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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 previous studies have indeed delved it under current period. Here, we investigated the characteristics of LSWT variation in major lakes of China over two centuries. First we used the AIR2WATER model to construct the data set of LSWT in 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 in Chinese major lakes. 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 Chinese major lakes and their changing mechanisms under the warming climate.

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.

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.

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

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

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.

Global warming is increasing the frequency and duration of heat waves, which is defined as when air temperature exceeds a threshold for more than specific consecutive days. Ecosystem around the globe will be impaired by heat waves just like the exposures to dangerously high temperatures as a public health threat to human. However, the knowledge of the response of lake and reservoir ecosystem to heat waves is largely unknown although it has been argued that climate warming may increase the incidence of harmful algal blooms. We examined the long-term trend of heat waves and how the variability of phytoplankton biomass responds to lake heat waves on a deep reservoir (Lake Qiandaohu). Long-term (1980–2020) meteorological observation in the lake watershed showed a significant warming trend of 0.36 °C per decade for the yearly average of daily average air temperature and the yearly average of daily maximum air temperature of 18.32 °C was observed in 2016. Meanwhile, a significant increasing number of heat wave events lasting longer was observed, and Lake Qiandaohu suffered an unusually severe lake heat wave in summer 2016. Significant correlations were found between the yearly average of daily maximum air temperature and heat days, heat wave events, and heat wave days. Nuisance phytoplankton bloom was found in Lake Qiandaohu by high frequency observation and remote sensing monitoring in summer 2016. Remote sensing estimation from two Landsat 8 Operational Land Imager (OLI) images showed that the average chlorophyll a (Chl a ) was 7.45 ± 4.89 μg/L on July 18 before heat wave and 18.96 ± 0.98 μg/L on August 19 during the heat wave. Two heat wave events lasting from July 20 to August 2 and August 11 to 26 with average surface water temperature of 29.93 and 31.99 °C promoted two marked phytoplankton blooms with average Chl a concentrations of 11.75 ± 4.08 and 10.53 ± 1.65 μg/L in the central lake region, respectively, as evidenced by high-frequency buoy data. These findings suggest that heat waves are likely to yield an increased threat of harmful algal bloom in freshwater ecosystems. With lake heat waves projected to increase in frequency, duration, and spatial extent with global climate change, more studies are needed to improve our understanding of lake heat waves and their potential effects on the species, communities, frequency of phytoplankton bloom, and also help providing advanced schemes of water quality management.

Warming-induced changes in lake thermal and mixing regimes present risks to water quality and ecosystem services provided by U.S. lakes and reservoirs. Modulation of responses by different physical and hydroclimatic settings are not well understood. We explore the potential effects of climate change on 27 lake “archetypes” representative of a range of lakes and reservoirs occurring throughout the U.S. Archetypes are based on different combinations of depth, surface area, and water clarity. LISSS, a one-dimensional dynamic thermal simulation model, is applied to assess lake response to multiple mid-21st century change scenarios applied to nine baseline climate series from different hydroclimatic regions of the U.S. Results show surface water temperature increases of about 77 % of increase in average air temperature change. Bottom temperature changes are less (around 30 %) for deep lakes and in regions that maintain mid-winter air temperatures below freezing. Significant decreases in length of ice cover are projected, and the extent and strength of stratification will increase throughout the U.S., with systematic differences associated with depth, surface area, and clarity. These projected responses suggest a range of future challenges that lake managers are likely to face. Changes in thermal and mixing dynamics suggest increased risk of summer hypoxic conditions and cyanobacterial blooms. Increased water temperatures above the summer thermocline could be a problem for cold water fisheries management in many lakes. Climate-induced changes in water balance and mass inputs of nutrients may further exacerbate the vulnerability of lakes to climate change.

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.