Daily Maximum Air Temperature Research Articles

Increasing duration of heatwaves poses a threat to oyster sustainability in the Gulf of Mexico

The future of the wild oyster fishery in the northern Gulf of Mexico is largely uncertain due to changing environmental conditions and declining abundance of harvestable oysters. Specifically, rising temperatures can directly impact the physiological thresholds of the eastern oyster (Crassostrea virginica) at all life history stages and alter the narrow ecological niche this oyster occupies. The impact of rising temperatures is likely most pronounced during atmospheric heatwaves, defined as three or more days above the 90th percentile of daily maximum air temperatures, which have been shown to be increasing in frequency. Increasing exposure to high temperature extremes may contribute to and exacerbate an already declining oyster fishery. Critical to fishery health is recruitment i.e., the addition of new harvestable biomass, which is a dynamic process strongly driven by temperature. Here, we examine the relationship between heatwave characteristics and the prediction of poor oyster recruitment, measured as the abundance of post-larval oysters (e.g. spat) below the site-specific median density observed in historically productive oyster fisheries over 46-years (1976 – 2020) in Mobile Bay, Alabama and 21-years (1993 – 2014) in Apalachicola Bay, Florida. We acquired daily maximum air temperature measurements measured over 50 years (1970 – 2020) at weather monitoring stations adjacent to the bays to identify site specific annual heatwave events (maximum yearly air temperature, yearly and consecutive heatwave days, and number of annual heatwaves). Then, years with extreme heatwaves that exceeded the 75th percentile for the 50-year measurements were compared to years with non-extreme heatwave events. Years with extreme total heatwave days and extreme consecutive heatwave days were correlated with low post-larval oyster density. Across both bay systems, if consecutive heatwave days exceeded 11 days, then poor recruitment of oysters occurred 83 % of the time. Extreme heatwave duration as an indicator for poor recruitment has the potential to be a powerful tool for fishery managers to forecast recruitment and inform sustainable oyster harvest based on year-to-year variability in heatwave duration and long-term warming trends. Our findings illustrate how extreme temperatures can exacerbate multiple physiological and ecological stressors resulting in the loss of a keystone species for healthy and resilient coastal ecosystems.

Winter warm spells over Italy: Spatial–temporal variation and large‐scale atmospheric circulation

AbstractThis article analyses the winter warm spells (WWS) that occurred in central Mediterranean over the period 1993–2022, examining the daily maximum temperatures collected at eight airport sites located in the Italian Peninsula, belonging to different climate zones. According to the definition proposed in 1999 by the Expert Team on Climate Change Detection and Indices (ETCCDI), a WWS is a sequence of at least six consecutive days when daily maximum air temperature exceeds the calendar day 90th percentile centred on a 5‐day window for a base period. WWS occurring over the entire Italian territory or only over northern/central/southern Italy have been identified and related to the peculiar synoptic conditions. It was found that December is the month most prone to WWS and, on average, WWS last 9.4 days in northern Italy, 6.6 days in central Italy, and 8.5 days in southern Italy. Over the period under investigation, the Italian Peninsula experienced only one common event characterized by persistent high‐pressure systems associated with air subsidence over western Mediterranean and, therefore, with exceptional warming. Finally, it has been proven that the definition of WWS proposed by ETCCDI allows to capture synoptic scale events but, in orographically complex areas such as Italy, underestimates moderate spells, which generally might have a duration of at least 3 days. Consequently, it is important to consider the possibility of reducing the period length threshold used for the detection of WWS when orographically heterogeneous regions are studied.

Spatial Distribution and Trends of Heat Stress in Vietnam

This study investigated the spatial distribution and trend of heat stress in Vietnam using data from 68 meteorological stations between 1979 and 2018. Daily maximum wet-bulb temperature (TWmax), an indicator of heat stress, was computed based on the daily maximum air temperature (Tx) and relative humidity at 13:00 LST (RH13). The results indicate a strong positive correlation (>0.72) between daily TWmax and Tx and a weak relationship between daily TWmax and RH13. Tx and TWmax generally increased across most stations over the study period, while RH13 displayed both negative and positive trends. The heat stress thresholds for each station are defined using the 95th percentile values of TWmax during the baseline period 1979-1998. We found that most parts of Vietnam experienced an increase in heat stress days, with the interquartile range across all stations spanning from 0.8 to 4.2 days per decade. Among seasons, summer contributed the most (typically 52-80%) to the annual number of heat stress days in most sub-regions, except for the Central Highlands and the South, where spring contributed the most (44% and 41%, respectively). Overall, this study provides useful benchmark values for future research on heat stress in Vietnam.

Evaluation of Daily Temperature Extremes in the ECMWF Operational Weather Forecasts and ERA5 Reanalysis

In weather forecasting and climate monitoring, daily maximum and minimum air temperatures (TMAX and TMIN) are fundamental for operational and research purposes, from early warning of extreme events to climate change studies. This study provides an integrated evaluation of TMAX and TMIN from two European Centre for Medium-range Weather Forecasts (ECMWF) products: ERA5 reanalysis (1980–2019) and operational weather forecasts (2017–2021). Both products are evaluated using in situ observations from the Global Historical Climatology Network (GHCN). While the analyses span globally, emphasis is given to four key regions: Europe, East and West United States, and Australia. Results reveal a general underestimation of TMAX and overestimation of TMIN in both operational forecasts and ERA5, highlighting the limitation of the ECMWF model in estimating the amplitude of the diurnal cycle of air temperature. ERA5′s accuracy has improved over the past decade, due to enhanced constrain of land–atmosphere analysis streaming from more and higher-quality satellite data. Furthermore, ERA5 outperforms one-day-ahead weather forecasts, indicating that non-real-time dependent studies should rely on ERA5 instead of real-time operational forecasts. This study underscores the importance of ongoing research in model and data assimilation, considering the relevance of daily temperature extremes forecasting and reanalysis for operational meteorology and climate monitoring.

Increased diurnal temperature range in global drylands in more recent decades

AbstractThe diurnal temperature range (DTR) has generally decreased at global scale according to the IPCC reports and previous publications. Here, we examined the change in DTR within the past four decades and found that DTR decreased in wet areas, but it increased in dry areas. This is because the daily minimum air temperature (Tmin) increased at a slower rate than the daily maximum air temperature (Tmax) in the dry areas, while it increased more rapidly in the wet areas. The changes in cloud cover, water vapour and moisture flux were observed to have contributed to the observed change in DTR. The change in moisture flux largely contributed to this change through the indirect effect on the change in water vapour across the aridity gradient. The significant impact of moisture flux is linked to the differential change in moisture flux between the dry and wet zones.

Association between air temperature and unintentional drowning risk in the United Kingdom 2012–2019: A nationwide case-crossover study

ObjectiveDrowning is a leading cause of death. The World Health Organization (WHO) and United Nations (UN) emphasise the need for population-level data-driven approaches to examine risk factors to improve water safety policies. Weather conditions, have the potential to influence drowning risk behaviours as people are more likely to spend time around water and/or undertake risky activities in aquatic spaces as a behavioural thermoregulatory response (e.g., seeking coolth). MethodsA case-crossover approach assessed associations between changes in daily maximum air temperature (data from the nearest weather station to each drowning event) and unintentional drowning risk using anonymous data from the validated UK Water Incident Database 2012–2019 (1945 unintentional deaths, 82% male). Control days were selected using a unidirectional time-stratified approach, whereby seven and 14 days before the hazard day were used as the controls. ResultsMean maximum air temperature on case and control days was 15.36 °C and 14.80 °C, respectively. A 1 °C increase in air temperature was associated with a 7.2% increase in unintentional drowning risk. This relationship existed for males only. Drowning risk was elevated on days where air temperature reached 15–19.9 °C (Odds Ratio; OR: 1.75), 20–24.9 °C (OR: 1.87), and ≥ 25 °C (OR: 4.67), compared with days <10 °C. The greatest elevations in risk appeared to be amongst males and when alcohol intoxication was suspected. Precipitation showed no significant association with unintentional drowning risk. ConclusionsIdentifying such relationships highlights the value of considering weather conditions when evaluating environmental risk factors for drowning, and may inform water safety policy and allocating resources to prevention and rescue.

Unprecedented Human‐Perceived Heat Stress in 2021 Summer Over Western North America: Increasing Intensity and Frequency in a Warming Climate

AbstractThe unprecedented 2021 June‐July heatwave in Western North America resulted in record‐breaking human‐perceived heat stress across the region, measured by the humidex considering both air temperature and humidity. During extended summer (June‐September), both 95th percentiles of daily maximum humidex (HX95) and air temperature (TX95) have increased over the 1940–2022 period, with even faster intensification in the last two decades (2001–2022). HX95 has increased more than TX95 because of the positive monotonic nonlinear relationship between humidex and air temperature at a given level of relative humidity. The Canadian Earth System Model version 5 (CanESM5) projects a larger increase in human‐perceived heat stress than air temperature across the region under low to high emission scenarios (HX95 increases 4.40–7.04°C and TX95 increases 2.92–4.65°C between 1981–2010 and 2041–2060). Moreover, CanESM5 projects significant increases in the frequency of HX and TX conditions that exceed the levels reached in 2021 under intermediate and high emission scenarios.

Forecasting Daily Maximum and Minimum Air Temperatures in The Cilacap District Using Arima and Exponential Smoothing

This research aims to predict daily maximum and minimum air temperatures in Cilacap Regency using ARIMA and Exponential Smoothing. Data was obtained from recordings carried out by BMKG Cilacap using maximum and minimum thermometers taken from January 1, 2016, to December 31, 2021. The results show that the best forecasting model uses the ARIMA (2,1,2) model for maximum temperature and the ARIMA (1,1,1) model for minimum temperature, with the MAPE value of 2.09% for the maximum temperature and 2.44% for the minimum temperature, while the RMSE value obtained is 0.9177 for the maximum temperature and 0.8001 for the minimum temperature. Based on the ARIMA model, Cilacap's daily maximum temperature in 2022 was predicted to be around 30.6ᵒC, with a 95% confidence interval between 28ᵒC - 35ᵒC, while the minimum temperature was predicted to be around 25.1ᵒC, with a 95% confidence interval between 23ᵒC - 28ᵒC.

Transient Thermal Analysis of Concrete Box Girders: Assessing Temperature Variations in Canadian Climate Zones.

This study examines the temperature distributions and thermal-induced responses in reinforced concrete bridge elements, focusing on the Canadian climate regions. The Canadian Highway Bridge Design Code (CHBDC) currently utilizes a fixed thermal gradient profile that does not account for regional climatic variations. Historical environmental data determines the effective maximum temperatures in the CHBDC. In order to investigate temperature behaviors and distributions, a transient finite element (FE) model is developed using recorded and calculated 3-month thermal loads data for representative cities in different climate regions. The results indicate that the predicted daily maximum effective mean temperatures and extreme daily positive vertical thermal gradients do not align. A linear correlation exists between the daily maximum effective mean temperature and the daily maximum air temperature, with a coefficient of determination (R2) of 0.935. The proposed effective mean temperatures obtained from the FE thermal analysis are higher than the CHBDC recommendations. New thermal gradient profiles are proposed for Canadian climate zones, consisting of two straight lines and a linear gradient at the top and bottom sections. A comparison between the proposed profiles and the CHBDC and AASHTO specifications reveals that a single fixed thermal gradient profile is inadequate to account for the variation in thermal gradients across Canadian climate regions.

Thermal stress during heat waves and cold spells in Poland

The study objective was the determination of the temporal and spatial variability/changes in the occurrence of heat waves and cold spells in Poland, and characteristics of biometeorological conditions during their occurrence based on the Universal Thermal Climate Index (UTCI). The research was based on data provided by the Institute of Meteorology and Water Management – National Research Institute from the period 1966–2021 for 37 stations in Poland. A heat wave was (HW) defined as a sequence of at least 3 days with maximum daily air temperature >30.0 °C, and a cold spell (CS) as a sequence of at least 3 days with maximum daily air temperature <-10.0 °C. After identifying HWs and CSs, the pattern of their occurrence was determined, i.e. their number and duration, first and last day of HWs and CSs, and the longest HW and CS. The analyses were performed for each station. Characteristics of biometeorological conditions were conducted based on the UTCI. Daily UTCI values provided the basis for the calculation of mean index values for individual HWs and CSs. Those with the highest and lowest mean UTCI were identified, as well as their days with the maximum and minimum UTCI values. The frequency of occurrence of individual classes of thermal stress during HWs and CSs was also determined. The study showed more and more frequent occurrence of HWs in Poland. Regions of Poland the most prone to the occurrence of HWs were the southern and central-western regions. They were also regions in which HWs were characterised by some of the highest UTCI values. The analysed years also showed high variability of occurrence of CSs both in temporal and spatial terms. The highest number and the longest CSs were recorded until the end of the 1980's. The study showed that the most strenuous biometeorological conditions were recorded in east, and particularly north-east Poland.

A novel statistical model for flood prediction in the Eel River watershed, New Brunswick, Canada

ABSTRACT A strong correlation between the effect of climate change and the increase in flooding frequency and magnitude has been reported in Canada. Consequently, there is a crucial need to examine the effects of future climate change scenarios on flooding conditions. The main objective of this research is to better understand the destructive effects of flood events under historical and future climate change conditions for a small watershed (Eel River watershed) in New Brunswick (NB), Eastern Canada. A practical model had been developed using the modified Artificial Neural Network (ANN) in MATLAB by the authors of this study. The architecture and data structure of ANN is characterized by a back propagation with the Levenberg–Marquardt method. The observed daily total precipitation, daily maximum and minimum air temperatures, daily discharge for the period 1967 to 1983, the simulated monthly maximum and minimum air temperatures, and monthly total precipitation for the period of 1996–2099 from the CanESM2, the second-generation Canadian Earth System Model (CGCM), were used as input of the model. The Representative Concentration Pathways (RCP 4.5 and 8.5), as suitable climate change scenarios, were selected based on the Intergovernmental Panel on Climate Change (IPCC) recommendations for flood studies. Daily values of temperatures, precipitations, and discharges were converted to monthly mean values for better prediction of the output results. In addition, two series of observed discharges were prepared using mean monthly (Qavg) and daily maximum discharges (Qd) as the Target of the model. For more accurate analysis, the time frames of 1996–2012 (for the historical) and 2022–2038, 2039–2055, 2056–2072, 2073–2089, and 2083–2099 (for the future) were considered with a duration of 16 years for each time frame. The output results of ANN were predicted daily maximum (Qd) and mean (Qavg) discharges under the impact of climate change scenarios. As a part of the developed model, Flood Frequency Analysis (FFA) was undertaken using the generalized extreme value (GEV) and the three-parameter lognormal (LN3) distributions based on the predicted and observed discharges. The performance of FFA and ANN were demonstrated using the Anderson–Darling (AD), the Chi-square (CS) tests and coefficient of correlation (R) and mean squared error (MSE), respectively. In conclusion, the three most critical time frames with the highest values of predicted discharges were 2022–2038, 2056–2072, and 2073–2089 for RCP4.5 and 2039–2055, 2073–2089, and 2083–2099 for RCP8.5. Also, based on the FFA, the magnitudes of flood recurrence for the future time period of 100 years will dramatically increase according to the most critical time frames of 2056–2072 and 2039–2055 for RCP 4.5 and 8.5, respectively. Findings indicated that the Eel River watershed will encounter severe floods, and about a 50% increase in mean discharge, especially for the critical time frames. Finally, flood occurrences show increasing trends due to climate change effects in the most critical time frames.

Effects of Artificial Green Land on Land–Atmosphere Interactions in the Taklamakan Desert

Land–atmosphere interactions are influenced by the earth’s complex underlying subsurface, which in turn indirectly affects atmospheric motion and climate change. Human activities are increasingly exerting an influence on desert ecosystems, and artificial green land with clear functional orientation has been established in many desert areas. Consequently, the previously dominant, shifting, sand-covered, underlying surface in these desert regions is gradually transforming. This transformation has significant implications for the characteristics of land–atmosphere interactions, causing them to deviate from their original state. At present, existing studies still have not presented a systematic understanding of this change and have ignored the impact of human activities on land–atmosphere interactions in artificial green land. To address these research gaps, this study specifically targets artificial green land in the Tazhong region of Taklamakan Desert. We carried out observation experiments on land–atmosphere interactions in three different functional units from outside to inside: natural shifting sands, the shelter forest, and the living area. We also analyzed the differences and attribution of land–atmosphere interactions characteristics of different functional units. Compared with the natural shifting sands, the daily average maximum values of wind speed in the shelter forest decreased by 78%, and the daily average maximum air temperature and soil (0 cm) temperature decreased by 2.6 °C and 7 °C, respectively. Additionally, the soil moisture level was significantly increased throughout the green land due to the shelter forest. The surface albedo experienced a decrease, with an annual average of 0.21. Furthermore, the aerodynamic roughness and bulk transport coefficient increased by two orders of magnitude. The daily average maximum values of sensible heat flux and soil heat flux (G05) decreased by 18.7% and 75%, respectively, and the daily average maximum value of latent heat flux increased by 70.3%. This effectively improved the microclimate environment of the green land. The living area was greatly reduced by the shelter forest coverage and influenced by the buildings. Consequently, the environmental improvement was not as large as it was inside the shelter forest. However, it still provided a good shelter for production and living in the desert area. Throughout the year, a total of 4.60 × 105 t water was consumed through evapotranspiration in the artificial green land. The findings of this study have the potential to enhance our comprehension of land–atmosphere interactions in desert regions, thereby offering valuable insights for the establishment and effective management of artificial desert green lands.

Does choice of outdoor heat metric affect heat-related epidemiologic analyses in the US Medicare population?

We calculated daily maximum, minimum, and mean outdoor air temperature (T), heat index (HI), wet-bulb globe temperature (WBGT), and Universal Thermal Climate Index (UTCI) for populous US counties and linked estimates with daily all-cause mortality and heat-related hospitalizations among Medicare beneficiaries (2006-2016). We fit distributed-lag nonlinear models for each metric and compared relative risks (RRs) at the 99th percentile. Across all heat metrics, extreme heat was statistically significantly associated with elevated risks of morbidity and mortality. Associations were more pronounced for maximum daily values versus the corresponding minimum for the same metric. The starkest example was between HImax (RR = 1.14; 95% confidence interval [CI] = 1.12, 1.15) and HImin (RR = 1.10; 95% CI = 1.09, 1.11) for hospitalizations. When comparing RRs across heat metrics, we found no statistically significant differences within the minimum and maximum heat values (i.e., no significant differences between Tmax/HImax/WBGTmax/UTCImax or between Tmin/HImin/WBGTmin/UTCImin). We found similar relationships across the National Climate Assessment regions. Among Medicare beneficiaries in populous US counties, daily maximum and mean values of outdoor heat are associated with greater RRs of heat-related morbidity and all-cause mortality versus minimum values of the same metric. The choice of heat metric (e.g., temperature versus HI) does not appear to substantively affect risk calculations in this population.

Monitoring intertidal golden tides dominated by Ectocarpus siliculosus using Sentinel-2 imagery

Golden seaweed tides are a global environmental and social problem, that have been occurring along Ireland's eastern coastline since the 1990s. This study focused on analysing the spatiotemporal dynamics of golden seaweed (Ectocarpus siliculosus) coverage at Dollymount Strand in Dublin Bay (Ireland), between 2016 and 2022, and its relationship with meteorological conditions. Hyperspectral measurements in the field and Sentinel-2 imagery were utilized to monitor macroalgal blooms with a spatiotemporal resolution of 10 m and minimum costs. The normalized difference vegetation index (NDVI) values filtered between 0.1 and 1 were used to identify the coverage of golden seaweeds. The results showed that the golden seaweed coverage extended over 99% of the study area (54 ha) in June 2020 with an average NDVI of 0.25. A seasonal pattern of golden seaweed abundance was observed and modelled from May to October using a Generalized Additive Model. Approximately 28% of the coverage was correlated with daily average global radiance, and 38% of the mean NDVI was associated with daily average maximum air temperatures and wind direction by means of the Generalized Linear Models. A greater biomass of Ectocarpus spp. was accumulated on the beach when the wind direction was from the north-east and south-east. The results also suggested that freshwater nutrient inputs in winter from nearby estuaries may be contributing to golden tides on the shoreline. These findings are useful in developing strategies aimed at controlling and managing golden tides globally, as well as using them as bioindicators of ecological status in coastal environments.

1991–2020 climate normal in the European Alps: focus on high-elevation environments

Alps are an important geographical area of the European continent and, in this area, temperature increase is most evident. However, the 1991–2020 climate normal in the Alps has still not been thoroughly investigated. Aiming to fill this gap with a focus on high-elevation environments, minimum and maximum daily air temperature acquired by 23 automatic weather station were used. The results show that the mean annual values of minimum and maximum temperature for the 1991–2020 climate normal in the Alps are −2.4°C and 4.4°C, respectively, with a warming rate of 0.5°C/10 years. The mean annual temperature comparison between 1961–1990 and 1971–2000, 1961–1990 and 1981–2010, 1961–1990 and 1991–2020 climate normal show an increase of 0.3°C, 0.5°C and 0.9°C, respectively. The results also confirm that seasonal and annual temperatures are rising through the whole Alpine arc, mainly in summer and autumn. This work highlights that annual minimum and maximum temperature do not seem to be affected by a positive elevation-dependent warming. Instead, a positive elevation-dependent warming in the maximum values of the annual minimum temperature was found. If anthropogenic emissions maintain the trend of the last decades, the expected mean annual temperature of the 2001–2030 climate normal is −0.2°C, with an increase of 0.5°C if compared to the 1991–2020 climate normal and with an increase of 1.5°C if compared to the 1961–1990 climate normal. This study highlights the warming rate that is now present in the European Alps, provides indications on the warming rate that will occur in the coming years and highlights the importance of carrying out investigations that consider not only the last 30-year climate normal, but also the most recent 30-year climate normal by comparing them with each other.

Compound drought and heatwave events in the eastern part of the Baltic Sea region

Droughts and heatwaves are natural phenomena that can cause severe damage to the economy, infrastructure, human health, and agriculture, among others. However, in recent years, it has been noted that their combined effect, known as compound drought and heatwave events (CDHE), often results in even greater harm. The main aim of this study was to identify CDHEs in this region during summers from 1950 to 2022 and assess the frequency and intensity of these events. To this end, the periods of droughts and heatwaves that occurred between 1950 and 2022 were determined, and the recurrence, extent, and intensity of these phenomena were evaluated. In this study, 1-month Standard Precipitation Index (SPI) values calculated for each summer day were used to identify droughts, while heatwaves were defined as a period of five or more consecutive days when the daily maximum air temperature (Tmax) was higher than the 90th percentile of Tmax. Precipitation and Tmax data (with a spatial resolution 0.25° x 0.25°) were obtained from the European Centre of Medium-Range Weather Forecast ERA-5 reanalysis dataset. The study showed that in most of the eastern part of the Baltic Sea region, the number of drought days had decreased from 1950 to 2022, while the number of heatwave days had increased significantly. In total, ten CDHEs were identified during the summers of 1950–2022. Eight of these events were recorded in 1994 or later. However, a statistically significant increase of CDHEs was found only in a small part of the study area.

Ozone variability driven by the synoptic patterns over China during 2014–2022 and its implications for crop yield and economy

China has experienced increasingly serious ozone (O3) pollution in recent years, posing a great threat to agricultural ecosystem and economy. In this study, the decadal records of surface ozone concentrations across China (2014–2022), combined with the spatial synoptic classification (SSC) and multiple linear regression (MLR) methods are used to determine the surface ozone variability driven by the synoptic weather patterns. The maximum daily average 8-h (MDA8) ozone concentrations increase significantly from 2014 to 2019 at 2.5–5.2 μg m−3 yr−1 and decrease over the years 2019–2021, with a rebound in 2022. Strong connections between surface O3 levels and SSC synoptic weather patterns are identified, with the dry tropical (DT) weather pattern featured by dry and hot air masses as the main contributor to 35.3% of the high ozone occurrences and partly explaining the observed ozone trends in recent decade (e.g., +9.7% ozone increase per tripling of the DT frequency). Quantitative MLR analysis further confirms that meteorological conditions play an important role in the seasonal ozone trends from 2014 to 2022 (up to a 28.6% contribution), and daily-maximum air temperature and solar radiation could explain 11.3–51.0% and 11.1–37.4% of the meteorological-driven ozone trends. Using the agricultural statistical data and empirical algorithms, it is estimated that the annual yields of winter wheat, single-cropping rice, double-cropping early rice and late rice are reduced by 9.0–17.0%, 6.0−8.2%, 4.8−9.4% and 3.9−7.0%, respectively, in China during 2014–2022 due to the ozone exposure, along with a total economic loss of 74.9–171.9 billion yuan per year. This study is meaningful to understand the driving factors for the increasingly serious ozone pollution and is beneficial for the synergetic control of air pollution and climate mitigation.

Distinct responses of PM2.5 and O3 extremes to persistence of weather conditions in eastern China

Climate change may aggravate air pollution through altering emissions, ventilation, chemical production, and deposition. Under a warming climate, persistent weather patterns are likely to increase in both number and intensity, yet their implications on future air quality have been less explored. Here we use ground-level observations of air pollutants and meteorological reanalysis dataset to explore how persistence of weather conditions would affect extremes of wintertime PM2.5 and summertime O3 in China. Associated changes in the shapes of their distributions are discussed also with contrasting weather conditions. As air stagnation days persist, median summertime O3 significantly increases, with 0.07, 0.12 and 0.19 standard deviation per day (about 1.97, 3.12 and 4.63 ppb per day) for the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) regions, respectively. As high temperature days continue, significant increases in median (about 0.31 ppb per day) and 90th percentile (about 0.78 ppb per day) of summertime O3 is found only in the YRD region, although the highest probability of O3 extreme (90th percentile) is identified on the last day of four-day high temperature events for both the BTH and YRD regions. In contrast, persistent stagnation causes a significant increase in the 90th percentile of PM2.5 of about 17.87 and 10.88 μg/m3 per day for BTH and YRD regions, rather than the median. Stagnation events lasting for five, four and five days are the best indicators for PM2.5 extreme (90th percentile) in the BTH, YRD and PRD regions, respectively. Analysis on contrasting monthly weather conditions suggests that summertime O3 extreme is amplified by higher mean daily maximum air temperature (T) in the BTH region and co-occurrences of high T and stagnation in the YRD region, while wintertime PM2.5 extreme is significantly amplified by stronger stagnation. These results suggest that megacities with relatively high emissions are more vulnerable to a hotter and more stable future.

Increasing Global Terrestrial Diurnal Temperature Range for 1980–2021

AbstractThe 2021 IPCC report found that most studies show declining trends for the global diurnal temperature range (DTR) since the 1950s, decreasing mainly during 1960–1980. This issue is revisited here using an up‐to‐date in‐situ data set, Hadley Center Integrated Surface Database, constrained by rigorous station selection conditions. The global observed DTR trend was found to reverse during 1980–2021, increasing significantly at a rate of 0.091 ± 0.008°C decade−1. The trend was dominated by a faster rate of increasing daily maximum air temperature. This increasing observed trend in the past four decades was not fully captured in raw CMIP6 models, as models only partially capture the spatial patterns. With global CMIP6 outputs and regionally‐available observations, the global land DTR was then estimated, through emergent constraints, to be 0.063 ± 0.012°C decade−1. The study raises concern for risks of increasing DTR globally and provides new insights into global DTR assessment.

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.