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Global warming has caused severe regional water security risks, and from the theory of the hydrological cycle, the daily extreme temperature could also bring an impact on the streamflow volume, which could be even more important than the average temperature. Therefore, based on the level of the maximum or minimum temperature warming scenarios, a variety of meteorological datasets were selected to assess the asymmetric effect of increased daily extreme temperature on the streamflow from a multi-scale perspective by using the Variable Infiltration Capacity (VIC) model. Model simulations indicate that the streamflow experiences more significant changes in response to the maximum temperature than in response to the minimum temperature, and the relationships of streamflow with both the maximum and minimum temperatures show an upwards parabolic response function, but the response function varies with the type of warming. Additionally, the seasonal and monthly duration curves results show that the increases in both the maximum and minimum temperatures demonstrate a similar response that leads the proportion of the flood period streamflow to be increased (Tmin : 0.16–0.53%/°C; Tmax : 0.11–0.51%/°C). When the minimum temperature increases, the higher the original temperature in the region, the greater the proportion of the flood period streamflow increases. However, when the maximum temperature increases, the opposite effect occurs. • This paper included a numerical analysis of the sensitivity of streamflow to daily extreme temperature increases. • This paper discusses in detail the regional asymmetric effect of daily extreme temperature increase to streamflow. • A relatively complete picture of the hydrological processes caused by daily extreme temperature rise is presented. [ABSTRACT FROM AUTHOR]

The intraseasonal reversal of the temperature over East Asia and the sudden stratospheric warming (SSW) are two abrupt events during the winter of 2020/21. The characteristics of these two events and their relationship are the focused issues in this paper. In winter 2020/21, there was a transition from cold to warm over East Asia. The surface air temperature (SAT) in East Asia suddenly changed from cold to warm with long duration. The cold period lasted from 21 November 2020 to 11 January 2021, while the warm period lasted from 19 January to 28 February 2021. And the transition of the cold to the warm was from 12 to 18 January 2021.There were significant differences of the atmospheric circulations between the cold and warm periods in the upper and lower troposphere. During the cold period, both of Ural Mountain High(UH) and Siberian High(SH) were stronger than normal, the Westerly Jet (WJ) was weakened and located to the area farther southerly than usual, the northerly wind anomalies and the cold advection occurred in East Asian; vice versa during the warm period. The mutations of the atmospheric circulations in the upper and lower levels were obvious during the transition period, especially for UH and SH. A strong SSW event occurred in the stratosphere at the beginning of 2021. As a result, the stratospheric polar vortex was largely weakened and deviated to the areas over Ural Mountains and nearby, the polar westerly wind turned to easterly wind and the temperature gradient reversed. The abnormal changes of the temperature and the geopotential height over the Ural Mountains area in the stratosphere were closely related to the SSW. These abnormal signals caused by the SSW gradually transmitted from stratosphere to troposphere over the Ural Mountains and the polar region, leading to the weakening and collapse of tropospheric UH and further caused the transition from cold to warm in East Asia. The SSW may be the most critical factor leading to the turning from cold to warm over East Asia during the winter of 2020/21. • Focuses on two newest and unexpected events during the winter 2020/21. • The intraseasonal reversal of the temperature over East Asia and the sudden stratospheric warming (SSW). • The characteristics of these two events and their relationship are examined.Apart from SSW. • Other factors of affecting the intraseasonal turning are also pointed out and discussed in this paper. [ABSTRACT FROM AUTHOR]

The present paper discusses development of a hybrid model for predicting near surface air temperature (2 m) over the city scale (∼100 m). The model is based on the premise that temperature at each place within the city is correlated with its mean temperature. The proposed model estimates the mean temperature of a city, using the forecast generated (5 km × 5 km resolution) by regional weather prediction model viz., Weather and Research Forecast (WRF) and downscale it to city scale, using the correlations estimated from the satellite based high-resolution (1 km × 1 km) surface characteristics i.e. Land Surface Temperature (LST). The downscaling coefficients at each grid (1 km x1 km) are generated by formulating the statistical relationship between the LST over the grid to the mean temperature of a city. LST from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor on-board Aqua satellite during the Indian summer season (April–June) of the year 2017–2018 has been employed for this purpose. The relationship between LST and air temperature is established using the collocated satellite estimated LST and monitored air temperature observations. As a case study, the model is demonstrated for the Indian city Ahmedabad, but the same procedure can be extended for any other cities. The existence of strong urban heat island during night hours over Ahmedabad city with a temperature gradient of 4-5 °C has been found in the study. During day hours the different parts of the city depicts different temperature based on their surface characteristics. The results have been validated using the in-situ data based on the ground based observational network temporally installed at different locations of the city for the year 2022. The developed model shows that it has a potential to be used in operational mode for generating location specific extreme temperature or heat wave warning within a city. • Development of a hybrid model for predicting near surface air temperature over the city scale. • Model involves downscaling numerical model forecast using LST and air temperature. • The developed model shows the potential for predicting location specific extreme temperature. [ABSTRACT FROM AUTHOR]

The main advantage of classifying weather types is its systematic use with pressure gridded values. In this paper, an extensive database covering 74 years was used to determine weather types and their temperature fields in the Iberian Peninsula. Moreover, two 37-year periods were compared to investigate climate change in this region. Lamb weather types were determined. The most frequent was the anticyclonic type (21%) followed by the unclassified type (16.5%), with the distribution of these weather types being nearly similar over time. The pressure field was quite uniform in the region and pressure increased 0.7 hPa between the two periods. Moreover, pressure of easterly flows was greater than the pressure of westerly flows, with their difference being around 7 hPa, and the centres of cyclonic types were closer to the Iberian Peninsula than the centres of the anticyclonic types. Isotherms follow geographic parallels, although the meridian gradient was greater over the continental surface than over the ocean. Northern Africa proved to be prominent, since temperatures were highest and specific flows might favour heat waves over the peninsula. The temperature increase between the two 37-year periods was 0.5 °C. In addition, the contrast between the meridian gradients in the east and west of the region evolved towards a lower contrast of these meridian gradients throughout the whole of the region. • Westerly flows were linked with lower pressure than easterly flows. • Cyclonic centres are closer to the Iberian Peninsula than anticyclonic centres. • The significant temperature gradient in North Africa favours heat waves. • Weather-type distribution was nearly similar over time. • Contrasts of the periods analysed showed increases of 0.7 hPa and 0.5 °C. [ABSTRACT FROM AUTHOR]

Based on the 18 realizations of three state-of-the-art climate models participated in the Coupled Model Intercomparison Project phase 6 (CMIP6), this paper examines the changes in climatology and trends of winter diurnal temperature range (DTR) over East Asia in response to natural and anthropogenic forcings. In the observation, the climatology of DTR during 1979–2014 displays a horizontal tile from southwest to northeast over East Asia. In addition, the observed DTR also presents an obviously declining trend in recent decades, with the magnitude of roughly −0.10 °C decade−1. The historical all-forcing simulations well reproduce the geographical distributions and magnitude of climatology and trends of winter DTR over East Asia in 1979–2014. Furthermore, the winter DTR exhibits a continuous declining trend from the historical scenario to the future SSP245 scenario over East Asia, with the most significant declining during the first half of twenty-first century (i.e., 2021–2060). Based on the individual forcing experiments, it suggests that the greenhouse gases forcing plays a primary role in driving the climatology and trends of DTR over East Asia, especially for the SSP245 scenario. Further analysis indicates that the increasing precipitation under greenhouse gases forcing largely explain the declining DTR trends over East Asia, especially over the northern East Asia both in historical and SSP245 scenarios. • The observed decreasing DTR trends are reasonably captured in the all-forcing historical simulations in CMIP6 models. • The greenhouse gas forcing plays a primary role in driving the declining DTR trends over East Asia • The declining of DTR is partly attributed to the increasing precipitation over East Asia in the greenhouse gas forcing. [ABSTRACT FROM AUTHOR]

In this paper, the discrete wavelet transform (DWT), the Mann–Kendall (MK) trend test, and the sequential Mann–Kendall test are applied to temperature series at different time scales in order to detect the long-term trends (1956–2010) in synoptic-scale surface temperatures in Iran, as well as the dominant time scales affecting these temperature time series. The relevant data was extracted from a gridded data file of the region (44°E to 63.5°E, 25°N to 40°N) and divided into 12 regular zones (of dimensions 5 × 5°), each of which was analyzed as an individual unit. The results of this research show that at the monthly, seasonal and annual time scales, the trends in temperature were significant and positive in all of the study zones. In addition, the 2-month and 4-month components were dominant at the monthly time scale, the 48-month component dominant at the seasonal time scale, and the 8-year and 16-year components dominant at the annual time scale. Also, the temperature trends in the northern, and especially central, regions of the study zones increased from west to east, and these increasing trends in temperature were most prominent for the spring and summer seasons. The methodology applied here is generally applicable and quite useful for studying both trends and the dominant time scales affecting climatic data series and could find significant applications in related fields. [ABSTRACT FROM AUTHOR]

This paper explored the projected changes in precipitation, maximum temperature (T max), and minimum temperature (T min) over the Yangtze River Basin (YRB) based on 23 Global climate models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6). The Empirical Quantile Mapping (EQM) approach was firstly applied to correct the biases in the GCMs. Next, the future changes were investigated by analyzing the multi-model ensemble (MME) of the bias-corrected dataset during 2025–2044 (near-term), 2045–2064 (mid-term), and 2081–2100 (long-term) periods, with reference to the baseline period 1995–2014, under three integrated scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) of the Shared Socioeconomic Pathways (SSPs) and the Representative Concentration Pathways (RCPs). Results show that: (1) the biases in CMIP6 GCMs can be effectively corrected by EQM, and the MME performs better than individual models for each climatic variable. (2) Precipitation over the YRB from 2025 to 2100 is projected to increase at the rate of 9.66 mm/decade, 13.45 mm/decade, and 21.01 mm/decade under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. In the long-term, the annual precipitation is projected to increase by 10.41%, 10.66% and 15.80%, under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5, respectively. (3) T max (T min) over the YRB is projected to increase by 0.09 (0.07) °C/decade, 0.29 (0.27) °C/decade, and 0.66 (0.64) °C/decade under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. In the long-term, T max (T min) averaged over the YRB is projected to increase by 1.75 (1.50) °C, 2.72 (2.54) °C and 5.04 (4.85) °C under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5, respectively. (4) Uncertainties in the projected precipitation and temperature over the YRB were reduced based on MME. But further researches, such as selecting the superior models with respect to the regional climate of the YRB from the CMIP6 and using the ensemble methods that assign weight based on the performance of each model, are still needed to provide more reliable climate projections. • Projected changes were explored by 23 CMIP6 GCMs under the three SSP-RCP scenarios. • Biases in the GCMs were corrected by Empirical Quantile Mapping (EQM) method. • Multi-model ensemble (MME) was used to reduce the uncertainties. [ABSTRACT FROM AUTHOR]