Occurrence Of Extremes Research Articles

Weakening of global terrestrial carbon sequestration capacity under increasing intensity of warm extremes.

The net ecosystem exchange (NEE), determining terrestrial carbon sequestration capacity, is strongly controlled by climate change and has exhibited substantial year-to-year fluctuations. How the increased frequency and intensity of warm extremes affect NEE variations remains unclear. Here, we combined multiple NEE datasets from atmospheric CO2 inversions, Earth system models, eddy-covariance data-driven methods and climate datasets to show that the terrestrial carbon sequestration capacity is weakened during warm extreme occurrences over the past 40 years, primarily contributed by tropical regions (81% ± 48%). The underlying mechanism can be rooted in the overwhelmingly decreased trend of gross primary productivity compared with terrestrial ecosystem respiration. Additionally, the weakened terrestrial carbon sequestration capacity is mainly driven by the transition from temperature or soil moisture control to vapour pressure deficit control, which is associated with the increasing intensity of warm extremes. Our findings suggest that warm extremes threaten the global carbon sequestration function of terrestrial ecosystems. Therefore, more attention should be given to the evolution of the increasing intensity of warm extremes in future climate projections.

Extremes and Short‐Term Fluctuations in Coastal Ocean Acidification and Hypoxia

AbstractIn Eastern boundary upwelling systems, such as the California Current System (CCS), seasonal upwelling brings low oxygen and low pH waters to the continental shelf, causing ocean acidification and hypoxia (OAH). The location, frequency, and intensity of OAH events is influenced by a combination of large‐scale climatic trends, seasonal changes, small‐scale circulation, and local human activities. Here, we use results from two 20‐year long submesoscale‐resolving simulations of the Northern and Southern U.S. West Coast (USWC) for the 1997–2017 period, to describe the characteristics and drivers of OAH events. These simulations reveal the emergence of hotspots in which seasonal declines in oxygen and pH are accompanied by localized short‐term extremes in OAH. While OAH hotspots show substantial seasonal variability, significant intra‐seasonal fluctuations occur, reflecting the interaction between low‐ and high‐frequency forcings that shape OAH events. The mechanisms behind the seasonal decreases in pH and oxygen vary along the USWC. While remineralization remains the dominant force causing these declines throughout the coast, physical transport partially offsets these effects in Southern and Central California, but contributes to seasonal oxygen loss and acidification on the Northern Coast. Critically, the seasonal decline is not sufficient to predict the occurrence and duration of OAH extremes. Locally enhanced biogeochemical rates, including shallow benthic remineralization and rapid wind‐driven transport, shape the spatial and temporal patterns of coastal OAH.

Soil Moisture Dominates the Land Surface Feedback in the Development of Compound Drought–Heat Extremes in Tropical South America

Abstract Observational evidence has demonstrated that heat extremes consistently coincide with droughts in tropical South America. However, the underlying causes and associated physical mechanisms remain relatively unexplored. In this study, we employ numerical experiments using Community Earth System Model, version 2 (CESM2), with prescribed oceanic forcing to assess how extreme soil temperature and moisture affect subsequent hydrometeorological conditions during the premonsoon season in tropical South America, a season when land can play an important role in regional climate. The results reveal a different diurnal pattern in the strength of the surface air temperature response, with a stronger response at night to soil temperature anomalies and a stronger response at noon to soil moisture anomalies. Elevated initial soil temperature or reduced initial soil moisture tends to yield warmer and drier hydrometeorological conditions and increase the occurrence of both drought and heat extremes, with stronger and longer-lasting responses to soil moisture anomalies (than soil temperature anomalies). The simulated responses to initial soil moisture anomalies alone closely resemble those in the experiment with combined soil temperature and moisture anomalies. These findings underscore the dominant role of soil moisture feedback in shaping compound drought–heat extremes, in regions where evaporation plays a significant role, highlighting its importance in the modeling and prediction of hydrometeorological extremes.

The construction of a Chinese fine-grained sentiment dictionary for Chinese domestic investors (CN-FSD) and its application

Stock bar comment texts are used to forecast stock market trends because they contain sentiment data provided by investors. However, previous research has relied on generic or polar sentiment dictionaries to construct sentiment indicators, which has limited progress in this field. This paper crawls nearly 1.8 million stock bar comment texts from the Eastmoney (www.eastmoney.com) Stock Bar Forum to perform sentiment analysis in this specialized area of the financial market. It reclassifies existing Chinese sentiment dictionaries, and utilizes Word2Vec and CNN models and word frequency statistics with a penalty mechanism to extract polar sentiment words. Additionally, the improved PMI (Pointwise Mutual Information) algorithm was utilized for fine-grained segmentation of negative emotion words. Finally, a fine-grained sentiment dictionary (four categories: happy, anger, sad and fear; with a total of 6155 words) was established for Chinese domestic investors (denoted by CN-FSD). We found that the improved PMI algorithm increased accuracy by 14%. In addition, we introduce the investor sentiment quantification indicators from two aspects, i.e., fine-grained sentiments and on the emotion theory dimension of psychology from the valence and arousal dimensions. This is done to verify the validity and robustness of the constructed dictionary. The case study from 2019 to 2022 discovered that under the influence of extreme occurrences like COVID-19, the fine-grained sentiment indicators constructed in this study are found to be significantly more strongly correlated with market trend indicators like index return and trading volume than the existing polar or fine-grained sentiment dictionaries. ‘fear’ emotions in fine-grained emotions dominate the stock market. Just as this case study emphasize the importance of ‘fear’ sentiment, studying the indicators of high arousal sentiment is also important. This case study further validates the efficacy of CN-FSD.

The Effectiveness of Asset Pricing Models Under Special Conditions: Evidence from the COVID-19 Pandemic, Emerging Markets, and Financial Crises

Asset pricing models are important tools in finance. However, special market conditions can make them highly ineffective. This paper evaluates the performance of classical asset pricing models (that is, CAPM and Fama-French models) under three specific scenarios: the COVID-19 pandemic, emerging markets, and financial crises. Recent research and market data are reviewed to conclude that these models have very low efficiency in times of extreme occurrences for capturing proper asset valuations. Our findings reveal that standard models lose a substantial part of their explanatory power in periods of crises while new context-specific risk factors come into existence in other exceptional conditions. This work argues for the inclusion of liquidity and tail risk considerations, especially during market stress, as we also observe a greater need for more dynamic and adaptive modeling approaches that capture time-varying risk premia and factor loadings. The findings drive home the point that standard asset pricing models cannot be applied across the board in all market conditions and stress the need for developing more flexible and situation-specific valuation tools. Therefore, this research lies in its dual contribution, both in academic terms and practical finance, by providing an understanding of the behavior of asset pricing models in extreme markets and giving indications for future model development.

Winter snowpack loss increases warm-season compound hot-dry extremes

Ongoing warming intensifies snowpack extremes, posing significant hydroclimatic risks to socio-ecological systems. However, the relation between snowpack extremes and subsequent compound hydroclimatic extremes remains unclear. Here, we investigated the impact of snowpack extremes on warm-season compound hydroclimatic extremes in the Northern Hemisphere using multisource datasets from 1980 to 2022. We found widespread increases in deficient, short, and deficient-short snowpack extremes, triggering more compound hot-dry extremes within a month after snowpack disappearance (mean coincidence rate over 0.6, p < 0.05). The impact of compound snowpack extremes exceeded that of individual snowpack extremes in both areas (over 10%) and coincidence rates (over 0.2). Meanwhile, increased intensity, rather than frequency, of snowpack extremes drove mainly the occurrence of compound hydroclimatic extremes. Furthermore, background climate factors, followed by vegetation, topography, and soil, affected relations between snowpack and compound hydroclimatic extremes. These findings will deepen our understanding of the emerging consecutive extremes and improve their predictability.

Microphysical characteristics of torrential predecessor rain events over the Yangtze River Delta Area and the related tropical cyclones

The precipitation structures and microphysical characteristics of predecessor rain events (PREs) over the Yangtze River Delta area and related tropical cyclones (TCs) from 2014 to 2019 were investigated using Dual-frequency Precipitation radar data from Global Precipitation Measurement (GPM) for drop size distributions (DSDs). Results showed that the total mean rain rate of PREs was larger than that of TCs, primarily due to higher convective and stratiform rain rates, with enhanced fractional coverage of convective rain in PREs. Examination of microphysical characteristics revealed that a greater quantity of small-sized droplets and a smaller quantity of medium- as well as large-sized droplets contributed towards PREs in comparison with TCs. The conclusion still holds when partitioning DSDs based on different precipitation rate categories. Further investigation of DSDs using gamma functions illustrated that precipitation in PREs had lower average mass-weighted diameters (Dm) and enhanced normalized number concentration (Nw) compared with TCs; partitioning precipitation into convective and stratiform components illustrated a larger Dm and lower Nw in TCs than PREs. The analysis of microphysical and thermodynamical processes using the reanalysis data indicates that relatively intense convective activity with drier conditions may be favorable to enhancing raindrop growth through collision-coalescence processes, as a result of larger Dm in TCs than PREs. The empirical relations (Z–R algorithms) applied in different rain regimes (stratiform, convective, and total PREs) revealed significant diversities, relying on weather conditions and geographical locations. Plain language summaryThe Yangtze River Delta area is an important economic belt in China. Under climate change, observed frequent occurrences of weather extremes of heavy rainfall and tropical cyclones (TCs) exert adverse effects on economic development in this region. Thus, a deep understanding of the mechanism of TCs torrential rainfall in the Yangtze River Delta area is urgently necessary. In this study, we investigated the precipitation patterns and microphysical characteristics of predecessor rain events (PREs) in the Yangtze River Delta region, and their association with TCs in the South China Sea-Western North Pacific Ocean (SCS-WNPO) area from 2014 to 2019. We found that PREs had a higher total mean precipitation rate than TCs. Further examination using gamma functions demonstrated that PREs exhibited lower average mass-weighted diameters (Dm) and higher normalized intercept parameters (Nw) than TCs. This pattern persisted when distinguishing between convective and stratiform precipitation components. We believe that our study makes a significant contribution to the literature because these results provide valuable insights into the distinct precipitation characteristics of PREs and TCs in the study region and contribute to a better understanding of tropical cyclone-related rainfall patterns, and act as a scientific basis for disaster mitigation.

Tree-ring anomalies as time markers for ice-core chronologies, with special reference to 5281 BCE as the possible date of the Kikai volcanic event

The synchronization of Greenland and Antarctica ice core data with tree-ring data, other proxies, and direct observations of natural processes and events is important to understand past climatic variation and environmental change. One of the methods that is used to correct the dating of ice layers is to match volcanic eruption footprints in ice cores with tree rings, manifested as sulphate spikes and anomalous rings, respectively. In this study, we inventoried the occurrence of tree-ring anatomical anomalies and extremes in ring width during three 200-year periods. These periods included three of the eight largest Holocene volcanic eruptions, each with a Volcanic Explosivity Index (VEI) of 7. The initial period spanned from 6560 to 6360 BCE and included the eruption of Ilyinsky Volcano in Kamchatka. The second period was from 5780 to 5580 BCE, during which Mount Mazama Volcano in North America erupted. The third period was from 5380 to 5180 BCE when the Kikai Volcano in the Japanese Islands erupted. Throughout the first two periods, no substantial tree-ring anomalies were observed suggesting the absence of any significant climate consequences of a major volcanic eruption. However, in the 5380–5180 BCE period, a clear sharp decline in tree growth and an exceptionally high frequency of tree-ring anomalies were identified in 5281 BCE and the subsequent 5 years. We propose that the exceptionally narrow light rings in these 6 years are indicative of the climatic impact resulting from the Kikai volcanic eruption. We suggest utilising the year 5281 BCE as the reference year for synchronising the Greenland and Antarctica ice core chronologies. In the case that our assumption is correct, this would imply the necessity to adjust the time of the event to an earlier date compared to the dates indicated by the existing ice core chronologies of the GICC05 (Greenland Ice Core Chronology 2005), adjusted according to Kobashi (2017), and WD2014 used for creating HolVol 1.0 (Holocene ice-core volcanic eruption catalogue from 9500 BCE - 1900 CE) by 67 and 54 years, respectively, for the period around 5300 BCE. A verification of our assumption could be conducted by examining the nearby Miyake event, a spike in cosmogenic radiocarbon, of 5258 BCE. Ice core layers displaying potential signs of this event should be approximately 25 years later than the markers of the Kikai volcanic eruption.

Enhanced Pacific Northwest heat extremes and wildfire risks induced by the boreal summer intraseasonal oscillation

The occurrence of extreme hot and dry summer conditions in the Pacific Northwest region of North America (PNW) has been known to be influenced by climate modes of variability such as the El Niño-Southern Oscillation and other variations in tropospheric circulation such as stationary waves and blocking. However, the extent to which the subseasonal remote tropical driver influences summer heat extremes and fire weather conditions across the PNW remains elusive. Our investigation reveals that the occurrence of heat extremes and associated fire-conducive weather conditions in the PNW is significantly heightened during the boreal summer intraseasonal oscillation (BSISO) phases 6-7, by ~50–120% relative to the seasonal probability. The promotion of these heat extremes is primarily attributed to the enhanced diabatic heating over the tropical central-to-eastern North Pacific, which generates a wave train traveling downstream toward North America, resulting in a prominent high-pressure system over the PNW. The ridge, subsequently, promotes surface warming over the region primarily through increased surface radiative heating and enhanced adiabatic warming. The results suggest a potential pathway to improving subseasonal-to-seasonal predictions of heatwaves and wildfire risks in the PNW by improving the representation of BSISO heating over the tropical-to-eastern North Pacific.

Indo-Pacific regional extremes aggravated by changes in tropical weather patterns

The Pacific Walker circulation and the closely connected El Niño/Southern Oscillation influence the climate and weather of the tropical Indo-Pacific region. They specifically exert a strong control on the regional occurrence of weather extremes, such as heatwaves, heavy precipitation and prolonged dry spells, which are becoming increasingly frequent and severe. However, climate models struggle to accurately simulate large-scale circulation changes in the tropics and thus their consequences for regional weather and future climate. Here we use high-resolution ERA5 reanalysis data from 1940 to 2022 to study the occurrence trends of weather patterns in the tropical Indo-Pacific region. We find that new large-scale synoptic situations that were rarely present before the 1990s have emerged in the Indo-Pacific, while some others that were prominent have disappeared. Those new synoptic situations are associated with an unusual proportion of heatwaves and extreme precipitation in the region. These weather patterns are physically consistent with a trend towards a stronger Pacific Walker circulation, wetter and warmer conditions in Southeast Asia and drier conditions in the equatorial Pacific. These changes cannot be fully explained by El Niño/Southern Oscillation and other relevant modes of interannual variability, and other factors such as global warming, aerosol forcing, external forcing mechanisms and nonlinear mode interactions may be contributing.

The Attribution of February Extremes over North America: A Forecast-Based Storyline Study

Abstract The importance of extreme event attribution rises as climate change causes severe damage to populations resulting from unprecedented events. In February 2019, a planetary wave shifted along the U.S.–Canadian border, simultaneously leading to troughing with anomalous cold events and ridging over Alaska and northern Canada with abnormal warm events. Also, a dry-stabilized anticyclonic circulation over low latitudes induced warm extreme events over Mexico and Florida. Most attribution studies compare the climate model simulations under natural or actual forcing conditions and assess probabilistically from a climatological point of view. However, in this study, we use multiple ensembles from an operational forecast model, promising statistical as well as dynamically constrained attribution assessment, often referred to as the storyline approach to extreme event attribution. In the globally averaged results, increasing CO2 concentrations lead to distinct warming signals at the surface, resulting mainly from diabatic heating. Our study finds that CO2-induced warming eventually affects the possibility of extreme events in North America, quantifying the impact of anthropogenic forcing over less than a week’s forecast simulation. Our study assesses the validity of the storyline approach conditional on the forecast lead times, which is hindered by rising noise in CO2 signals and the declining performance of the forecast model. The forecast-based storyline approach is valid for at least half of the land area within a 6-day lead time before the target extreme occurrence. Our attribution results highlight the importance of achieving net-zero emissions ahead of schedule to reduce the occurrence of severe heatwaves.

Potential distribution of rice thrips (Stenchaetothrips biformis) in India under changing climate

Sudden outbreaks of agricultural pests are increasing in India due to the growing prominence of environmental issues like global warming, the occurrence of weather extremes, and hydrological extremes. Climate change impacts the developmental rates and population dynamics of agricultural pests. As rice is a staple food widely used in India, it often suffers intensive damage by various pests, leading to a considerable decrease in crop yield. One of the most common pests in rice fields in India and neighboring countries is the rice thrips (Stenchaetothrips biformis). However, the degree of pest infestation of rice crops under climate change scenarios is poorly known. Therefore, this study aimed to understand the potential geographical distribution of the rice thrips in India for future scenarios using the MaxEnt modeling framework. Future projections from global climate models (GCMs), such as HadGEM3-GC31 and MPI-ESMI-2, are used to predict the most affected regions in 2050 under three greenhouse gas emission scenarios: SSP1-2.6, SSP3-4.5, and SSP5-8.5. The results from the MaxEnt indicate that the southern and northeastern parts of India will be highly affected by rice thrips in 2050. The affected number of districts are found to be 256, 330 and 313 under SSP1-2.6, SSP3-4.5, and SSP5-8.5 scenarios, respectively. It has been observed that the maximum temperature is the most influential climatic parameter for the geographical distribution of rice thrips, followed by the annual mean and minimum temperatures. It can be concluded from these results that the elevated temperature enhances the growth and distribution of rice thrips.

Detailed analysis of local climate at the CTAO-North site on La Palma from 20 yr of MAGIC weather station data

ABSTRACT The Observatorio del Roque de los Muchachos will host the northern site of the Cherenkov Telescope Array Observatory (CTAO), in an area about 200 m below the mountain rim, where the optical telescopes are located. The site currently hosts the MAGIC (Major Atmospheric Gamma-ray Imaging Cherenkov) telescopes, which have gathered a unique series of 20 yr of weather data. We use advanced profile-likelihood methods to determine seasonal cycles, the occurrence of weather extremes, weather downtime, and long-term trends correctly taking into account data gaps. The fractality of the weather data is investigated by means of multifractal detrended fluctuation analysis. The data are published according to the Findable, Accessible, Interoperable, and Reusable (FAIR) principles. We find that the behaviour of wind and relative humidity show significant differences compared to the mountain rim. We observe an increase in temperature of $0.55\pm 0.07\mathrm{(stat.)}\pm 0.07\mathrm{(syst.)}$$^{\circ }$C decade−1, the diurnal temperature range of $0.13\pm 0.04\mathrm{(stat.)}\pm 0.02\mathrm{(syst.)}$$^{\circ }$C decade−1 (accompanied by an increase of seasonal oscillation amplitude of $\Delta C_m=0.29\pm 0.10\mathrm{(stat.)}\pm 0.04\mathrm{(syst.)}$$^{\circ }$C decade−1), and relative humidity of $4.0\pm 0.4\mathrm{(stat.)}\pm 1.1\mathrm{(syst.)}$ per cent decade−1, and a decrease in trade wind speeds of $0.85\pm 0.12\mathrm{(stat.)}\pm 0.07\mathrm{(syst.)}$ (km h−1) decade−1. The occurrence of extreme weather, such as tropical storms and long rains, remains constant over time. We find a significant correlation of temperature with the North Atlantic Oscillation Index and multifractal behaviour of the data. The site shows a weather-related downtime of 18.5 per cent–20.5 per cent, depending on the wind gust limits employed. No hints are found of a degradation of weather downtime under the assumption of a linear evolution of environmental parameters over time.

Intraseasonal Variations and Extreme Occurrence in the Local Sea Level Along the Western Coast of India Remotely Controlled by a Basin‐Scale Climate Variability

AbstractThe equatorial Kelvin waves, remotely excited by basin‐scale climate modes, and subsequent coastal trapped waves significantly influence the intraseasonal variations, their low‐frequency modulations, and the frequency of extreme sea level events along the western coast of India. This study demonstrates that the frequency of extreme events are linked to the phase of the Indian Ocean Dipole mode. The temporal changes in the occurrence frequency of extremes are simulated in an eddy‐resolving ocean model consistently with observations. However, a non‐eddying model significantly underestimate the occurrence frequency of extreme sea level events, suggesting the importance of coastal trapped wave propagations regulated by the horizontal scale with the Rossby radius of deformation. This result implies that many state‐of‐the‐art climate models with a one‐degree ocean horizontal resolution may underestimate future coastal sea level variability and the frequency of extreme events under global warming and potential modulations of major internal climate modes.

The driving of North American climate extremes by North Pacific stationary-transient wave interference

Wave interference between transient waves and climatological stationary waves is a useful framework for diagnosing the magnitude of stationary waves. Here, we find that the wave interference over the North Pacific Ocean is an important driver of North American wintertime cold and heavy precipitation extremes in the present climate, but that this relationship is projected to weaken under increasing greenhouse gas emissions. When daily circulation anomalies are in-phase with the climatological mean state, the anomalous transport of heat and moisture causes the enhanced occurrence of cold extremes across the continental U.S. and a significant decrease of heavy precipitation extremes in the western U.S. In a future emissions scenario, the climatological stationary wave over the eastern North Pacific weakens and shifts spatially, which alters and generally weakens the relationship between wave interference and North American climate extremes. Our results underscore that the prediction of changes in regional wave interference is pivotal for understanding the future regional climate variability.

Accuracy of daily extreme air temperatures under natural variations in thermometer screen ventilation

AbstractAccurate air temperatures underpin environmental research. Most professional meteorological air temperature measurements still expose thermometers within traditional, naturally ventilated screens. Their representation of true air temperature depends on screen airflow, and therefore local winds. Accuracies of daily maximum (Tmax) and minimum (Tmin) air temperatures are assessed by comparison between a naturally ventilated large conventional screen and a co‐located aspirated reference screen. In over 1200 days' data, the naturally ventilated Tmin and Tmax both showed small (median &lt; 0.06°C) cold bias, but, in 1% of cases, warm Tmax bias and cold Tmin bias &gt;|1°C|. The Tmin cold bias is associated with calm clear nights, and the Tmax warm bias events with calm winter days at low sun angles, allowing solar heating of the screen. The prevalence of poor natural ventilation, potentially affecting Tmin and Tmax, is estimated across European sites. Poor ventilation occurred at Tmin for 12% of values, and at Tmax for 4%. Climatological averaging will reduce these effects, but, without corroborating wind data, statistical changes in Tmin or Tmax, including identifying “Tropical Nights” (Tmin &gt; 20°C) or occurrences of winter extremes, may have limited value. Wider adoption of aspirated thermometer screens, with an initial overlap period, will largely eliminate these effects.

Antarctic Warm Extremes Across Seasons and Their Response to Advection

AbstractAntarctic warm extremes impact the cryosphere, with very warm extremes driving surface melt on ice shelves. Here, we analyze temperatures exceeding the 90th percentile and the associated circulation patterns and radiation anomalies. ERA5 reanalysis data show positive geopotential height anomalies related to the occurrence of warm extremes. The highest temperature during warm extremes appears on the western periphery of high‐pressure systems, consistent with anticyclonic advection. Temperature anomalies during warm extremes are strongest in winter due to the transport of warm and moist air and a strong meridional temperature gradient. In summer, the weak meridional gradients of top‐of‐atmosphere downward solar radiation flux and surface air temperature contribute to weak temperature anomalies. Warm extremes are associated with positive longwave radiation anomalies in all seasons, but with negative shortwave radiation anomalies at the surface except during polar night. These relationships are verified by station observations. Our results confirm that Antarctic warm extremes are mostly driven by meridional advection of warm air, and suggest that these warm air masses are predominantly moist and cloudy.

Spatial and temporal trend analysis of temperature extremes in Tanzania

This study examines the effects of extreme temperature events, defined explicitly as the 98th percentile temperature for heat, in Tanzania from 1981 to 2023. The study utilises percentile analysis, z-score calculations, and trend mapping to highlight the prevalence of extreme temperatures. The z-score analysis examines the occurrence of extreme temperatures and their distribution in terms of frequency and space. It identified Tanzania&amp;rsquo;s mid-eastern and north-western regions as areas with a high concentration of extreme temperatures over the previous 43 years. The study analyses the length of extreme occurrences and reveals changing patterns. In 2020, the events were shorter, while in 2021 and 2022, they became longer-lasting, and there was a notable concentration of extended anomalies near the northern coastline. In 2023, the highest temperatures ever recorded were observed, with more than half of the regions seeing prolonged periods of extreme temperatures lasting for a week and significant deviations from the norm staying for four weeks or more. This was particularly notable in the southern regions of Lindi and Mtwara. The harmonic trend analysis of temperature shows an upward temperature trend noticeably in regions such as Arusha, Kilimanjaro, Kagera, Morogoro, Simiyu, and Shinyanga and is somewhat inconspicuous in most areas.

Variations of compound warm, dry, wet, and cold climate extremes in India during 1951 to 2014

The simultaneous occurrence of climate extremes significantly impacts ecosystems, increasing the vulnerability to physical risks. Despite extensive research on hot extremes and droughts globally, there remains a significant gap in comprehending the occurrence, magnitude, spatial extent, and associated risks of compound extremes, encompassing scenarios like warm/dry, cold/dry, warm/wet, and cold/wet. This study investigates various compound extreme scenarios by examining combinations of maximum temperature (Tx) and the Standardized Precipitation Evapotranspiration Index (SPEI) using monthly data from 1951 to 2014 acquired from the India Meteorological Department (IMD) for Indian landmass. From the results, the spatial extent of warm/dry events has increased at 1.8 % per decade, while cold/wet events decreased by 1.1 % over India. The warm/wet events have shown an increased trend of about 0.3 %, and cold/dry events at modest rise of 0.7 % per decade. Furthermore, compound warm/dry and cold/wet extremes over India exhibit extreme frequency and shorter return periods, posing greater risk. Conversely, compound cold/dry and warm/wet extremes occur less often, indicating longer return periods and lower risk. Across much of the country, the frequency of warm/dry, cold/dry, warm/wet, and cold/wet extremes ranges from 30 to 45, 15–30, 20–30, and 25–45 months, respectively. Notably, warm/dry conditions exhibit increased frequency in the recent period (1983–2014) with 31 years compared to the base period (1951–1982) which had approximately 24 years for a spatial extent exceeding 5 %. The findings of this study contribute to an enhanced understanding of the changes in compound climate extremes from a multivariate perspective.

Synchronous Eurasian heat extremes tied to boreal summer combined extratropical intraseasonal waves

Heat extremes frequently hit different regions synchronously during boreal summer over the Eurasian continent. A remarkable coupling is first revealed between Eurasian heat extreme occurrence and individual extratropical intraseasonal oscillation (EISO). Further, the combined EISOs facilitate and largely increase the occurrence probabilities of synchronous Eurasian heat extremes. These dominant combined EISOs together contribute 20–45% to the total heat extreme days over the five Eurasian regions where the climatological heat extremes occur most frequently. A multi-model hindcast further shows that the subseasonal prediction exhibits higher skills for synchronous heat extremes over the combined-EISO hotspot regions when the associated combined EISOs are active, supporting the notion that the monitoring and prediction of EISOs are crucial for heat extremes’ early warning. Skillful prediction of EISOs opens a pathway for heat extremes’ prediction by extending it from the weather to the subseasonal timescales.