Warm Temperature Extremes Research Articles

Warm Temperature Extremes Across Greenland Connected to Clouds

AbstractGreenland ice sheet melt is a large contributor to rising global sea level and melt is dependent on surface air temperature. Arctic temperatures are strongly coupled to clouds but spatial connections between clouds and temperature have yet to be established across Greenland. By mapping spaceborne lidar measurements and surface observations, it is shown that radiatively opaque clouds generally coincide with anomalously warm near‐surface temperatures at Greenland sites. These results indicate that both temperatures over 0 °C as well as positive daily temperature anomalies relate to spatially extensive opaque cloud cover. While prior studies indicate that clouds enhance extreme melt events, this research shows that opaque cloud cover and surface warming are closely related across the Greenland ice sheet, particularly in the ablation region. These findings establish broadly the spatial relationships between opaque clouds and temperatures and demonstrate the importance of direct observations across Greenland.

Projected changes in extreme warm and cold temperatures in China from 1.5 to 5°C global warming

AbstractLinking regional extreme temperature changes to global warming levels is important for understanding the impacts of global emission targets on regional climate. Here, we investigate how the temperature extremes in China change with different global warming levels using large ensemble runs from Canadian Earth System Model version 2. With the global mean near‐surface temperature increasing from 1.5 to 5°C above the preindustrial level, the absolute intensity of the warmest and coldest temperatures in China will change linearly, while the percentile‐based frequency of warm and cold temperatures will change nonlinearly. All the changes in the intensity and the frequency show clear regional differences, with the most obvious changes observed in northeastern China. The probability distribution functions (PDFs) for the intensity indices show clear shifts but with little change in shape, while the PDFs for the frequency indices show changes in both position and shape. Quantified analyses of risk ratio show that the risk changes in the frequency of temperature extremes will be larger than those for the intensity indices. The changes in the nighttime extremes are faster than those in the daytime extremes. The rarer the event is, the larger the change in the risk ratio. At the 2°C warming level, the cold days and nights with return periods of 5, 10, and 50 years in the current climate will become almost disappeared. At the 3°C level and beyond, the once‐in‐5‐year, 10‐year and 50‐year warm events in the current climate will occur every year.

Decadal Predictions of the Probability of Occurrence for Warm Summer Temperature Extremes

AbstractWe use a decadal prediction system with the Coupled Model Intercomparison Project Phase 6 version of the coupled Max Planck Institute Earth System Model to predict the probability of occurrence for extremely warm summers in the Northern Hemisphere. An assimilation run with Max Planck Institute Earth System Model shows a robust response of summer temperature extremes in northern Europe and northeast Asia to North Atlantic sea surface temperature via a circumglobal Rossby wavetrain. When the North Atlantic is warm, warm summer temperature extremes occur with a probability of 20% and 24% in northern Europe and northeast Asia, respectively. In a cold North Atlantic phase, these probabilities are 0% and 8%. A similar difference in probability of occurrence is found in the initialized climate predictions. Consequently, the likelihood of a warm summer temperature extreme occurring in the examined regions in the next 10 years can be inferred from predictions of North Atlantic temperature.

Springtime Bark-Splitting of Acer pseudoplatanus in Germany

A large-scale regional event of springtime bark-splitting in Acer pseudoplatanus was observed in Germany in May 2018, where bark dissected from the wood. In young trees, an average of about 30% of the circumference was affected by cracks that were up to 8 m long. The damage occurred on the south-facing side of the trees after a warm period in March, followed by an extreme cold spell and warm temperatures. In this study, we investigated the possible causes of this damage. The damage occurred in the expanding xylem with cambial cells remaining in the bark. These cells-initiated growths of new, bark-based stems. The unprotected xylem was attacked by several fungi and wood-boring bark beetles. The mode of damage-recovery suggested that this event will eventually lead to a frost-scar-like structure in the future that will not totally heal, because the new stems attached to the old bark were in the process of forming new bark. Due to the increase in variation of springtime temperatures observed over the past 40 years, such damage may become more common in the future.

On the potential impact of a half-degree warming on cold and warm temperature extremes in mid-latitude North America

This study focuses on potential impacts of climate change on the early spring (March–April) temperature and its extremes in the mid-latitudes of North America, discriminated between the 1.5 °C and 2 °C levels of global warming, as projected by a suit of numerical experiments. The results suggest relatively mild seasonal average warming (0.25 °C–1.5 °C), but also an intensification of both warm and cold temperature extremes. The derived changes feature much stronger warming over the West of the United States and weak to no warming to the East, which is congruent with the ventilating effect of the intensified northerly wind over central Canada and the East of the United States. The intensified northerly component of the mid-latitude jet is likely a contributing factor to the derived increased seasonal variability of March–April temperatures over parts of Manitoba and Ontario in Canada, and the Midwest of the United States. The projected changes in temperature extremes agree to some extent with the previous studies: warm extremes intensify especially over southern mid-latitudes, while cold extremes are weakening over the north mid- to high-latitudes. However, high-resolution simulations with the Community Atmospheric Model 5 (CAM5) indicate much sharper spatial gradients, which translate into higher magnitudes and also more complex patterns of changes. Particularly, cold extremes feature not only reductions north of ∼45°N latitudes, but also a very strong intensification of cold extremes (by −4 °C for 20 year return values) in the band 25°–45°N, centered in the Midwest of the United States. While general warming and intensification of the warm extremes may accelerate the arrival of early spring, the intensifying cold extremes may increase the risk of early spring frost damage, and hence may yield a profound impact on the regional agriculture of North America. Combined with reliable information on expected temperature variability at interannual-to-decadal timescales, the background longer-term projections can help inform decision makers in the food security sector.

Corrigendum to “Spatio‐temporal Variability and Trends in Extreme Temperature Events in Finland over the Recent Decades: Influence of Northern Hemisphere Teleconnection Patterns”

Fifteen temperature indices recommended by the ETCCDI (Expert Team on Climate Change Detection and Indices) were applied to evaluate spatiotemporal variability and trends in annual intensity, frequency, and duration of extreme temperature statistics in Finland during 1961–2011. Statistically significant relationships between these high-resolution (10 km) temperature indices and seven influential Northern Hemisphere teleconnection patterns (NHTPs) for the interannual climate variability were also identified. During the study period (1961–2011), warming trends in extreme temperatures were generally manifested by statistically significant increases in cold temperature extremes rather than in the warm temperature extremes. As expected, warm days and nights became more frequent, while fewer cold days and nights occurred. The frequency of frost and icing days also decreased. Finland experienced more (less) frequent warm (cold) temperature extremes over the past few decades. Significant lengthening in warm spells was observed in Finland during 1961–2011, while no clear changes are found in cold spells. Interannual variations in the temperature indices were significantly associated with a number of NHTPs. In general, warm temperature extremes show significant correlations with the East Atlantic and the Scandinavia patterns and cold temperature extremes with the Arctic Oscillation and the North Atlantic Oscillation patterns.

Reduction in Northern Midlatitude 2-m Temperature Variability due to Arctic Sea Ice Loss

Abstract In this study, we investigate links between Arctic sea ice loss and the variability of 2-m temperatures over a 6-month period (November–April) over two domains centered over northern Eurasia and northern North America. Based on data from the Climate Forecast System Reanalysis (CFSR), there has been an increase (a decrease) in recent seasonal temperature variability over Eurasia (North America), which can be attributed to cooling (warming) during the winter months. Decreases in the intraseasonal variability of temperature anomalies, however, are noted in both regions for the November–April period. This study investigates the role of different forcings on the changes seen in the reanalysis product using Atmospheric Model Intercomparison Project simulations forced with repeating sea surface temperature, sea ice, and carbon dioxide concentration relative to climatologies from two different base periods, 1981–90 and 2005–14. The seasonal temperature and intraseasonal anomaly variabilities are examined, and we find that only the simulations with reduction in sea ice (2005–14 base-period sea ice concentration) produce significant decreases in intraseasonal temperature anomaly variability over these regions, agreeing with the CFSR analysis. Runs that reduce sea ice also result in a significant decrease in the frequency and magnitude of extreme warm and cold temperature anomalies. It is proposed that the weakened latitudinal temperature gradient, resulting from decreased sea ice, leads to reduced meridional temperature advection variability, which in turn contributes to the reduction in the variability of temperature anomalies.

ENSO Bimodality and Extremes

AbstractTropical sea surface temperature (SST) and winds vary on a wide range of timescales and have a substantial impact on weather and climate across the globe. Here we study the variability of SST and zonal wind during El Niño‐Southern Oscillation (ENSO) between 1982 and 2014. We focus on changes in extreme statistics using higher‐order moments of SST and zonal winds. We find that ENSO characteristics exhibit bimodal distributions and fat tails with extreme warm and cold temperatures in 1982–1999, but not during 2000–2014. The changes in the distributions coincide with changes in the intensity of ENSO events and the phase of the Interdecadal Pacific Oscillation. We also find that the strongest Easterly Wind Bursts occur during extreme El Niños and not during La Niñas. Maps of SST kurtosis can serve as a diagnostic for the thermocline feedback mechanism responsible for the differences in ENSO diversity between the two periods.

Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles

Abstract Extreme temperature events can have considerable negative impacts on sectors such as health, agriculture, and transportation. Observational evidence indicates the severity and frequency of warm extremes are increasing over much of the United States, but there are sizeable challenges both in estimating extreme temperature changes and in quantifying the relevant associated uncertainties. This study provides a simple statistical framework using a block maxima approach to analyze the representation of warm temperature extremes in several recent global climate model ensembles. Uncertainties due to structural model differences, grid resolution, and internal variability are characterized and discussed. Results show that models and ensembles differ greatly in the representation of extreme temperature over the United States, and variability in tail events is dependent on time and anthropogenic warming, which can influence estimates of return periods and distribution parameter estimates using generalized extreme value (GEV) distributions. These effects can considerably influence the uncertainty of model hindcasts and projections of extremes. Several idealized regional applications are highlighted for evaluating ensemble skill and trends, based on quantile analysis and root-mean-square errors in the overall sample and the upper tail. The results are relevant to regional climate assessments that use global model outputs and that are sensitive to extreme warm temperature. Accompanying this manuscript is a simple toolkit using the R statistical programming language for characterizing extreme events in gridded datasets.

Evaluation of synoptic‐scale patterns during extreme temperature and precipitation events in Alaska

Synoptic‐scale patterns associated with daily temperature and precipitation extremes in Alaska are identified and evaluated for daily variability in order to understand consistency in forcing mechanisms associated with extreme events as well as the tendency for each pattern to produce an extreme event. Daily station data at five locations for the 29‐year period from 1982 to 2010 are used. The widely recognized ClimDex indices are used to identify extreme high temperature, low temperature, and single‐day precipitation events. Pressure patterns during extreme events are evaluated seasonally for summer (JJA) and winter (DJF) at mean sea level pressure, 700 and 500‐hPa geopotential heights. Temperature extremes are largely characterized by synoptic‐scale patterns associated with substantial warm and cold air advection, and precipitation extremes are associated with moisture advection from the North Pacific. Extreme warm and cold temperature events are generally associated with more consistent daily synoptic‐scale patterns than precipitation. Considerable dissimilarities among daily synoptic‐scale patterns associated with extreme events based on location, type of extreme event and season highlight the need for careful consideration in the practice of applying synoptic‐scale forcing patterns to forecast or downscale extreme weather events in Alaska.

Spatial and temporal analysis of changes in temperature extremes in the non-monsoon region of China from 1961 to 2016

Changes in temperature extremes have far-reaching consequences for fragile ecological processes, hydrologic cycles, and human society in semiarid and arid regions. Therefore, detecting the spatial and temporal variations in temperature extremes and their driving mechanisms is crucial. Based on daily temperature records from 1961 to 2016 at 154 meteorological stations in the non-monsoon region of China, the spatial and temporal variations in 12 extreme temperature indices and the diurnal temperature range (DTR) were analyzed, and the association of these variations with atmospheric circulation patterns was also investigated. The major conclusions are as follows. (1) DTR and cold temperature extremes decreased significantly except for the coldest days (TXn) and coldest nights (TNn), which increased significantly, while all the warm temperature extremes significantly increased in the past 56 years; that is, all the temperature-based indices show patterns consistent with a general warming trend. (2) Cold extremes slowed during the global warming hiatus (1998–2016) compared with 1961–2016; however, the variation trends in the warm extremes did not change dramatically between the two periods. (3) Almost all temperature indices display the largest trend magnitudes in the cold-half year (autumn and winter), the variation trends in most cold extremes are significantly higher than those in warm extremes, and the warming rates of the nighttime indices are also significantly faster than those of the daytime indices. (4) Spatially, for most significant temperature indices, the stations located on the Tibetan Plateau show larger variation trends. (5) Both annual and seasonal Atlantic multidecadal oscillation (AMO) index are significantly correlated with almost all of the temperature indices, except for TXn; further, the summer and winter western Pacific subtropical high intensity (WPI) index, the summer and winter North Atlantic oscillation (NAO) index, and the winter Arctic oscillation (AO) index display significant relationships with most of the temperature extremes, which indicates that the large atmospheric circulation patterns play significant roles in the changing temperature extremes in this region. Overall, the results provide scientific references for understanding and predicting the spatial and temporal trends of extreme climate in the future.

Extreme winter warm event causes exceptionally early bud break for many woody species

AbstractIn many ecosystems, climate is changing faster during winter compared to other seasons. However, we lack basic information about the responses of many species to winter climate change, including extreme warm events. Extreme warm events may have particularly strong influences at the end of winter, when some species begin to break dormancy while the risk of freezing remains high. Here, we monitored bud burst of 101 temperate woody species following an extreme warm event during winter to investigate species responses to this anomalous event and determine whether functional traits predicted species responses. Following six consecutive days of extreme warm temperatures in winter, nearly half the surveyed tree and shrub species had an advanced stage of bud phenology. Responding species were most likely to be shade‐intolerant, phylogenetically related, and have weaker dormancy requirements. Based on established species response thresholds to spring temperatures in the region, species were expected to be unresponsive to warm temperatures this early in the year, yet many species broke dormancy. Species responses to this extreme winter warm event highlighted how climate change can alter well‐established species–climate associations. In an era of increasing climate change creating novel winter conditions, continued monitoring both long‐term and following extreme events is needed to understand new species–climate dynamics.

Vegetation Dynamics and Diverse Responses to Extreme Climate Events in Different Vegetation Types of Inner Mongolia

As the global climate has changed, studies on the relationship between vegetation and climate have become crucial. We analyzed the long-term vegetation dynamics and diverse responses to extreme climate changes in Inner Mongolia, based on long-term Global Inventory Monitoring and Modelling Studies (GIMMS) NDVI3g datasets, as well as the eight extreme precipitation indices and six extreme temperature indices that are highly correlated with the occurrence of droughts or floods, heat or cold temperature disasters, and vegetation growth in Inner Mongolia. These datasets were analyzed using linear regression analysis, the Hurst exponent index, residual analysis, and the Pearson correlation analysis. The results showed the following: (1) The vegetation dynamical changes exhibited trends of improvement during 1982 to 2015, and 68% of the vegetation growth changes in Inner Mongolia can be explained by climate changes. (2) The extreme precipitation indices exhibited a slight change, except for the annual total wet–day precipitation (PRCPTOT). The occurrence of extreme cold temperatures showed a significant decline, while the occurrence of extreme warm temperatures showed an upward trend in Inner Mongolia. (3) The typical steppe, desert steppe, and forest steppe regions are more sensitive to extreme large precipitation, and the forest regions are more sensitive to extreme warm temperatures. (4) Extreme precipitation exhibits a one-month lagged effect on vegetation that is larger than the same-month effects on the grassland system. Extreme temperature exhibits same-month effects on vegetation, which are larger than the one-month lagged effects on the forest system. Therefore, studies of the relationship between extreme climate indices and vegetation are important for performing risk assessments of droughts, floods, and other related natural disasters.

Environmental extremes drive population structure at the northern range limit of Atlantic salmon in North America.

Conservation of exploited species requires an understanding of both genetic diversity and the dominant structuring forces, particularly near range limits, where climatic variation can drive rapid expansions or contractions of geographic range. Here, we examine population structure and landscape associations in Atlantic salmon (Salmo salar) across a heterogeneous landscape near the northern range limit in Labrador, Canada. Analysis of two amplicon-based data sets containing 101 microsatellites and 376 single nucleotide polymorphisms (SNPs) from 35 locations revealed clear differentiation between populations spawning in rivers flowing into a large marine embayment (Lake Melville) compared to coastal populations. The mechanisms influencing the differentiation of embayment populations were investigated using both multivariate and machine-learning landscape genetic approaches. We identified temperature as the strongest correlate with genetic structure, particularly warm temperature extremes and wider annual temperature ranges. The genomic basis of this divergence was further explored using a subset of locations (n=17) and a 220K SNP array. SNPs associated with spatial structuring and temperature mapped to a diverse set of genes and molecular pathways, including regulation of gene expression, immune response, and cell development and differentiation. The results spanning molecular marker types and both novel and established methods clearly show climate-associated, fine-scale population structure across an environmental gradient in Atlantic salmon near its range limit in North America, highlighting valuable approaches for predicting population responses to climate change and managing species sustainability.

Changes in daily maximum temperature extremes across India over 1951–2014 and their relation with cereal crop productivity

This study used gridded daily maximum temperature data (1° × 1°) for 1951–2014 period to analyze the trend in monthly extreme warm days (ExWD) and changes in its probability distribution in each grid. It also analyzed the trend in spatial spread of annual ExWD over the study period at four exceedance levels and further related the number of ExWDs with cereal crop productivity of India. Extreme warm days have increased throughout India but were statistically significant in 42% grids. The increase was consistent over all the months in north-eastern region, southern plateau and both the coastal plains. It also increased significantly over north-western and central India during April to June summer period. The probability distribution of ExWD also changed significantly in many grids, especially in southern plateau and both the coastal plains. The changes indicated increased frequency in the existing levels of extremes and new occurrences of higher frequency of extremes. The analysis of land area affected by different levels of extremes indicated significant increase, with the rate being highest for higher extremes. In terms of extreme warm day temperatures, the study identified southern plateau, east and west coast plains, and north-eastern India as highly vulnerable. Using copula probability model, study showed that increase in ExWD from 20 to 60% may increase the probability of 5% or more yield loss from 17 to 53% for Kharif cereals, 11 to 43% for Rabi cereals and 19 to 63% for wheat crop. The results may be used for devising zone specific adaptation strategies.

Spatiotemporal Variability and Trends in Extreme Temperature Events in Finland over the Recent Decades: Influence of Northern Hemisphere Teleconnection Patterns

Fifteen temperature indices recommended by the ETCCDI (Expert Team on Climate Change Detection and Indices) were applied to evaluate spatiotemporal variability and trends in annual intensity, frequency, and duration of extreme temperature statistics in Finland during 1961–2011. Statistically significant relationships between these high-resolution (10 km) temperature indices and seven influential Northern Hemisphere teleconnection patterns (NHTPs) for the interannual climate variability were also identified. During the study period (1961–2011), warming trends in extreme temperatures were generally manifested by statistically significant increases in cold temperature extremes rather than in the warm temperature extremes. As expected, warm days and nights became more frequent, while fewer cold days and nights occurred. The frequency of frost and icing days also decreased. Finland experienced more (less) frequent warm (cold) temperature extremes over the past few decades. Interestingly, significant lengthening in cold spells was observed over the upper part of northern Finland, while no clear changes are found in warm spells. Interannual variations in the temperature indices were significantly associated with a number of NHTPs. In general, warm temperature extremes show significant correlations with the East Atlantic and the Scandinavia patterns and cold temperature extremes with the Arctic Oscillation and the North Atlantic Oscillation patterns.

Understanding the role of sea surface temperature-forcing for variability in global temperature and precipitation extremes

The oceans are a well-known source of natural variability in the climate system, although their ability to account for inter-annual variations of temperature and precipitation extremes over land remains unclear. In this study, the role of sea-surface temperature (SST)-forcing is investigated for variability and trends in a range of commonly used temperature and precipitation extreme indices over the period 1959 to 2013. Using atmospheric simulations forced by observed SST and sea-ice concentrations (SIC) from three models participating in the Climate of the Twentieth Century Plus (C20C+) Project, results show that oceanic boundary conditions drive a substantial fraction of inter-annual variability in global average temperature extreme indices, as well as, to a lower extent, for precipitation extremes. The observed trends in temperature extremes are generally well captured by the SST-forced simulations although some regional features such as the lack of warming in daytime warm temperature extremes over South America are not reproduced in the model simulations. Furthermore, the models simulate too strong increases in warm day frequency compared to observations over North America. For extreme precipitation trends, the accuracy of the simulated trend pattern is regionally variable, and a thorough assessment is difficult due to the lack of locally significant trends in the observations. This study shows that prescribing SST and SIC holds potential predictability for extremes in some (mainly tropical) regions at the inter-annual time-scale.

Forest loss and Borneo’s climate

The equatorial island of Borneo is a deforestation hotspot. However, the influence of forest loss on the island’s climate remains largely unexplored. Here, we examine how forest loss is related to changes in ground-based records of temperature (1961–2007) and precipitation (1951–2007), and MODIS data for temperature (2002–2016). Analyses were performed for the entire island, lowland areas (<200 m ASL), and nine selected watersheds. We found a strong island-wide relationship between forest loss and increases in daily temperature and reductions in daily precipitation. The relationship between deforestation and changes in local climate was most pronounced for watersheds in southeast Borneo, which have lost 40%–75% of their forests since 1973. These watersheds also had a significantly higher frequency of temperatures above 31 °C. Watersheds in north and northwest Borneo, which have lost 5%–25% of their forest cover, maintained a more stable climate with a similar distribution of mean and extreme warm temperatures between forest and modified forest areas. Watersheds with >15% forest loss had a >15% reduction in rainfall. We conclude that loss of forest in Borneo has increased local daily temperatures and temperature extremes, and reduced daily precipitation.

High‐Resolution Dynamical Downscaling Ensemble Projections of Future Extreme Temperature Distributions for the United States

AbstractThe aim of this study is to examine projections of extreme temperatures over the continental United States (CONUS) for the 21st century using an ensemble of high spatial resolution dynamically downscaled model simulations with different boundary conditions. The downscaling uses the Weather Research and Forecast model at a spatial resolution of 12 km along with outputs from three different Coupled Model Intercomparison Project Phase 5 global climate models that provide boundary conditions under two different future greenhouse gas (GHG) concentration trajectories. The results from two decadal‐length time slices (2045–2054 and 2085–2094) are compared with a historical decade (1995–2004). Probability density functions of daily maximum/minimum temperatures are analyzed over seven climatologically cohesive regions of the CONUS. The impacts of different boundary conditions as well as future GHG concentrations on extreme events such as heat waves and days with temperature higher than 95°F are also investigated. The results show that the intensity of extreme warm temperature in future summer is significantly increased, while the frequency of extreme cold temperature in future winter decreases. The distribution of summer daily maximum temperature experiences a significant warm‐side shift and increased variability, while the distribution of winter daily minimum temperature is projected to have a less significant warm‐side shift with decreased variability. Using “business‐as‐usual” scenario, 5‐day heat waves are projected to occur at least 5–10 times per year in most CONUS and ≥95°F days will increase by 1–2 months by the end of the century.

Nonstationary extreme value analysis of temperature extremes in China

In a changing climate, the common assumption of stationarity of climate extremes has been increasingly challenged, raising the need to incorporate non-stationarity in extreme value modeling. In this study, quantile regression is used to identify the trends of annual temperature extremes and their correlations with two large climate patterns, the western Pacific subtropical high (WPSH) and the Arctic Oscillation (AO) at 357 stations in China. Statistical significant positive trends and correlations between warm (or cold) temperature extremes and WPSH (or AO) have been detected at most stations. The influence of WPSH on warm extremes is significant in southern China, while the AO mainly affects the cold extremes in northern and eastern China. Then, annual temperature extremes are fitted to generalized extreme value (GEV) distributions with time-varying parameters. The summer (or winter) mean daily maximum (or minimum) temperatures and two climate indices, the WPSH index and the AO index, are chosen as covariates. In total, 16 candidate GEV distribution models are constructed, and the best fitting model with the lowest Bayesian information criterion (BIC) is selected. The 20-year return levels of annual warm (or cold) extremes in the period 1961–1980 and 1991–2010 are computed and compared. The changes of 20-year return levels of annual warm and cold extremes are jointly determined by trend and distributional changes of annual temperature extremes. Analysis of large scale atmospheric circulation changes indicate that a strengthening anticyclonic circulation and increasing geopotential height in recent decades may have contributed to the changes in temperature extremes in China.