Climate change is a global challenge with manifold and widespread consequences, including the intensification and increased frequency of numerous extreme weather phenomena. In response to this pressing issue, we introduce ClimaMeter, a platform designed to assess and contextualize extreme weather phenomena in relation to climate change. The platform provides near-real-time information on the dynamics of extreme events, serving as a resource for researchers, policymakers, and acting as a scientific outreach tool for the general public. ClimaMeter currently analyzes heatwaves, cold spells, heavy precipitation, and windstorms.Our methodology is based on looking for weather conditions similar to those that caused the extreme event of interest with physics-informed machine-learning methodologies. We focus on the satellite era, namely the period since 1979, when widespread observations of climate variables from satellites have become available. The object studied (i.e. "the event") is asurface-pressure pattern over a certain region and averaged over a certain number of days, that has lead to a extreme weather conditions. We split the dataset 1979-Present in two parts of equal length and consider the first half of the satellite era  as "past" and the second part as "present" separately. We use data from MSWX. We then compare how the selected weather conditions have changed between the two periods, and whether such changes are likely due to natural climate variability or anthropogenic climate change.

This presentation sheds light on the methodology, data sources, and analytical techniques that ClimaMeter relies on, offering a comprehensive overview of its scientific foundations. To illustrate ClimaMeter, we present some examples of recent extreme weather events. Additionally, we highlight the role of ClimaMeter in promoting a profound understanding of the complex interactions between climate change and extreme weather phenomena, with the hope of ultimately contributing to informed decision-making and climate resilience. Follow us on X @ClimaMeter and visit www.climameter.org.

How to cite: Faranda, D. and the The ClimaMeter team: ClimateMeter: Putting Extreme Weather Phenomena in Climate Perspective , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-126, https://doi.org/10.5194/ems2024-126, 2024.

According to climate and socioeconomic projections, a global development based on high-emission scenarios is likely to see a progressive increase in magnitude of climate extremes, resulting in large impacts who can lead to devastating impacts and hinder the stability of ecosystems. To minimize the risk of such events, the Paris Agreement recommended pursuing the strongest efforts to keep the global temperature increase – compared to pre-industrial level – to 1.5°C or, at least, well below 2°C. Considering the global temperature tendency of the last decades, it is likely that the World would exceed the 1.5°C threshold, and so the efforts to limit the temperature increase, and possibly revert it, fit into the 1.5°C (or 2°C) overshoot pathways. However, if the World will follow low-emission scenarios, will such overshoot trajectory prevent us from unprecedented climate extremes or are we unavoidably bound to experience new record-breaking events? To answer this question, we used the bias-adjusted multi-model ISIMIP3b data, we constructed a dataset of 12 hazards (including heat and cold waves, droughts, precipitation extremes, fire danger, snowfall, and compound events), and we evaluated – for each hazard – whether, where, and when the most severe event recorded in the past is likely to be exceeded in the future. The results are presented at grid point scale (0.5°) and separately for the SSP1-1.9 and SSP1-2.6 scenarios. To expand the analyses, we coupled the integrated assessment model WITCH with a climate emulator to create multiple overshoot curves (still at 1.5°C, but with different timing and duration), and we again estimated the possible occurrence of such unprecedented events. In this presentation, we also discussed the population and land-use shares exposed to such events, depending on the scenario and the shape of the temperature overshoot.

How to cite: Spinoni, J., Chiani, L., Dosio, A., Ghirri, J., Mastropietro, M., Rodriguez-Pardo, C., and Tavoni, M.: Are unprecedented climate extremes unavoidable with temperature overshoot pathways?, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-546, https://doi.org/10.5194/ems2024-546, 2024.

Both extremely low and high temperatures can be relevant for the operation of nuclear power plants (NPPs). For example, temperatures higher than 40 °C might endanger safe shutdown of an NPP (STUK, 2011). The MAWECLI project aims to provide reliable estimates about the likelihood of extreme events affecting NPPs in the changing climate of Finland.

In this study, we evaluate the annual probabilities of low and high temperature extremes in the past and future climate of Finland. To produce estimates for events with low annual probabilities (2%, 1%, 0.2%, and 0.1%), we utilise statistical extreme value analysis, employing both the generalized extreme value distribution with block maxima approach and the generalized Pareto distribution with peak-over-threshold approach. Furthermore, any potential non-stationarity in the time series is addressed by utilising distribution parameter covariates based on linear trends identified from the time series.

For the past, the extreme value analysis is performed for 35 weather stations across Finland. The analysis is based on a 60-year period of observations (1963-2022), which is common for all stations, as well as longer station-specific historic records, starting from 1844 for the longest operational weather station. In addition to instantaneous extreme temperatures, estimates are also calculated for high (or low) temperatures that have prevailed for time periods of six and 24 hours.

For the future projections, bias-corrected daily mean temperature data from 25 CMIP6 global climate models (GCM) is utilised (Ruosteenoja and Jylhä, 2023). To produce extensive data sets for 20-year periods representing current (1999-2018) and mid-century (2041-2060) climatic conditions, 20-year time series of each climate model were combined to create a 500-year ensemble depicting the climatic conditions of each period. The use of this 500-year ensemble enables more robust estimates in comparison to extreme value analysis relying on only 20-year data periods for each individual GCM. Our results show higher warming for extremes compared to changes in mean temperatures, the difference increasing towards smaller annual probabilities, i.e., more rare events.

References:

Ruosteenoja, K., Jylhä, K. Average and extreme heatwaves in Europe at 0.5–2.0 °C global warming levels in CMIP6 model simulations. Clim Dyn 61, 4259–4281 (2023). https://doi.org/10.1007/s00382-023-06798-4

STUK, 2011. European Stress Tests for Nuclear Power Plants. National Report. Finland 3/0600/2011 2011 Tomi Routamo (ed.) Radiation and Nuclear Safety Authority 2011. Available at: https://www.ensreg.eu/sites/default/files/EU_Stress_Tests_-_National_Report_-_Finland.pdf

How to cite: Laapas, M., Jylhä, K., and Ruosteenoja, K.: Annual probabilities of extreme air temperatures in the past and future climate of Finland, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-706, https://doi.org/10.5194/ems2024-706, 2024.

This paper aims to evaluate the precipitation extremes over Northwest of Iran based on 5th generation reanalysis precipitation data (ERA5) for long-term historical period from 1941 to 2020. The trend of 10 selected extremes precipitation indices (EPI) were evaluated in monthly, seasonal, annual and decadal time scales. The nonparametric Mann–Kendall test were applied to 960 grid (600,000 km2) with a high spatial resolution of 0.25° × 0.25° to detect the possible temporal trends in all EPI on the significance levels of 1% and 5%, include frequency and intensity indices. Notably, anomalies in precipitation for the recent reference period (1991-2020) compared to a different subperiods (e.g. 1961-1990 and 1941-2020) seeking to distinguish shifts in precipitation patterns. 

Our findings have uncovered a significant increase in the frequency of daily heavy precipitation events over the studied period, particularly in the latter decades. Trends showing mostly positive patterns, many of which are statistically significant. Moreover, in some areas over the past 30 years, the total rainfall has increased by approximately 25%. Although most events occur during the winter (DJF) and spring (MAM) seasons, the highest number of events belongs to March. The largest positive trend in terms of intensity and frequency is associated with the fall season (SON) and the month of October and November in this region. Also the highest number of grid points with significant positive trends is related to fall in seasonal timescale and November in monthly timescale. Although the results of the annual study indicate that 56.5% of the respected territory have a positive trend which statistically significant in at least one of the indices, this percentage increases to 80.3% on a seasonal scale for (SON). In some cases, negative trends were observed for few number of grids, none of which were statistically significant in annual scale. Also, periodic behavior observed, characterized by oscillations spanning on interdecadal scales.

Overall, the combination of the results of the EPI trends, illustrate a considerable changes towards more intense and more frequent precipitation on interannual scales. Consequently, this region can be considered as a high risk area in terms of extreme events and flash floods.

How to cite: Fakour, P. and Ustrnul, Z.: Long-Term Trends in Extreme Precipitation Events in Northwest Iran: A Comprehensive Analysis Using ERA5 Reanalysis Data , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-479, https://doi.org/10.5194/ems2024-479, 2024.

The recent increase in extreme and intense rainfall events is a pressing concern, mainly due to the effects of climate change. These heavy downpours contribute significantly to urban damage, triggering floods, landslides and other related disasters. Such natural disasters pose serious risks to human life and infrastructure. Seoul, South Korea, is particularly vulnerable. In 2001, for example, the city recorded a staggering 273 mm of rainfall in a single day. More recently, in 2011 and again in 2022, Seoul suffered extensive urban flooding. These events were largely due to the inability of rivers and stormwater systems to handle the sudden deluge, which exceeded the design frequencies that these infrastructures were originally built to withstand. As climate change accelerates, the frequency of such extreme weather events is expected to increase, requiring advanced research to fully understand their impact on urban infrastructure. This includes vital structures such as dams, embankments and stormwater pipes, all of which require robust designs to cope with such unprecedented environmental stress. This study focuses on collecting and analysing rainfall data from meteorological stations across Korea. By using the Generalised Extreme Value (GEV) distribution along with time series analysis, our research aims to meticulously map flood risk patterns. These statistical methods are crucial for understanding and predicting the behaviour of rare but severe weather phenomena. In addition, our research calculates flood loads based on specific return periods and design frequencies. These calculations are essential for designing infrastructure that can withstand future climatic challenges, thereby ensuring the safety and resilience of urban environments against the increasing threat of flooding. This comprehensive approach not only highlights historically flood-rich or flood-poor periods, but also assists in the strategic planning and upgrading of urban infrastructure to cope with the realities of climate change.

Acknowledgement

This research was supported by a (2022-MOIS63-002(RS-2022-ND641012)) of Cooperative Research Method and Safety Management Technology in National Disaster funded by Ministry of Interior and Safety(MOIS, Korea).

How to cite: Choi, S., Choo, K., and Kim, B.: Evaluation of Flood Risk in South Korea Through Time Series Analysis of Precipitation Data, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-628, https://doi.org/10.5194/ems2024-628, 2024.

Mountainous regions present a distinct vulnerability to climate change, serving as valuable barometers for tracking these shifts. Studies conducted across various global locales reveal a recent uptick in temperatures within mountainous terrains, notably evident since the 1980s, with variations in intensity across specific altitude bands. While temperature rises are not consistently accompanied by notable trends in annual precipitation totals, they do prompt alterations in the annual precipitation patterns and types, thereby impacting the frequency and duration of snow cover.

Since snow cover plays a pivotal role in the Earth's systems, influencing hydrology, climate, and ecological environments, any alterations in snowpack patterns will yield multifaceted effects. This study aims to investigate the variability of snow cover metrics, including snow depth, duration, persistence, and extent.

The study focused on mountainous regions in Central Europe, defined as areas with elevations exceeding 500 meters above sea level within the domain of 10°E - 25°E longitude and 47°N - 55°N latitude. Data analysis spanned from 1961 to 2023 and drew upon various meteorological sources, including on-site snow depth measurements and regional ERA5-Land reanalysis. To better capture the details of complex terrain, a new gridded dataset was generated by integrating measurements with reanalysis data at a spatial resolution of 0.5 kilometers.

The primary findings underscore the significant influence of ongoing climate change on snow cover characteristics, with the intensity of this impact varying based on geographical and terrain factors such as altitude, landform, slope, and aspect. However, a consistent multiannual decrease in snowpack persistence was observed across the entire research area.

A comprehensive understanding of the timing of snow accumulation and ablation is essential, as it governs springtime mountain runoff rates, water infiltration, groundwater storage, and transpiration rates—all critical components of the hydrological cycle.

How to cite: Wypych, A., Ustrnul, Z., and Sałaja, J.: Variability and changes of snow cover in Central European mountains , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-929, https://doi.org/10.5194/ems2024-929, 2024.

Six gridded datasets of mean, minimum and maximum temperatures and one dataset of 22 individual sites distributed over mainland Spain and the Balearic Islands are compared. The gridded datasets vary in spatial resolution, temporal coverage and interpolation procedures as well as in the number of individual predictor sites used to create them. The six gridded datasets start in 1901 (1951) in the longest (shortest) case and extend until present or some date within the last decade. The spatial resolution ranges from 0.11^◦ to 1 km. The site-resolved dataset includes the 22 longest data records over Spain, reaching back until the mid-19^th century and thus complementing the temporal analysis of this study. The availability of datasets with different levels of predictor data, methodologies and final characteristics allows for an assessment of the observational uncertainties in temperature climatologies and trends at the local/regional scales.

The structure of the spatial patterns for the climatologies of the mean, minimum and maximum temperatures is quite consistent across datasets, with the coldest temperatures over areas of the Pyrenees and mountain ranges in winter and the hottest ones over the Guadalquivir Valley in summer. However, notable differences are observed at a regional scale, particularly over the highest elevation regions where average discrepancies greater than 3 ^◦C are found. Mean temperatures increase since 1970s between 1-2 ^◦C, and uncertainties range from 0.2 to 0.5 ^◦C across datasets. The spatial distribution of trends shows that warming is widespread but there are notable differences in magnitude, spatial distribution and even sign of the trends depending on the database considered. The relevant variability estimated among datasets leads to a low confidence on where the warming peaks annually and seasonally. A Principal Component  Analysis is used to filter out signals that account for low amounts of regional variance and focus on those responsible for the long term responses. The first principal component reproduces the trends in each dataset and season, and is consistent with the forced response to external forcing found at global and hemispherical scales. This PC accounts for a large percentage of variance in each dataset and season. The corresponding spatial patter has uniform sign and its regional variability varies considerably across datasets with differences being larger in scale than the resolution of the datasets. The results can be relevant for model evaluation and detection and attribution studies over this region.

How to cite: Garrido, J. L., González-Rouco, J. F., Brunet, M., Sigró, J., Herrera, S., Chazarra-Bernabé, A., Serrano-Notivoli, R., Beguería, S., González-Hidalgo, J. C., Peña-Angulo, D., Rodríguez-Camino, E., Rodríguez-Guisado, E., Peral, C., García, M., and García-Bustamante, E.: Temperature trends and uncertainties over peninsular Spain and the Balearic Islands, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-996, https://doi.org/10.5194/ems2024-996, 2024.

Pan-European summer drought of 2022 affected different sectors as agriculture, energy, ecosystems, and population health, with both large-scale and local remarkable impacts. In the last decades, similar extreme droughts hit Europe, to the point that in some areas as the Mediterranean Region such events are becoming the new normal. This study moves from two questions: can we find comparable events as we move back in time? Has Europe ever experienced a period with more frequent and/or severe droughts? To provide the answers, we constructed a new database of meteorological drought events from 1921 to 2014. As input, we used the E-OBS gridded daily precipitation and temperature data, we computed the Standardized Precipitation Index (SPI) and the Standardized Precipitation-Evapotranspiration Index (SPEI) at multiple accumulation scales, and we set a multi-parameter system (including duration, severity, intensity, extent, and peak) to assign a 0-100 score to droughts at country scale. The single events are therefore classified into five big categories to provide decadal statistics and investigate the trends of mega-droughts over the last one hundred years. For the events occurred after 1950, we also estimated population and land-use exposure. According to our analyses, no other summer drought in 1921-2023 exceeded that of 2022, confirming its exceptional features, in particular regarding intensity and extent. Our findings show that - in the last two decades - the meteorological drought severity actually increased through most of Europe, if compared with the second half of the 20th century. However, they also show that Europe already experienced a period of comparable drought hazard from the late 1930s to the late 1940s, including long multi-country droughts never recorded again. Being aware that high-quality climatic data are sparser as we go backwards in time, the results presented here suggest that recent extreme droughts might not be record-breaking – in particular regarding duration - if we extend the analyses to a hundred years ago. 

How to cite: Spinoni, J., Cammalleri, C., Chiani, L., Dosio, A., Ghirri, J., Mastropietro, M., Toreti, A., and Tavoni, M.: Record-breaking European meteorological droughts in 1921-2024, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-537, https://doi.org/10.5194/ems2024-537, 2024.

How to cite: Altava-Ortiz, V., Barnolas, M., Barrera-Escoda, A., Assimenidis, S., Cortès, M., Prohom, M. J., Moré, J., and Martín-Vide, J.: Is Catalonia facing a Megadrought episode?, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-783, https://doi.org/10.5194/ems2024-783, 2024.

Droughts at the global scale can be described by many variables, expressing their extent, duration, dynamics and severity. To identify common features in global land drought events (GLDEs) based on soil moisture, we present a robust method for their identification and classification (cataloging). Gridded estimates of root-zone soil moisture from the SoilClim model and the mesoscale Hydrologic Model (mHM) were calculated over global land from 1980–2023. Using the 10th and 20th percentile thresholds of soil moisture anomalies, outputs of the two models were merged into a united dataset of drought affected areas in a 10-day step. OPTICS clustering of the gridded data was then used to identify a total of 736 GLDEs. By utilizing four spatiotemporal and three motion-related characteristics for each GLDE, we established threshold percentiles based on their distributions. This information enabled us to categorize droughts into seven severity categories (ranging from extremely weak to extremely severe) and seven dynamic categories (ranging from extremely static to extremely dynamic). The severity and dynamic categories overlapped substantially for extremely severe and extremely dynamic droughts but very little for less severe/dynamic categories, despite some very small droughts that have occasionally been very dynamic. The frequency of GLDEs has generally increased in recent decades across different drought categories but is statistically significant only in some cases. Overall, the cataloging of GLDEs presents a unique opportunity to analyze the evolving features of spatiotemporally connected drought events in recent decades and provides a basis for future investigations of the drivers and impacts of dynamically evolving drought events.

How to cite: Řehoř, J., Brázdil, R., Rakovec, O., Hanel, M., Fischer, M., Kumar, R., Balek, J., and Trnka, M.: Cataloguing global soil moisture droughts since 1980, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-437, https://doi.org/10.5194/ems2024-437, 2024.

The study of the precipitation regime aims to verify the hypothesis about the decrease of rainfall in Spain. Is the climate of peninsular Spain and the Balearic Islands moving towards a drier and drier climate?

In principle, global warming (GW), at the planetary level, represents an increase in precipitation. The progressive increase in temperature causes greater evaporation, and therefore greater humidity in the atmosphere, which should produce increasing precipitation. However, this trend is not occurring homogeneously. The various scenarios developed by the IPCC suggest that in Mediterranean latitudes the GW generates a tendency towards greater drought.

One of the objectives of this work is to analyze whether, in Spain, there is a temporal evolution of the precipitation regime towards less precipitation, as well as to study the relationship between annual precipitation and the trend towards progressive warming. Specifically, to verify the hypothesis that the increase in temperature resulting from the GW process implies a trend towards progressive drought.

Two different databases are used in this work: on the one hand, the information provided by AEMET from a selection of meteorological stations throughout Spain, with the daily series of precipitation (rr) and maximum (tx) and minimum (tn) temperatures, from January 1, 1971 to December 31, 2023; on the other hand, information from the Copernicus climate service. Specifically, the one resulting from E-OBS, from January 1, 1950 to December 31, 2022 with a resolution of 0.25 degrees.

In addition to multiple regression, the techniques used are the Mann-Kendall test and the Kendall-Theil-Sen regression.

The Mann-Kendall test confirms the statistical significance of the relationship between rr and tx, with a statistical reliability close to 99.9%. The KTS regression predicts a reduction of 38.49 mm for each 1°C increase.

If the warming trend experienced in recent years (1973-2022) continues, it is foreseeable that by 2050 there will be a reduction in precipitation in Spain of between 14% and 20% with respect to current precipitation (understood as the average between 2000 and 2022). Spain's climate is likely to vary from a Mediterranean climate to a warm steppe climate in the Köppen classification system.

How to cite: Roca, J., Arellano, B., and Zheng, Q.: Spain: towards a drier and warmer climate?, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-932, https://doi.org/10.5194/ems2024-932, 2024.

The Amazon rainforest is one of the most ecologically diverse and important ecosystems on Earth and plays a fundamental role in regulating the global climate. Extreme weather events, such as droughts, heatwaves, and floods, can lead to various consequences such as increased soil erosion, altered water availability, and changes in vegetation dynamics, which can affect the Amazon forest's carbon balance, biodiversity, and overall ecosystem functioning. Moreover, global-scale studies employing Coupled Model Intercomparison Project Phase 6 (CMIP6) future climate scenarios project an increase in extreme events over South America, further emphasizing the vulnerability of the Amazon forest ecosystem. 

Previous studies have examined heatwaves, droughts, and extreme precipitation events within the Amazon forest as singular extreme events. However, few studies have considered these weather extremes simultaneously as compound or concurrent extreme events. These compound events can have a more significant impact than individual extreme events alone; recent research has shown that the occurrence of combined drought and heatwave can increase the probability of vegetation decline compared to the individual effects of these events. Furthermore, existing research has primarily focused on hot-dry compound events, leaving a knowledge gap regarding the occurrence of combined hot and wet extremes.

In this context, the main research question addressed in this study is: What is the climatology of compound hot-dry and hot-wet extreme events in the Amazon region? To answer this question, our analysis involves identifying compound events of heatwaves and dry spells (hot-dry) and heatwaves and extreme precipitation (hot-wet) events that have occurred in the Amazon region over the last few decades. For this, we utilize data on maximum temperature and precipitation from rain gauges and weather stations, as well as from gridded datasets. The intensity and frequency of the compound events will be discussed, as well as the main atmospheric drivers.

How to cite: Ferreira, V. and Ramming, A.: Identifying compound extremes in the Amazon rainforest: dry-hot and dry-wet events, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-786, https://doi.org/10.5194/ems2024-786, 2024.

Drought is one of the extreme events that will be increasingly present in the context of climate change. As revealed in recent studies, meteorological droughts will become the principal factor modulating compound hot-dry events and analysis; thereof is therefore fundamental with regard to understanding future climate patterns. The average citizen knows little of geometry, but it plays an essential role in the characteristics of the droughts, by means of "fractional lengths". We analysed the fractality of the meteorological droughts under the most recent climate change scenarios. In addition, a new criterion for defining the beginning and end of meteorological droughts is established, which allows the analysis of seasonal variability. A temporal fractality measure based upon the Cantor set reveals consensual changes in the behavior of droughts worldwide. Most regions will undergo a slight increase in fractality (up to +10% on average), particularly associated with an acceleration of the hydrological cycle and the Hadley cell expansion, with a shift towards the higher latitudes of the tropical edge in both hemispheres. Geometrical measures were applied to the dry spells (<1mm) simulated by Earth System Models of the CMIP6, showing more concentrated or unequal distribution of droughts in mid latitudes. Simultaneously, the polar regions might benefit from more regular precipitation patterns. Other inequality measures, such as the indices of Gini and Monjo, showed similar results. In general terms, the earth’s climate will be more fractal in the rainfall-related patterns, which likely means that the consequences will be more catastrophic for the human population.

How to cite: Galiano, L., Monjo, R., Royé, D., and Martin-Vide, J.: Future changes in global drought fractality, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-794, https://doi.org/10.5194/ems2024-794, 2024.

Wildfires pose a significant natural disaster risk to populations and contribute to accelerated climate change. As wildfires are also affected by climate change, extreme wildfires are becoming increasingly frequent. Although they occur less frequently globally than those sparked by human activities, lightning-ignited wildfires play a substantial role in carbon emissions andaccount for the majority of burned areas in certain regions. While existing computational models, especially those based on machine learning, aim to predict lightning-ignited wildfires, they are typically tailored to specific regions with unique characteristics, limiting their global applicability. In this study, we present machine learning models designed to characterize and predict lightning-ignited wildfires on a global scale. Our approach involves classifying lightning-ignited versus anthropogenic wildfires globally over a long timespan, and estimating with high accuracy of over 91% the probability of lightning to ignite a fire based on a wide spectrum of factors such as meteorological conditions and vegetation. Utilizing these models, we analyze seasonal and spatial trends in lightning-ignited wildfires shedding light on the impact of climate change on this phenomenon. Our findings highlight significant global differences between anthropogenic and lightning-ignited wildfires. Moreover, we demonstrate that, even over a short time span of less than a decade, climate changes have steadily increased the global risk of lightning-ignited wildfires. We also find that models trained to predict lightning-ignited wildfires and models trained to predict anthropogenic wildfires are very different. This dramatically reduces the predictive performance of models trained on anthropogenic wildfires when applied to lightning-ignited ignitions, and vice versa. This distinction underscores the imperative need for dedicated predictive models and fire weather indices tailored specifically to each type of wildfire.

How to cite: Shmuel, A., Glickman, O., Lazebnik, T., Heifetz, E., and Price, C.: Lightning-Ignited Wildfires On A Global Scale: Prediction and Climate Change Projections based on Explainable Machine Learning Models, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-480, https://doi.org/10.5194/ems2024-480, 2024.

We investigate the Oceanic Niño Index (ONI), for simplicity called in this paper an El Nino Southern Oscillation (ENSO) index in 1950-2023  by applying  the wavelet spectral transform and the IBM SPSS correlations analysis.  ONI follows the three months current measurements of an average temperature of the sea surface  in the East-Central tropical part of the Pacific ocean nearby the international line of the date change over the average sea surface temperature over the past 30 years. The ENSO index is found to have a strong  ($>$0.87) correlation with the Global Land-Ocean Temperature (GLOT). The scatter plots  of the  ENSO-GLOT correlation with the linear and cubic fits have shown that the ENSO index is better fit by the cubic polynomial increasing proportionally to a cubic power of the GLOT variations.  The  wavelet analysis  allowed us to detect the two key periods in the ENSO (ONI) index: 4-5 year and 12 years.  The smaller period of 4.5 years can be linked to the motion of tectonic plates while the larger period of 12 years is shown to have a noticeable correlation of 0.25 with  frequencies of the under-water (submarine) volcanic eruptions in  the areas with ENSO occurrences.  Not withstanding any local terrestrial factors considered to contribute to the ENSO  occurrences, we investigated the possibility of these volcanic eruptions to be also induced by tidal forces of the Jupiter and Sun showing the correlation of  the under-water volcanic eruption frequency to be 0.12 with the Jupiter-Earth distances and 0.15 with the Sun-Earth distances in January, induced by the solar inertial motion, when the Earth is turned to the Sun with the southern hemisphere where the ENSO occurs.  Hence,  the underwater volcanic eruptions induced by tidal forces  of Jupiter and Sun can be the essential additional factors imposing this 12 year period of the ENSO (ONI) index variations. 

How to cite: Zharkova, V. and Vasilieva, I.: The ENSO  variations and their link with solar and volcanic activity , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-23, https://doi.org/10.5194/ems2024-23, 2024.

The Arctic ozone layer is characterised by high interannual variability, especially in the springtime period, which depends on stratospheric circulation and polar vortex forcing. Since the 1990s, there has been a gradual ozone recovery in the Arctic polar stratosphere. However, the increasing ozone trend may be different in unlike pressure levels of the stratosphere in connection with the ongoing climate change, which is related to the strengthening of the Brewer-Dobson circulation. This strengthening is related to changes in vertical distribution of ozone concentrations in different levels of the stratosphere. The aim of this study is a spatiotemporal analysis of the stratospheric ozone trend at different pressure levels between 10–100 hPa in the spring periods 1980–2023. In the following section, trends of geopotential height, temperature or potential vorticity at pressure levels between 10–100 hPa are analysed in particular. The monthly means of ERA-5 reanalysis layers were utilized. The trend was assessed using linear regression, and its statistical significance was determined using the non-parametric Mann-Kendall test. In March, the statistically significant decreasing trend (p=0.05) of the ozone mass mixing ratio was detected over Siberia at 10 hPa, while at 100 hPa, the statistically significant decreasing trend was registered over eastern Siberia and Greenland. In April, there was a statistically significant increasing trend at 10 hPa over eastern Siberia, and at 100 hPa, the trend over uniform parts of the central Arctic is spatially heterogeneous. In May, a statistically insignificant increasing trend occurred over the Arctic Ocean at 10 hPa, while at 100 hPa statistically significant decreasing trend was registered in the peripheral parts of the Arctic between 60–70°N.  

How to cite: Tichopad, D. and Láska, K.: Springtime stratospheric ozone trends in the Arctic since 1980, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-682, https://doi.org/10.5194/ems2024-682, 2024.