Daily Maximum Temperature Data Research Articles

Spatiotemporal analysis of compound droughts and heat waves in the Horn of Africa

AbstractClimate change has increased the frequency and severity of weather extremes, including droughts, heat waves and compound drought and heatwave (CDHW) events. CDHW events profoundly impact water availability, agriculture, public health and energy production, particularly in the Horn of Africa (HOA). This study examined the historical spatiotemporal patterns of CDHW periods in the HOA during three periods (1951–1980, 1971–2000 and 1991–2020) using the ERA5 reanalysis dataset. This study utilized monthly Standardized Precipitation Evapotranspiration Index (SPEI) data to detect droughts and daily maximum temperature data to identify heatwaves for characterizing the duration, severity and magnitude of CDHW events. The results show a substantial increase in the duration of CDHW events in recent years, with durations reaching up to 25 days. The average duration of heat waves also increased from 7 days before 1993 to 18 days by 2011, culminating in a record‐breaking 43‐day heat wave in 2019. Most areas experienced a significant increase in heatwave duration by more than 12 days from the early period of 1951–1980 to the late period of 1991–2020. Although around 76% and 69% of the HOA exhibited insignificant heatwave trends in the first two periods, Ethiopia and Kenya experienced substantial increases of more than 18 days during the most recent period, with some durations exceeding 25 days in recent decades. The magnitude of CDHW events generally decreased as drought duration intensified, but specific areas, particularly southwest Kenya and Eritrea, exhibited higher CDHW values in the last period. These findings underscore the urgent need to understand and address CDHW events in the HOA. Targeted interventions for disaster risk reduction and resilience‐building are needed to mitigate the adverse effects of these events in this vulnerable region. This study provides a basis for future research and policy formulation in the HOA.

A global attribution analysis of increasing risk of successive runoff-heat extreme events driven by anthropogenic forcing

The successive runoff-heat extreme (SRHE) events, defined as the occurrence of an extreme runoff event followed by a heatwave event, have become more frequent under recent global warming. However, the impact of anthropogenic climate change (ACC) on global changes in SRHE characteristics during the past decades remains unclear. Here, we evaluate the impact of ACC on SRHE frequency and characteristics during 1950–2014, based on the daily maximum and minimum temperature and total runoff data simulated by five global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under the “anthropogenic and natural both (ALL)” and “natural only (NAT)” experiments. Attribution of SRHE changes with different durations and intervals between extreme runoff and heatwave (IRHs) is achieved by using the fraction attributable risk (FAR) method. Results indicate that ACC exacerbates SRHE occurrence for most global regions, especially in the Southern Hemisphere (> 60 % increase in probability), but the spatial variability of this impact decreases over time. The globally-average probability of SRHE occurrence due to ACC has increased significantly by 40 % during 1971–2014 (∼10 %/decade), and the probability of SRHE with the short IRHs and long duration tends to be larger than other types of SRHE. ACC also prolongs the duration of SRHE in most global regions, with an increase of 1–4.5 folds in northern South America, central Africa, and southeastern Asia during 1950–2014. Partial correlation analysis indicates that the intensification of heatwave due to ACC contributes more than that of extreme runoff to SRHE occurrence in nearly all global regions, particularly in the Southern Hemisphere.

Using SARIMA Modeling and Forecasting of Metrological Parameters: A Conceptual Framework

Objective: The current study aims to predict the metrological factor of temperature in the region of Karachi Methodology: using the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model. Daily maximum and minimum temperature data from the region from 1st January, 2012 to 31st December, 2022 using as training data for the model. These datasets are clean and modify to obtain monthly averages of the maximum and minimum temperature data for the region. These datasets using for the model development. The two datasets pass through time series analysis separately and best fitted models are developing for both. Finding: The study shows the presence of seasonality in the temperature data alongside the presence of a growing mean in the minimum and maximum temperature dataset, signifying global warming over the past decade. Implications: These results will help in future studies regarding the variations in temperature in Karachi and in developing strategies to accommodate for such variations in the region

Spatial modeling of extreme temperature in the Canadian Prairies using max-stable processes

The Prairie region of Canada is an agriculture-intensive area and is strategic to Canada's food security. Moisture deficit due to extreme temperatures leading to high evapotranspiration can have significant impacts on water availability, resulting in poor crop yield. The primary objective of this study is to quantify the spatial structure and dependency of extreme temperatures by employing Max-Stable Process (MSP) modeling on daily annual maximum temperature data spanning 1970–2020. The spatial trend surface of the marginal parameters of the Spatial Generalized Extreme Value (SGEV) shows that geographical coordinates, topography due to the Rocky Mountains, and proximity (Euclidean distance) to Hudson Bay are important covariates in capturing the spatial trend of extreme temperatures. Furthermore, through the SGEV, important products such as the point-wise return periods and levels were derived using the best selected model as determined by Takeuchi's information criteria. The return levels show that the southern portion of the Canadian Prairies shows a consistent increase in extreme temperature for all the return periods. The results show that all the return periods mentioned above have extreme temperatures exceeding 37 °C in the southern portion of the Canadian Prairies. An unconditional simulation using the fitted MSP model provided various realizations of temperature extremes. The results from this study provide important insights into extreme temperatures in this important region, where water resources management is crucial. This study will be beneficial to the hydrologists, water resource specialists, climate change scientists and policy makers involved in the monitoring of extreme events in the Canadian Prairies.

Variations of Extreme Temperature Event Indices in Six Temperature Zones in China from 1961 to 2020

In this study, eight extreme temperature event indices were calculated based on daily maximum, minimum, and mean temperature data recorded at 699 National Reference Stations in China during 1961–2020. The yearly change of mean temperature and the magnitude, frequency, and duration of extreme temperature events in six temperature zones were evaluated. All temperature zones had a trend of an increase in mean temperature (rate: 2.1–3.3 °C per 10 years), and the warming was more significant in the warm temperate zone and the Qinghai–Tibet Plateau zone (QPZ). For extreme temperature events, the extreme maximum and minimum temperatures in most temperature zones showed significant trends of increase, and the rates of increase were greater in the northern zones and QPZ. The rate of increase in extreme minimum temperature was substantially (up to three times) higher than the rate of increase in extreme maximum temperature in the same temperature zone; however, the finding was the opposite for the cold temperate zone (CTZ), which is the northernmost region of China. The rate of increase in extreme maximum temperatures was the greatest (0.35 °C per 10 years), whereas the rate of increase in extreme minimum temperatures was the smallest (0.17 °C per 10 years). The number of warm days/nights and the warm spell duration index also showed significant trends of increase that were most obvious in the southern zones and QPZ. In the tropical zone (TZ), which is the southernmost part of mainland China, the number of warm nights was only 15.3 days in 1961–1970, whereas it increased to 61.9 days in 2011–2020 (an increase of 303.9%). The rate of increase in warm nights in TZ (8.8 days per 10 years) was four times that in CTZ (2.2 days per 10 years). The number of cold days/nights and the cold spell duration index showed significant trends of decrease, with the greatest rates of reduction in QPZ and TZ. In evaluating the frequency of extreme temperature events, the amplitude of warming of the night index was found to be greater than that of the day index. In evaluating the duration of extreme temperature events, the variation of the cold index was found to be greater than that of the warm index. The notable asymmetries found in the variations of the minimum/maximum temperatures, day/night indices, and cold/warm spell durations in China are direct manifestations of global warming.

Deep learning for daily potential evapotranspiration using a HS-LSTM approach

Accurate estimation of potential evapotranspiration (ET0) is important for the sound design of irrigation schedules, management of water resources, assessment of hydrological drought, and research on atmospheric variations. The present study proposed a novel deep learning (DL) approach for daily ET0 estimations with limited daily climate data: HS- LSTM. This approach was constructed based on a classic ET0 model and a long short-term memory neural network (LSTM). Specifically, the Hargreaves-Samani (HS) model was employed as the classic model, and the predictors were restricted to the daily maximum and minimum air temperature data. Ground truth data for ET0 were employed to train, validate, and test the models. Traditional machine learning (ML) algorithms comprising adaptive neuro-fuzzy inference system (ANFIS), genetic programming (GP), multi-gene genetic programming (MGGP), and one-dimensional CNN (1D-CNN), as well as the HS-ML models (HS-ANFIS, HS-GP, HS-MGGP, HS-1D-CNN), were also established and assessed for daily ET0 estimations. Compared to the other tested approaches, the errors of the HS-LSTM technique significantly decreased, demonstrating that the novel HS-LSTM approach significantly outperformed the other techniques beyond the study area (in Songliao Basin, Northeast China, which is a semi-humid zone with temperate continental climate). The developed models can then be used to estimate future ET0 with only air temperature forecasts, which can be readily obtained from public weather forecasts. The present study provides a new and promising strategy that can provide more accurate estimations of daily ET0 with limited meteorological data, along with significant implications for enhancing atmospheric research.

Extreme Heat Events over Southeast Europe Based on NEX-GDDP Ensemble: Present Climate Evaluation and Future Projections

Southeast Europe is considered one of the most vulnerable regions in the context of climate change, and projected future summer warming is expected to exceed global rates significantly. Despite the importance of this problem, there have been few studies that utilized Coupled Model Intercomparison Project Phase 5 (CMIP5) Global Circulation Models (GCMs) and the multi-model ensemble approach to examine extreme heat events on a regional scale. Additionally, the NEX-GDDP dataset, successfully applied in other parts of the world to assess extreme heat, has not yet been utilized for Southeast Europe. This study aims to fill that gap, presenting the time evolution and spatial distribution of extreme heat events in Southeast Europe for the historical period 1950–2005 and for the expected future climate up to the end of the 21st century based on the NEX-GDDP dataset. In order to leverage the strengths of the multi-model ensemble approach, a set of purpose-tailored indicators, such as the annual number of hot days, the maximum number of consecutive hot days, and hot spell duration at different thresholds, is computed by the daily maximum temperature data from all datasets, produced by NEX-GDDP (21 for the historical period and 42 for the future period). The E-OBS dataset is used as a reference for evaluating the NEX-GDDP’s capability to simulate the features of the observed historical extreme heat events. The results show that the multi-model ensemble can satisfactorily capture the occurrence of extreme heat events in the historical period, and therefore it is reasonable to assume that the NEX-GDDP dataset has the potential to reproduce such extremes in the projected future. The study provides clear evidence that the persistence and spatial extent of extreme heat will increase significantly. Some indicators that were not relevant for the historical period due to the high-temperature threshold will become helpful in assessing extreme heat in Southeast Europe in the latter part of the century. Thus, under the RCP8.5 scenario, the area-averaged duration of hot spells at 32 °C and 34 °C will increase from near zero in 1976–2005 to 60 and 45 days, respectively, by the end of the century. The indicators used in the study may be helpful for decision-makers to implement climate change mitigation strategies and actions adequately. The findings are consistent with general tendencies in maximum temperatures considered in our previous works but also with the outcomes of recent studies dedicated to the future climate of the region.

Examining the Relationship Between Extreme Temperature, Microclimate Indicators, and Gestational Diabetes Mellitus in Pregnant Women Living in Southern California.

We utilized 2008-2018 data for pregnant women from Kaiser Permanente Southern California electronic health records. GDM screening occurred between 24 and 28 gestational weeks for most women using the Carpenter-Coustan criteria or the International Association of Diabetes and Pregnancy Study Groups criteria. Daily maximum, minimum, and mean temperature data were linked to participants' residential address. We utilized distributed lag models, which assessed the lag from the first to the corresponding week, with logistic regression models to examine the exposure-lag-response associations between the 12 weekly extreme temperature exposures and GDM risk. We used the relative risk due to interaction (RERI) to estimate the additive modification of microclimate indicators on the relation between extreme temperature and GDM risk. GDM risks increased with extreme low temperature during gestational weeks 20--24 and with extreme high temperature at weeks 11-16. Microclimate indicators modified the influence of extreme temperatures on GDM risk. For example, there were positive RERIs for high-temperature extremes and less greenness, and a negative RERI for low-temperature extremes and increased impervious surface percentage. Susceptibility windows to extreme temperatures during pregnancy were observed. Modifiable microclimate indicators were identified that may attenuate temperature exposures during these windows, which could in turn reduce the health burden from GDM.

The unprecedented spatial extent and intensity of the 2021 summer extreme heatwave event over the Western North American regions

A record-breaking heatwave event occurred in western North America in the summer of 2021. Previous studies on a similar event's uniqueness and return period are typically estimated based on extreme value analysis and statistics of selected stations or area-mean temperature data. The extreme statistics results are no longer valid if moved to another location. Further, the detailed evolution of spatial extent and intensity through its duration is not quantified and compared to other historical heatwave events to claim its rarity and severity. By applying an event detection and tracking algorithm to heatwaves, we first construct an archive for all the events over the region since 1979 based on daily maximum temperature data from reanalysis. It documented the daily mean temperature anomalies, affected areas, and durations of all individual heatwave events. It is, therefore, possible to objectively compare and rank the magnitudes of all historical heatwave events by examining the total integrated effects from the intensity, impact region, and duration. With the global mean warming background increase of about 0.4 °C from 1979 to 2000 to 2000–2021, we found that the mean integrated magnitude and size of the top 100 heatwave events increased significantly by 155 ± 88%. The dominant relative contributions to this integrated severity measure increase are +78 ± 39% from the mean affected area, followed by +54 ± 21% from duration and +7 ± 4% from the intensity. With the above analyzed general historical increasing trend of heatwave event metrics, the severity of the 2021 summer heatwave over the region was due to a combination of its record-breaking event-mean spatial coverages (2090383 km2), highest normalized daily maximum temperature anomalies and long duration (11 days, in the top 4% of all heatwave events). This integrated severity measure of the event was more than double of any other historical heatwave event and about 17 times larger than the mean severity of all heatwave events over the region.

Meteorological Pre-warning Grade of High Temperature During Flowering Stage for Summer Maize in North China Plain

High temperatures during flowering stage affects the seed setting in maize and thereby result in significant yield penalties in recent changing climate era. Based on the daily maximum temperature data of the maize growing season from 1971 to 2019 at the surface meteorological observatory in Henan Province of China and maize observation data at the agricultural meteorological observatory, an early warning grade of high temperatures during the summer maize flowering stage was constructed. The daily maximum temperature of the summer maize flowering stage was ≥ 32 °C as the critical threshold of high temperature disaster, and ≥ 35 °C as the threshold of high temperature disaster. The number of high temperature days (HD) ≥ 32 °C and ≥ 35 °C during 10 days of the summer maize flowering stage were counted. The sequence of high temperature days of summer maize flowering stage was constructed and its normal distribution characteristics were verified. The quartile value of the normal distribution sequence was selected as the threshold value of high temperature in different grades, and the high temperature heat damage in the summer maize flowering stage was divided into three early warning meteorological grades. The index verification of the early warning meteorological grade was carried out by using the observation data of kernel number per ear in the agricultural meteorological station for maize. The results showed that the three grade indexes corresponding to the number of high temperature days with daily maximum temperature ≥ 32 °C were: 3 ≤ HD < 5 (Grade I), 5 ≤ HD < 7 (Grade II), and HD ≥ 7 (Grade III) and the three grade indexes corresponding to high temperature days ≥ 35 °C were: 2 ≤ HD < 3 (Grade I), 3 ≤ HD < 5 (Grade II), HD ≥ 5 (Grade III). The constructed indexes were verified as follows, the number of high temperature days ≥ 32 °C at about 90% stations was significantly negatively correlated with the number of maize grains per ear, and 74% stations which the number of high temperature days ≥ 35 °C was significantly negatively correlated with the number of maize grains per ear. Applying this index to evaluate the high temperature risk in the flowering period, we can indicate that the high temperature risk had trend of increasing gradually from north to south. In conclusion, the constructed high temperature warning meteorological grade index during the flowering stage could accurately reflect the damage grade of maize during the flowering stage.

Spatial and temporal variations of extreme climate index in the Songhua River Basin during 1961-2020.

Understanding climate change and extreme climate is of great significance for ensuring food security and socio-economic development of the Songhua River Basin. Based on the daily precipitation, maximum temperature and minimum temperature data during 1961-2020 from 69 meteorological stations in and around the Songhua River Basin, we analyzed the temporal and spatial variations of extreme temperature and precipitation in the Songhua River Basin using 27 extreme climate indices recommended by the World Meteorological Organization, and linear trend method, Mann-Kendall trend test and ordinary Kriging interpolation methodology. The results showed that, from 1961 to 2020, except for cold speel duration, the extreme cold index in the study area showed a downward trend, while the extreme warm index, extreme value index and other temperature indices showed an upward trend. The increasing trend of the minimum temperature was greater than that of the maximum temperature. Icing days, cold speel duration and warm speel duration showed an increasing trend from south to north, while the minimum value of maximum temperature and that of minimum temperature showed opposite spatial characteristics. The high value areas of summer days and tropical nights were mainly distributed in the southwestern region, while there was no obvious spatial variations of cool days, warm nights, and warm days. Overall, except for cold speel duration, other extreme cold indices had a rapid decreasing trend in the north and west of the Songhua River Basin. In the warm index, summer days, warm nights, warm days, and warm speel duration had a rapid upward trend in the north and west, and tropical nights had the fastest rise in the southwest. In the extreme value index, the maximum of temperature rose fastest in the northwest, while the minimum rose fastest in the northeast. Except for consecutive dry days, the rest of precipitation indices showed an increasing trend, and the fastest rising areas were mainly in the north-central part of the Nenjiang River Basin, while some areas in the south of the Nenjiang River Basin became dry. Heavy precipitation days, very heavy precipitation days, heaviest precipitation days, consecutive wet days, very wet day precipitation, extremely wet day precipitation, and annual precipitation showed a gradual decreasing pattern from southeast to northwest. Overall, the Songhua River Basin was warming and wetting, but there were some differences among different regions, especially the northern and southern parts of the Nenjiang River Basin.

Heat-related mortality in U.S. state and private prisons: A case-crossover analysis.

Rising temperatures and heatwaves increase mortality. Many of the subpopulations most vulnerable to heat-related mortality are in prisons, facilities that may exacerbate temperature exposures. Yet, there is scare literature on the impacts of heat among incarcerated populations. We analyzed data on mortality in U.S. state and private prisons from 2001-2019 linked to daily maximum temperature data for the months of June, July, and August. Using a case-crossover approach and distributed lag models, we estimated the association of increasing temperatures with total mortality, heart disease-related mortality, and suicides. We also examined the association with extreme heat and heatwaves (days above the 90th percentile for the prison location) and assessed effect modification by personal, facility, and regional characteristics. There were 12,836 deaths during summer months. The majority were male (96%) and housed in a state-operated prison (97%). A 10°F increase was associated with a 5.2% (95% CI: 1.5%, 9.0%) increase in total mortality and a 6.7% (95% CI: -0.6%, 14.0%) increase in heart disease mortality. The association between temperature and suicides was delayed, peaking around lag 3 (exposure at three days prior death). Two- and three-day heatwaves were associated with increased total mortality of 5.5% (95% CI: 0.3%, 10.9%) and 7.4% (95% CI: 1.6%, 13.5%), respectively. The cumulative effect (lags 1-3) of an extreme heat day was associated with a 22.8% (95% CI: 3.3%, 46.0%) increase in suicides. We found the greatest increase in mortality among people ≥ 65 years old, incarcerated less than one year, held in the Northeast region, and in urban or rural counties. These findings suggest that warm temperatures are associated with increased mortality in prisons, yet this vulnerable population's risk has largely been overlooked.

Future changes in extreme heatwaves in terms of intensity and duration over the CORDEX-East Asia Phase Two domain using multi-GCM and multi-RCM chains

An extreme heatwave, in terms of intensity and duration, is projected to occur at the end of the 21st century (2071–2100) over the whole of East Asia. The projection is calculated using daily maximum temperature data of 25 km horizontal resolution produced by 12 general circulation model-regional climate model chains participating in the CORDEX-East Asia Phase 2 project. An ‘extreme’ heatwave is defined as one in which the heatwave magnitude (HWM), which is the accumulated daily intensity of a heatwave during the heatwave period, is higher than the 95th percentile of the HWM for the reference period (1981–2005). In historical simulations, heatwaves have occurred mainly from April to June in India, in April and May in Indochina, from June to August in China and Mongolia, and in July and August in the Korean Peninsula and Japan; most heatwaves last three to four days. In India and Indochina, long-lasting and intense heatwaves occur more often than in other regions. In future, heatwave intensity will increase, the average duration of heatwaves will be approximately two to three weeks, and the heatwave season will be lengthened. Therefore, extreme heatwaves will occur more frequently and strongly. Under two representative concentration pathway scenarios (RCP2.6 and RCP8.5) and two shared socioeconomic pathway scenarios (SSP1-2.6 and SSP5-8.5), the proportion of extreme heatwaves to all heatwave events will increase from 5.0% (historical) to 8.0%, 20.8%, 19.3%, and 36.3%, and the HWM of the extreme heatwave will be 1.4, 3.5, 3.0, and 9.0 times stronger, respectively. The main reason for the increase in the HWM of extreme heatwaves is the increased duration rather than the daily intensity of the heatwaves. In East Asia, the temporal and regional disparities of heatwave damage will be much more prominent as extreme heatwaves become stronger and more frequent in these regions and during the periods that are more affected by heatwaves in the present day.

The interannual variation of the first regional extreme hot events in southeastern China and the possible mechanism

Have the onset dates of regional extreme hot events threatening human health been advancing? Based on the daily maximum temperature data from Chinese metrological stations and reanalysis datasets, we quantified the variations in the onset dates of the first regional extreme hot events (EHE-ODs) and revealed the early-onset characteristics of the regional EHEs in southeastern China. The results show that the EHE-ODs in southeastern China have a significant early-onset trend with an average advanced rate of 7.1 days/decade since 1987. The rate in the middle and lower reaches of the Yangtze River and South China exceeds 10.3 days/decade. The interannual variation in the sea ice over the Barents Sea (SICBS) in March and the snow depth over western Eurasia (SDWEA) in April are closely related to the first regional EHE-ODs in southeastern China, which both provide favorable atmospheric circulation patterns and significant descending motion for the occurrence of regional EHEs. The pathway of the physical process originates from the northern Eurasia propagating southeastward, and spreads to southeastern China, which can enhance the anticyclonic anomaly in southeastern China and favor the occurrence of regional EHEs in southeastern China in the early summer. Therefore, the SICBS in March might be a critical potential predictor for the onset of the regional EHEs in southeastern China.

Cold waves in Terai region of Nepal and farmer’s perception of the effect of fog events and cold waves on agriculture

Cold waves are considered one of the important extreme weather events affecting winter crop production in the Indo-Gangetic Plain (IGP). In spite of media coverage of extreme cold events in the Terai area of Nepal (Nepal section of IGP) in recent years, few studies on this topic were found. This study investigates cold waves and their impact on agriculture during winter in the Terai region of Nepal. Historical daily maximum and minimum temperature data from six stations in the Terai (Dhangadhi, Nepalgunj, Bhairahawa, Simara, Janakpur, and Biratnagar) during 1971–2015 were analyzed to study the occurrence of cold days, cold nights, extreme cold days, extreme cold nights, cold wave days, and extreme cold wave days in the Terai. The average number of cold days per annum ranges from 15.6 to 17.9 days and the extreme cold days per annum ranges from 3.2 to 3.6 days in the Terai. Except for Nepalgunj, all the Terai stations show statistically significant increasing trends in the frequency of cold days and extreme cold days over the last four decades. Similarly, the average number of cold wave days varies from 9.2 to 13.8 per annum and the average number of extreme cold wave days varies from 1.4 to 3.8 days in the Terai region of Nepal. By comparing the co-occurrence of foggy days and cold and extreme cold wave days at Biratnagar, Simara, Bhairahawa, and Nepalgunj airport, it is also observed that most of the cold and extreme cold wave days are also foggy days. The perception of farmers regarding the effect of fog and cold wave events was explored through focus group discussions at Dhanusha and Sunsari districts of Nepal and found that the fog and cold events have significantly affected their winter crops, livestock, and their day-to-day life.

The fast‐changing climate reality of Arequipa, Peru

AbstractDespite the availability of global and continental climate datasets and climate change information, locally relevant quantification of historic trends in climate variables is still lacking in developing countries, especially at local scales. This is particularly true in the Department of Arequipa, Peru. An arid region with a booming population, substantial mining activities, and large irrigated agriculture, which is highly susceptible to climate change. This study aims to evaluate climate trends from 1988 to 2017 in the Arequipa Department and provide information that can facilitate stakeholders' adaptation to the rapid‐changing climate. The daily precipitation (Prec), and maximum (Tmax) and minimum (Tmin) daily air temperature data used in this study came from the Servicio Nacional de Meteorología e Hidrología del Perú (SENAMHI) and the National Ocean and Atmospheric Administration's (NOAA) Global Summary of the Day (GSOD). Data passed through a quality checking process for removal of implausible data, data gap filling, and inhomogeneity detection. The Mann–Kendall test, at a significance level of 0.10, was used to determine trends and the Theil–Sen slope (Sen's slope) was used to estimate the magnitude of the change. Sen's slope was also calculated spatially, using the gridded Arequipa Climate Maps (ACM) dataset. Results indicate that precipitation seasonality has been increasing, as the observed increase in annual precipitation is happening mostly in the rainy season (December–March) and the start and end of the rainy season are delayed. Positive temperature trends were dominant in the whole region. Tmin is increasing more than Tmax, especially at higher altitudes. Exceptions to increasing temperatures were found in areas influenced by irrigation projects that underwent great expansion. The effect of increasing temperature on glaciers was evaluated by mapping the change in the area with average annual temperature below 0°C between the decades of 1988–1997 and 2008–2017, which reduced by 73.2%, with small areas disappearing and larger contiguous areas shrinking.

Characterisation of Hot Days and Heatwaves: A Case Study for Queensland State in Australia

Due to climate change, the temperature is increasing; however, its spatial and temporal distribution and trend are lessunderstood. To fill this research gap, this study examines the nature of temperature rise in the Queensland State of Australia. Based on the daily maximum and minimum temperature data of 17 weather stations covering 1969-2018, the geographical patterns as well as the temporal variations of hot days and heatwaves over the state are examined. The Mann-Kendall trend test and simpleregression methods are employed for detecting trends. The majority of the stations show a significant increase in temperature indices, indicating the warming of the state. Out of 17 stations, 12/14 display a significant increase in the annual frequency ofwarm days/nights, and 2/11 reveal a significant decrease in the frequency of cool days/nights. The restof the stations show no change, except two stations showing an upward trend for cold nights. The Excess Heat Factor (EHF) index is used to identify theheat waves. Over Southern Queensland, frequency, total duration, and maximum duration of heatwaves are found to have increased significantly with agreater magnitude compared to other parts of the state. These findings will be useful for climate adaption and mitigation measures in Queenslandis compulsory.

Characteristics of extreme temperature variation in the Loess Plateau and its correlation with average temperature

In recent decades, extreme climate occurred frequently on the Loess Plateau. It is thus particularly important to study and predict the occurrence of extreme climate. Available researches on extreme climate mainly focus on the changing characteristics of the event itself, but ignore the correlation between average temperature and its changing trend. We used linear trend estimation, Mann-Kendall test, sliding t test and Pearson correlation analy-sis to study the variation trend of extreme temperature and its correlation with average temperature on the Loess Plateau based on the daily maximum temperature, minimum temperature and average temperature data of 79 meteo-rological stations from 1986 to 2019. The results showed that the extreme warmth index in the Loess Plateau region showed a significant upward trend, the extreme cold index showed a significant downward trend, and the frequency of extreme high temperature events increased. Most of the extreme temperature indices had abrupt changes in the middle and late 1990s and in 2012, and the extreme temperature showed a downward trend from 1998 to 2012, which better responded to the phenomenon of global warming hiatus. The increasing trend of mean temperature in the gully region, the rocky mountain region and the valley plain region was more obvious than that in other regions. The stations with large trend of extreme temperature index almost all occurred in the region with large increases of mean temperature. The increases of average temperature in small increments increased the frequency of extreme high temperature event, with the change range of extreme low temperature and its rate being greater than the extreme high temperature. Climate warming on extreme temperature index had a different effect, small changes in the average temperature in the Loess Plateau made the climate transfer towards the direction of more frequent heat.

Dispersal and seasonal movements of Pacific halibut (Hippoglossus stenolepis) in the eastern Bering Sea and Aleutian Islands, as inferred from satellite-transmitting archival tags

BackgroundUnderstanding connectivity is critical to the management of exploited fish stocks, but migratory dynamics of Pacific halibut (Hippoglossus stenolepis) in the Bering Sea and Aleutian Islands region are not well-understood. In the current study, 145 Pacific halibut ≥ 82 cm fork length were tagged with Pop-up Archival Transmitting (PAT) tags to evaluate interannual dispersal, seasonal migration, and depth-specific habitat use.ResultsEndpoint locations obtained after 1 year at liberty (n = 79), fishery recoveries after 2–3 years at liberty (n = 5), and at-liberty geopositions based on light data (n = 5313 estimates from 109 fish) indicated geographically distinct movement patterns: Pacific halibut tagged in the Western and Central Aleutian Islands remained within the island groups in which the fish had been tagged; fish in the eastern Bering Sea remained in that ocean basin, moving among International Pacific Halibut Commission (IPHC) regulatory areas and into Russian waters; those tagged south of Unimak Pass in IPHC Regulatory Area 4A displayed the greatest amount of emigration, dispersing eastward both seasonally and interannually to as far south as Washington State. Analysis of daily maximum depth and temperature data from 113 individuals demonstrated group-level variation in summer temperatures experienced by the fish and in the timing, duration, and synchrony of movement to deep-water wintering grounds.ConclusionsDepth-specific habitat use was suggestive of regionally explicit migratory contingents, while interannual dispersal patterns were consistent with the existence of multiple functional spawning units. The results may guide future research to examine cross-basin connectivity in the Northern Bering Sea and provide inputs for numerical modelling of individual movements, larval advection, and recruitment analyses.

Heat risk of mortality in two different regions of the United Kingdom

Heatwaves pose a protracted health risk depending on its intensity and exposure time. Not only cities but countryside areas are also exposed to risk of summertime heat which has not been recently updated at the bucolic scale. This study aims to associate temperature and mortality and explore its temporal variation. A Poisson regression model combined with a distributed lag non-linear model was applied over daily mortality and maximum temperature data from 1981 to 2018 to formulate the lagged response of summer temperature. The relative risk (RR) and mortality attributable fraction (AF) with respect to minimum mortality temperature (MMT) in Southeast England and Aberdeenshire, UK was calculated. The RR and AF for high and extreme (95th and 99th percentile) temperature with respect to MMT have increased (RR– 1% and 7%; AF– 1.33 and 1.9 times, respectively) in Southeast England but reduced in Aberdeenshire (RR– 2% and 6%; AF– 0.49 and 0.15 times, respectively) in last two decades. However, lagged risk persists for very extreme temperature after several days of exposure at both sites and the hazard cannot be underestimated and neglected. Hence, action is needed to update the heat action plan for extreme temperature management formulating appropriate heat-mitigation strategies focused on vulnerable populations.