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.

Storm surges caused by extreme meteorological conditions are a major natural risk in coastal areas, especially in the context of global climate change. The increase of future sea-levels caused by continuing global warming, may endanger human lives and infrastructure through inundation, erosion and salinization.
Hence, the reliable estimation of the occurrence probability of these extreme events is crucial to quantify risk and to design adequate coastal defense structures. The probability of event occurrence is typically estimated by modelling observed sea-level records using one of a few statistical approaches.
The traditional Extreme Value Theory is based on the use of the Generalized Extreme Value distribution (GEV),  fitted either by considering block (typically yearly) maxima, or values exceeding a high threshold (POT). This approach does not make full use of all observational information, and thereby does not minimize estimation uncertainty.
The recently proposed Metastatistical Extreme Value Distribution (MEVD), instead, makes use of most of the available observations and has been shown to outperform the classical GEV distribution in several applications, including hourly and daily rainfall, flood peak discharge and extreme hurricane intensity.
Here, we comparatively apply the MEVD and the GEV distribution to long time series of sea-level observations distributed along European coastlines (Venice (IT), Hornbaek (DK), Marseille (FR), Newlyn (UK)). A cross-validation approach, dividing available data in separate calibration and test sub-samples, is used to compare their performances in high-quantile estimation.
The MEVD approach is based on the definition of an “ordinary values” distribution (here a Generalized Pareto distribution), whose parameters are estimated using the Probability Weighted Moments method on non-overlapping sub-samples of fixed size (5 years). To address the problems posed by observational samples of different sizes, we explore the effect on uncertainty of different calibration sample sizes, from 5 to 30 years. In this application, we find that the GEVD-POT and MEVD approaches perform similarly, once the above parameter choices are optimized. In particular, when considering short samples (5 years) and events with a high return time, the estimation errors show no significant differences in their median value across methods and sites, all approaches producing a similar underestimation of the actual quantile. When larger calibration sample sizes are considered (10-30 yrs), the median error of MEVD estimates tends to be closer to zero than those obtained from the traditional methods.
Future projections of sea-level rise until 2100 are also analyzed, with reference to intermediate and extreme representative concentration pathways (RCP 4.5 and RCP 8.5). The probability of future storm surges along European coastlines are then estimated assuming a changing mean sea-level and an unchanged storm regime. The projections indicate future changes in mean sea-level lead to increases in the height of storm surges for a fixed return period that are spatially heterogeneous across the coastal locations explored.

How to cite: Caruso, M. F. and Marani, M.: Extreme Storm Surge estimates and projection through the Metastatistical Extreme Value Distribution, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10706, https://doi.org/10.5194/egusphere-egu21-10706, 2021.

In recent years, flash floods occurred repeatedly in temperate regions of central Western Europe (e.g., Orlacher Bach (GER), Hupselsebeek (NL), White Ernz (LUX)). This type of extreme flood events is unusual for these regions, as opposed to Mediterranean catchments that are more prone to flash floods. In the second half of the 20^th century, and more specifically in the 1990’s, westerly atmospheric fluxes were the dominating triggering factor of large scale (winter) floods in central Western Europe.

With a view to gain a better understanding of the mechanisms controlling the recent flash flood events at higher latitudes, we explore various avenues related to the non-stationarity of environmental systems. We hypothesize that an increase in the occurrence of flash flood prone atmospheric conditions has recently led to higher precipitation totals and a subsequent increase in flash flood events in central Western Europe.

Therefore, we first analysed relevant atmospheric parameters from the ERA 5 reanalysis dataset. Second, we linked the atmospheric parameters to the concept of general circulation patterns as per Hess and Brezowsky (1977). Third, we analysed precipitation data from a set of stations located in the Moselle river basin (35.000 km²). These three pillars build the base for identifying flash flood prone atmospheric conditions over space and time that are then compared to actual occurrences of extreme discharge events in streams within the Moselle river basin.

To validate our hypothesis, spatial and temporal patterns in the occurrence of extreme precipitation and discharge events need to match atmospheric patterns. Preliminary results suggest that daily precipitation data and meridional circulation patterns do not show a clear trend towards an increased occurrence of precipitation events or higher precipitation amounts. Due to the limitations inherent to the available long-term dataset of daily data, the hypothesis can only be partly evaluated, and more detailed analyses are added. For that reason, discharge data with a 15-minute resolution, along with precipitation radar data of 5-minute time steps will be employed at a limited spatial extent in future analyses. In case of rejection of our working hypothesis this may pinpoint to other flash flood triggering mechanisms, such as changes in land use, soil moisture conditions or cultivation methods.

How to cite: Meyer, J., Douinot, A., Neuper, M., Mathias, L., Tamez-Meléndez, C., Zehe, E., and Pfister, L.: Identifying and linking flash flood prone atmospheric conditions to flooding occurrences in central Western Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12522, https://doi.org/10.5194/egusphere-egu21-12522, 2021.

In Europe, flash floods are one of the most significant natural hazards, causing serious risk to life and destruction of buildings and infrastructure. The intense rain causing those floods has a few different names, however, with very similar meaning. The term chosen in this study, ‘cloudburst’, was introduced by Woolley (1946) as “…a torrential downpour of rain which by its spottiness and relatively high intensity suggests the bursting and discharge of the whole cloud at once”. While these events play an important role in the ongoing flood risk management discussion, they are under-represented among flood models.

The main aim of this study is to demonstrate an approach by showing how methods and techniques can be integrated together to construct a catastrophe model for flash flooding of Jönköping municipality in Sweden. The model is developed in the framework of the ‘Oasis Loss Modelling Framework’ platform, jointly with end-users from the public sector and the insurance industry. Calibration and validation of the model were conducted by comparisons against three historical cloudburst events and corresponding insurance-claim data.

The analysis has shown that it is possible to get acceptable results from a cloudburst catastrophe model using only rainfall data, and not surface-water level as driving variable. The approach presented opens up for such loss modelling in places where complex hydraulic modelling cannot be done because of lacking data or skill of responsible staff. The Swedish case study indicates that the framework presented can be considered as an important decision making tool, by establishing an area for collaboration between academia; insurance businesses; and local authorities, to reduce long-term disaster risk in Sweden.

Woolley, Ralf R., "Cloudburst Floods in Utah 1850-1938" (1946). Elusive Documents. Paper 55.

How to cite: Karagiorgos, K., Halldin, S., Haas, J., Knos, D., Blumenthal, B., and Nyberg, L.: Cloudburst catastrophe modelling: Case study – Jönköping municipality, Sweden, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12684, https://doi.org/10.5194/egusphere-egu21-12684, 2021.

Global warming is expected to enhance El Niño Southern Oscillation (ENSO) with potential impacts on rainfall and flood risk in numerous countries of the Asia-Pacific region. Modeling studies have suggested that positive and negative ENSO phases may intensify by as much as 25% under extreme climate projections. However, the influence of ENSO variability on flood risk in Asia-Pacific countries is still largely unexplored. Here, we aim to shed light into the link between ENSO, flood risk, and insured losses in New Zealand by combining rainfall observations and state-of-the-art flood risk models. We draw on 60 years of daily precipitation measurements to quantify the statistical correlations between the rainfall principal components and the ENSO historical time series. This allows us to generate 50,000 years of stochastic daily rainfall maps correlated with a long-term, synthetic ENSO time series. The stochastic precipitation maps are then used to drive streamflow and flood simulations at 20 m spatial resolution. Our results indicate that positive and negative ENSO phases increase the flood risk in different regions of New Zealand, and that extreme ENSO events tend to cause more severe flood events. We finally investigate the potential differences in economic losses during positive and negative ENSO phases by combining modeled flood footprints with exposure and vulnerability data. These results may guide the implementation of effective adaptation and mitigation strategies against the increasing risk of flood events in warming climate.

How to cite: Comola, F., Scudeler, C., Satyam, S., and Nicotina, L.: Impacts of El Niño Southern Oscillation on flood risk and insured losses in New Zealand, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15107, https://doi.org/10.5194/egusphere-egu21-15107, 2021.

The presentation highlights the TAMIR project (2020-2022), its mid-term results, and final objectives.

Hazards created by convective storms and heavy rainfall, e.g. flooding, turn into disasters when and where they encounter exposed and vulnerable societal systems, e.g. human life and activities, assets, and infrastructure. The recent progress in probabilistic multi-source rainfall-induced hazard forecasting has enabled predictions from the nowcast (minutes) to short-medium ranges (5 days), allowing for consistent decision making at both emergency response and planning stages. Nevertheless, civil protection agencies still face challenges that hamper their ability to make active decisions when preparing for emergencies in severe weather situations. The challenges include e.g. high false-alarm rates, lack of multi-hazard forecasts (e.g. the combined effects of heavy rainfall, flood, lightings, wind gusts, hail), difficulties in translating the hazard forecasts into impact forecasts, and inadequate risk assessments. The TAMIR project, funded by the EU Civil Protection Mechanism, addresses these challenges using innovative, state-of-the-art science, and integration of the developed tools and services into existing systems. Experimental additional products are delivered e.g. via the European Flood Awareness System (EFAS) platform, part of the Copernicus Emergency Management Service, and as new information in regional civil protection systems.

In the project, pro-active emergency management is supported by developing forecast products covering different spatial scales (regional to pan-European) and lead times (15 minutes to 5 days). In particular, the project focuses on improving existing products and tools with enhanced impact assessment and preparedness capacity. The uncertainty related to precipitation type in flood forecasts is considered by utilizing a model-based precipitation type estimate to guide the radar-based flood hazard estimate, as snowfall is far less prone to cause severe flood hazard than rainfall. Flash flood hazard forecasting is improved by developing lead-time dependent flood warning thresholds, utilizing the information on precipitation type and the information from gauge adjusted EUMETENET OPERA weather radar composite data and NWP data. Flood risk assessments are improved by combining the flash flood hazard forecasts with enhanced vulnerability and exposure data, covering information about population, transportation infrastructure, energy infrastructure, education facilities and health facilities, and by developing methods to turn the combined information into improved flood rapid risk impact assessments. To account for hazards and risks caused by convective weather, a nowcasting tool for multi-hazards caused by thunderstorms is being developed which combines a cell-based storm nowcast model with a classification model that estimates the hazard level of convective storm situations based on historical data on meteorological observations and the emergency calls they have caused. The multi-hazard nowcasts are again combined with vulnerability layers to produce risk nowcasts for damages from thunderstorms.

Another important aspect of supporting pro-active emergency management is integrating the products and tools developed in the project to operational platforms. Accordingly, the developed products are delivered to end-users utilizing e.g. the EFAS platform and integration into existing civil protection platforms as new web services. This allows for assessing the usefulness of the products and further refinements based on end-user experiences.

How to cite: Niemi, T., Baugh, C., Berenguer, M., Berruezo, A., Leinonen, M., von Lerber, A., Park, S., Prudhomme, C., Pulkkinen, S., and Ritvanen, J.: Advanced Tools for pro-Active Management of Impacts and Risks Induced by Convective Weather, Heavy Rain and Flash floods in Europe – TAMIR project, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8194, https://doi.org/10.5194/egusphere-egu21-8194, 2021.

Temperature extremes like hot days or prolonged episodes of high air temperature like heat waves can cause adverse human health effects. Heat-related mortality only represents the extreme end of a variety of possible health outcomes like heat exhaustion or heat stroke.

Exposure to ground-level ozone provokes negative impacts on human health primarily affecting the cardio-pulmonary system causing respiratory or cardiovascular diseases. These diseases include, but are not limited to, lung inflammation and tissue damage, asthma, heart attacks or heart failure.

High levels of ozone and temperature often coincide due to the underlying ozone formation characteristics. As synergistic effects lead to a risk beyond the sum of their individual effects, the co-occurrence of elevated levels of air temperature and ground-level ozone concentrations represents an even intensified human health risk.

The current contribution deals with statistical models and analysis of the interplay between large-scale meteorological and synoptic conditions, prevailing air pollution levels and combined ozone and temperature events under present and future climatic conditions. In this context, meteorological mechanisms representing main drivers of these concurrent ozone and temperature events were identified. Large-scale atmospheric circulation dynamics and their relationships with ground-level ozone and temperature conditions were evaluated.

The methodological focus was primary on statistical modeling approaches and different machine learning methods. Self-Organizing Maps, an artificial neural network algorithm based on unsupervised machine learning, were used to classify synoptic types based on daily mean sea level pressure reanalysis data. The resulting synoptic types were evaluated with regard to the European ozone and temperature characteristics in order to identify types associated with high ozone and temperature. Regression analyses with e.g. shrinking methods were used to identify main predictors for concurrent ozone and temperature events. Due to data availability and research foci, two varying time windows from 1993 to 2012 as well as from 2004 to 2018 were used within the study. The European area built the regional focus.

Anthropogenic-induced global climate change affects not only mean but also extreme temperatures as well as associated ground-level ozone concentrations due to changing synoptic circulation and chemical environment conditions. Future frequency changes of concurrent ozone and temperature events were evaluated exemplarily for Central Europe. Statistical downscaling projections until the end of the twenty-first century were assessed by using the output of seven models of the Coupled Model Intercomparison Project Phase 5 (CMIP5). A sharp increase was projected under RCP4.5 and RCP8.5 scenario assumptions. Respective multi-model mean changes amounted to 8.94% and 16.84% as well as 13.33% and 37.52% for mid- (2031–2050) and late-century (2081–2100) European climate, respectively (Jahn and Hertig 2020). Hotspot regions with more frequent occurrences of these combined events in Central Europe were identified for which, due to their associated individual and combined health effects, a higher future vulnerability can be expected.

How to cite: Jahn, S. and Hertig, E.: Health-relevant, concurrent ground-level ozone and temperature events in recent and future European climate, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-451, https://doi.org/10.5194/egusphere-egu21-451, 2021.

Small variations in the mean state of the atmosphere can cause large changes in the frequency of extreme events. In order to deepen and extend previous results in time, in this work we analyzed the linear relationship between extreme and mean temperature (Τ) on a climate change scale in Argentina. Two monthly extreme indices, cold nights (TN10) and warm days (TX90), were calculated based on the quality-controlled daily minimum and maximum temperature data provided by the Argentine National Meteorological Service from 58 conventional weather stations located over Argentina in the 1977–2017 period. Subsequently, we evaluated the relationship between the linear trends of extremes and mean temperature on a seasonal basis (JFM, AMJ, JAS, and OND). Student's T-test was performed to analyze their statistical significance at 5%. Firstly, positive (negative) and significant linear regressions were found between TX90 (TN10) trends and mean temperature trends for the four studied seasons. Therefore, an increase in the Τ-trend maintains a linear relationship with significant increase (decrease) of warm days (cold nights). Moreover, we found that JFM was the one with the highest coefficient of determination (0.602 for hot extremes and 0.511 for cold extremes), implying that 60.2% (51.1%) of the TX90 (TN10) trend could be explained as a function of the Τ-trend by a linear regression. In addition, in the JFM (OND) quarter, the TX90 index increased by 7.02 (6.02) % of days each with a 1 ºC increase in the mean temperature. Likewise, the TN10 index decreased by 4.94 (and 4.99) % of days from a 1ºC increase in the mean temperature for the JFM (AMJ) quarter. Finally, it is worthwhile to highlight the uneven behavior between hot and cold extremes and the mean temperature. Specifically, it was observed that the slopes of the linear regression calculated for the TX90 index and Τ presented a higher absolute value than those registered for the TN10 index and Τ. Therefore, a change in the mean temperature affects hot extremes to a greater extent than cold ones in Argentina.

How to cite: Suli, S., Rusticucci, M., and Collazo, S.: A study about the linearity of trends between extreme and mean temperatures in Argentina, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-741, https://doi.org/10.5194/egusphere-egu21-741, 2021.

The Mediterranean coast of Spain is well known for its pleasant weather, which results in high population densities and large number of tourists. The littoral area is constituted by a rich variety of ecosystems combined with a well-developed industry and agricultural land. The attractive Mediterranean climate has another side of the story, due partially to the Spanish mountain ranges along the east coast. This results in extreme rainfall events that drive flash floods that carry significant economic, environmental and social impact to the affected areas. The mentioned scenario gets more complex when considering the climate change that is already experienced in the Mediterranean region. Among others, the increase in extreme precipitation events envisioned by global climate models. Considering that storms and flash floods are the highest occurrence and most expensive events, it is fair to analyse the adaptation measures in place for the studied area.

The present work shows the comparative analysis of three recent case studies of major compound hazard events happened in the Mediterranean coast of Spain with special focus on littoral impacts and within a short time frame of 4 months: September 2019, October 2019, and January 2020. The nearness of the events left short time for recovery between them, as well as added aggravation due to the accumulated environmental and economic impacts caused to the region and the Covid-19 pandemics. The work presents a wide range of data (meteorological, hydrological, economical, impact data, etc.), collected from the press and social media as well as from official sources such as CCS, Meteorological agencies, Civil Protection, and others. This allows developing a multidisciplinary approach from the point of view of hydrology, meteorology, sea sciences and social science.

The analysis of the events is made from a holistic point of view including details as varied as the geographical areas affected up to municipality level, circumstances of casualties, location of extreme hydrometeorological values recorded during the events, environmental impact and economic loss. Furthermore, the different factors driving to each compound hazard event (floods, windstorms, sea surges, ...) and cascade effects have been analysed. Moreover, an analysis of the adaptation measures present at the time is done, along with suggestions of complementary or better adaptation measures for the three cases. Even though the data collection and analysis are made for the entire affected area within the Iberian Peninsula, the impacts and adaptation measures considered in this communication have a focus on the coastal area, including its various littoral ecosystems, coastal infrastructures, tourist sector, etc.

This work has been done in the framework of the M-CostAdapt (CTM2017-83655-C2-1&2-R) research project, funded by the Spanish Ministry of Science and Innovation (MICINN-AEI/FEDER, UE).

How to cite: Caballero-Leiva, I., Llasat-Botija, M., and Llasat, M. C.: Some lessons to improve adaptation measures in basis to the analysis of three extreme rainfall events and its associated impacts in the Spanish Mediterranean Coast., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2060, https://doi.org/10.5194/egusphere-egu21-2060, 2021.

North Atlantic tropical cyclones are the costliest natural hazard affecting the US, and are capable of causing hundreds of billions of dollars of insured losses in a single season.  Tropical cyclone activity has been observed to show considerable decadal variability, linked with variations in sea surface temperatures in regions of the North Atlantic such as the main hurricane development region (MDR) and sub-polar gyre (SPG).

In this presentation we show that a multi-model ensemble of decadal prediction systems can skilfully predict north Atlantic hurricane activity and consequent US insured losses on multi-annual timescales, with a correlation coefficient of greater than 0.7 for 5 year mean hurricane activity.  Rather than tracking tropical cyclones directly in the dynamical models, we make predictions using an index based on predicted temperatures over the north Atlantic.  The skill of the dynamical models outperforms persistence, and could aid decision making for the (re)insurance industry over the US.  As part of the Copernicus Climate Change Service, a publicly available probabilistic forecast of 5 year mean north Atlantic hurricane activity and US insured losses has been produced and will be presented here.

How to cite: Lockwood, J., Dunstone, N., Hermanson, L., Scaife, A., Smith, D., and Thornton, H.: Skilful predictions of multi-year US hurricane insured losses by decadal prediction systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2719, https://doi.org/10.5194/egusphere-egu21-2719, 2021.

Heavy and prolonged rainfall from one of the most destructive natural hazards, Tropical Cyclone (TC) generate devastating inland coastal flooding in the US. In this study, we introduce a pluvial hydrodynamic model to translate rainfall intensity of TCs as the main driver into extreme flooding hazard in coastal areas on the west side of Buzzard Bay in Massachusetts. The model implements a 2D hydraulic modeling and landscape characteristics, including geometry, land use, surface roughness, river networks, and soil infiltration. Using the continuity of mass and momentum equations, the model translates rainfall intensity of TCs that make landfall in the area into dynamic flooding during each event. The rainfall intensity data are derived from a large number of synthetic TCs (generated from historical climate through 1979-2019). The high spatial resolution rainfall intensity with short and long duration scenarios (1-hr, 2-hr, 3-hr, 6-hr, 12-hr, 24-hr, 48-hr, and 72-hr) are then used to simulate the corresponding extreme flooding during each TC. The accuracy of the developed model is evaluated by comparing flood inundation areas during observed TCs (extracted from the Synthetic-Aperture Radar (SAR) image processing) with those simulated by the model from NEXRAD data for the same events. The maximum simulated flood depth during each synthetic TC is then applied in a probabilistic framework to estimate flood levels in different return periods (up to 200 year) for each of the short and long duration scenario. The results of flood depth and inundated extent from low probable and high consequence TC floods provide critical insight for designing resilient infrastructure and reducing damages and cost against these destructive extremes. Our methodology can be applied for other susceptible coastal regions, helping identify vulnerable areas to extreme flooding induced by short and long duration TCs.  

How to cite: Sarhadi, A. and Emanuel, K.: Inland Coastal Flooding Risk by Tropical Cyclone, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8997, https://doi.org/10.5194/egusphere-egu21-8997, 2021.

Do you want to translate your work on extreme weather into human and economic impacts? Do you want to investigate the statistical risk from climate change to your region, country or planet? Do you need to identify vulnerable populations and find the most effective climate adaptation measures?

The CLIMADA platform (CLIMate ADAptation) is built to support these analyses. The model is an open source, globally consistent, fully probabilistic risk assessment tool. It is designed with both academics and decision-makers in mind and is used in international financial planning, regional climate adaptation projects and impact forecasting.

CLIMADA combines hazard, vulnerability and exposure data to produce risk assessments, allowing you to supply any (or none) of the data required. The model includes event data for hazards including tropical storm wind and surge, windstorms, earthquake, flood, drought, wildfire and agricultural risk, at different stages of maturity. It includes the LitPop exposure model for estimating economic and population exposure, and impact/vulnerability functions to combine them with hazards. It is suitable for case studies and climate studies.

In this session we will present the model, highlight recent additions, and discuss our work supporting users in government, industry and the third sector. We want to hear questions from potential new users and collaborators and hope to spark conversations about new data sources, improved methodologies and integrations with other workflows.

How to cite: Fairless, C., Kropf, C., and Bresch, D.: CLIMADA: an open-source climate risk assessment model. The latest developments!, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9205, https://doi.org/10.5194/egusphere-egu21-9205, 2021.

Climate change poses significant challenges for many industrial activities around the world, including mining. Changes in precipitation patterns and the increasing frequency of extreme weather events can trigger severe droughts or flash floods that can easily disrupt the minerals value-chain and increase environmental pollution risks. This research focuses on evaluating climate change risks faced by the mining industry in Chile during the period 2035-2065 under the assumptions of the RCP 8.5 scenario (business as usual).  This research presents risk maps, at the national scale, based on different databases that describe the location and characteristics of the mining infrastructure and spatiotemporal analysis of daily precipitation changes between present climate conditions and future predictions. The present climate conditions are depicted by historical observations for the period 1980-2010 while the future predictions are represented by an ensemble of 34 downscaled Global Circulation Models (GCMs) from the CMIP5.  On one hand, the results show that mining operations located in northern and central Chile (Atacama, Coquimbo and Valparaiso regions), will face significant flash flood risks due to the predicted increase of extreme precipitation events for 2035-2065. On the other hand, the results suggest that mining operations located in the regions of Coquimbo, Valparaiso, Biobio, Libertador G.B.O, and Metropolitan area of Santiago are those under the most significant risks due to droughts. The results obtained in this research are part of a more comprehensive project titled “Climate Risk Atlas of Chile”, developed by the Center for Climate and Resilience Research (CR2) and the Center for Global Change of Universidad Católica de Chile (https://arclim.mma.gob.cl/), which analyses the risks of climate change for different industries of the Chilean economy.

How to cite: Perez, G., Pagliero, L., McIntyre, N., Aitken, D., and Rivera, D.: Evaluation of climate change risks faced by the mining industry in Chile: spatiotemporal analysis of extreme precipitation for 2035-2065, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10500, https://doi.org/10.5194/egusphere-egu21-10500, 2021.

It is common today to consider that climate is expected to change or even climate change is present and evident.  A changing climate leads to changes in the frequency, intensity, spatial extent, duration, and timing of climate extremes, and may result in unprecedented events. Changes in extremes of a climate variable are not always related in a simple way to changes in the mean of the same variable or a hydrological variable, and in some cases may be of opposite sign to a change in the mean of the variable.  Also, the changes vary from one geographical region to another.   In this review paper, examples of climate change impact studies on hydro-meteorological extremes, i.e. extreme precipitation, floods and droughts, in the Mediterranean region, are presented and discussed.  In this geographical area, agriculture is the main consumer of water, demanding 60-90% of the total water use. The impacts of the climate change induced modifications of hydro-meteorological extremes and water management practices on the availability of surface water and groundwater resources are also discussed.

How to cite: Loukas, A.: Climate change induced modifications of hydro-meteorological extreme events and their impacts on water resources for agriculture in the Mediterranean, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15596, https://doi.org/10.5194/egusphere-egu21-15596, 2021.

Due to climate change the frequency and character of precipitation are changing as the hydrological cycle intensifies. With regards to snowfall, global warming thereby has two opposing influences. Increasing humidity enables potentially intense snowfall, whereas warming temperatures decrease the likelihood of snowfall in the first place. Here we show an intensification of extreme snowfall under future warming, which is robust across all global coupled climate models when they are bias-corrected with observational data. While mean daily snowfall decreases drastically in the model ensemble, both the 99th and the 99.9th percentiles of daily snowfall increase strongly in the next decades. Additionally, the magnitude of high snowfall events increases, which is likely to pose considerable challenge to municipalities in mid to high northern latitudes. We propose that the almost unchanged occurrence of temperatures just below the freezing point of water in combination with the strengthening of the hydrological cycle enables this intensification of extreme snowfall. Thus extreme snowfall events are likely to become an increasingly important impact of climate change on society in the next decades.

How to cite: Quante, L., Willner, S., Middelanis, R., and Levermann, A.: Intensification of extreme snowfall under future warming, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10589, https://doi.org/10.5194/egusphere-egu21-10589, 2021.

Meteorological considerations of grapevine damage due to temperature variations: the 2019 late spring frost and summer heat wave events in Burgundy

During 2019, the occurrence of two contrasting weather events, a cold snap and a heat wave, caused extensive damage to the vineyards of Northern Burgundy. The late spring cold snap, that occurred from May 5^th to 7^th, generated frost like conditions across the northern and north-western areas of the Côte-d'Or department. The weather stations of the Northern Auxois area, where the three observation and study sites are located, recorded minimum temperatures ranging between -1 and -2°C. On the 24^th and the 25^th of July vineyards were exposed, yet again, to an extreme temperature variation. A brief but unusually intense heat wave increased daily maximum temperatures up to 42°C in the department’s far north. Landforms such as plateaus were less exposed to the increase in temperatures due the limiting effect of higher elevations. This led to temperatures not exceeding 40°C above 300 m, elevation at which the vineyard sites of this study are located.

Weather conditions that caused the early May frost event were related to a northern circulation present over Western Europe that persisted from the 28^th of April to the 6^th of May. The strong anticyclonic ridge stretching from Greenland to the Iberian Peninsula directed an air mass of arctic origin towards France. On July 24^th and 25^th, the presence of a surface high pressure system above Scandinavia, associated with a low-pressure center located near the Atlantic Ocean, generated an influx of a very hot air mass from the northern part of the African continent through France and neighbouring countries.

The local impact of these two weather events was modulated by the topographical features specific to the study area: a limestone plateau strongly dissected by parallel valleys of S.E. / N.W. orientations. The three observation sites have similar soil characteristics and are located on south facing slopes. However, damage to vegetation was uneven across sites as well as within each site. These observations rise up the question of the influence of very fine-scale environmental conditions and the impact they might have on the different vegetative growth stages. Lastly, the variation in physiological response among grapevines and its effect on their sensitivity to the occurrence of different weather hazards is also to be considered.

How to cite: Planchon, O., Cantat, O., Bois, B., Beauvais, F., Gavrilescu, C., and David, M.: Meteorological considerations of grapevine damage due to temperature variations: the 2019 late spring frost and summer heat wave events in Burgundy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-76, https://doi.org/10.5194/egusphere-egu21-76, 2021.