Magnitude Of Extremes Research Articles

Uncertainty in the extreme flood magnitude estimates of large-scale flood hazard models

The growing worldwide impact of flood events has motivated the development and application of global flood hazard models (GFHMs). These models have become useful tools for flood risk assessment and management, especially in regions where little local hazard information is available. One of the key uncertainties associated with GFHMs is the estimation of extreme flood magnitudes to generate flood hazard maps. In this study, the 1-in-100 year flood (Q100) magnitude was estimated using flow outputs from four global hydrological models (GHMs) and two global flood frequency analysis datasets for 1350 gauges across the conterminous US. The annual maximum flows of the observed and modelled timeseries of streamflow were bootstrapped to evaluate the sensitivity of the underlying data to extrapolation. Results show that there are clear spatial patterns of bias associated with each method. GHMs show a general tendency to overpredict Western US gauges and underpredict Eastern US gauges. The GloFAS and HYPE models underpredict Q100 by more than 25% in 68% and 52% of gauges, respectively. The PCR-GLOBWB and CaMa-Flood models overestimate Q100 by more than 25% at 60% and 65% of gauges in West and Central US, respectively. The global frequency analysis datasets have spatial variabilities that differ from the GHMs. We found that river basin area and topographic elevation explain some of the spatial variability in predictive performance found in this study. However, there is no single model or method that performs best everywhere, and therefore we recommend a weighted ensemble of predictions of extreme flood magnitudes should be used for large-scale flood hazard assessment.

Impacts of regional climate change on hydrological drought characteristics in headwaters of the Ore Mountains

AbstractChanges in the frequency, magnitude, and seasonality of hydrological extremes are one of the expected consequences of climate change. Recent years suggested an increasing risk of drought occurrence and restricted water supply even in usually humid mountain regions. Our research is focused on the evaluation of regional climate change and its effect on hydrological drought characteristics in headwater areas in the Ore Mountains along the Czech/German border. The main aim was to evaluate and compare streamflow regime changes and trends in long‐term hydrological and climatic time series within selected sub‐catchments of the Svatava (Zwota) River, the Rolava River, and the Načetínský Brook (Natzschung). A comparative statistical analysis for the 1967–2018 period was performed using various homogeneity and trend tests. Longer runoff time series with earlier beginnings were examined for two catchments as well. The results pointed to significant, continuous warming in the region which is accompanied by decreasing trends in snow cover depths in the colder half of the year. Drought indices based on runoff data were derived in the Indicators of Hydrologic Alterations (IHA 7.1) software environment. The seasonality of hydrological drought occurrence changed markedly after 1992, when most severe drought events started to concentrate between August and October in the study area. Increasing trends in deficit volumes were found in two catchments characterized by lower elevation. Furthermore, there was a significant decrease in average and minimum flows in spring and summer months in all catchments observed between 1967 and 2018 despite relatively stable amounts of precipitation and land cover.

Shifting magnitude and timing of streamflow extremes and the relationship with rainfall across the Hawaiian Islands

Flooding is a significant threat to life and property in Hawaiʻi. As climate warming continues to alter precipitation patterns and hydrological processes in the tropics, characterizing the shifting patterns in magnitude, seasonality, and location of floods would improve our understanding of the consequences and better prepare us for future flood events. In this study, 84 rain gauges and 111 crest gauges across five major Hawaiian Islands were analyzed from 1970 to 2005. We estimated trends in the annual maximum daily rainfall (RFmax) and the annual peak flow (PFmax) using the Mann-Kendall test and Senʻs slope. Subsequently, we examined the association between PFmax and rainfall. Then, we assessed temporal shifting by combining circular analysis with Senʻs slope. The main identified trends showed decreases in RFmax and PFmax(67% and 61% of all gauges, respectively). The physiography of the Hawaiian islands (i.e., windward vs. leeward) has little contribution to both trends. In addition, PFmax trend cannot be fully attributed to RFmaxtrend, and in many cases, RFmax andPFmax did not occur coincidently. We also found the timing of RFmax and PFmax occurred in early wet season during the El Niño years and shifted even earlier from 1970 to 2005, likely due to the change of El Niño. These findings have implications for assessing flood risk. Our finding will aid watershed management and flood mitigation, and can increase resilience of downstream communities and near-shore environments.

Modulations of Synoptic Weather Patterns on Warm-Sector Heavy Rainfall in South China: Insights From High-Density Observations With Principal Component Analysis

Based on hourly high-density precipitation data in Guangdong Province, China, 134 warm-sector heavy rainfall (WSHR) events were selected from 2016 to 2018. The synoptic weather patterns of these WSHR events were objectively classified using T-mode principal component analysis. Six WSHR weather patterns were identified, as follows: Type 1-southwest (T1-SW), Type 2-southeast (T2-SE), Type 3-coastal jets I (T3-CJI), Type 4-coastal jets II (T4-CJ II), Type 5-western low vortex (T5-WL), and Type 6-high-pressure (T6-HP). Three high-occurrence WSHR centers were finally extracted: the areas of Yangjiang and Shanwei, and the urban agglomeration of Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Compared with the other five patterns, T6-HP is a newly identified WSHR weather pattern, which is related to a local/small-scale weather system in the context of anomalous northward movement of the western Pacific subtropical high. Notably, the precipitation area of the T6-HP type of WSHR event is smaller, which can only be captured by high-density observations. In addition, the occurrence locations of six large-scale extreme precipitation events were closely associated with the urban agglomerations in GBA, implying that urbanization plays an important role in extreme magnitudes of large-scale WSHR events and their occurrence centers.

Prolonged Siberian heat of 2020 almost impossible without human influence

Over the first half of 2020, Siberia experienced the warmest period from January to June since records began and on the 20th of June the weather station at Verkhoyansk reported 38 °C, the highest daily maximum temperature recorded north of the Arctic Circle. We present a multi-model, multi-method analysis on how anthropogenic climate change affected the probability of these events occurring using both observational datasets and a large collection of climate models, including state-of-the-art higher-resolution simulations designed for attribution and many from the latest generation of coupled ocean-atmosphere models, CMIP6. Conscious that the impacts of heatwaves can span large differences in spatial and temporal scales, we focus on two measures of the extreme Siberian heat of 2020: January to June mean temperatures over a large Siberian region and maximum daily temperatures in the vicinity of the town of Verkhoyansk. We show that human-induced climate change has dramatically increased the probability of occurrence and magnitude of extremes in both of these (with lower confidence for the probability for Verkhoyansk) and that without human influence the temperatures widely experienced in Siberia in the first half of 2020 would have been practically impossible.

SSP 시나리오에 따른 동아시아 극한기후 미래전망

This study investigates the extreme temperature and precipitation indices as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). We calculate 14 indices representing extreme climate over East Asia. Here, we utilized the ensemble results of NIMS-KMA (National Institute of Meteorological Sciences and Korea Meteorological Administration) climate models (UKESM1 and K-ACE) for analysis of long-term variation in present-day (1995 ~ 2014) and future (2015 ~ 2100) periods. The spatial distributions of simulated daily maximum and minimum temperatures and daily mean precipitation are comparable to those of ERA5 reanalysis data. Simulated extreme indices are well reproduced in the present-day period, although the NIMS-KMA climate models tend to underestimate annual minimum temperature extremes and overestimate extremes in precipitation intensity. In the future scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), intensity and frequency of warm temperature extremes increased for the late 21st century, but magnitude and number of cold temperature extremes significantly decreased. Changes in extreme precipitation associated with a warming climate are becoming more intense and frequent in Southern China. But the changes in precipitation duration indices tend to have high uncertainty under SSP scenarios.

Chronology and Eccentricity Phasing for the Early Turonian Greenhouse (∼93–94 Ma): Constraints on Astronomical Control of the Carbon Cycle

AbstractThe Early Turonian interval represents a unique confluence of climatic and oceanographic conditions including peak surface temperatures, high greenhouse‐gas concentrations, and maximum Phanerozoic sea level. The susceptibility of this climate mode to astronomical insolation forcing remains poorly understood partly due to a limited time control and unknown phasing of astronomical cycles in this interval. Here, we offer a refined astrochronology of the Early Turonian based on laterally consistent precession signals preserved in offshore strata of the Bohemian Cretaceous Basin (central Europe). Pristine amplitude modulation verified through interference patterns in depth‐frequency plots provides a robust indication of ∼100 and 405‐kyr eccentricity phases (maxima and minima) that are pinned to ammonite biozones and new carbon‐isotope data from two cores. The Early Turonian is estimated as 885 ± 41 (2σ) thousand years (kyr) in duration, with the Cenomanian/Turonian boundary predating the first Turonian 405‐kyr maximum (no. 232 in the Geological Time Scale 2020) by 82 ± 70 (2σ) kyr. The results support a possible link of the recovery from Oceanic Anoxic Event II to increasing magnitude of seasonal insolation extremes due to rising eccentricity on 405‐kyr and million‐year (Myr) time scales. Superimposed upon this trend are small‐scale carbon‐isotope anomalies the pacing of which passes from ∼110 kyr, resembling short eccentricity, to ∼170 kyr, possibly related to obliquity modulation. This eccentricity‐to‐obliquity transition, paralleling the rising phase of Myr‐scale eccentricity, suggests decoupling of the carbon‐cycle perturbations from low‐latitude seasonal insolation. Mid‐latitude to high‐latitude carbon reservoirs became involved toward the Middle Turonian.

Remote sensing of droplet number concentration of aerosol-induced clouds during the 2019 fire event in Borneo, Indonesia

The droplet number concentration of liquid water clouds N is estimated based on the optical thickness τ and particle effective radius reff retrieved from the measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard of Terra satellite. Two measurements of MODIS taken from 15 June 2019 (case A) and 15 September 2019 (case B) are analyzed. They represent a condition before and during the 2019 fire event in Borneo, Indonesia. The result shows, that the domains of τ and reff are comparable for the two cases. While there is no sign of systematic bias in case of τ, the analysis of r eff tells differently. Clouds with smaller droplets, less than 12 µ m, are more prominent in case B, indicating the present of aerosol-induced clouds. In case A, the frequency distributions of clouds with larger droplets, more than 12 µ m, are systematically higher. The magnitudes of N in case A are mostly less than 100 cm-3, which exhibit a background condition. Extreme magnitudes of N, from 200 to 800 cm −3, are found in case B. Those excessive numbers are attributed to the region where the aerosol-induced clouds are observed.

Regional frequency analysis of annual daily rainfall maxima in Skåne, Sweden

AbstractExtreme daily rainfall events are critical for the urban drainage system, human life, agriculture and small catchments. The information about extreme rainfall magnitudes and frequencies is immensely important for civil engineers, city planners, scientists related to water management, rescue operations and flood control works. This study illustrates the results of regional frequency analysis (RFA) of annual maximum daily rainfall (AMDR) of Skåne County, Sweden. L‐moments based heterogeneity measure (H) reveals that the Skåne County is a homogeneous region. Based on the L‐moment ratio diagram and ZDist statistic results, the generalized normal (GNO) distribution is selected as the most suitable regional distribution. The accuracy measures used in K‐fold cross validation indicate that support vector machine (SVM) model is an appropriate model to find the index rainfall at ungauged sites in the region. The sites characteristics, elevation and latitude are identified as the most important variables to explain the variation in mean annual maximum daily rainfall (MAMDR). Finally, spatial maps of predicted MAMDR for different return periods are constructed by using index rainfall combined with regional quantiles. Spatial maps offer an overall view of the expected MAMDR in the region that is helpful for multiple decision makers including infrastructure planners, city planners, emergency managers, engineers and many others.

Spatial analysis of drought severity and magnitude using the standardized precipitation index and streamflow drought index over the Upper Indus Basin, Pakistan

Droughts are one of the multidimensional extreme events that negatively influence agriculture, water availability and food security in the World. The Upper Indus Basin (UIB) is the major river system in the world that contributes 80% of the surface water availability in Pakistan. The objective of the current study is to analyze the spatial patterns of drought severity and magnitude using the standardized precipitation index and streamflow drought index. These drought indices were used to estimate the characteristics and a variation of the droughts annual, pre-monsoon and post-monsoon seasons for the period of 1991–2017. Results revealed that the northeastern side of the Upper Indus Basin was greatly influenced by the droughts as compared to the southwestern side. The duration from 1997 to 1998 was strongly correlated with El Nino and La Nina (ENSO) in a strong way. This type of ENSO indicates the severe drought for 5 years from 1998 to 2002 in the UIB. The episode of the warm water oceans mechanism was also strengthened in 1996, 1997, 1998, 2000, 2001 and 2002 drought years. La Nina effect has been observed in the wet and cold phase during the year of 1999. The study indicated the severe SDI drought in the years of 1991 and 2001. The study also indicated that the streamflow drought index in the post-monsoon season was marked by extreme level drought magnitude at G. Habibullah (2001), Muzaffarabad (2001), Yogo (1993) and Dhok Pathan (1998). The results of the current investigation would help policymakers to introduce new types of drought risk reduction measures for drought preparedness at the regional and the national level in Pakistan.

Inter-basin and Multi-time Scale Interactions in generating the 2019 Extreme Indian Ocean Dipole

AbstractAn unprecedented extreme positive Indian Ocean Dipole event (pIOD) occurred in 2019, which has caused widespread disastrous impacts on countries bordering the Indian Ocean, including the East African floods and vast bushfires in Australia. Here we investigate the causes for the 2019 pIOD by analyzing multiple observational datasets and performing numerical model experiments. We find that the 2019 pIOD is triggered in May by easterly wind bursts over the tropical Indian Ocean associated with the dry phase of the boreal summer intraseasonal oscillation, and sustained by the local atmosphere-ocean interaction thereafter. During September-November, warm sea surface temperature anomalies (SSTA) in the central-western tropical Pacific further enhance the Indian Ocean’s easterly winds, bringing the pIOD to an extreme magnitude. The central-western tropical Pacific warm SSTA is strengthened by two consecutive Madden Julian Oscillation (MJO) events that originate from the tropical Indian Ocean. Our results highlight the important roles of cross-basin and cross-timescale interactions in generating extreme IOD events. The lack of accurate representation of these interactions may be the root for a short lead time in predicting this extreme pIOD with a state-of-the-art climate forecast model.

Cool season precipitation projections for California and the Western United States in NA-CORDEX models

Understanding future precipitation changes is critical for water supply and flood risk applications in the western United States. The North American COordinated Regional Downscaling EXperiment (NA-CORDEX) matrix of global and regional climate models at multiple resolutions (~ 50-km and 25-km grid spacings) is used to evaluate mean monthly precipitation, extreme daily precipitation, and snow water equivalent (SWE) over the western United States, with a sub-regional focus on California. Results indicate significant model spread in mean monthly precipitation in several key water-sensitive areas in both historical and future projections, but suggest model agreement on increasing daily extreme precipitation magnitudes, decreasing seasonal snowpack, and a shortening of the wet season in California in particular. While the beginning and end of the California cool season are projected to dry according to most models, the core of the cool season (December, January, February) shows an overall wetter projected change pattern. Daily cool-season precipitation extremes generally increase for most models, particularly in California in the mid-winter months. Finally, a marked projected decrease in future seasonal SWE is found across all models, accompanied by earlier dates of maximum seasonal SWE, and thus a shortening of the period of snow cover as well. Results are discussed in the context of how the diverse model membership and variable resolutions offered by the NA-CORDEX ensemble can be best leveraged by stakeholders faced with future water planning challenges.

The influence of system heterogeneity on peat-surface temperature dynamics

Temperatures at the soil–atmosphere interface influence ecosystem function by driving nonlinear terrestrial biogeochemical, ecohydrological, and micrometeorological processes. Whilst climate, soil and vegetation controls on spatially average ecosystem temperatures are recognised, how interacting and heterogeneous ecosystem layers create spatio-temporal complex thermal ecosystems has not been determined. Such thermal hot spots and hot moments may underpin the capability of ecosystems to support biological and biogeochemical diversity and control the likelihood of tipping points in system-regulating feedbacks being locally exceeded. This is of notable importance in peatlands, where soil temperatures control the storage of their associated globally important carbon stocks. Here, through the application of high spatio-temporal resolution surface temperature data and peat thermal modelling, we assess the impact of system heterogeneity (spatio-temporal impact of the following system layers: tree, shrubs, microtopography, groundcover species and sub-surface ice) on surface temperature regimes. We show (a) that peat-surface thermal hotspot intensity and longevity is linked to system heterogeneity and (b) that not all system layers have an equal influence over the peat-surface thermal regime and extreme temperatures; thermal heterogeneity increases up to a maximum of five layers of heterogeneity and decreases thereafter. The results crucially demonstrate that such changes in the spatio-temporal thermal dynamics and extremes may occur without significant changes in median temperatures. This is important to the conceptual understanding of peatland responses and ecosystem resilience to disturbance. It emphasises the need to determine the potential for transitions in magnitude, longevity and locality of small-scale thermal extremes to induce functional transitions that propagate through given ecosystems, and to characterise the impact of such small-scale spatio-temporal complexity on ecosystem scale biogeochemical and ecohydrological function.

A flow-weighted ensemble strategy to assess the impacts of climate change on watershed hydrology

Evaluation of hydrological response to future climate change is essential for water quality risk assessment and adaptive management implementation within a watershed context. In this study, we present a modeling framework that integrates a hydrological model with projections of General Circulation Models (GCMs). Temperature and precipitation projections of six GCMs with two downscaling methods are used to force the Soil and Water Assessment Tool (SWAT) model in the Hamilton Harbour watershed in Ontario, Canada. A flow-based weighting strategy was developed to integrate the projections of multiple GCMs based on their ability to recreate empirical flow frequency distributions in multiple monitoring sites. Our study renders support to the ability of the weighted model ensemble to draw meaningful hydrological forecasts. Nonetheless, we also note that the ensemble strategy understates the frequency and magnitude of flow extremes, and therefore the domain that is collectively delineated by individual GCMs can still provide complementary planning information. Climate change is projected to trigger a distinct increase in air temperature and precipitation during the late winter-early spring period, which in turn will likely result in an earlier snowmelt and changes in the magnitude and timing of peak flow events. Analysis of the water cycle shows that the sensitivity of the individual hydrological components to climate change may vary along the urban-to-agriculture gradient. The projected declining soil–water content in agricultural catchments highlights the likelihood of more intensified drought conditions in the croplands. Evapotranspiration rates will likely increase across the entire watershed, whereas surface runoff could be reduced but less so in urbanized locations. Overall, our findings suggest that any future planning exercise to ameliorate the prevailing water quality conditions will only be insightful if we consider the interplay between climate change and urbanization processes in the area.

Intensification characteristics of hydroclimatic extremes in the Asian monsoon region under 1.5 and 2.0 °C of global warming

Abstract. Understanding the influence of global warming on regional hydroclimatic extremes is challenging. To reduce the potential risk of extremes under future climate states, assessing the change in extreme climate events is important, especially in Asia, due to spatial variability of climate and its seasonal variability. Here, the changes in hydroclimatic extremes are assessed over the Asian monsoon region under global mean temperature warming targets of 1.5 and 2.0 ∘C above preindustrial levels based on representative concentration pathways (RCPs) 4.5 and 8.5. Analyses of the subregions classified using regional climate characteristics are performed based on the multimodel ensemble mean (MME) of five bias-corrected global climate models (GCMs). For runoff extremes, the hydrologic responses to 1.5 and 2.0 ∘C global warming targets are simulated based on the variable infiltration capacity (VIC) model. Changes in temperature extremes show increasing warm extremes and decreasing cold extremes in all climate zones with strong robustness under global warming conditions. However, the hottest extreme temperatures occur more frequently in low-latitude regions with tropical climates. Changes in mean annual precipitation and mean annual runoff and low-runoff extremes represent the large spatial variations with weak robustness based on intermodel agreements. Global warming is expected to consistently intensify maximum extreme precipitation events (usually exceeding a 10 % increase in intensity under 2.0 ∘C of warming) in all climate zones. The precipitation change patterns directly contribute to the spatial extent and magnitude of the high-runoff extremes. Regardless of regional climate characteristics and RCPs, this behavior is expected to be enhanced under the 2.0 ∘C (compared with the 1.5 ∘C) warming scenario and increase the likelihood of flood risk (up to 10 %). More importantly, an extra 0.5 ∘C of global warming under two RCPs will amplify the change in hydroclimatic extremes on temperature, precipitation, and runoff with strong robustness, especially in cold (and polar) climate zones. The results of this study clearly show the consistent changes in regional hydroclimatic extremes related to temperature and high precipitation and suggest that hydroclimatic sensitivities can differ based on regional climate characteristics and type of extreme variables under warmer conditions over Asia.

Temperature and rainfall extremes change under current and future global warming levels across Indian climate zones

Mean surface temperature is projected to rise by about 4.4 °C by the end of the century compared to the period between 1976 and 2005 when following the most extreme scenario of the greenhouse gas emissions pathway (Krishnan et al., 2020). With this rise in mean temperature, there is a lot of uncertainty on how weather and climate extremes would unfold, especially for various climate zones of India. It is therefore essential that the potential changes in both magnitude and direction of weather and climate extremes at the regional level when the global temperature reaches the different warming levels from 1 °C to 3 °C be established to allow for informed policy formulation.The present study explores the potential changes in the Expert Team on Climate Change Detection and Indices of rainfall and temperature estimated from the coupled model inter-comparison project CMIP5 multi-model ensemble over different climatic zones of India at 1 °C, 1.5 °C, 2 °C, 2.5 °C and 3 °C global temperature rise relative to pre-industrial levels under two Representative Concentration Pathways, RCP4.5 and RCP8.5. Projected changes in temperature extremes indicate significant changes at all warming levels across the nine climate zones of India. Hot temperature extremes are expected to increase while cold temperature extremes decrease. For India, country average at 3 °C under the RCP8.5 and 2 °C under the RCP4.5 scenarios, ensemble median shows that Warm Spell Duration Index will increase by 131 days and 66 days; hot days increase by 44% and 52%, warm nights increase by 23% and 13%; cold days decrease by 10% and 9%, and cold nights decrease by 13% and 12% relative to pre-industrial levels. The greatest changes in temperature based indices are projected in the colder northern parts of the country followed by the arid zone. Ensemble median for rainfall indices shows an increase in high precipitation indices, though with large model spread indicating the large uncertainties in the projections. Annual total precipitation and heavy rainfall related extreme indices show statistically significant increases in the tropical, temperate and semi-arid regions of India, moving from 1 °C to 3 °C warming level under RCP8.5 scenario whereas there is generally no significant change in the maximum number of consecutive dry and wet days.Moreover, the potential changes in climate extremes at the regional level are expected to have far-reaching impacts on the social and economic statuses of the respective climate zones. This information at a regional scale also calls attention to the national and state action plan on climate change and adaptation to be more responsive in order to take coherent and integrated policy decisions.

Changes in seasonality and magnitude of sub-daily rainfall extremes in Emilia-Romagna (Italy) and potential influence on regional rainfall frequency estimation

Study regionThe present study focuses on Emilia-Romagna (northern Italy), a densely populated region affected in the last few years by several destructive flood events. Study focusA rich and detailed regional dataset of annual maximum series (AMS) of sub-daily rainfall is considered with a three-fold aim: to detect possible changes in (1) seasonality and (2) magnitude of sub-daily rainfall extremes, (3) to assess the current reliability and accuracy of a previously developed regional model for rainfall frequency estimation based on the local value of mean annual precipitation (MAP). New hydrological insights for the regionThe main findings of the study include: (1) delay (towards Autumn) in the mean timing of sub-daily rainfall extremes, more marked for higher elevations and longer durations; (2) significant increase in the magnitude of sub-daily extreme events for some areas, especially in the Apennine; (3) high sensitivity of the regional predictions of the frequency regime of sub-daily rainfall extremes to local changes in AMS and MAP.

Increased Flood Exposure Due to Climate Change and Population Growth in the United States

AbstractPrecipitation extremes are increasing globally due to anthropogenic climate change. However, there remains uncertainty regarding impacts upon flood occurrence and subsequent population exposure. Here, we quantify changes in population exposure to flood hazard across the contiguous United States. We combine simulations from a climate model large ensemble and a high‐resolution hydrodynamic flood model—allowing us to directly assess changes across a wide range of extreme precipitation magnitudes and accumulation timescales. We report a mean increase in the 100‐year precipitation event of ~20% (magnitude) and >200% (frequency) in a high warming scenario, yielding a ~30–127% increase in population exposure. We further find a nonlinear increase for the most intense precipitation events—suggesting accelerating societal impacts from historically rare or unprecedented precipitation events in the 21st century.

Climate change‐mediated temperature extremes and insects: From outbreaks to breakdowns

Insects are among the most diverse and widespread animals across the biosphere and are well‐known for their contributions to ecosystem functioning and services. Recent increases in the frequency and magnitude of climatic extremes (CE), in particular temperature extremes (TE) owing to anthropogenic climate change, are exposing insect populations and communities to unprecedented stresses. However, a major problem in understanding insect responses to TE is that they are still highly unpredictable both spatially and temporally, which reduces frequency‐ or direction‐dependent selective responses by insects. Moreover, how species interactions and community structure may change in response to stresses imposed by TE is still poorly understood. Here we provide an overview of how terrestrial insects respond to TE by integrating their organismal physiology, multitrophic, and community‐level interactions, and building that up to explore scenarios for population explosions and crashes that have ecosystem‐level consequences. We argue that TE can push insect herbivores and their natural enemies to and even beyond their adaptive limits, which may differ among species intimately involved in trophic interactions, leading to phenological disruptions and the structural reorganization of food webs. TE may ultimately lead to outbreak–breakdown cycles in insect communities with detrimental consequences for ecosystem functioning and resilience. Lastly, we suggest new research lines that will help achieve a better understanding of insect and community responses to a wide range of CE.

Global surface air temperatures in CMIP6: historical performance and future changes

Surface air temperature outputs from 16 global climate models participating in the sixth phase of the coupled model intercomparison project (CMIP6) were used to evaluate agreement with observations over the global land surface for the period 1901–2014. Projections of multi-model mean under four different shared socioeconomic pathways were also examined. The results reveal that the majority of models reasonably capture the dominant features of the spatial variations in observed temperature with a pattern correlation typically greater than 0.98, but with large variability across models and regions. In addition, the CMIP6 mean can capture the trends of global surface temperatures shown by the observational data during 1901–1940 (warming), 1941–1970 (cooling) and 1971–2014 (rapid warming). By the end of the 21st century, the global temperature under different scenarios is projected to increase by 1.18 °C/100 yr (SSP1-2.6), 3.22 °C/100 yr (SSP2-4.5), 5.50 °C/100 yr (SSP3-7.0) and 7.20 °C/100 yr (SSP5-8.5), with greater warming projected over the high latitudes of the northern hemisphere and weaker warming over the tropics and the southern hemisphere. Results of probability density distributions further indicate that large increases in the frequency and magnitude of warm extremes over the global land may occur in the future.