Displays

The prediction of extreme events has been a main focus in subseasonal forecasting due to their potentially high impacts. Despite their undebatable significance, it is not clear that extremes are forecast any better than events close to the mean of the climatology. In our work, we address the question of whether subseasonal forecasting systems show different performance for extreme than for average events. For this, we focus on forecasts of area-averaged European land temperatures in 20 years of hindcasts from the ECMWF system. To compare the prediction skill of extremes at both ends of the distribution to that of average events in summer and winter, we use the Extremal Dependence Index (EDI) which is a forecast performance measure suitable for rare events. Our results suggest that there is higher prediction skill for summer warm extremes as compared to average events at lead times of 3 – 4 weeks, with some regional dependence. The same is not true for summer cold extremes, indicating an asymmetry in the processes causing opposite summer temperature extremes. In winter, our analyses indicate that the situation is reversed: here, the cold events are better predicted. The difference in EDI between extreme and average events is, however, less pronounced than in summer. Further, we find that the forecast performance is strongly improved by the most severe and persistent events inside the analyzed period. We hypothesize that the enhanced warm extreme skill in summer is related to persistent flow patterns and land-atmosphere interaction.

How to cite: Wulff, O. and Domeisen, D.: Subseasonal prediction of average vs extreme European land temperatures in S2S hindcasts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10053, https://doi.org/10.5194/egusphere-egu2020-10053, 2020.

Abstract:

During the summer of 2015, heatwave events claimed 2422 lives in India. Following that disaster, India’s National Disaster Management Authority (NDMA), formulated a Heat Action Plan to protect citizens and minimize fatalities. Improved forecasts from the India Meteorological Department (IMD) together with NDMA’s heat action plan played a major role in the reduction of heatwave mortality since 2016. However, forecasts at longer lead times are required to improve action plans ahead of the heatwave events.

IMD uses extended-range forecast products provided by the National Centre for Medium Range Weather Forecasting (NCMRWF), but we show the improved prediction of high probability from a mutli-model ensemble of the subseasonal-to-seasonal (S2S) database (Vitart et al. 2017). The S2S prediction project that provides Global weather forecasts at lead time of 15 to 60 days, is a joint project of the World Weather Research Program (WWRP) and the World Climate Research Program (WCRP). This provides an opportunity to study the skill of predicting heatwaves over India at extended-range (15 to 30 days). 

In a recent study Ratnam et al., 2016 showed that atmospheric blocking patterns over the north Atlantic region have linkages with heatwave events over northwest India at 2-day lag using ERA-Interim reanalysis and IMD observation. Using ERA5 reanalysis, we found that during 1979-2018, a third of the blocking events over North Atlantic caused heat events over India.

Using the "reforecast" outputs in the S2S database to bias correct the real-time extended range forecast results in improved prediction of frequency, timing,  and spatio-temporal pattern evolution of heatwaves and severe heatwaves at 2 to 3 weeks forecast lead time. The atmospheric blocking anomalies at high-latitudes which precede the heatwave events in India could be predicted three weeks in advance. Based on the S2S models’ skills, the prospects for early warning and disaster preparedness look promising in the coming years.

References:

• Vitart, F., C. Ardilouze, A. Bonet, A. Brookshaw, M. Chen, C. Codorean, M. Déqué, et al. 2017. "The Subseasonal to Seasonal (S2S) Prediction Project Database", Bulletin of the American Meteorological Society 98: 163–173. doi:10.1175/BAMS-D-16-0017.1.
• V. Ratnam, Swadhin K. Behera, Satyaban B. Ratna, M. Rajeevan and Toshio Yamagata.: "Anatomy of Indian heatwaves", Scientific Reports, volume 6, Article number: 24395 (2016) doi:10.1038/srep24395

How to cite: Thanigachalam, A., AchutaRao, K., Mitra, A. K., Ashrit, R., and Gupta, A.: Extended-range prediction of heatwave events over North India: role of atmospheric blocking over North Atlantic, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-750, https://doi.org/10.5194/egusphere-egu2020-750, 2020.

We evaluate how hotspots of different types of the most extreme summer heat change under global warming increase of up to 4°C, to determine the level of global warming that allows us to avert the risk of these hotspots considering the irreducible range of possibilities defined by well-sampled internal variability. We use large samples of low-probability extremes simulated by the 100-member Max Planck Institute Grand Ensemble (MPI-GE) for five metrics of extreme heat: maximum reachable temperatures, return periods of extreme temperatures, maximum temperature variability, sustained tropical nights, and wet bulb temperatures. At 2°C of warming, MPI-GE projects maximum summer temperatures below 50°C over most of the world. Beyond 2°C, this threshold is overshot in all continents, with projected temperatures above 60°C in hotspots such as the Arabic Peninsula. Extreme 1-in-100-years pre-industrial temperatures occur every 10-25 years already at 1.5°C of warming. At 4°C, these 1-in-100-years extremes are projected to occur every one to two years over most of the world. The range of maximum temperature variability increases by 10-50% at 2°C of warming, and by 50-100% at 4°C. Beyond 2°C, heat stress is aggravated substantially over non-adapted areas by sustained tropical night and hot and humid conditions that occur rarely in a pre-industrial climate. At 4°C of warming, tropical night hotspots spread polewards globally, and prevail for at least 95% of the summer months; whilst extreme monthly mean wet bulb temperatures beyond 26°C spread over large tropical as well as mid-latitude regions.

How to cite: Suarez-Gutierrez, L., Müller, W. A., Li, C., and Marotzke, J.: Hotspots of Extreme Heat under Global Warming, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5192, https://doi.org/10.5194/egusphere-egu2020-5192, 2020.

An increasing fraction of people living in urban areas and the expected increase in long lasting heat waves highlight the important role of urban climates in terms of future climate change impacts, especially with relation to the heat-health sector. Due to the urban heat island (UHI) effect and its (generally) increased intensity particularly during nighttime, people living in urban areas happen to be more affected by heat-related discomfort and health risks than those in non-urban regions. In this contribution, temperatures of both rural and urban sites (station couples) in Switzerland and Southern Germany are analyzed, using (i) observed as well as (ii) bias-corrected and downscaled climate model data for daily minimum (tmin) and daily maximum temperature (tmax) to account for the UHI in future climates. As meteorological data are often restricted to locations of long-term measurements at rural sites only, they need to be transferred to urban sites first. For this purpose, the well-established quantile mapping technique (QM) is tested in a two-step manner. The resulting products are urban time series at daily resolution for tmin and tmax. By analyzing the temperature differences of the observed climate at rural sites and their respective urban counterparts and by assuming a stationary relationship between both, we can represent the UHI in future climates, which is quantified in terms of heat indices based on tmin and tmax (tropical nights, summer days, hot days).

The QM performance is evaluated using long-term weather station data of a Zurich station couple in a comprehensive cross-validation framework. Results reveal a promising performance in the present-day climate, given very low biases in the validation.

Applying the proposed method to the employed station couples, projections indicate distinct urban-rural temperature differences (UHI) during nighttime (considering the frequency of tropical nights based on tmin) compared to weak differences during the day (considering the frequency of summer days and hot days based on tmax). Moreover, scenarios suggest the frequency of all indices to dramatically rise at the urban site by the end of the century under a strong emission scenario (RCP8.5): compared to the rural site, the number of tropical nights almost doubles while the number of summer days reveals about 15% more days at the urban site when focusing on the station couple in Zurich and the late scenario period. The lack of nighttime relief, indicated by tmin not falling below 20°C (i.e. a tropical night), is especially problematic in terms of human health and makes the study of the urban climate in general and the UHI effect in particular indispensable.

How to cite: Burgstall, A., Casanueva, A., Hertig, E., Fischer, E., Knutti, R., and Kotlarski, S.: Representing the Urban Heat Island Effect in Future Climates, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1352, https://doi.org/10.5194/egusphere-egu2020-1352, 2020.

Global warming is leading to increased heat stress in many regions around the world. An extensive number of heat stress indicators has been developed to measure the associated impacts on human health. Here we calculate eight heat stress indicators for global climate models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) and compare their future trends and exceedances of critical physiological thresholds with particular focus on highly populated regions. The heat stress indicators are selected to represent a range of different applications, such as extreme heat events, heat-related losses in worker productivity, heat warnings, and heat-related morbidity and mortality. Projections of the analyzed heat stress indicators reveal that they increase significantly in all considered regions as function of global mean temperature. Moreover, heat stress indicators reveal a substantial spread ranging from trends close to the rate of global mean temperature up to an amplification of more than a factor of two. Consistently, exceedances of critical physiological thresholds are strongly increasing globally, including in several densely populated regions, but also show substantial spread across the selected heat stress indicators. Additionally, the indicators with the highest exceedance vary for different threshold levels, suggesting that the large indicator spread is associated both to differences in trend magnitude and threshold levels. The usage of heat stress indicators that are suitable for each specific application is thus crucial for reliably assessing impacts of future heat stress, while inappropriate indicators might lead to substantial biases.

How to cite: Schwingshackl, C., Sillmann, J., Sandstad, M., and Aunan, K.: Heat stress indicators in CMIP6: Estimating future trends and exceedances of critical physiological thresholds, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8701, https://doi.org/10.5194/egusphere-egu2020-8701, 2020.

Comparable estimates of the heat-related work productivity loss (WPL) in different countries over the world are difficult partly due to the lack of exact measures and comparable data for different counties. In this study, we analysed 4363 responses to a global online survey on the WPL during heat waves in 2016. The participants were from both developed and developing countries, facilitating estimates of the heat-related WPL across the world for the year. The heat-related WPL for each country involved was then deduced for increases of 1.5, 2, 3 and 4 °C in the global mean surface temperature under the representative concentration pathway scenarios in climate models. The average heat-related WPL in 2016 was 6.6 days for developing countries and 3.5 days for developed countries. The estimated heat-related WPL was negatively correlated with the gross domestic product per capita. When global surface temperatures increased by 1.5, 2, 3 and 4 °C, the corresponding WPL was 9 (19), 12 (31), 22 (61) and 33 (94) days for developed (developing) countries, quantifying how developing countries are more vulnerable to climate change from a particular point of view. Moreover, the heat-related WPL was unevenly distributed among developing countries. In a 2°C-warmer world, the heat-related WPL would be more than two months in Southeast Asia, the most influenced region. The results are considerable for developing strategy of adaptation especially for developing countries.

How to cite: Yu, S., Yan, Z., Xia, J., Zhao, A., Zhang, A., Xia, Y., Guan, D., Han, J., Wang, J., Chen, L., and Liu, Y.: Loss of work productivity in a warming world: Differences between developed and developing countries, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8779, https://doi.org/10.5194/egusphere-egu2020-8779, 2020.

Extreme heat is associated with increased risks of human mortality. In a warming climate, extreme heat events are projected to intensify and become more frequent, potentially adversely affecting human health. The Paris Agreement aims at limiting global mean temperature rise this century to well below 2°C above pre-industrial levels, but mitigation ambition as established in nations’ initial Nationally Determined Contributions still implies ~3°C warming. Quantifying the differences in extreme heat-related mortality between 1.5, 2 and 3°C warming is essential to understanding the public health impacts of climate policies and how societies may adapt to a warming climate.

In this talk, I will show a new approach to projecting extreme heat-related mortality using the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) large ensemble and health models. The large ensemble of HAPPI simulations of the 1.5, 2 and 3°C warmer worlds allows extreme heat events and their health impacts in these worlds to be examined, rather than the mean climates. Using published case studies of the United States and Europe; I will demonstrate that limiting global mean warming from 3°C to 2°C or 1.5°C above pre-industrial levels could reduce heat-related mortality associated with extreme heat events, with the 1.5°C limit being substantially more beneficial to public health than 2°C. In addition to climate change, I will discuss the roles of urbanisation, population changes and adaptation in future extreme heat exposure and heat-related mortality.

How to cite: Lo, E., Mitchell, D., Gasparrini, A., and Vicedo-Cabrera, A.: HAPPI-Health: The Paris Agreement avoids substantial extreme heat-related mortality , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5805, https://doi.org/10.5194/egusphere-egu2020-5805, 2020.

Marine heatwaves (MHWs) are periods of extreme warm temperatures in the ocean and have been seen to exert substantial pressure to marine ecosystems around the world. For instance, they may drive widespread marine species die-offs, force coastal marine ecosystem regime shifts, promote toxic algal blooms, and/or alter the distribution of commercial fisheries on a scale of weeks to months. Recent studies have indicated a significant increase in MHW frequency and intensity throughout the 20^th century, a trend which is likely to aggravate in the 21^st century, according to future projections.  Therefore, it is crucial to understand what are the climate drivers and physical processes governing MHWs in different regions of the global ocean and how these may change under the climate change regime. Here, we perform a mixed layer heat budget analysis, using a global ocean reanalysis product, to diagnose the relative role of ocean advection and atmosphere fluxes on the development of past MHWs around the world. Significant events are first identified using a consistent framework. Then, the heat budget results reveal that certain physical processes tend to be dominant in different regions, which can be traced back to specific local-scale dynamics. The global scale of this analysis provides a significant addition to the current literature which has, so far, been focused on the examination of the underlying mechanisms behind individual events. It also contributes to a better understanding of the variability and processes governing MHWs, offering also a potential ability for future event predictability.

How to cite: Darmaraki, S. and Oliver, E.: Global Analysis of Marine Heatwave physical Processes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22597, https://doi.org/10.5194/egusphere-egu2020-22597, 2020.

Currently, heat-wave warning systems are based on temperature in many countries. However, heat-wave impacts depend not just on temperature but by socio-economic contexts, including age, occupation, income, household type, etc. This study developed a heatwave health impacts forecast model by considering socio-economic characteristics. In addition, this study evaluated the developed forecasting model by using Area Under the Curve (AUC).
This study used health and meteorological data from 2011 to 2017. For the health data, we used two different measures, the number of mortality and the number of emergency department visits with heat-wave related diseases (respiratory diseases, cardiovascular diseases, trauma, infectious diseases, mental and behavioral disorders). Those numbers were obtained from the National Statistical Office and the National Health Insurance Corporation, respectively. For meteorological data, we used temperature and humidity data, which were interpolated at 1 km spatial resolution.
We analyzed the health impacts of heat-wave on health by age, type of work, and income. In addition, we analyzed the weighted effects of humidity on health. The results showed age over 65, outdoor workers and low-income groups are relatively vulnerable to heat-wave. Moreover, high relative humidity was a factor that increased the risk of mortality for the population of age over 65. 
Based on the analysis results, we categorized warning level to 5 levels (from 0 to 4), level 0 means low risk and level 4 means high risk. Warning levels were classified by considering the increased risk of disease and mortality with temperature. We developed warning levels for three different groups, the general public, the elderly, and the outdoor workers.
The performance of the model measured based on AUC by using 2018 Heat-related illness monitoring data obtained from the Korea Centers for Disease Control. In the assessment for the risk level 4, the AUC ranged from 0.71 to 0.92, with an average of 0.80. The AUC value of above the risk level 3 also ranged from 0.71 to 0.92, with an average of 0.85.
These results indicate that the health impact forecasting model suggested in the study is applicable as an operational forecast model. The results are expected to be used to develop a heat-wave early warning system in Korea.

How to cite: Park, J. and Chae, Y.: Heat-wave health impacts forecasting model in Korea: development and evaluation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21062, https://doi.org/10.5194/egusphere-egu2020-21062, 2020.

This study has formulated artificial neural network models to predict thermal comfort evaluation in outdoor urban areas in Seoul for summer. The artificial neural network models were considerably improved by including preceptions of microclimate, perception of environmental features(e.g urban spatial characteristics and visual stimuli, etc) and personal traits as additional predictor variables. Thermal comfort in outdoor environments has been repeatedly shown to be influenced also by human perceptions and preferences. Despite numerous attempts at refining these thermal comfort, there still have been large discrepancies between the results predicted by the theoretical models and the actual thermal comfort evaluation votes. indeed Thermal comfort model using microclimatic factors including air temperature, air velocity, solar radiation and relative humidity as predictor variables could explain only 7–42% of thermal comfort evaluation votes.

Accordingly, this study aims to formulate models to predict thermal comfort evaluation in outdoor urban areas for summer in Korea, which is located in temperate climate zone. ANN models were formulated to portray intricate interrelationships among a multitude of personal traits, urban residents’ environmental perception, microclimatic and spatial perception and physiological factors. The prediction performances of the formulated ANN models were compared with those of the commonly used thermal comfort models(PMV, PET). Also, this study aims to identify important factors that influence thermal comfort evaluation in outdoor urban areas. In addition, it is intended to compare whether the important factors and the magnitude of their contributions are different in urban spatial environment. The findings should provide valuable insights for informing urban planning designers on formulating effective strategies to improve the thermal environments in outdoor urban areas in the temperate climate zone.

How to cite: Kim, E. S. and Lee, D. K.: Development of artificial neural network models for thermal comfort evaluation in outdoor urban spaces, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12882, https://doi.org/10.5194/egusphere-egu2020-12882, 2020.

Impact-based forecasts provide information about the risk of a hazard so that it can be prepared and responded appropriately. In order to mitigate and respond to disasters better, it is necessary to identify the most vulnerable areas, called hotspots. This study identifies hotspots for a heatwave, one of the fatal hazards in South Korea, using high-resolute data in four major cities (Seoul, Busan, Daegu, and Gwangju). High-resolution (100m×100m) income data and floating population data based on Long-Term Evolution (LTE) signals are used as a socio-economic factor of hotspots. The daily maximum temperature that downscaled from the short-range forecast system into 1km×1km is used as a meteorological factor. Each grid point is categorized on the relationship between temperature and floating population by the time. The categories are classified into four groups; points where population increases with temperature, points where population decrease with temperature, points that have low variability, and the others. The areas where the population density increases with temperatures are mainly avoidable to heat, such as parks, subway stations, and indoor shopping centers. The population decreased with temperature in universities, tourist sites, and residential areas. The third group, which is areas of low variability with a coefficient of variation of less than 20%, is areas that do not respond properly to heatwaves. Hotspots are defined as low-income old-age residential areas with low population variability. Those identified hotspots can be concerned as areas that need prior public care to disaster mitigation and response.

How to cite: Kim, S.-W., Park, J., Kim, T., and Chae, Y.: Identification of Hotspots for Heatwaves using Big Data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20803, https://doi.org/10.5194/egusphere-egu2020-20803, 2020.

The risks of Emergency Room (ER) visits for cerebral infarction (CI) and intracerebral hemorrhage (ICH) is found to differ in different age groups under different climatic thermal environments. Based on CI and ICH related ER-visit records from three major hospitals in Beijing, China, from 2008 to 2012, the advanced universal thermal climate index (UTCI), was adopted in this study to assess the climatic thermal environment. Particularly, daily mean UTCI was used as a predictor for the risk of ER visits for CI and ICH. A generalized quasi-Poisson additive model combined with a distributed lag non-linear model was performed to quantify their association. The results indicated that (ⅰ) the highest growth rate of ER visits for ICH occurred in age 38 to 48, whereas an increasing ER admissions for CI maintained at age 38 to 78. (ⅱ) The frequency distribution of UTCI in Beijing peaked at -8 and 30 ℃, corresponding to moderate cold stress and moderate heat stress, respectively. (ⅲ) Correlation analysis indicated that ICH morbidity was negatively correlated with UTCI, whereas occurrence of CI showed no significant association with UTCI. (ⅳ) The estimated relative risk of ER visits corresponding to 1℃ change in UTCI, which was then stratified by age and gender, indicated that all sub-groups of ICH patients responded similarly to thermal stress. Namely, there is an immediate ICH risk (UTCI = -13℃, RR=1.35, 95% CIs: 1.11~1.63) from cold stress on the onset day, but non-significant impact from heat stress. As for CI occurrences, no effect from cold stress was identified, except for only those aged 45 to 65 were threatened by heat stress (UTCI = 38℃, RR=1.64, 95% CIs: 1.10~2.44) on lag 0~2d. 

How to cite: Ma, P.: Differences of Hemorrhagic and Ischemic Strokes in Age Spectra and Responses to Climatic Thermal Conditions , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8124, https://doi.org/10.5194/egusphere-egu2020-8124, 2020.

Extreme heat waves as in 2003 and 2010 can have severe consequences for the economy and society. This raises the question how anomalous they could have gotten. Addressing this question is challenging given the lack of long coherent reliably daily data. Multi-millennial GCM simulations and single-model initial condition large ensembles offer a new opportunity to investigate the very upper tail of temperature distribution. Here, we use a nearly 5,000-year long pre-industrial control run and a 84-member large initial condition ensemble performed with CESM1.2. Evaluations show that the simulated climate variability and temperature response to circulation anomalies agree well with the ERA5 reanalysis over large parts of the global land regions.

We show that highest temperature extremes in the long pre-industrial control simulation exceed the temperature records of 2003 by several degrees in the related hotspot region over Western Europe. The anomalies are caused by large anticyclonic circulation anomalies and very dry land surface conditions, leading to amplifying feedbacks in the surface energy budget. Moreover, the simulation results reveal that summer temperature maxima as a function of return period have an asymptotic , suggesting an upper temperature limit.

In a next step, we use a novel method of ensemble boosting to generate even more extreme temperatures. To that end, 100-member ensembles are reinitialized with perturbed atmospheric conditions weeks before the most intense events. Thereby, we gain insight into short-term mechanisms that underly these hot extremes. The result of the ensemble calculation shows that using this method even more extreme event anomalies can be generated, substantially exceeding highest values in the long pre-industrial control simulations. We investigate how the physical mechanisms of these rare and unseen simulated events differ from more moderate events. We further compare the simulated very rare events with maximum anomalies estimated based on statistical methods.

How to cite: Gessner, C., Fischer, E., Beyerle, U., and Knutti, R.: Very rare heat extremes: how anomalous could they get?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1628, https://doi.org/10.5194/egusphere-egu2020-1628, 2020.

Europe experienced several major heat waves in the recent summers, substantially affecting human society and environment. Heat waves are generally related to joint effect of perturbed atmospheric circulation and anomalies in surface energy budget, and they are often linked to hydrological preconditioning. Contributions of these driving mechanisms, however, vary across European climatic zones. Climate models struggle to simulate the spatial differences properly, ultimately leading to large uncertainties in future heat waves’ characteristics. As the first step towards identifying spatial patterns of differences between driving mechanisms of temperature extremes, a pan-European database of observed major heat waves has been created. Heat waves are studied using the E-OBS 20.0e dataset in 0.1° horizontal grid spacing, which is analogous to that used in the ERA5 reanalysis and CORDEX regional climate models. Magnitude of heat waves is defined with respect to local daily maximum temperature (Tmax) variance, using multiples of standard deviation of Tmax summed across individual events. For each heat wave, circulation conditions and surface energy fluxes are analysed using the ERA5 reanalysis, in order to study their links to the heat wave magnitude and geographical location. In the next step, these findings are used for analyzing spatial patterns of heat wave mechanisms and as a source of reference data for evaluation of relevant processes in climate models.

How to cite: Lhotka, O. and Kyselý, J.: Database of major European heat waves from 1950 to present, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2973, https://doi.org/10.5194/egusphere-egu2020-2973, 2020.

Global temperatures have shown a warming trend over the last century, mainly as a result of anthropogenic activities. Rising temperatures are a potential cause for increase of extreme climate events, such as heat waves, both in severity and frequency. Under an increasing extreme event scenario, the world population of mid- and low-latitude countries is more vulnerable to heat related mortality and morbidity. In India, the events occurred in recent years have made this vulnerability clear, since the numbers of heat related deaths are on a rise.

Over India, the heat waves occur during the months of April to June and can impact various sectors including health, agriculture, ecosystems and the national economy. In May 2015, a severe heat wave due to the delayed onset of southwest monsoon affected parts of south-eastern India, which claimed more than 2500 lives.

Preliminary results show the prevalence of Heat events in North-West, Central and South-Eastern regions of India during the pre-monsoon (March, April, May) and transitional (May, June, July) months. We consider the Heat Index (HI), a combination of temperature and relative humidity, also known as apparent temperature, gives an insight into the discomfort because of increment in humidity, that reduces the efficiency of body’s cooling mechanism as it blocks evaporation. Thus, along with temperature anomalies, humidity also plays a role in transitional period.

Heatwaves over India are known to be linked with El-Niño-Southern Oscillation or ENSO, but some studies indicated that the processes generating heat waves over northwest-central and coastal eastern India could be linked to anomalous blocking over North Atlantic and to the cooling over central and east equatorial Pacific. While other studies demonstrated that anomalous persistent high-pressure systems, supplemented with clear skies and depleted soil moisture, are primarily responsible for the occurrence of heat waves over India.

The changes in the frequency and intensity of extreme events have profound impact on human society and the natural environment. The heat stress and underlying anomalous conditions can exacerbate an increase in the number of deaths. While global heat wave and health impact research is prolific in some regions, the global population most incline to risk of death and conspicuous harm caused by extreme heat is under-represented. Heat wave and health impact research are needed in regions where this impact is expected to be most severe.

How to cite: Ritika, K., Scoccimarro, E., Alvarez-Castro, C., Materia, S., and Gualdi, S.: Heat Events in the Indian Subcontinent under a warming climate scenario: Detection and its Drivers, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5539, https://doi.org/10.5194/egusphere-egu2020-5539, 2020.

    The large-scale circulation anomalies associated with extreme heat (EH) in South Korea and southern–central Japan are examined using data during the time period 1979–2016. Statistical analysis indicates that EH days in these two regions are concentrated in July and August and tend to occur simultaneously. These EH days are therefore combined to explore the physical mechanisms leading to their occurrence. The composite results indicate that the anomalous atmospheric warming during EH days is dominantly caused by a significant subsidence anomaly, which is associated with a deep anomalous anticyclone over East Asia. Further investigation of the evolution of circulation anomalies suggests that the anomalous anticyclone over East Asia related to EH is primarily initiated by wave trains originating from upstream regions, which propagate eastward along the Asian westerly jet in the upper troposphere. These wave trains can be categorized into two types that are characterized by the precursor anticyclonic and cyclonic anomalies, respectively, over central Asia. The distinction between these two types of wave train can be explained by the wavenumbers of the Rossby waves, which are modulated by both the intensity and the shape of the Asian westerly jet as the background basic flow.

How to cite: Xu, K.: Large-Scale Circulation Anomalies Associated with Extreme Heat in South Korea and Southern–Central Japan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8477, https://doi.org/10.5194/egusphere-egu2020-8477, 2020.

Extreme heat events are well identified as a climate threat for human health. Less studied but at least as important as heat waves, extreme hot and humid conditions can lead to conditions where human survivability is not possible because in such environments bodies cannot cool down (evaporation becomes impossible). Wet-bulb temperature (TW) is a combined measurement of dry-bulb temperature and relative humidity (RH) and can be used to study hot and humid conditions. TW summarizes the complex interaction between humidity and temperature and allows more easy analysis. Here we investigate how TW has changed in the recent decades over Eastern China, a region already identified as vulnerable to such conditions.

For any observational analysis, reliable datasets are needed. Temperature data have traditionally received a lot of attention from the community while humidity observation remains poorly evaluated. We used a dense network of Chinese observation and compared it with the new ERA5 reanalysis during the 1979-2017 period. A first analysis indicate a weak increase in TW in both dataset due to a sharp drop in RH around 2000s. However, a new homogenised RH data have revealed that this decrease was an artifact due to a change in Chinese observation network. Newly homogenised data show no drop in RH and consequently a much larger increase in TW. ERA5 has assimilated biased data over China and is not reliable to study TW without performing RH correction. We did so by using an independent model approach, and recalculated RH and TW in ERA5. After correction, increase in TW becomes much larger and we could identified several location with already dangerous TW levels.

How to cite: Freychet, N., Tett, S. F. B., Yan, Z., and Li, Z.: Recent changes in hot and humid extreme over China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9704, https://doi.org/10.5194/egusphere-egu2020-9704, 2020.

Hot extremes are typically instigated by a combination of favorable large-scale conditions and positive land surface feedbacks: as heatwaves evolve, the soil dries out and the decreased evaporation is accompanied by further heating of the atmosphere. Extreme high temperatures are known to cause increased mortality, and thus dry soils are typically thought to be associated with higher risk for human health. However, empirical studies indicate that health-threatening consequences and overall human discomfort during heatwaves not only depend on air temperature, but on air humidity as well. Drying soils are expected to reduce air humidity, which may — to a yet-unknown degree — offset the detrimental effect of soil dryness on increased temperatures in what relates to human heat discomfort. Here, we provide observational evidence for the role of anomalies in soil moisture on heat stress worldwide. We use a novel framework that combines weather balloons, reanalysis and satellite data with a mechanistic model of the atmospheric boundary layer. The health-threatening nature of hot spells is diagnosed by adopting a definition based on the concept of wet-bulb temperature and findings from recent meta-analysis of global human lethal impact data. Results indicate that the detrimental effect of drying soils on air temperature is overcompensated by the beneficial effect on reduced air humidity, which is partly related to the enhanced dry air entrainment. These findings can be used to design climate change adaptation strategies, being aware that ongoing trends in land and atmospheric dryness will impact human heat stress during future heatwaves.

How to cite: Wouters, H., Miralles, D. G., Keune, J., Petrova, I. Y., Teuling, A. J., van Heerwaarden, C. C., and Vilà-Guerau de Arellano, J.: The effect of soil-moisture on human heat stress during hot spells, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11530, https://doi.org/10.5194/egusphere-egu2020-11530, 2020.