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Several studies reported statistically significant associations of blood pressure (BP) with short-term air temperature variability (TV), but the effect of TV on BP was found to differ in different areas. This study aimed to detect the association between BP and TV in Kaunas, Lithuania. Data from the international Health, Alcohol, and Psychosocial Factors in Eastern Europe study was used to gather information on the participants’ BP during 2006–2008. The TV variables were the diurnal temperature range (DTR), the standard deviation (SD) of hourly temperature during the 24 h (TSD) and the first 12 h of the day (TSDF), and the SD of daily minimum and maximum temperatures during the exposure days (DTV). Multiple linear regression was used after controlling for potential confounders. Among the participants, 45.5% were men, 30.9% were aged > 65 years, and 9.8% were normotensive. A positive association of systolic BP with TSD on the same day (TSD0), DTR with lags of 0–2 days, and DTV with lags of 0–1 day and of diastolic BP with TSD0 and TSDF was found, a stronger impact being observed in males and in physically active participants. The impact of TV was stronger at lower temperatures, and a statistically significant negative interaction between air temperature and TV variables was found. In May–June, a negative association of DTV with diastolic BP was observed. We found a positive association of BP with TV. Sex, the level of physical activity, and air temperature may modify the relationship between TV and BP.

Over the last 71 years, the air temperature in Poland has increased on average by 0.28 °C per decade—which gives a total change in this period exceeding 2 °C. The subject of this study was an analysis of the long-term variability of the Polish climate in terms of thermal characteristics. The aim of the research was to verify the hypothesis on the lack of homogeneity of this change and to identify points of significant acceleration of the observed tendencies. The analysis utilized the average monthly air temperature at selected synoptic stations in Poland over the period 1951–2021. The values were then processed into a reference series using Alexandersson’s method, which provided synthetic information on the variability in thermal conditions in the country. The analyses were carried out on an annual and seasonal basis. The values of the trend coefficients (and their statistical significance) were also calculated in shorter periods (minimum 30 years), which enabled determination of the stability of the observed changes’ tendencies. In addition to the analysis of the basic characteristics, non-parametric tests (Wilcoxon, Kruskal–Wallis) were used to verify shifts between decades. The annual and seasonal analyses showed the existence of sub-periods with different directions and scales of the observed tendencies. Additionally, statistically significant changes in decadal characteristics were noted, e.g., in the decades 2001–2010 and 2011–2020 in the case of annual temperature, and 1961–1970 and 1971–1980 in the case of the winter season.

Temperature data from three Automatic Weather Stations and twelve Temperature Loggers are used to investigate the spatiotemporal variability of temperature over a glacier, its main atmospheric controls, the suitability of extrapolation techniques and their effect on melt modeling. We use data collected on Juncal Norte Glacier, central Chile, during one ablation season. We examine temporal and spatial variability in lapse rates (LRs), together with alternative statistical interpolation methods. The main control over the glacier thermal regime is the development of a katabatic boundary layer (KBL). Katabatic wind occurs at night and in the morning and is eroded in the afternoon. LRs reveal strong diurnal variability, with steeper LRs during the day when the katabatic wind weakens and shallower LRs during the night and morning. We suggest that temporally variable LRs should be used to account for the observed change. They tend to be steeper than equivalent constant LRs, and therefore result in a reduction in simulated melt compared to use of constant LRs when extrapolating from lower to higher elevations. In addition to the temporal variability, the temperature‐elevation relationship varies also in space. Differences are evident between local LRs and including such variability in melt modeling affects melt simulations. Extrapolation methods based on the spatial variability of the observations after removal of the elevation trend, such as Inverse Distance Weighting or Kriging, do not seem necessary for simulations of gridded temperature data over a glacier. Key Points Analsysis of spatio‐temporal variability of air temperature on a glacier Temperature extrapolation using lapse rates with different variability degrees Effect of different temperature extrapolation techniques on melt modeling

Differences in land surface characteristics across a city produce great spatial and temporal variability in air temperature. This fact is particularly pronounced between urban and surrounding rural areas giving rise to the canopy-layer urban heat island (CL-UHI) phenomenon. In the present study, we apply the dimensional analysis technique to develop a simple semi-empirical equation to map daily maximum CL-UHI (UHImax) intensities during nighttime over the city of Singapore for specific weather conditions. By adopting the methodology proposed by Theeuwes et al., but selecting meteorological and morphological parameters that affect UHImax intensity most for Singapore, evaluation of the developed equation shows good agreement with observations (RMSE = 1.13 K and IOA = 0.76). Model performance depends strongly on wind conditions and is best during weak winds when ‘ideal’ conditions for UHI development are approached (RMSE = 0.65 K and IOA = 0.85). Results using the simple equation developed to map UHImax intensities in Singapore under dry weather conditions are comparable to those obtained from more sophisticated numerical models, which demand significant computational resources, and the complex parameterizations involved require expertise to carry out the simulations. The resulting maps of the present study can be used to investigate less favorable thermal conditions and assess population vulnerability to a certain temperature excess, as well as provide insights for urban planning strategies of mitigation measures according to the land cover and morphology of a location.

Efforts have been made to appreciate the extent to which we can predict the dominant modes of December–January–February (DJF) 2 m air temperature (TS) variability over the Asian winter monsoon region with dynamical models and a physically based statistical model. Dynamical prediction was made on the basis of multi-model ensemble (MME) of 13 coupled models with the November 1 initial condition for 21 boreal winters of 1981/1982–2001/2002. Statistical prediction was performed for 21 winters of 1981/1982–2001/2002 in a cross-validated way and for 11 winters of 1999/2000–2009/2010 in an independent verification. The first four observed modes of empirical orthogonal function analysis of DJF TS variability explain 69 % of the total variability and are statistically separated from other higher modes. We identify these as predictable modes, because they have clear physical meaning and the MME reproduces them with acceptable criteria. The MME skill basically originates from the models’ ability to capture the predictable modes. The MME shows better skill for the first mode, represented by a basin-wide warming trend, and for second mode related to the Arctic Oscillation. However, the statistical model better captures the third and fourth modes, which are strongly related to El Niño and Southern Oscillation (ENSO) variability on interannual and interdecadal timescales, respectively. Independent statistical forecasting for the recent 11-year period further reveals that the first and fourth modes are highly predictable. The second and third modes are less predictable due to lower persistence of boundary forcing and reduced potential predictability during the recent years. In particular, the notable decadal change in the monsoon–ENSO relationship makes the statistical forecast difficult.

Two long‐term regional climate model simulations with and without subsurface soil temperature feedbacks are performed to investigate the role of soil temperature–atmosphere coupling in influencing interannual variability of summer climate over East Asia. Results indicate that soil temperature–atmosphere coupling depends on climate regimes, mainly affecting summer climate variability over the arid/semi‐arid regions. Over these areas, subsurface soil temperature feedbacks play an important role in amplifying summer surface air temperature variability, accounting for about 30–70% of total variance. The feedbacks on precipitation variability are weaker than those on surface air temperature variability over the arid/semi‐arid regions, but are still significant over many areas of western part.

From January to March 2018, one of the strongest Madden–Julian Oscillation (MJO) events of the last 45 years progressed eastward along the equator from the Indian Ocean to the Pacific Ocean then back to the Indian Ocean. In response to strong tropospheric heating in the MJO's active convective envelope, several pronounced Rossby wave trains developed and extended from the equatorial tropics, across the extratropical Pacific and North America, and into the extratropical Atlantic. The origins of these Rossby wave trains evolved eastward with time, generally following the eastward progression of the MJO, but preferentially clustered in subtropical India and Southeast Asia and in two locations in the subtropical Pacific Ocean: along 160°E and 170°W. Over eastern North America, surface and lower‐tropospheric temperatures rose to more than 12 °C above normal when the MJO convective envelope was over the Indian Ocean (in mid‐January) and Western Hemisphere (in late February). In between those warm periods, temperatures cooled to below normal while the MJO convection was over the western Pacific. These temperature anomalies evolved in time with the pronounced Rossby wave trains that linked eastern North America with the Tropics in the Eastern Hemisphere: warm temperatures occurred when ridging was present over eastern North America and cooler temperatures occurred when troughing was present. This variability is discussed and placed in context of recent work showing the MJO's role in modulating temperature and circulation. Surface temperatures in the northeastern United States exhibited significant intra‐seasonal variability in winter 2018, concurrent with a record‐breaking MJO event. Very warm temperatures in early January were followed by slightly below‐normal temperatures in early February, only to be followed by very warm temperatures again in mid‐February. These temperature anomalies were connected to a train of Rossby waves emanating from the Tropics.

Prediction of spatial and temporal variability of air temperature in areas with complex topography is still a challenge for numerical weather prediction models. Simulation of atmosphere over complex terrain requires dense and accurate horizontal and vertical grids. In this study, verification results of three configurations of the Aire Limitée Adaptation Dynamique Développement International High-Resolution Limited Area Model (ALADIN-HIRLAM) numerical weather prediction (NWP) system, using two different horizontal and vertical resolutions and applied to the Polish Western Carpathian Mountains, are presented. One model of the ALADIN-HIRLAM NWP system is tested in two horizontal and vertical resolutions. Predicted air temperatures are compared with observations from stations located in different orographies. A comparison of model results with observations was conducted for three cold season intervals in 2017 and 2018. Statistical validation of model output demonstrates better model representativeness for stations located on hill and mountain tops compared to locations in valley bottoms. A comparison of results for two topography representations (2 × 2 km and 1 × 1 km) showed no statistically significant differences of root mean square error (RMSE) and bias between model results and observations.

Interannual variations of sea surface temperature (SST) in the midlatitudes of the Southern Hemisphere play an important role in the rainfall variability over the surrounding countries by modulating synoptic-scale atmospheric disturbances. These are frequently associated with a northeast–southwest-oriented dipole of positive and negative SST anomalies in each oceanic basin, referred to as a subtropical dipole. This study investigates the role of tropical SST variability on the generation of subtropical dipoles by conducting SST-nudging experiments using a coupled general circulation model. In the experiments where the simulated SST in each tropical basin is nudged to the climatology of the observed SST, the subtropical dipoles tend to occur as frequently as the case in which the simulated SST is allowed to freely interact with the atmosphere. It is found that without the tropical SST variability, the zonally elongated atmospheric mode in the mid- to high latitudes, called the Antarctic Oscillation (AAO), becomes dominant and the stationary Rossby waves related to the AAO induce the sea level pressure (SLP) anomalies in the midlatitudes, which, in turn, generate the subtropical dipoles. These results suggest that the tropical SST variability may not be necessary for generating the subtropical dipoles, and hence provide a useful insight into the important role of the AAO in the midlatitude climate variability.

The estimates ofthe long-term variability ofair temperature are obtained from the century series of its monthly mean values. The warming is observed in autumn and winter (November-February) and the cooling takes place in March and October. In summer the strongly pronounced trend is absent. The average annual temperature slowly rises due to the autumn-winter warming.