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Seasonal differences of temperature are crucial components of the Earth’s climate system. However, the relatively short observational record, especially for East Asia, has limited progress in understanding seasonal differences. In this study, we identify ten tree-ring chronologies separately correlated with local winter (December–February) temperatures and twelve different tree-ring chronologies separately correlated with summer (June–August) temperatures across East Asia. Using these discrete seasonal tree-ring chronologies, we develop two independent winter and summer temperature reconstructions covering the period 1376–1995 CE for East Asia, and compare them with model simulations. Our reconstructions show a more significant volcanic cooling and earlier onset of modern warming in summer than in winter. The reconstructed summer-minus-winter temperature decreased since as early as the late 19th century, which has driven the current state of seasonal temperature difference to out of the natural variability since the 1370s. Climate models could generally reproduce the variability and trends in seasonal reconstructions, but might largely underestimate seasonal differences due to the fact that seasonal expressions on external forcing and modes of internal variability are too small. Our study highlights the importance of using proxy-based seasonal reconstructions to evaluate the performance of climate models, and implies a substantial weakening of seasonal temperature differences in the future.

Tropical rainfall variations are of direct societal relevance and drive climate variations worldwide via teleconnections. The convective rainfall tends to occur when sea surface temperature (SST) exceeds a threshold, SST thr , usually taken to be constant in time and space. We analyze 40-yr monthly observations and find that SST thr varies by up to 4°C in space and with season. Based on local convective instability, we develop a quantitative theory that largely explains the SST thr variations using the climatological state of the tropical atmosphere. Although it is often assumed that spatial variations of tropical upper-tropospheric temperature are small and can be neglected, it is shown that lower climatological values favor a lower SST thr . Similarly, a small increase in climatological surface relative humidity also leads to a decrease in SST thr , as does a lower climatological air–sea temperature difference. Consequently, efforts to understand and predict natural or forced variations in tropical rainfall must account for, in addition to SST, the temperatures aloft and the near-surface humidity and temperature and requires improved understanding of what controls their distribution in space and time.

Lakes are sensitive indicators of climate change. There are thousands of lakes on the Tibetan Plateau (TP), and more than 1200 of them have an area larger than 1 km2; they respond quickly to climate change, but few observation data of lakes are available. Therefore, the thermal condition of the plateau lakes under the background of climate warming remains poorly understood. In this study, the China regional surface meteorological feature dataset developed by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences (ITPCAS), MODIS lake surface temperature (LST) data and buoy observation data were used to evaluate the performance of lake model FLake, extended by simple parameterizations of the salinity effect, for brackish lake and to reveal the response of thermal conditions, radiation and heat balance of Qinghai Lake to the recent climate change. The results demonstrated that the FLake has good ability in capturing the seasonal variations in the lake surface temperature and the internal thermal structure of Qinghai Lake. The simulated lake surface temperature showed an increasing trend from 1979 to 2012, positively correlated with the air temperature and the downward longwave radiation while negatively correlated with the wind speed and downward shortwave radiation. The simulated internal thermodynamic structure revealed that Qinghai Lake is a dimictic lake with two overturn periods occurring in late spring and late autumn. The surface and mean water temperatures of the lake significantly increased from 1979 to 2012, while the bottom temperatures showed no significant trend, even decreasing slightly from 1989 to 2012. The warming was the strongest in winter for both the lake surface and air temperature. With the warming of the climate, the later ice-on and earlier ice-off trend was simulated in the lake, significantly influencing the interannual and seasonal variability in radiation and heat flux. The annual average net shortwave radiation and latent heat flux (LH) both increase obviously while the net longwave radiation and sensible heat flux (SH) decrease slightly. Earlier ice-off leads to more energy absorption mainly in the form of shortwave radiation during the thawing period, and later ice-on leads to more energy release in the form of longwave radiation, SH and LH during the ice formation period. Meanwhile, the lake–air temperature difference increased in both periods due to shortening ice duration.

Elevated annual average temperature has been found to impact macro-economic growth. However, various fundamental elements of the economy are affected by deviations of daily temperature from seasonal expectations which are not well reflected in annual averages. Here we show that increases in seasonally adjusted day-to-day temperature variability reduce macro-economic growth independent of and in addition to changes in annual average temperature. Combining observed day-to-day temperature variability with subnational economic data for 1,537 regions worldwide over 40 years in fixed-effects panel models, we find that an extra degree of variability results in a five percentage-point reduction in regional growth rates on average. The impact of day-to-day variability is modulated by seasonal temperature difference and income, resulting in highest vulnerability in low-latitude, low-income regions (12 percentage-point reduction). These findings illuminate a new, global-impact channel in the climate–economy relationship that demands a more comprehensive assessment in both climate and integrated assessment models.Increases in daily temperature variability could reduce economic growth. Analysis of 40 years of subnational economic data and daily temperature observations from across the world shows that higher temperature variability reduces annual income, with greatest vulnerability in low-latitude regions.

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.

Sea surface temperature (SST)–heat flux feedback refers to a surface heat flux response to SST anomalies. Under global warming, an overall weakening trend of SST–heat flux feedback over global oceans has been reported. However, the seasonality of the feedback change remains unknown. In this study, the wintertime air–sea feedback in the North Pacific is estimated over the past six decades, and a notable increasing trend is found. The air–sea specific humidity difference is the key determinant of the trends in the SST–heat flux feedback process. While the wind speed and air–sea temperature difference are secondary factors, the enhanced ocean eddy heat transport mechanism in the Kuroshio Extension also plays a contributing role. These findings suggest that trends in specific seasons could run opposite to the overall trend and should be considered in impact assessments.

Iraq, the land of two rivers, has a history that extends back millennia and is the subject of much archaeological research. However, little environmental research has been carried out, and as such relatively little is known about the interaction between Iraq’s vegetation and climate. This research serves to fill this knowledge gap by investigating the relationship between the Normalized Difference Vegetation Index (NDVI) and two climatic factors (precipitation and air temperature) over the last decade. The precipitation and air temperature datasets are from the Water and Global Change Forcing Data ERA-Interim (WFDEI), and the NDVI dataset was extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m spatial resolution and 16 day temporal resolution. Three different climatic regions in Iraq, Sulaymaniyah, Wasit, and Basrah, were selected for the period of 2001–2015. This is the first study to compare these regions in Iraq, and one of only a few investigating vegetation’s relationship with multiple climatic factors, including precipitation and air temperature, particularly in a semi-arid region. The interannual, intra-annual and seasonal variability for each region is analysed to compare the different responses of vegetation growth to climatic factors. Correlations between NDVI and climatic factors are also included. Plotting annual cycles of NDVI and precipitation reveals a coherent onset, fluctuation (peak and decline), with a time lag of 4 months for Sulaymaniyah and Wasit (while for the Basrah region, high temperatures and a short rainy season was observed). The correlation coefficients between NDVI and precipitation are relatively high, especially in Sulaymaniyah, and the largest positive correlation was (0.8635) with a time lag of 4 months. The phenological transition points range between 3 and 4 month time lag; this corresponds to the duration of maturity of the vegetation. However, when correlated with air temperature, NDVI experiences an inverse relationship, although not as strong as that of NDVI and precipitation; the highest negative correlation was observed in Wasit with a time lag of 2 months (− 0.7562). The results showed that there is a similarity between temporal patterns of NDVI and precipitation. This similarity is stronger than that of NDVI and air temperature, so it can be concluded that NDVI is a sensitive indicator of the inter-annual variability of precipitation and that precipitation constitutes the primary factor in germination while the air temperature acts with a lesser effect.

Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.

The interannual‐to‐decadal variability of sea surface temperature and height in the North Atlantic exhibits a tripolar pattern. Here, we explore the spatiotemporal structure, including the vertical, of the North Atlantic tripole using observations and reanalysis data in 1993–2021. For the first time, we demonstrate that the tripole's vertical structure across the Mid‐Atlantic Bight continental shelf and slope differs from that in the ocean interior. The tripole strongly projects in the Slope Water north of the Gulf Stream mean path, marked with temperature changes across the water column not maintained by air‐sea heat flux. Over the shelf, the tripole‐associated sea level, temperature, and ocean current are weak. In the ocean interior, the tripole temperature variability is apparent in the upper 100 m in the tropics and three times as deep in the subtropics. The tripole imprints resemble those of the North Atlantic Oscillation, peaking after the dominant atmospheric mode's winter maximum.

High-quality and continuous radiosonde, aerosol and surface meteorology datasets are used to investigate the statistical characteristics of meteorological parameters and their effects on aerosols. The data were collected at the Atmospheric Radiation Measurement Southern Great Plains climate research facility during 2000–15. The parameters and vertical distribution of temperature inversion layers were found to have strong diurnal and seasonal changes. For surface-based temperature inversion (SBI), the mean frequency and depth of temperature inversion layers were 39.4% and 198 m, respectively. The temperature difference between the top and bottom of SBI was 4.8°C, and so the temperature gradient was 2.4°C (100 m) −1 . The detailed vertical distributions of temperature inversion had been determined, and only the temperature inversion layers below 1000 m showed diurnal and seasonal variations. Mean surface aerosol number concentrations increased by 43.0%, 21.9% and 49.2% when SBIs were present at 0530, 1730 and 2330 LST, respectively. The effect of SBI on surface aerosol concentration was weakest in summer (18.1%) and strongest in winter (58.4%). During elevated temperature inversion events, there was no noticeable difference in surface aerosol number concentrations. Temperature differences and temperature gradients across SBIs correlated fairly well with aerosol number concentrations, especially for temperature gradients. The vertical distribution of aerosol optical properties with and without temperature inversions was different. Surface aerosol measurements were representative of the air within (below), but not above, SBIs and EIs. These results provide a basis for developing a boundary layer aerosol accumulation model and for improving radiative transfer models in the lower atmosphere.