Daily Temperature Variations Research Articles

Seasonal and Temporal Ensemble Models for Accurate Near-Surface Air Temperature Estimation

The near-surface air temperature (NSAT) is crucial for understanding thermal and urban environments. Traditional estimation methods using general remote sensing images often focus on the types of spatial data or machine learning models used, neglecting the importance of seasonal and temporal variations, limiting their accuracy. This study introduces a novel ensemble model that incorporates both seasonal and temporal information integrated with satellite-derived land surface temperature (LST) data to enhance NSAT estimation, along with a rigorous feature importance analysis to identify the most impactful parameters. Data from 2022, collected from 147 South Korean weather stations, were used to develop and evaluate the models. Thirteen initial variables, including the LST and other auxiliary data, were considered. Random forest regression was employed to build separate models for each season. This novel approach of separating data by season allowed optimized feature selection tailored to each season, improving the model efficiency and capturing finer seasonal and daily temperature variations. These seasonal models were then combined to form an ensemble model. The seasonal models demonstrated varying accuracy, with the R2 values indicating a strong correlation between the predicted and actual NSAT, particularly high in spring and fall and lower in summer and winter. The ensemble model showed improved performance, achieving an MAE of 0.534, an RMSE of 0.391, an R2 of 0.996, and a cross-validated R2 of 0.968. These findings highlight the effectiveness of incorporating seasonal and temporal information into NSAT estimation models, offering significant improvements over traditional approaches. The developed models support precise temperature monitoring and forecasting, aiding environmental and urban management.

On some features of diurnal dynamics of solonchak emissivity in summer

ABSTRACT Widely represented in many arid regions of the world, dry marshes pose a potential threat to the adjacent areas because of soil salinization caused by wind transfer (for long distances) of toxic mineral salts from the dried surface. For environmental monitoring of such soils, remote microwave sensing methods are used. In this work, we study at a 2.3 cm wavelength the summer diurnal dynamics of microwave radiation of sodic solonchak formed on the dried bottom of a hypersaline lake. It has been established that the study indicator (in the centimetre range) depends on the cumulative changes in temperature, moisture and salinity of soils. With rise in soil temperature, intensive evaporation and topsoil drying, a salt crust and the underlying layer with less moisture (accompanied by microwave radiation interference on the layered structure of solonchak) can be formed. In the experiment, a daily variation in the brightness temperature of the study object (a specific object) by 25 K occurred with change in thermodynamic temperature of the skin layer by 10 K.

The first firn core from Peter I Island – capturing climate variability across the Bellingshausen Sea

Abstract. Peter I Island is situated in the Bellingshausen Sea, a region that has experienced considerable climate change in recent decades. Warming sea surface temperatures and reduced sea ice cover have been accompanied by warming surface air temperature, increased snowfall, and accelerated mass loss over the adjacent ice sheet. Here we present data from the first firn core drilled on Peter I Island, spanning the period 2001–2017 CE. The stable water isotope data capture regional changes in surface air temperature and precipitation (snow accumulation) at the site, which are highly correlated with the surrounding Amundsen–Bellingshausen seas and the adjacent Antarctic Peninsula (r > 0.6, p < 0.05). The firn core data, together with the unique in situ data from an automatic weather station, confirm the high skill of the ERA5 reanalysis in capturing daily mean temperature and inter-annual precipitation variability, even over a small sub-Antarctic island. This study demonstrates the suitability of Peter I Island for future deep-ice-core drilling, with the potential to provide a valuable archive to explore ice–ocean–atmosphere interactions over decadal to centennial timescales for this dynamic region.

How does temperature affect rural income: Channels and implication of adaptation

AbstractThis paper examines the impact of daily temperature variations on rural income per capita and evaluates the effectiveness of adaptation based on China's county‐level socioeconomic statistics and household survey data. We find that high temperatures have significant negative effects on rural income per capita. Relative to the physically comfortable reference temperature between 10 and 15°C, an additional day with a daily average temperature above 30°C reduces the rural income per capita by 0.12% in the year. Evidence from micro‐data indicates that agricultural income of rural households is sensitive to high temperatures relative to other components of household income, such as wage income and transfer income, which leads to a reallocation of rural laborers across sectors. In addition, we indeed find some evidence of adaptation, but we conclude that the current adaptation pattern of rural households probably magnifies the threat of future climate change to China's agricultural sector.

Genotype‐by‐environment interaction for wheat falling number performance due to late maturity α‐amylase

AbstractLate maturity α‐amylase (LMA) is known to reduce falling number (FN) in wheat (Triticum aestivum L.), similar to the effect of preharvest sprouting (PHS) and frost, which can result in grain parcels testing below trading thresholds. Hence, Grains Australia mandates that new Australian wheat cultivars must be at a low risk of LMA expression to receive a milling classification. The multi‐environment trial dataset contained 34 environments not affected by PHS or frost and was analyzed using a five‐factor analytic linear mixed model. Factor 1 accounted for 71.4% of the genetic variation in FN, factor 2 accounted for 8.8%, factor 3 accounted for 5.7%, factor 4 accounted for 3.5%, and factor 5 accounted for 3.1%. The interaction class (iClass) summary method was used to assist in the characterization of crossover genotype‐by‐environment interaction (G × E). Poorer FN performance was best observed in the “ppp” iClass, which indicated a minor, but significant, response to crossover G × E. The environment loadings for factor 1 were associated with mild ripening conditions, characterized by fewer days above 28°C, increased rainfall, and increased variation in daily maximum temperature and relative humidity. Factors 2 and 3 were associated with “cool shock” conditions, where the maximum temperature for 1 day was above 24°C followed by at least three consecutive days below 18°C during the grain fill period. This study provides further evidence of the crossover G × E present for FN associated with LMA, poor FN performance in genotypes that express higher levels of LMA, and the environmental conditions that contribute to LMA expression.

Adaptive Variable Emissivity Reflector for Seasonal and Daily Thermal Regulation in Regions with Significant Temperature Variations

AbstractTemperature‐adaptive variable emissivity reflectors offer an electricity‐free and environmentally friendly cooling strategy, holding substantial potential to improve the global energy landscape. However, challenges associated with unoptimized solar absorptance can lead to overcooling or overheating when the modulation of infrared emittance is held constant, thereby increasing extra energy consumption. Herein, a temperature‐adaptive variable emissivity reflector based on W‐Mg co‐doped VO2 (W‐Mg‐VER) is proposed, engineered to optimize solar absorptance (αL = 0.40) while maintaining effective infrared emittance modulation (Δɛ = 0.69) near ambient temperature. To maximize year‐round energy savings in regions experiencing significant daily temperature fluctuations, W‐Mg co‐doped VO2 is employed to reduce solar absorptance by modifying electron occupancy in the V 3d orbital and expanding the optical bandgap. Outdoor experiments have validated the remarkable temperature management and energy‐saving capabilities of the W‐Mg‐VER, facilitated by its seamless transition between radiative cooling and heat‐retaining modes. Numerical simulation indicates that a W‐Mg‐VER roof covering 100 m2 would save 152.9 GJ of energy annually in locations with significant daily temperature variations. Moreover, W‐Mg‐VER demonstrates robust performance, exhibiting less than 1% degradation in emittance tunability and solar absorptance after 10 000 cycles. This approach provides valuable insights and practical guidance for significantly enhancing global energy savings.

Temperature variability regulates the interactive effects of warming and pharmaceutical on aquatic ecosystem dynamics

Climate warming and pharmaceutical contaminants have profound impacts on population dynamics and ecological community structure, yet the consequences of their interactive effects remain poorly understood. Here, we explore how climate warming interacts with pharmaceutical-induced boldness change to affect aquatic ecosystems, built on an empirically well-informed food-chain model, consisting of a size-structured fish consumer, a zooplankton prey, and a fish predator. Climate warming is characterized by both daily mean temperature (DMT) and diurnal temperature range (DTR) in our model. Results show that DMT and high levels of species’ boldness are the primary drivers of community instability. However, their interactive effects can lead to diverse outcomes: from predator collapse to coexistence with seasonality-driven cycles and coexistence with population interaction-driven cycles. The interactive effects are significantly modulated by daily temperature variability, where moderate DTR counteracts the destabilizing interactive effects by increasing consumer reproduction, while large temperature variability considerably reduces consumer biomass, destabilizing the community at high mean temperatures. Our analyses disentangle the respective roles of DMT, DTR and boldness in mediating the response of aquatic ecosystems to the impacts from pharmaceutical contaminants in the context of climate warming. The interactive effects of the environmental stressors reported here underscore the pressing need for studies aimed at quantifying the cumulative impacts of multiple environmental stressors on aquatic ecosystems.

Microtopographic effect on soil temperature during a burn by shifting cultivation in a tropical rain forest

Fire is an important component of shifting cultivation systems. Although its effects on soil properties depend on its severity and duration, few studies have measured soil temperature during burns at different depths and in relation to topography in tropical forests. Frequent rain keeps the soil moist in these ecosystems, but soil moisture does not only depend on precipitation but also on relief position. Here we monitor soil temperature at 1 and 10 cm depth before the burn along elevation gradients of three logged hillsides to establish the daily temperature variation, and to compare it with the soil temperature under forested sites and during the burn. Additionally, moisture content and physicochemical properties of the soil were measured before and after the burn.Results before the burn indicate a larger daily temperature amplitude in logged versus forested areas, and specially at crests and shoulder positions in comparison to backslopes and toeslopes. The fire increased soil temperature only in 1 cm depth, and for less than 40 degrees at most of the sites. Soil moisture content was negatively correlated with maximum temperature during the burn, however, also fuel loads and litter thickness determined fire intensity, with the spatial distribution of these variables being unrelated to topography. Nutrient contents changed only in the litter layer, in which C and N diminished by 35 % and 24 %, respectively, while total Ca, Mg, K and P increased by 210, 202, 318, 235 % respectively. We conclude that soil moisture contents contributed to maintain fire intensity low at the studied site.

To study the outcomes of scrotal exploration for acute scrotal pain in relation to monthly and daily temperature variations: A large contemporary series from a Teaching Hospital

ABSTRACT: BACKGROUND & OBJECTIVE: Acute testicular pain requires emergency exploration for suspected testicular torsion (TT). It has been suggested that temperature variation is associated with TT. However, it was not possible to conclude whether the higher occurrence of TT was related to TT itself or the effect of the overall higher presentation of acute scrotum. We studied the impact of seasonal and daily temperature variations on acute testicular pain and outcomes. METHODOLOGY: Retrospective data collection on patients required testicular exploration between January 2006 and December 2017 for TT. The Kruskal Wallis test & chi-square tests were used to assess the group difference between continuous and categorical variables, respectively. RESULTS: In total, 502 patients required testicular exploration. The median age and duration of symptoms were 16.4 yrs. (1.3 – 77) and 4 hrs. (1 – 336). Respectively, TT was found in 231 (46%), torsion of the testicular appendix (TTA) in 126 (25%), epididymal inflammation (EI) in 46 (9.2%), and no identifiable cause (NIC) was found in 99 (19.7%). Scrotal explorations were more common in groups with mean daily temperature >6.20C, 60.2% Vs 39.8% while there was no difference in TT 46% Vs 46% in these groups. CONCLUSION: There was no seasonal variation for testicular torsion or operative findings. Cold weather and average low daily temperatures were not related to testicular torsion.

Temperature history reconstruction in steel box girders using limited data and proper orthogonal decomposition-based dimension reduction representation

The monitoring temperature history of steel box girders inevitably contains gaps or anomalies due to the malfunctions of the structural health monitoring system. Traditional methods for reconstructing temperature histories face the challenge in accounting for the randomness of temperature variations caused by the uncertainty of complex environmental factors. This research proposes a framework for reconstructing the long-term temperature of steel box girders by combining the limited measurements with a proper orthogonal decomposition (POD) based dimension reduction representation approach, to account for the stochastic nature of daily temperature variations. Unlike the conventional Monte Carlo-based POD method, the developed POD-based dimension reduction representation approach effectively reduces the number of elementary random variables from thousands to two by introducing random functions serving as constraints, overcoming the challenge of high-dimensional random variables inherent in the Monte Carlo methods. The proposed approach divides the measured temperature histories into random high-frequency (HF) and deterministic low-frequency (LF) components and establishes the theoretical models of power spectral density and coherence functions of HF temperature components to accommodate the generation of HF temperature component samples, and finally reconstructs temperature samples by superimposing the LF components and generated HF component samples. The results from a practical example demonstrate that the statistical characteristics of representative HF temperature component samples generated by the POD-based dimension reduction representation align well with the corresponding targeted values, and the proposed method outperforms the traditional POD method, yielding a 60% efficiency enhancement without compromising computational accuracy. The developed framework owns apparent superiority in accuracy compared to the traditional POD and the long short-term memory methods, particularly in continuous and extensive missing data. Moreover, the reconstructed temperature samples with assigned probabilities present complete probability information from the level of total probability. These results advance the probabilistic methods in tackling long-term temperature history reconstruction.

Meteorological factors and risk of ischemic stroke, intracranial hemorrhage, and subarachnoid hemorrhage: A time-stratified case-crossover study.

Stroke risks associated with rapid climate change remain controversial due to a paucity of evidence. To examine the risk of subarachnoid hemorrhage (SAH), intracranial hemorrhage (ICH), and ischemic stroke (IS) associated with meteorological parameters. In this time-stratified case-crossover study, adult patients hospitalized for their first stroke between 2011 and 2020 from the insurance claims data in Taiwan were identified. The hospitalization day was designated as the case period, and three or four control periods were matched by the same day of the week and month of each case period. Daily mean and 24-h variations in ambient temperature, relative humidity, air pressure, and apparent temperature were measured. Conditional logistic regression models were applied to assess the risk of stroke associated with exposure to weather variables, using the third quintile as a reference, controlling for air pollutant levels. There were 7161 patients with SAH, 40,426 patients with ICH, and 107,550 patients with IS. There was an inverse linear relationship between mean daily temperature and apparent temperature with ICH. Elevated mean daily atmospheric pressure was associated with an increased risk of ICH. A greater decrease in apparent temperature over a 24-h period was associated with increased risk of ICH but decreased risk of IS (odds ratio (95% confidence interval) for the first vs. third quintile of changes in apparent temperature, 1.141 (1.053-1.237) and 0.946 (0.899-0.996), respectively). There were considerable differences in short-term associations between meteorological parameters and three main pathological types of strokes. The authors have no permission to share the data.

The impact of intra-annual temperature fluctuations on agricultural temperature extreme events and attribution analysis in mainland China

Daily temperature variations, which tend to exhibit non-constant and non-linear patterns, are often characterized by intra-annual fluctuations that cause severe and frequent extreme temperature events that have an enormous impact on agricultural production. However, the quantitative relationship between intra-annual temperature fluctuations and extreme agricultural temperatures remains unclear. We aimed to investigate intra-annual temperature fluctuation changes based on daily meteorological data in nine agricultural regions across China from 1960 to 2022 and quantify the impact of temperature fluctuations on extreme agricultural temperatures during crop growth periods. Moreover, an attribution analysis of intra-annual temperature fluctuations was performed using climate indicators. Main results showed: (1) intra-annual temperature fluctuations in each region exhibited a certain decrease, and the spatial distribution showed a significant decreasing trend from north to south. (2) Intra-annual temperature fluctuations have moderately exacerbated extreme agricultural hot and cold events during growth periods, which have brought serious challenges to agriculture owing to advances in phenology and unsynchronized rain heat compared to climate warming. The proportion of positive correlations between temperature fluctuations and extreme temperatures was much larger than that of the negative correlations in nearly all of China (percentage of stations: 20.5 %), and the negative correlation was concentrated only in southern China (percentage of stations: 3.7 %). (3) Hydrothermal coupling and elevation moderately affected intra-annual temperature fluctuations. Average temperature, relative humidity, and elevation had negative correlations with intra-annual temperature fluctuations, the correlation coefficients (R) were − 0.62, −0.42 and − 0.12, respectively; however, reference crop evapotranspiration (ET0) exhibited a positive correlation (R: 0.29), and all reached highly significant levels (P < 0.01). Climate indicators mainly affected intra-annual temperature fluctuations in eastern China. Specifically, the El Niño-Southern Oscillation (ENSO), unstable Asia Polar Vortex, and increased Western Pacific Subtropical High have enhanced temperature fluctuations. The deepened East Asian Trough and Arctic Oscillation of the negative phase weakened the intra-annual temperature fluctuations. This investigation highlights the crucial function of temperature fluctuation in intensifying extreme temperature occurrences and provides a more reasonable scientific foundation for extreme event prediction and agricultural planning.

Time series analysis of the interaction between ambient temperature and air pollution on hospitalizations for AECOPD in Ganzhou, China

This study aimed to investigate the univariate and bivariate effects of ambient temperature and air pollutants on 57,251 inpatients with AECOPD (Acute Exacerbation of Chronic Obstructive Pulmonary Disease) in Ganzhou from January 1, 2016, to December 31, 2019. We categorized the daily mean temperature and air pollutant variables based on the exposure–response curve of the Distributed Lag Non-Linear Model. Poisson regression model was used for interaction and stratification analysis. The Relative Excess Risk due to Interaction (RERI) with 95% confidence intervals (95% CI) between daily mean temperature (Tmean) and air pollutants including NO2, PM2.5, and PM10 were − 0.428 (95% CI − 0.637, − 0.218), -− 0.227 (95% CI − 0.293, − 0.161), and − 0.119 (95% CI − 0.159, − 0.079). Further stratification analysis showed the relative risk (RR) (95% CI) of high NO2 (> 33 μg/m3) at low Tmean (≤ 28 °C) was 1.119 (95% CI 1.096, 1.142). Low temperatures with high PM10 in men and high PM2.5 in women were associated with a higher risk of AECOPD hospitalization. The results indicate a higher risk of hospitalization for AECOPD when there is with high concentrations of air pollution at low temperatures.

Arbovirus Transmission in Australia from 2002 to 2017.

Arboviruses pose a significant global public health threat, with Ross River virus (RRV), Barmah Forest virus (BFV), and dengue virus (DENV) being among the most common and clinically significant in Australia. Some arboviruses, including those prevalent in Australia, have been reported to cause transfusion-transmitted infections. This study examined the spatiotemporal variation of these arboviruses and their potential impact on blood donation numbers across Australia. Using data from the Australian Department of Health on eight arboviruses from 2002 to 2017, we retrospectively assessed the distribution and clustering of incidence rates in space and time using Geographic Information System mapping and space-time scan statistics. Regression models were used to investigate how weather variables, their lag months, space, and time affect case and blood donation counts. The predictors' importance varied with the spatial scale of analysis. Key predictors were average rainfall, minimum temperature, daily temperature variation, and relative humidity. Blood donation number was significantly associated with the incidence rate of all viruses and its interaction with local transmission of DENV, overall. This study, the first to cover eight clinically relevant arboviruses at a fine geographical level in Australia, identifies regions at risk for transmission and provides valuable insights for public health intervention.

Numerical modeling of microclimate of re-waterlogged lands of Belarusian Polesie

Based on the mesoscale hydrodynamic model WRF (Weather Research and Forecasting), the balance model of atmospheric moisture and the remote sensing data, we obtained the estimates of microclimate changes as a result of reswamping of lands in Belarusian Polesie. The calculations were performed on the example of the Khoiniki district of the Gomel region of Belarus. Numerical experiments considered the driest summer periods of the last two decades. Based on the modeling results, the maps of changes in mean daily temperature, amplitude of daily temperature variations, evapotranspiration and precipitation were constructed, which can be used to predict the consequences of land reclamation in different scenarios of adaptation to climate changes.

Spatiotemporal variability identification and analysis for non-stationary climatic trends for a tropical river basin of India

The non-stationary behavior of climatic variables has been increasingly recognized as a challenge that disrupts the equilibrium of human-defined climate-based stationary processes, including hydrological and agricultural practices, and irrigation systems. This study aims to investigate long-term trends and non-stationarity in climatic variables across 23 stations of the Krishna River basin, India. Prominent trends in rainfall, temperature, and their extreme indices were identified using the Modified Mann-Kendall (MMK), Bootstrapped Mann-Kendall (BMK), and Sen's Slope Estimator tests, while the Innovative Trend Analysis (ITA) test uncovered hidden trends and potential shifts in climatic patterns. This study addresses a critical research gap by exploring both significant and hidden trends in climatic variables, providing a better understanding of future dynamics. Traditional methods like MMK and Sen's Slope were insufficient to reveal these hidden trends, but ITA offered a more comprehensive analysis. The findings revealed an increase in total annual rainfall for almost 50% of the basin, which aligns with rising maximum temperatures, suggesting enhanced evaporation rates and subsequent fluctuations in rainfall patterns. Seasonal analysis indicated a shift towards decreased rainfall during winter and pre-monsoon seasons, contrasted by increased precipitation during the monsoon and post-monsoon periods, highlighting a clear alteration in rainfall distribution. The Simple Daily Intensity Index (SDII) and other indices suggest intensified rainfall events despite a decrease in the number of rainy days, indicating fewer but more intense events. Temperature analysis showed an overall increase in maximum temperatures, with the Diurnal Temperature Range (DTR) significantly increasing across all stations, implying greater daily temperature variations and potential for intensified water cycles and extreme climatic events. Furthermore, the study simplifies these trends by classifying them into two attributes: intensity and frequency, aiding policymakers in site-specific management of water resources and planning for future climatic scenarios. The presence of non-stationarity in extreme rainfall was confirmed by the Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests. These findings are significant as they conclude how climate change is altering hydrological patterns at each station. The study emphasizes the necessity for adaptive management strategies to mitigate the adverse impacts on agriculture, infrastructure, and human safety.

Ontogeny of daily rhythms in the expression of metabolic factors in Nile tilapia (Oreochromis niloticus) kept at two different temperature regimes: Thermocycle and constant temperature

The daily variations of temperature are one of the main synchronizers of the circadian rhythms. In addition, water temperature influences the embryonic and larval development of fish and directly affects their metabolic processes. The application of thermocycles to fish larvae has been reported to improve growth and the maturation of the digestive system, but their effects on metabolism are poorly understood. The aim of the present study was to evaluate the effect of two different temperature regimes, cycling versus constant, on the daily rhythms of metabolic factors of Nile tilapia (Oreochromis niloticus) larvae. For this purpose, fertilized eggs were divided into two groups: one reared in a 31 °C:25 °C day:night thermocycle (TCY) and another group maintained in a constant 28 °C temperature (CTE). The photoperiod was set to a 12:12 h light/dark cycle. Samples were collected every 4 h during a 24-h cycle on days 4, 8 and 13 post fertilization (dpf). The expression levels of alanine aminotransferase (alt), aspartate aminotransferase (ast), malic enzyme, glucose-6-phosphate dehydrogenase (g6pd), phosphofructokinase (pfk) and pyruvate kinase (pk) were analyzed by qPCR. Results showed that, in 13 dpf animals, most of the genes analyzed (alt, ast, malic, g6pd and pfk) showed daily rhythms in TCY, but not in the group kept at constant temperature, with most acrophases detected during the feeding period. An increase in nutrient metabolism around feeding time can improve food utilization and thus increase larval performance. Therefore, the use of thermocycles is recommended for tilapia larviculture.

Daily Variation of Body Temperature: An Analysis of Influencing Physiological Conditions

Daily Variation of Body Temperature: An Analysis of Influencing Physiological Conditions

Прогноз сильной жары в Красноярске с использованием региональной модели WRF-ARW

The reasons for the occurrence of a severe heatwave in Krasnoyarsk have been studied, and the accuracy of the forecasts of the WRF-ARW regional model has been assessed. Four episodes of hot weather in 2020, 2021, and 2023 have been analyzed. The heatwave in June 2023 was especially intense and met the criteria for a severe weather event. All examined cases were associated with the intense advection of warm tropical air in the leading edge of the upper-air trough. The formation of the urban heat island has been investigated, and its intensity in the nighttime was found to be 6.5°C. During daytime hours, the heat island was weaker than at nighttime. It has been determined that on the second day of forecasting, the WRF-ARW model accurately reproduces the urban heat island and the impact of the Yenisei River, although it underestimates the predicted amplitude of daily temperature variations. The absolute error in air temperature predictions for the time moments close to the time of daily maximum temperatures was 2.6°C (33 hours of model time).

Latitudinal variation in thermal performance of the common coral Pocillopora spp.

Understanding how tropical corals respond to temperatures is important to evaluating their capacity to persist in a warmer future. We studied the common Pacific coral Pocillopora over 44° of latitude, and used populations at three islands with different thermal regimes to compare their responses to temperature using thermal performance curves (TPCs) for respiration and gross photosynthesis. Corals were sampled in the local autumn from Moorea, Guam and Okinawa, where mean±s.d. annual seawater temperature is 28.0±0.9°C, 28.9±0.7°C and 25.1±3.4°C, respectively. TPCs for respiration were similar among latitudes, the thermal optimum (Topt) was above the local maximum temperature at all three islands, and maximum respiration was lowest at Okinawa. TPCs for gross photosynthesis were wider, implying greater thermal eurytopy, with a higher Topt in Moorea versus Guam and Okinawa. Topt was above the maximum temperature in Moorea, but was similar to daily temperatures over 13% of the year in Okinawa and 53% of the year in Guam. There was greater annual variation in daily temperatures in Okinawa than Guam or Moorea, which translated to large variation in the supply of metabolic energy and photosynthetically fixed carbon at higher latitudes. Despite these trends, the differences in TPCs for Pocillopora spp. were not profoundly different across latitudes, reducing the likelihood that populations of these corals could better match their phenotypes to future more extreme temperatures through migration. Any such response would place a premium on high metabolic plasticity and tolerance of large seasonal variations in energy budgets.