Heat Wave Characteristics Research Articles

Thermodynamic characteristics of extreme heat waves over the middle and lower reaches of the Yangtze River Basin

In August 2022, an exceptionally long-lasting heat wave (HW) affected the middle and lower reaches of the Yangtze River basin. This study uses the JRA55 daily reanalysis datasets to elucidate the thermodynamic characteristics of the daily evolution of historical extreme HWs in this region via the heat budget equation. HWs are generally characterized by the occurrence of anticyclonic circulation anomaly throughout the troposphere and positive air temperature anomaly with the maximum amplitude in the boundary layer. The anticyclonic anomaly can induce compression heating in the entire troposphere and warm zonal advection in the boundary layer. Meanwhile, due to the reduced cloud cover, more shortwave radiation reaches the ground surface, and the sensible heat flux becomes an important source of diabatic heating before the onset of HWs. The accumulated excessive heat in the HWs is primarily damped through the emission of longwave radiation and meridional thermal advection. For the HW in August 2022, its extreme persistence is mainly caused by prolonged adiabatic heating, enhanced diabatic heating during the developing stage and weakened diabatic cooling during the decay stage. The upper-level portion of the anticyclonic circulation anomalies is linked to the strengthened South Asia High. After applying the state-of-the-art dynamic metric, i.e., local finite wave activity, we reveal that the formation of the anomalous South Asia High in August 2022 is associated with the Stokes drift flux rather than the dispersion of Rossby wave energy. This characteristic sets it apart from other extreme HWs.

Intensifying human-driven heatwaves characteristics and heat related mortality over Africa

Heatwaves in Africa are expected to increase in frequency, number, magnitude, and duration. This is significant because the health burden is only expected to worsen as heatwaves intensify. Inadequate knowledge of the climate’s impact on health in developing nations such as Africa makes safeguarding the health of vulnerable groups at risk challenging. In this study, we quantify possible roles of human activity in heatwave intensification during the historical period, and project the future risk of heat-related mortality in Africa under two Representative Concentration Pathways (RCP26) and (RCP60). Heatwaves are measured using the Excess Heat Factor (EHF); the daily minimum (Tn ) and maximum (Tx ) are used to compute the EHF index; by averaging Tx and Tn . Two heat factors, significance and acclimatization are combined in the EHF to quantify the total excess heat. Our results confirm the intensification of heatwaves across Africa in recent years is due anthropogenic activity (increase in greenhouse gas concentration and changes in land use). The Return event highlights the potential future escalation of heatwave conditions brought on by climate change and socioeconomic variables. RCP26 projects a substantial rise in heat-related mortality, with an increase from about 9000 mortality per year in the historical period to approximately 23 000 mortality per year at the end of the 21st century. Similarly, RCP60 showed an even more significant increase, with heat-related mortality increasing to about 43 000 annually. This study highlights the potentially growing risk of intensifying heatwaves in Africa under different emission scenarios. It projects a significant increase in heatwave magnitude, number, duration, frequency, and heat-related mortality. Africa’s low adaptive capacity will amplify the impact, emphasizing the need for emissions reduction and effective adaptation measures.

Projected heatwaves in Xinjiang Uygur autonomous region, China

Introduction: Heatwaves (HWs) are the serious natural disaster that exert great impacts on human health and social economy. Projecting future changes in HWs is crucial for the development of effective adaptation strategies.Method: This study investigates the variations of HWs in Xinjiang for three time periods (near-term, mid-term, and long-term) under four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) using multi-model ensemble (MME). To enhance confidence in HWs projection, we evaluate the performance of 17 climate models in simulating HWs in terms of interannual variability and spatial patterns using interannual variability skill score (IVS) and distance between indices of simulation and observation (DISO), respectively, and generate MME using the overall performance.Results: Compared to the observed data, the overall performance of MME outperforms most of individual models, although many models effectively capture the characteristics of HWs. Projections indicate that HWs in Xinjiang will become more longer lasting and severe. Specifically, heatwave frequency (HWF) and heatwave duration (HWD) are projected to reach 7.5 times and 61 days, respectively, in the long-term under the SSP5-8.5 scenario. The spatial distribution of HWs exhibits significant heterogeneity, with high value regions primarily distributed in eastern Xinjiang and the eastern part of southern Xinjiang. Although the HWs area is projected to expand under all scenarios, the spatial pattern is anticipated to remain largely unchanged.Conclusion: These findings provide a comprehensive assessment of future variations in HWs, which are necessary for improving regional adaptive capacity to extreme heat risk.

Observational Evidence Reveals Compound Humid Heat Stress‐Extreme Rainfall Hotspots in India

AbstractSequential climate hazards, such as “warm and wet” compound extremes, have direct societal implications for highly urbanized regions and agricultural production. While typically extreme temperatures and rainfall are inversely correlated during the summer, extreme humid heatwaves often lead to atmospheric instability and moisture convection, increasing the likelihood of extreme precipitation (EP). Little is known about how heatwave characteristics, such as peak intensity and duration, influence EP at a regional scale. Using high‐resolution, sub‐daily station‐based observational records over five decades (1971–2021) across India, we find a robust increase in the frequency of compound humid heat‐peak precipitation events in all seasons. Our sensitivity analysis of the impact of humid heatwave characteristics on the subsequent sub‐daily rainfall extremes reveals that, with an increase in peak heatwave intensity for a given heatwave duration, >50% of sites show an increase in the magnitude of rainfall; conversely, with an increase in heatwave duration for a given peak heatwave intensity, around 67% sites show a decline in sub‐daily rainfall extremes. An asymmetrical shift toward above‐average precipitation extremes in response to humid heat stress is mainly clustered around low‐elevation, densely populated coastal areas and the irrigation‐intensive Indo‐Gangetic Plains.

Heat wave characteristics: evaluation of regional climate model performances for Germany

Abstract. Heat waves are among the most severe climate extreme events. In this study, we address the impact of increased model resolution and tailored model settings on the reproduction of these events by evaluating different regional climate model outputs for Germany and its near surroundings between 1980–2009. Outputs of an ensemble of six EURO-CORDEX models with 12.5 km grid resolution and outputs from a high-resolution (5 km) WRF (Weather Research and Forecasting) model run are employed. The latter was especially tailored for the study region regarding the physics configuration. We analyze the reproduction of the maximum temperature, number of heat wave days, heat wave characteristics (frequency, duration and intensity), the 2003 major event, and trends in the annual number of heat waves. E-OBS is used as the reference, and we utilize the Taylor diagram, the Mann–Kendall trend test and the spatial efficiency metric, while the cumulative heat index is used as a measure of intensity. Averaged over the domain, heat waves occurred about 31 times in the study period, with an average duration of 4 d and an average heat excess of 10 ∘C. The maximum temperature was only reproduced satisfactorily by some models. Despite using the same forcing, the models exhibited a large spread in heat wave reproduction. The domain mean conditions for heat wave frequency and duration were captured reasonably well, but the intensity was reproduced weakly. The spread was particularly pronounced for the 2003 event, indicating how difficult it was for the models to reproduce single major events. All models underestimated the spatial extent of the observed increasing trends. WRF generally did not perform significantly better than the other models. We conclude that increasing the model resolution does not add significant value to heat wave simulation if the base resolution is already relatively high. Tailored model settings seem to play a minor role. The sometimes pronounced differences in performance, however, highlight that the choice of model can be crucial.

Future Projections of Heat Waves and Associated Mortality Risk in a Coastal Mediterranean City

Climate change has been linked to the escalating frequency, duration, and intensity of heat waves in the Mediterranean region, intensifying health concerns for the general populace. Urban environments face elevated health risks due to concentrated populations and the urban heat island effect, further amplifying nighttime heat conditions. This study aims to project changes in heat wave characteristics and the associated population exposure risk in a large Mediterranean city, Thessaloniki, Greece. High-resolution climate simulations, using the WRF model, were conducted for three 5-year periods (2006–2010, 2046–2050, 2096–2100) under the RCP8.5 emission scenario, covering Thessaloniki with a 2 km grid. By the end of the century, Thessaloniki is projected to experience over 60 annual heat wave days, compared to ~8 in the present climate, while some episodes were found to persist beyond 30 days. The relative risk during heat wave days is expected to rise, which is primarily due to nighttime heat stress. Interestingly, the results indicate that minimum apparent temperature might be a more reliable indicator in predicting heat-related mortality compared to maximum apparent temperature. These findings emphasize the growing importance of informed heat mitigation and adaptation strategies and healthcare preparedness in urban areas facing escalating heat-related health challenges.

Heatwave characteristics complicate the association between PM2.5 components and schizophrenia hospitalizations in a changing climate: Leveraging of the individual residential environment

BackgroundIn the era characterized by global environmental and climatic changes, understanding the effects of PM2.5 components and heatwaves on schizophrenia (SCZ) is essential for implementing environmental interventions at the population level. However, research in this area remains limited, which highlights the need for further research and effort. We aim to assess the association between exposure to PM2.5 components and hospitalizations for SCZ under different heatwave characteristics. MethodsWe conducted a 16 municipalities-wide, individual exposure-based, time-stratified, case-crossover study from January 1, 2017, to December 31, 2020, encompassing 160736 hospitalizations in Anhui Province, China. Daily concentrations of PM2.5 components were obtained from the Tracking Air Pollution in China dataset. Conditional logistic regression models were used to investigate the association between PM2.5 components and hospitalizations. Additionally, restricted cubic spline models were used to identify protective thresholds of residential environment in response to environmental and climate change. ResultsOur findings indicate a positive correlation between PM2.5 and its components and hospitalizations. Significantly, a 1 μg/m3 increase in black carbon (BC) was associated with the highest risk, at 1.58% (95%CI: 0.95–2.25). Exposure to heatwaves synergistically enhanced the impact of PM2.5 components on hospitalization risks, and the interaction varied with the intensity and duration of heatwaves. Under the 99th percentile heatwave events, the impact of PM2.5 and its components on hospitalizations was most pronounced, which were PM2.5 (2–4d: 4.59%, 5.09%, and 5.09%), sulfate (2–4d: 21.73%, 23.23%, and 25.25%), nitrate (2–4d: 17.51%, 16.93%, and 20.31%), ammonium (2–4d: 27.49%, 31.03%, and 32.41%), organic matter (2–4d: 32.07%, 25.42%, and 24.48%), and BC (2–4d: 259.36%, 288.21%, and 152.52%), respectively. Encouragingly, a protective effect was observed when green and blue spaces comprised more than 17.6% of the residential environment. DiscussionPM2.5 components and heatwave exposure were positively associated with an increased risk of hospitalizations, although green and blue spaces provided a mitigating effect.

Assessing the escalating human-perceived heatwaves in a warming world: The case of China

With increased global warming, heatwaves are expected to become more intense, frequent, and persistent. Although the spatiotemporal characteristics of heatwaves have been extensively studied, the vast majority of these studies have solely used near-surface air temperatures, particularly daily maximum temperatures (Tmax), to identify heatwaves. Given that air temperature alone proves inadequate as a metric for human heat stress. Here, using the relative threshold in conjunction with the absolute threshold and basing it on wet bulb globe temperature (WBGT), we develop a novel definition of human-perceived heatwaves. The combined effect of temperature and humidity is considered in this definition. On this basis, we quantify the climatology of and long-term changes in heatwaves in China based on homogenized in situ observations and outputs from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results show that the distribution of human-perceived heatwaves coincides with densely populated areas in the southeastern part of China, despite their limited spatial extent. The observed trends in human-perceived heatwaves have accelerated since the 1960s. It is now anticipated that moderate or worse human-perceived heatwaves will affect more than half of China's population. Moreover, CMIP6 climate projections suggest that the percentage of China's population exposed to historically unprecedented human-perceived heatwaves would increase rapidly in a warmer future, except for the sustainability scenario. It is noted that the increase in severe human-perceived heatwaves is more rapid than that in severe traditional Tmax-based heatwaves, suggesting that the hazard of heatwaves to humans may have been underestimated by previous Tmax-based studies. Our findings demonstrate the urgent need for additional planning and adaptation actions beyond the framework for short-term disaster reduction frameworks currently in place. Although we concentrated on China in this article, our method for evaluating human-perceived heatwaves is easily extended to handle comparable issues everywhere.

Coupling heat wave and wildfire occurrence across multiple ecoregions within a Eurasia longitudinal gradient

Understanding the relationship between heat wave occurrence and wildfire spread represents a key priority in global change studies due to the significant threats posed on natural ecosystems and society. Previous studies have not explored the spatial and temporal mechanism underlying the relationship between heat waves and wildfires occurrence, especially over large geographical regions. This study seeks to investigate such a relationship with a focus on 37 ecoregions within a Eurasia longitudinal gradient. The analysis is based on the wildfire dataset provided by the GlobFire Final Fire Event Detection and the meteorological dataset ERA5-land from Copernicus Climate service. In both cases we focused on the 2001–2019 timeframe. By means of a 12 km square grid, three wildfire metrics, i.e., density, seasonality, and severity of wildfires, were computed as proxy of fire regime. Heat waves were also characterized in terms of periods, duration, and intensity for the same period. Statistical tests were performed to evaluate the different patterns of heat wave and wildfire occurrence in the 37 ecoregions within the study area. By using Geographically Weighted Regression (GWR) we modeled the spatial varying relationships between heat wave characteristics and wildfire metrics. As expected, our results suggest that the 37 ecoregions identified within the Eurasia longitudinal gradient differ in terms of fire regimes. However, the occurrence of heat waves did not show significant differences among ecoregions, but a more evident variability in terms of relationship between fire regime metrics and heat waves within the study area. The outcome of the GWR analysis allowed us to identify the spatial locations (i.e., hotspot areas) where the relationship between heat waves and wildfires is positive and significant. Hence, in hotspots the presence of heat waves can be seen as a driver of wildfire occurrence in forest and steppe ecosystems. The findings from this study could contribute to a more comprehensive assessment of wildfire patterns in this geographical region, thus supporting cross-regional prevention strategies for disaster risk mitigation.

Univariate and bivariate spatiotemporal characteristics of heat waves and relative influence of large-scale climate oscillations over India

Heat waves (HWs) have grown in frequency, duration, and intensity in last few decades globally and even the latest climate projections (CMIP6) reveal these to get worsen under enhanced global warming. HWs have adverse impacts over biophysical and human systems, therefore, to analyse the impacts of HWs accurately, it is required to understand the regional trend and variability associated with HWs. We carried out a bivariate copula-based joint exceedance probabilistic analysis of HW characteristics to better assess the vulnerability of Indian regions. The regions with comparatively higher HW frequency, higher intensity, longer duration, and lower joint return period of HW frequency and intensity were selected as vulnerable to HWs. Rajasthan, Madhya Pradesh, Uttar Pradesh, Punjab, Bihar, Chhattisgarh, Gujrat, Karnataka, Maharashtra, Telangana, Andhra Pradesh, and Odisha states of India have been found vulnerable and are projected to get even more vulnerable in the future as per the recent climate projections. To unveil the spatiotemporal variability in HW characteristics, we investigated the relative influence of crucial large-scale climate oscillations (LSCOs) i.e., ENSO, IOD, and AMO on HW by introducing these atmospheric variables as covariates in nonstationary extreme value theory (EVT)-based models. The statistical analysis from generalized extreme value distribution of temperature extreme with LSCOs reveals that ENSO and IOD simultaneously have been influencing the extreme in 21.2%, 25.9% and 23.1% of total region taken into consideration in April, May and June month respectively.

Towards improved knowledge about water-related extremes based on news media information captured using artificial intelligence

Recent advances in machine learning have enabled near real-time retrieval of information from textual documents impacting a wide range of knowledge domains. This advantage makes of the insight extracted from text-based documents (e.g., reports and news) on water-related events an invaluable source of information that complements traditional approaches. By leveraging machine learning, we can not only characterize the event and determine its magnitude or phases of extreme weather event, but also identify its core elements. This is especially crucial in the current era of climate change, where extreme weather events such as floods or thunderstorms are becoming more frequent and unpredictable. By improving our ability to detect and analyse such events, we can enhance our alert systems and take more effective action to mitigate their impact. In this paper we discuss the role of worldwide media observation in extracting and estimating hydrological characteristics of floods, droughts, and heat waves, through the analysis of three case studies, complementing the information provided by traditional monitoring and measurement methods as an earlier but weaker signal. The results presented in this study indicate that the news media signal can be regarded as relatively good proxy of flood dynamics. It can capture the temporal dynamics of the event, and, in some cases, there could be a clear up to 1-day lag between the peak discharge values (i.e., the most extreme flood situation) and the peak in the number of published news. This lag can be attributed to the time needed by journalists to respond to the situation in publishing related news articles covering the event. Our result show that national and regional news can cover well local events. When compared to floods, drought conditions are less explicitly detected from the media. Our result show that European April 2022 drought did not produce much activity in the media while the combination of drought and extreme heat in July 2022 yielded a significant media coverage throughout the Europe. Hence, this can be attributed to the fact that hydrological drought such as low river flows do not attract much attention by the media unless there is a significant impact on the society. Therefore, media signal can be regarded as a relatively good proxy of the hydro-meteorological conditions in case there is a significant impact on the society such as extreme floods causing many casualties and large economic damage.

The Role of the North Atlantic for Heat Wave Characteristics in Europe, an ECHAM6 Study

AbstractThe recent severe European summer heat waves of 2015 and 2018 co‐occurred with cold subpolar North Atlantic (NA) sea surface temperatures (SSTs). However, a significant connection between this oceanic state and European heat waves was not yet established. We performed two AMIP‐like model experiments: (a) employing daily 2018 SSTs as observed and (b) applying a novel approach to remove the negative NA SST anomaly, while keeping SST daily and small‐scale variability. Comparing these experiments, we find that cold subpolar NA SSTs significantly increase heat wave duration and magnitude downstream over the European continent. Surface temperature and circulation anomalies are connected by the upper‐tropospheric summer wave pattern of meridional winds over the North Atlantic European sector, which is enhanced with cold NA SSTs. Our results highlight the relevance of the subpolar NA region for European summer conditions, a region that is marked by large biases in current coupled climate model simulations.

Understanding the Magnification of Heatwaves over Spain: Relevant changes in the most extreme events

Heatwaves (HW) are a growing climate change-related risk affecting people's lives every year. A consensus exists about the magnification of this phenomena under climate change; however, less is known about their regional-to-local evolution characteristics' undermining appropriate policy and preparedness actions.This study investigates, from 1951 to 2019, regional changes in the occurrence and characteristics of HWs over peninsular Spain and the Balearic Islands (PSBI), highly exposed to climate change. State-of-the-art high-resolution observations provided by the SPAIN02 gridded data set are used for this purpose. Our results show an indisputable magnification of the HW phenomena but with relevant regional differences in the evolution of their characteristics. HWs occur two times more frequently inland, while most intense events take place in coastal areas. More extreme HWs in terms of frequency, maximum intensity, duration, and spatial extent are identified, rather than a progressive intensification in time affecting all HWs population. In the last decades, high frequencies are 8 times more likely, unprecedented high intensities are registered, the yearly maximum spatial extent is larger than ever and the probability to have a long-duration event has doubled. All of this is in the context of a longer HW season, increasing at a rate of about 4 days/decade, particularly in its onset. About 75% of the most severe and impactful HWs occurred after 1985. The most severe HWs, generally, are found not to be the most harmful to human health. This study highlights the importance of detailed knowledge of the spatiotemporal characteristics of HWs to accurately inform local practice for the effective design and implementation of adaptation plans.

Association between Tibetan Heat Sources and Heat Waves in China

Abstract The Tibetan Plateau (TP), known as the “Third Pole,” profoundly affects weather and climate at regional and global scales. In this study, we investigate the characteristics of heat waves in China and their association with the TP heat source. The results show that during the summertime from 1980 to 2020, the frequency of heat wave days that hit eastern China and northwest China increased at rates of 0.09 and 0.24 days yr−1, respectively, accompanied by an increase in the atmospheric heat source (AHS) over the TP by over 2 W m−2 yr−1 under the background of global warming. The enhanced TP heat source induces an anomalous upper-tropospheric anticyclone, which caused the western Pacific subtropical high and South Asia high to be stronger and closer to each other, causing descending motions over eastern China and consequently more heat waves. At the same time, the enhanced TP heat source weakened the westerlies, thereby favoring the occurrence and maintenance of the anticyclone centered in northwest China and creating more heat waves due to strong descending motions. Therefore, the association between the TP heat source and heat waves in China provides relevant information for studying the mechanism and future changes of heat waves. Significance Statement The Tibetan Plateau significantly affects the surrounding weather and climate. We find that there are some linkages between the Tibetan heat source and heat waves in China. The stronger the Tibetan heat source is, the more heat waves hit China. The enhancement of the Tibetan heat source could induce an anomalous upper-tropospheric anticyclone by affecting the western Pacific subtropical high and South Asia high, consequently causing descending motions over eastern China and the resulting heat waves. The weaker westerly due to the enhanced Tibetan heat source favors the occurrence of the anticyclone centered in northwest China, leading to more heat waves by descending motions. This study reveals a potential contributor to heat waves in China, providing some clues for better prediction in the future.

Assessment of Changes in Heatwave Aspects over Saudi Arabia during the Last Four Decades

Heatwave (HW) number (HWN), frequency (HWF), duration (HWD), magnitude (HWM), and amplitude (HWA) are key aspects for interpreting and understanding HW characteristics worldwide. Most previous HW studies over the Kingdom of Saudi Arabia (KSA) focused only on the temperature extremes, so this study aims to assess the decadal changes, anomalies, and spatiotemporal variations in the five HW aspects over KSA during the last four decades (1982–2021) using the ClimPACT2 software. Daily gridded (0.25° × 0.25°) maximum (TX) and minimum (TN) temperatures from the ECMWF-ERA5 reanalysis dataset were used to compute these heat wave (HW) aspects. The HW aspects were derived in ClimPACT2 using the Excess Heat Factor (EHF), the 90th percentile of TX (TX90), and the 90th percentile of TN (TN90), all based on the reference climate period of 1982–2011. The results showed that the decadal sum and anomaly of the five HW aspects increased gradually during the last four decades (1982–2021). The three indices showed that the maximum decadal sum of HWN (42 events), HWF (255 days), and HWD (145 days) occurred in the last decade. Additionally, the last decade has the maximum decadal sum of HWM (175–463 °C) and HWA (189–471 °C) as derived from TX90 and TN90, which is confirmed by EHF, with ranges of 7–58 and 15–185 °C2, respectively. Finally, the periods 2015–2021 and 1984–1986 recorded the highest and lowest values of annual HW aspects, respectively, across the study period.

Comprehensive characterization of Marine Heatwaves in a coastal Northern Humboldt Current System regional model over recent decades

In this study, a high-resolution hydrodynamic model simulation was used to analyze the three-dimensional characteristics of marine heatwaves (MHWs) in the coastal region of the Northern Humboldt Current System (NHCS) over the period 2000–2019. Three distinct vertical layers were identified. The near-surface layer, extending down to 75 m depth, is identified as the region where both the maximum MHW intensity and cumulative intensity are found, predominantly concentrated along the thermocline depth. The intermediate layer, spanning from 75 to 125 m depth, exhibited the longest MHW durations and the lowest MHW frequencies. MHWs in this layer tend to be of moderate intensity and are often associated with ENSO variability. In contrast, the deep layer, from 125 to 250 m depth, was characterized by short-lived MHWs of low intensity. These MHWs exert a lesser thermal impact on the region. To understand the drivers of MHWs, a composite analysis was performed. The results highlighted the significant role exerted by wind-driven anomalies in the life cycle of MHWs. It was found that the combined effect of a sluggish coastal upwelling circulation and suppressed oceanic heat loss, as a result of weakened winds, preceded the MHW formation. Conversely, the strengthening of winds, which enhanced net heat loss, primarily driven by latent heat flux anomalies, and an enhancement of the cooling effect due to the recovery of the coastal upwelling circulation, are key processes in ending a typical MHW event in the coastal region. These findings expand our understanding of the MHWs and their associated potential drivers and help to reveal which regions are most susceptible to extreme climate events in one of the most productive marine ecosystems worldwide.

Analysis of heatwaves based on the universal thermal climate index and apparent temperature over mainland Southeast Asia.

Heatwaves have caused significant damage to human health, infrastructure, and economies in recent decades, and the occurrences of heatwaves are becoming more frequent and severe across the globe under climate change. The previous studies on heatwaves have primarily focused on air temperature, neglecting other variables like wind speed, relative humidity, and radiation, which could lead to a serious underestimation of the adverse effects of heatwaves. To address this issue, this study proposed to the use of more sophisticated thermal indices, such as universal thermal climate index (UTCI) and apparent temperature (AT), to define heatwaves and carry out a comprehensive heatwave assessment over mainland southeast Asia (MSEA) from 1961 to 2020. The traditional temperature-based method was also compared. The results of the study demonstrate that the annual maximum temperature in heatwave days (HWA) and the annual average temperature in heatwave days (HWM) are significantly underestimated if only air temperature is considered. However, UTCI and AT tend to predict a lower frequency of yearly heatwave occurrences and shorter durations. Trend analysis indicates a general increase in heatwave occurrences across MSEA under all thermal indices in the past six decades, particularly in the last 30years. This study's approach and findings provide a holistic view of heatwave characteristics based on thermal indices and highlight the risk of intensified heat stress during heatwaves in MSEA.

Frequency dominates intensity of future heat waves over India

Future changes in heat wave characteristics over India have been analyzed using Coordinated Regional Climate Downscaling Experiments (CORDEX) for South Asia (SA) regional climate model simulations for mid-term (2041-2060) and long-term (2081-2099) future under the representative concentration pathway (RCP) 4.5 and RCP 8.5 emission scenarios, respectively. SMHI_CSIRO-MK3.6 was found to be the best model in simulating heat wave trend over India for historical period. Future projections show a four-to-seven-fold increase in heat wave frequency for mid-term and long-term future under RCP 4.5 scenario, and five-to-ten-fold increase under RCP 8.5 scenario with increase in frequency dominating intensity in both the scenarios. Northwestern, Central, and South-central India emerged as future heat wave hotspots with largest increase in the south-central region. This high-resolution regional future projection of heat wave occurrence will serve as a baseline for developing transformational heat-resilient policies and adaptation measures to reduce potential impact on human health, agriculture, and infrastructure.

Projected changes in heat wave characteristics over India

Projected changes in heat wave characteristics over India

Seasonal Prediction of Arabian Sea Marine Heatwaves

AbstractMarine heatwaves are known to have a detrimental impact on marine ecosystems, yet predicting when and where they will occur remains a challenge. Here, using a large ensemble of initialized predictions from an Earth System Model, we demonstrate skill in predictions of summer marine heatwaves over large marine ecosystems in the Arabian Sea seven months ahead. Retrospective forecasts of summer (June to August) marine heatwaves initialized in the preceding winter (November) outperform predictions based on observed frequencies. These predictions benefit from initialization during winters of medium to strong El Niño conditions, which have an impact on marine heatwave characteristics in the Arabian Sea. Our probabilistic predictions target spatial characteristics of marine heatwaves that are specifically useful for fisheries management, as we demonstrate using an example of Indian oil sardine (Sardinella longiceps).