Explore the vast range of titles available.

The annual minimum of lake surface water temperature influences ecological and biogeochemical processes, but variability and change in this extreme have not been investigated. Here, we analysed observational data from eight European lakes and investigated the changes in annual minimum surface water temperature. We found that between 1973 and 2014, the annual minimum lake surface temperature has increased at an average rate of + 0.35 °C decade−1, comparable to the rate of summer average lake surface temperature change during the same period (+ 0.32 °C decade−1). Coherent responses to climatic warming are observed between the increase in annual minimum lake surface temperature and the increase in winter air temperature variations. As a result of the rapid warming of annual minimum lake surface temperatures, some of the studied lakes no longer reach important minimum surface temperature thresholds that occur in winter, with complex and significant potential implications for lakes and the ecosystem services that they provide.

Globally the lake surface water temperature (LSWT) has shown an upward trend and exhibits significant spatial heterogeneity,but revious studies have indeed delved it under current period. Here, we utilized the characteristics of LSWT variation in major lakes of China over two centuries. First we used the AIR2WATER model to constructed the data set of LSWT in Chin Chinese major lakes based on the data from CMIP6. Considering the rapid urbanization and climate change, the year of 1900–2014 can be divided into two phases: A stable period (1900–1970‐Phase I, 0.01°C/10a) and a warming period (1971–2014‐ Phase II, 0.16°C/10a). For the future (2015–2100), under the low emission scenario model (SSP1‐RCP2.6, 0.12°C/10a); under the medium emission scenario model (SSP2‐RCP4.5, 0.18°C/10a); and under the high emission scenario model (SSP5‐RCP8.5, 0.38°C/10a). We have designed three spatial classes to analyze the characteristics of LSWT of China major lake. Particularly, when analyzing the spatial pattern based on China's famous population‐economic demarcation line (the Hu Huanyong Line), we found that the LSWT growth rate in lakes east of the Hu Huanyong Line is higher than that in lakes to the west in Phase II, as well as under the SSP1‐RCP2.6 and SSP2‐RCP4.5 scenario models. However, in Phase I and under the SSP5‐RCP8.5 scenarios, the LSWT growth rate in lakes east of the Hu Huanyong Line is lower than that in the west. This study helps improve our understanding of China's major lakes and their changing mechanisms under the warming climate. Plain Language Summary Recent trends in lake surface water temperature (LSWT) indicate a warming pattern, there is needed a comprehensive understanding of the past, present, and future dynamics with LSWT. Here, we utilized the AIR2WATER model to reconstruct LSWT data set of Chinese major lakes under 1900–2100. Considering the rapid urbanization and climate change, the warming rate is Phase I (1900–1970,0.08°C/10a), Phase II (1971–2014, 0.16°C/10a), and under the low emission scenario model (SSP1‐RCP2.6, 0.12°C/10a); under the medium emission scenario model (SSP2‐RCP4.5, 0.18°C/10a); and under the high emission scenario model (SSP5‐RCP8.5, 0.38°C/10a). Interestingly, when we analyzed the spatial variation characteristics of LSWT based on China's population‐economic demarcation line (the Hu Huanyong Line), we found that the LSWT growth rate in lakes east of the Hu Huanyong Line is higher than that in the west during Phase II, as well as under the SSP1‐RCP2.6 and SSP2‐RCP4.5 scenario models. However, in Phase I and under the SSP5‐RCP8.5 scenarios, the LSWT growth rate in lakes east of the Hu Huanyong Line is lower than that in the west. Future research may focus on understanding how changes in LSWT affect the habitat suitability for aquatic species. Key Points We constructed a data set of lake surface water temperature for Chinese lakes during 1900–2100 LSWT warming west of the Hu Huanyong Line is increasingly exceeding that in the east Deep lake LSWT warming will influence and dominate the regional LSWT changes

Lake surface water temperatures (LSWTs) are sensitive to climate change, but previous studies have typically focused on temperatures from only the last few decades. Thus, while there is good appreciation of LSWT warming in recent decades, our understanding of longer-term temperature change is comparatively limited. In this study, we use a mechanistically based open-source model (air2water), driven by air temperature from a state-of-the-art global atmospheric reanalysis (ERA-20C) and calibrated with satellite-derived LSWT observations (ARC-Lake v3), to investigate the long-term change in LSWT worldwide. The predictive ability of the model is tested across 606 lakes, with 91% of the lakes showing a daily root mean square error smaller than 1.5 °C. Model performance was better at mid-latitudes and decreased towards the equator. The results illustrated highly variable mean annual LSWT trends during the twentieth century and across climatic regions. Substantial warming is evident after ~ 1980 and the most responsive lakes to climate change are located in the temperate regions.

Hurricane Ian rapidly intensified from Category 3 to 5 as it transited the wide West Florida Shelf (WFS). This is ascribed to heating by the anomalously warm shelf waters, despite the water depth being shallow when compared to the thicker, mixed layer areas of the deeper ocean. By examining temperature from long‐term moorings, we found that the sea surface and subsurface temperatures exceeded the climatologies by 1–2°C and 2–3°C, respectively. Additionally, these anomalously high temperatures in summer/fall of 2022 were related to the absence of Gulf of Mexico Loop Current interactions with the WFS slope at its “pressure point”. Without such offshore forcing to induce an upwelling circulation, the warmer waters on the shelf were not flushed and replaced by colder waters of deeper ocean origin. This work highlights the importance of subsurface temperature and ocean circulation monitoring on shallow continental shelves, which are largely overlooked in hurricane‐related ocean heat content observational programs. Plain Language Summary Rapid intensification of tropical cyclones can be fueled by upper ocean warm water. The favorable environment of high ocean heat potential is thought to be more likely during marine heatwaves. However, both the hurricane heat potential and marine heatwaves are primarily calculated from satellite‐derived sea surface data, with subsurface data largely overlooked due to lack of in situ measurements, particularly in coastal oceans where hurricanes may rapidly intensify before making landfall. Here we examine an unprecedented set of coastal ocean temperature records from long‐term (26 years) moorings on the wide West Florida Shelf for the cause of Hurricane Ian's rapid intensification to a Category 5 hurricane in 2022. We found that while sea surface temperatures exceeded their climatological mean values by 1–2°C in summer/fall of 2022, the subsurface temperature exceedances were even higher (2–3°C). These anomalously warm waters were further ascribed to a lack of a coastal ocean upwelling circulation due to the absence of offshore forcing by the Gulf of Mexico Loop Current. This work highlights the importance of subsurface temperature and current monitoring on shallow continental shelves, which are largely overlooked in hurricane‐related ocean heat content observing programs. Key Points Hurricane Ian (2022) rapidly intensified over a wide continental shelf with subsurface water 2–3°C warmer than climatology The anomalously warm water was related to the absence of Gulf of Mexico Loop Current interactions with the shelf slope Coastal ocean circulation and subsurface temperature monitoring is important for future hurricane intensification forecasts

Using the WRF-Lake model, this study tried to improve the prediction of lake surface water temperature (LSWT) and how it affects the atmospheric conditions over the hypersaline Urmia Lake (UL). Several changes were made to the lake model to fix its problems with showing how the temperature changes in UL. Because the salinity was abnormally high in this lake, changes had to be made to the formulas for water density, freezing point, and saturation vapor pressure. A dynamic data assimilation method was also used to use in-situ observations to keep the lake surface temperature up to date. For the cold season of 2016–2017, model simulations were run and the model’s performance was assessed using field observations, reanalysis data, and satellite retrievals. The results indicated significant discrepancies between the results from model with default configuration and in-situ observations. The model underestimated LSWT and had cold biases, particularly during nighttime. After trying out different combinations, the SLake model, which included all of the changes, worked the best. It enhanced the accuracy of LSWT prediction and eliminated thermal stratification patterns. The model was still not able to reproduce the lake’s complicated thermal behavior; hence, a dynamic LSWT assimilation method was employed that used in-situ data to keep the lake surface temperature up to date. This new method cut down the cold bias in LSWT by a large amount and improved the simulation of near-surface air temperatures significantly. The dynamic LSWT updates also improved simulations of lake evaporation, with the SLake_LSWT configuration outperformed other models and GLEAM predictions. The study shows importance of inclusion of physical properties of hypersaline lakes in to numerical models to have realistic results.

Satellite remote sensing is widely used to monitor lake surface water temperature (LSWT) due to its global coverage and relatively long‐term record. A common practice in previous studies is to exclude observations during cloudy periods, as most satellite‐based LSWT products rely on optical sensors that cannot penetrate cloud cover. Using synthetic LSWT data sets, we demonstrate that cloud cover tends to coincide with specific thermal conditions, introducing geographically structured biases in annual mean LSWT estimates. We further conducted global‐scale synthetic numerical experiments to examine the impact of cloud cover propagates into model predictions when satellite‐derived LSWTs are used for model development. Two modeling approaches were evaluated: a physically‐based model (Air2water) and a data‐driven model based on a Long Short‐Term Memory (LSTM) neural network. While the LSTM model achieved an overall higher predictive accuracy, it exhibited significantly amplified biases when trained on cloud‐affected data sets, particularly in the warmest‐month temperature and ice duration. In contrast, Air2water showed relatively stable bias patterns, reflecting its resistance to data gaps due to its physical constraints. Our findings underscore the importance of accounting for cloud‐induced selection bias in optical satellite remote sensing and environmental modeling.

The inverse temperature layer (ITL) beneath water‐atmosphere interface within which temperature increases with depth has been observed from measurement of water temperature profile at an inland lake. Strong solar radiation combined with moderate wind‐driven near‐surface turbulence leads to the formation of a pronounced diurnal cycle of the ITL predicted by a physical heat transfer model. The ITL only forms during daytime when solar radiation intensity exceeds a threshold while consistently occurs during nighttime. The largest depth of the ITL is comparable to the e‐fold penetration depth of solar radiation during daytime and at least one order of magnitude deeper during nighttime. The dynamics of the ITL depth variation simulated by a physical model forced by observed water surface solar radiation and temperature is confirmed by the observed water temperature profile in the lake. Plain Language Summary An idealized one‐dimensional heat transfer equation reveals the physical mechanisms of water temperature increasing with depth beneath the water‐atmosphere interface known as inverse temperature layer (ITL). Solar radiation is the dominant forcing of water temperature profile while wind‐driven turbulent mixing is a critical process determining whether the ITL forms. The limited depth of the ITL poses a constraint on the rate of heat transfer from the water body into the atmosphere. The dynamics of the ITL plays an important role in the water and energy cycle of large water bodies such as lakes and oceans. Key Points The formation of inverse temperature layer (ITL) is driven by strong solar radiation and moderate wind‐driven turbulence The ITL depth has pronounced diurnal cycle shallower during daytime than during nighttime A physical model using observed solar radiation and water surface temperature captures the ITL dynamics

Water temperature impacts many processes in rivers, and it is determined by various environmental factors. This study proposed an extreme learning machine (ELM)-based model to predict daily water temperature for rivers. Air temperature (Ta), discharge (Q) and the day of the year (DOY) were used as predictors. Three rivers characterized by different hydrological conditions were investigated to test the modeling performances and the model results were compared with multilayer perceptron neural network (MLPNN) and simple multiple linear regression (MLR) models. Results showed that inclusion of three inputs as predictors (Ta, Q and the DOY) yielded the best modeling accuracy for all the developed models. It was also found that Q played a minor role and Ta and DOY are the most important explanatory variables for river water temperature predictions. Additionally, sigmoidal and radial basis activation functions within the ELM model performed the best for river water temperature forecasting. ELM and MLPNN models outperformed MLR model, and ELM model with sigmoidal and radial basis activation functions performed comparably to MLPNN model. Overall, results indicated that the ELM model developed in this study can be effectively used for river water temperature predictions.

This study reveals that the Arctic Oscillation (AO) in boreal spring has a marked impact on the Indian Ocean Dipole (IOD) in the following autumn. When the spring AO is in its positive (negative) phase, a positive (negative) IOD tends to occur in the following autumn. Possible physical processes for the impact of the spring AO on the autumn IOD are further examined. Positive spring AO is accompanied by a dipole precipitation anomaly pattern over North Atlantic, with positive anomalies over high latitude and negative anomalies over mid-latitude. The associated atmospheric heating anomalies over mid-high latitudes North Atlantic further induces an atmospheric wave train from the North Atlantic to the Indian Ocean (IO), leading to pronounced easterly wind anomalies over the tropical northern IO. These easterly wind anomalies can cause warm sea surface temperature (SST) anomalies in the western tropical Indian Ocean (WTIO) by modulating surface heat fluxes and oceanic heat transport. The warm SST anomalies in the WTIO persist into the following autumn, which increase the zonal gradient of SST anomalies in the equatorial IO and lead to easterly wind anomalies over there. Moreover, the equatorial IO easterly wind anomalies can induce cold SST anomalies in the southeastern tropical Indian Ocean (SETIO) via increasing upwelling of cold water. In addition, previous studies have indicated that a positive spring AO could lead to significant positive precipitation anomalies in the tropical central Pacific in the following summer. Our results show that the associated atmospheric heating over the tropical central Pacific can enhance the southeasterly wind anomalies off the west coast of Sumatra via anomalous Walker circulation, which also play a role in contributing to cold SST anomalies in the SETIO. Therefore, the spring AO may exert a significant impact on the subsequent autumn IOD through the above processes and can be used as a potential predictor of the IOD event.

The thermal regime in large reservoirs plays a significant role in the water quality and ecosystem succession; however, little is known about the impacts of regional climate changes and hydrological conditions on a sizeable stratified reservoir with strong inflow conditions, i.e., the Xiangjiaba Reservoir. Using measured data from 2014 to 2018, the monthly and seasonal variations of the water temperature, thermal stability, and their influencing factors were addressed by using empirical models. The results showed substantial variability and seasonality in the reservoir water temperature, which correlated highly with the air temperature, inflow water temperature, and discharge. Correspondingly, there was a seasonal varying thermal stratification in the reservoir’s yearly cycle, with its duration being up to 4 ~ 5 months, the maximum surface-bottom water temperature difference being up to 7 ~ 10 °C. There were significant positive correlations between Schmidt’s stability index of the thermal structure and inflow-reservoir temperature difference and the surface-bottom temperature differences, while negative correlations with large discharge. Moreover, the inflow tends to influence thermal stability by retaining hypolimnion cold water, with its maximum bottom hysteresis residence time being up to ~ 4 months. Research findings indicated that climate warming in the recent 30 years (1988 ~ 2017) would cause a 0.213 °C/decade and 0.153 kJ/m 2 /decade increase in reservoir surface water temperature and Schmidt’s stability index, respectively. Among these variations, the inflow temperature increase caused by climate change accounted for the largest proportion, i.e., 0.16 °C/decade and 0.115 kJ/m 2 /decade. Therefore, climate warming significantly affected the thermal regimes in this large reservoir, and the inflow water temperature increase due to warm air was the main factor altering the reservoir’s thermal structure. Findings from the present study provide a fresh perspective on how to best optimize the deep channel-type reservoirs’ water quality in the face of anticipated climate change.