within the plains. Our preliminary results show that the occurrence and intensity of extreme precipitation events increased in all regions, while the trends of duration and frequency of dry spells show both intra- and inter regional variability across the plains.

How to cite: Berényi, A., Pongrácz, R., and Bartholy, J.: Trend in precipitation and drought extremes in southern lowland regions of Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12005, https://doi.org/10.5194/egusphere-egu21-12005, 2021.

Industrial facilities, like any building or installation, are designed to withstand defined levels of natural hazards during their lifetime. These levels are requested by the regulations, and are commonly estimated by use of the Statistical Extreme Value theory ahead of the building in order to support the design of the planned asset. However, for long lasting installations, climate change may change the frequency of the level defined at the time of the building, so that the protection is not as high as initially expected. This work presented here aims at describing and testing a way to estimate Return Levels for precipitation in different locations in Europe at the 2050 time horizon. The methodology is based on the definition of a variable whose extremes can be considered as stationary, so that future Return Levels are obtained from those of this variable and the climate model changes in mean, standard deviation and rainy day frequency at the desired future horizon (Acero et al. 2017). The methodology is first tested in a cross-validation setting over the historical period using 15 rainfall observation time series in Europe provided by the ECA&D dataset and CMIP5 climate model simulations. Then, estimates of the 50-year Return Levels in 2050 are computed. The methodology is then applied to the gridded E-OBS dataset with the objective of producing risk maps at the European scale. The first step is then to compare the estimations previously obtained for the station time series to those obtained for the nearest E-OBS grid points, in order to assess the ability of gridded data to faithfully represent the behavior of the extremes. Depending on the results, advices can be given about the most suited way to map future rainfall extremes in Europe in relation to the adaptation of industrial facilities.

Reference:

Acero F.J., Parey S., Hoang T.T.H., Dacunha-Castelle D., Garcia J.A. and Gallego M.C.: Non-stationary future Return Levels for extreme rainfall over Extremadura (SW Iberian Peninsula). Hydrological Sciences Journal, 2017, DOI: 10.1080/02626667.2017.1328559

How to cite: Parey, S. and Michelangeli, P.-A.: Estimation of future precipitation Return-Levels in Europe for the adaptation of industrial facilities, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2101, https://doi.org/10.5194/egusphere-egu21-2101, 2021.

Enduring and extensive heavy precipitation associated with widespread river floods are among the main natural hazards affecting Central Europe. Since such events are characterized by long return periods, it is difficult to adequately quantify their frequency and intensity solely based on the available observations of precipitation. Furthermore, long-term observations are rare, not homogeneous in space and time, and thus not suitable to run hydrological models (HMs). To overcome this issue, we make use of the recently introduced LAERTES-EU (LArge Ensemble of Regional climaTe modEl Simulations for EUrope) data set, which is an ensemble of regional climate model simulations providing 12.000 simulated years. LAERTES-EU is adapted and applied for the use in an HM to calculate discharges for large river catchments in Central Europe, where the Rhine catchment serves as the pilot area for calibration and validation. Quantile mapping with a fixed density function is used to correct the bias in model precipitation. The results show clear improvements in the representation of both precipitation (e.g., annual cycle and intensity distributions) and simulated discharges by the HM after the bias correction. Furthermore, the large size of LAERTES-EU improves the statistical representativeness also for high return values of precipitation and discharges. While for the Rhine catchment a clear added value is identified, the results are more mixed for other catchments (e.g., the Upper Danube).

How to cite: Ehmele, F., Kautz, L.-A., Feldmann, H., He, Y., Kadlec, M., Kelemen, F. D., Lentink, H. S., Ludwig, P., Manful, D., and Pinto, J. G.: Adaptation and Application of the large LAERTES-EU RCM Ensemble for Hydrological Modeling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9598, https://doi.org/10.5194/egusphere-egu21-9598, 2021.