Influence of the Atlantic Multidecadal Oscillation and Interdecadal Pacific Oscillation on Antarctic surface air temperature during 1900 to 2015

The importance of the Atlantic Multidecadal Oscillation(AMO)and Interdecadal Pacific Oscillation(IPO)in influencing zonally asymmetric changes in Antarctic surface air temperature(SAT)has been established.However,previous studies have primarily concentrated on examining the combined impact of the contrasting phases of the AMO and IPO,which have been dominant since the advent of satellite observations in 1979.This study utilizes long-term reanalysis data to investigate the impact of four combinations of+AMO+IPO,–AMO–IPO,+AMO–IPO,and–AMO+IPO on Antarctic SAT over the past 115 years.The+AMO phase is characterized by a spatial mean temperature amplitude of up to 0.5℃over the North Atlantic Ocean,accompanied by positive sea surface temperature(SST)anomalies in the tropical eastern Pacific and negative SST anomalies in the extratropical-mid-latitude western Pacific,which are indicative of the+IPO phase.The Antarctic SAT exhibits contrasting spatial patterns during the+AMO+IPO and+AMO–IPO periods.However,during the–AMO+IPO period,apart from the Antarctic Peninsula and the vicinity of the Weddell Sea,the entire Antarctic region experiences a warming trend.The most pronounced signal in the SAT anomalies is observed during the austral autumn,whereas the combination of–AMO and–IPO exhibits the smallest magnitude across all the combinations.The wavetrain excited by the SST anomalies associated with the AMO and IPO induces upper-level and surface atmospheric circulation anomalies,which alter the SAT anomalies.Furthermore,downward longwave radiation anomalies related to anomalous cloud cover play a crucial role.In the future,if the phases of AMO and IPO were to reverse(AMO transitioning to a negative phase and IPO transitioning to a positive phase),Antarctica could potentially face more pronounced warming and accelerated melting compared to the current observations.

El Niño and the AMO Sparked the Astonishingly Large Margin of Warming in the Global Mean Surface Temperature in 2023

In 2023,the majority of the Earth witnessed its warmest boreal summer and autumn since 1850.Whether 2023 will indeed turn out to be the warmest year on record and what caused the astonishingly large margin of warming has become one of the hottest topics in the scientific community and is closely connected to the future development of human society.We analyzed the monthly varying global mean surface temperature(GMST)in 2023 and found that the globe,the land,and the oceans in 2023 all exhibit extraordinary warming,which is distinct from any previous year in recorded history.Based on the GMST statistical ensemble prediction model developed at the Institute of Atmospheric Physics,the GMST in 2023 is predicted to be 1.41℃±0.07℃,which will certainly surpass that in 2016 as the warmest year since 1850,and is approaching the 1.5℃ global warming threshold.Compared to 2022,the GMST in 2023 will increase by 0.24℃,with 88%of the increment contributed by the annual variability as mostly affected by El Niño.Moreover,the multidecadal variability related to the Atlantic Multidecadal Oscillation(AMO)in 2023 also provided an important warming background for sparking the GMST rise.As a result,the GMST in 2023 is projected to be 1.15℃±0.07℃,with only a 0.02℃ increment,if the effects of natural variability—including El Niño and the AMO—are eliminated and only the global warming trend is considered.

Using satellite-derived land surface temperatures to clarify the spatiotemporal warming trends of the Alborz Mountains in northern Iran

The Alborz Mountains are some of the highest in Iran,and they play an important role in controlling the climate of the country’s northern regions.The land surface temperature(LST)is an important variable that affects the ecosystem of this area.This study investigated the spatiotemporal changes and trends of the nighttime LST in the western region of the Central Alborz Mountains at elevations of 1500-4000 m above sea level.MODIS data were extracted for the period of 2000-2021,and the Mann-Kendall nonparametric test was applied to evaluating the changes in the LST.The results indicated a significant increasing trend for the monthly average LST in May-August along the southern aspect.Both the northern and southern aspects showed decreasing trends for the monthly average LST in October,November,and March and an increasing trend in other months.At all elevations,the average decadal change in the monthly average LST was more severe along the southern aspect(0.60°C)than along the northern aspect(0.37°C).The LST difference between the northern and southern aspects decreased in the cold months but increased in the hot months.At the same elevation,the difference in the lapse rate between the northern and southern aspects was greater in the hot months than in the cold months.With increasing elevation,the lapse rate between the northern and southern aspects disappeared.Climate change was concluded to greatly decrease the difference in LST at different elevations for April-July.

Surface air temperature change in the Wuyi Mountains,southeast China

Detecting changes in surface air temperature in mid-and low-altitude mountainous regions is essential for a comprehensive understanding of warming trend with altitude.We use daily surface air temperature data from 64 meteorological stations in Wuyi Mountains and its adjacent regions to analyze the spatio-temporal patterns of temperature change.The results show that Wuyi Mountains have experienced significant warming from 1961 to 2018.The warming trend of the mean temperature is 0.20℃/decade,the maximum temperature is 0.17℃/decade,and the minimum temperature is 0.26℃/decade.In 1961-1990,more than 63%of the stations showed a decreasing trend in annual mean temperature,mainly because the maximum temperature decreased during this period.However,in 1971-2000,1981-2010 and 1991-2018,the maximum,minimum and mean temperatures increased.The fastest increasing trend of mean temperature occurred in the southeastern coastal plains,the quickest increasing trend of maximum temperature occurred in the northwestern mountainous region,and the increase of minimum temperature occurred faster in the southeastern coastal and northwestern mountainous regions than that in the central area.Meanwhile,this study suggests that elevation does not affect warming in the Wuyi Mountains.These results are beneficial for understanding climate change in humid subtropical middle and low mountains.

Spatiotemporal variation of land surface temperature and its driving factors in Xinjiang,China

Land surface temperature(LST) directly affects the energy balance of terrestrial surface systems and impacts regional resources, ecosystem evolution, and ecosystem structures. Xinjiang Uygur Autonomous Region is located at the arid Northwest China and is extremely sensitive to climate change. There is an urgent need to understand the distribution patterns of LST in this area and quantitatively measure the nature and intensity of the impacts of the major driving factors from a spatial perspective, as well as elucidate the formation mechanisms. In this study, we used the MOD11C3 LST product developed on the basis of Moderate Resolution Imaging Spectroradiometer(MODIS) to conduct regression analysis and determine the spatiotemporal variation and differentiation pattern of LST in Xinjiang from 2000 to 2020. We analyzed the driving mechanisms of spatial heterogeneity of LST in Xinjiang and the six geomorphic zones(the Altay Mountains, Junggar Basin, Tianshan Mountains, Tarim Basin, Turpan-Hami(Tuha) Basin, and Pakakuna Mountain Group) using geographical detector(Geodetector) and geographically weighted regression(GWR) models. The warming rate of LST in Xinjiang during the study period was 0.24℃/10a, and the spatial distribution pattern of LST had obvious topographic imprints, with 87.20% of the warming zone located in the Gobi desert and areas with frequent human activities, and the cooling zone mainly located in the mountainous areas. The seasonal LST in Xinjiang was at a cooling rate of 0.09℃/10a in autumn, and showed a warming trend in other seasons. Digital elevation model(DEM), latitude, wind speed, precipitation, normalized difference vegetation index(NDVI), and sunshine duration in the single-factor and interactive detections were the key factors driving the LST changes. The direction and intensity of each major driving factor on the spatial variations of LST in the study area were heterogeneous. The negative feedback effect of DEM on the spatial differentiation of LST was the strongest. Lower latitudes, lower vegetation coverage, lower levels of precipitation, and longer sunshine duration increased LST. Unused land was the main heat source landscape, water body was the most important heat sink landscape, grassland and forest land were the land use and land cover(LULC) types with the most prominent heat sink effect, and there were significant differences in different geomorphic zones due to the influences of their vegetation types, climatic conditions, soil types, and human activities. The findings will help to facilitate sustainable climate change management, analyze local climate and environmental patterns, and improve land management strategies in Xinjiang and other arid areas.

Spatiotemporal dynamics of land use/land cover(LULC)changes and its impact on land surface temperature:A case study in New Town Kolkata,eastern India

Rapid urbanization creates complexity,results in dynamic changes in land and environment,and influences the land surface temperature(LST)in fast-developing cities.In this study,we examined the impact of land use/land cover(LULC)changes on LST and determined the intensity of urban heat island(UHI)in New Town Kolkata(a smart city),eastern India,from 1991 to 2021 at 10-a intervals using various series of Landsat multi-spectral and thermal bands.This study used the maximum likelihood algorithm for image classification and other methods like the correlation analysis and hotspot analysis(Getis–Ord Gi^(*) method)to examine the impact of LULC changes on urban thermal environment.This study noticed that the area percentage of built-up land increased rapidly from 21.91%to 45.63%during 1991–2021,with a maximum positive change in built-up land and a maximum negative change in sparse vegetation.The mean temperature significantly increased during the study period(1991–2021),from 16.31℃to 22.48℃in winter,29.18℃to 34.61℃in summer,and 19.18℃to 27.11℃in autumn.The result showed that impervious surfaces contribute to higher LST,whereas vegetation helps decrease it.Poor ecological status has been found in built-up land,and excellent ecological status has been found in vegetation and water body.The hot spot and cold spot areas shifted their locations every decade due to random LULC changes.Even after New Town Kolkata became a smart city,high LST has been observed.Overall,this study indicated that urbanization and changes in LULC patterns can influence the urban thermal environment,and appropriate planning is needed to reduce LST.This study can help policy-makers create sustainable smart cities.

Decadal prediction skill for Eurasian surface air temperature in CMIP6 models

评估CMIP6年代际预测试验对季节平均SAT的预测技巧的结果表明:模式不能有效预测冬季和秋季SAT的年代际变率.IPSL-CM6A-LR和多模式集合平均对于春季SAT展现了预测技巧,其中对于变率的预测技巧好于振幅的结果.基于蒙古和我国华北地区的显著预测技巧,模式对于夏季SAT表现出最佳的预测水平.与外部强迫相比,模式对于SAT的预测技巧可能来自初始化.模式中的一个明显系统性误差值得注意,即模式中冬季SAT的变率可以持续到其他季节,而在观测中其他季节的SAT变化与冬季SAT相对独立.

Seasonal constraint of dynamic water temperature on riverine dissolved inorganic nitrogen transport in land surface modeling

水体温度变化对河流可溶性无机氮(DIN)输送有着强烈控制作用.然而,在全球尺度上河流DIN输送量对水温度变化的响应尚不清楚.因此,本文基于陆面过程模式,耦合河流水温估算和DIN传输方案,设定有,无动态水温情景,对比研究陆面模拟中水温变化对河流DIN通量变化的影响.结果表明:在考虑水温动态变化后,在30°N和30°S之间, DIN通量年振幅减小5%–25%.在中国东部地区,水温动态变化使河流DIN通量在夏季减少1%–3%,在冬季增加1%–5%,对DIN通量具有明显的季节性约束作用,表明动态水温的表达在河流DIN输送模拟中的重要性.

Estimating Monthly Surface Air Temperature Using MODIS LST Data and an Artificial Neural Network in the Loess Plateau, China

Air temperature(Ta)datasets with high spatial and temporal resolutions are needed in a wide range of applications,such as hydrology,ecology,agriculture,and climate change studies.Nonetheless,the density of weather station networks is insufficient,especially in sparsely populated regions,greatly limiting the accuracy of estimates of spatially distributed Ta.Due to their continuous spatial coverage,remotely sensed land surface temperature(LST)data provide the possibility of exploring spatial estimates of Ta.However,because of the complex interaction of land and climate,retrieval of Ta from the LST is still far from straightforward.The estimation accuracy varies greatly depending on the model,particularly for maximum Ta.This study estimated monthly average daily minimum temperature(Tmin),average daily maximum temperature(Tmax)and average daily mean temperature(Tmean)over the Loess Plateau in China based on Moderate Resolution Imaging Spectroradiometer(MODIS)LST data(MYD11A2)and some auxiliary data using an artificial neural network(ANN)model.The data from 2003 to 2010 were used to train the ANN models,while 2011 to 2012 weather station temperatures were used to test the trained model.The results showed that the nighttime LST and mean LST provide good estimates of Tmin and Tmean,with root mean square errors(RMSEs)of 1.04℃ and 1.01℃,respectively.Moreover,the best RMSE of Tmax estimation was 1.27℃.Compared with the other two published Ta gridded datasets,the produced 1 km×1 km dataset accurately captured both the temporal and spatial patterns of Ta.The RMSE of Tmin estimation was more sensitive to elevation,while that of Tmax was more sensitive to month.Except for land cover type as the input variable,which reduced the RMSE by approximately 0.01℃,the other vegetation-related variables did not improve the performance of the model.The results of this study indicated that ANN,a type of machine learning method,is effective for long-term and large-scale Ta estimation.

Correlation Between the Arabian Sea Surface Temperature and the Onset Period of South Asian Summer Monsoon with Trend Analysis on the Intensity

The long-term trend of the Arabian Sea surface temperature(ASST)during the formation of the South Asian summer monsoon(SASM)is discussed in this manuscript.From April to June,ASST changed from a meridional gradual distribution to a spatially uniform distribution and then to a zonal gradual distribution.The South Asian summer monsoon intensity(SASMI)and South Asian summer monsoon direction(SASMD)indicate that the variation of the ASST is highly related to the formation of the SASM during the summer monsoon period and can contribute to the spread of the SASM from the Southwest Arabian Sea throughout all of South Asia.Results of the correlation between the ASST and SASMI for the same month and its adjacent months were the same,and the areas of the positive correlation between the ASST and SASMI significantly increased from May–June as compared to April–May.The maximum correlation coefficient was 0.86.The results of the ASST and SASMD for the same month and its adjacent months were substantially different.However,the ASST and SASMD for May and April also showed a high positive correlation with a maximum correlation coefficient of 0.61 in the southwestern Arabian Sea.Existence of the ASST had a spatially consistent and significant upward trend with a mean increase of 0.6℃during the summer monsoon period from 1980 to 2020(between April and September),whereas the SASMI had a strengthening trend along the western and southwestern regions of the Arabian Sea and the southeastern region of the Arabian Peninsula.Meanwhile,the rest of the study regions showed a declining trend.Overall,the entire study region showed a slight downward trend,and the average value decreased by 0.02ms^(−1).

On the long-term memory characteristic in land surface air temperatures:How well do CMIP6 models perform?

利用去趋势涨落分析(DFA)方法计算序列的长程记忆性(LTM),以CRUTEM5数据集的结果作为观测参照,评估了60个参与第六次国际耦合模式比较计划(CMIP6)的气候模式对地表气温LTM的再现能力.结果表明:大部分模式可以再现全球平均地表气温序列的LTM特征,其中AWI-ESM-1-1-LR和E3SM-1-0的模拟效果最好;60个模式均能模拟LTM随纬度带的变化;综合来说,全球水平上CNRM-CM6-1和HadGEM3-GC31-LL对地表气温LTM的模拟性能最好;多模式平均相比单一模式模拟性能更好;多模式平均与观测结果的偏差以及模式之间的模拟差异显著体现在赤道和沿海区域,这种偏差可能源于模式对海气耦合过程的模拟差异.

Analysis of Sea Surface Temperature Cooling in Typhoon Events Passing the Kuroshio Current

The aim of this study is to investigate the sea surface temperature(SST) cooling as typhoons pass the Kuroshio Current.A numerical circulation model,denoted as the Stony Brook Parallel Ocean Model(sbPOM),was used to simulate the SST,which includes four wave-induced effect terms(i.e.,radiation stress,nonbreaking waves,Stokes drift,and breaking waves) simulated using the third-generation wave model,called WAVEWATCH-Ⅲ(WW3).The significant wave height(SWH) measurements from the Jason-2 altimeter were used to validate the WW3-simulated results,yielding a root mean square error(RMSE) of less than 0.50 m and a correlation coefficient(COR) of approximately 0.93.The water temperature measured from the Advanced Research and Global Observation Satellite was applied to validate the model simulation.Accordingly,the RMSE of the SST is 0.92℃ with a COR of approximately 0.99.As revealed in the sbPOM-simulated SST fields,a reduction in the SST at the Kuroshio Current region was observed as a typhoon passed,although the water temperature of the Kuroshio Current is relatively high.The variation of the SST is consistent with that of the current,whereas the maximum SST lagged behind the occurrence of the peak SWH.Moreover,the Stokes drift plays an important role in the SST cooling after analyzing four wave-induced terms in the background of the Kuroshio Current.The sensitivity experiment also showed that the accuracy of the water temperature was significantly reduced when including breaking waves,which play a negative role in the inside part of the ocean.The variation in the mean mixing layer depth(MLD) showed that a typhoon could enhance the mean MLD in the Kuroshio Current area in September and October,whereas a typhoon has little influence on the mean MLD in the Kuroshio Current area in May.Moreover,the mean MLD rapidly decreased with the weakening of the strong wind force and wave-induced effects when a typhoon crossed the Kuroshio Current.

Transition Periods Between Sea Ice Concentration and Sea Surface Air Temperature in the Arctic Revealed by an Abnormal Running Correlation

This study used the synthetic running correlation coefficient calculation method to calculate the running correlation coefficients between the daily sea ice concentration(SIC) and sea surface air temperature(SSAT) in the Beaufort-Chukchi-East Siberian-Laptev Sea(BCEL Sea), Kara Sea and southern Chukchi Sea, with an aim to understand and measure the seasonally occurring changes in the Arctic climate system. The similarities and differences among these three regions were also discussed. There are periods in spring and autumn when the changes in SIC and SSAT are not synchronized, which is a result of the seasonally occurring variation in the climate system. These periods are referred to as transition periods. Spring transition periods can be found in all three regions, and the start and end dates of these periods have advancing trends. The multiyear average duration of the spring transition periods in the BCEL Sea, Kara Sea and southern Chukchi Sea is 74 days, 57 days and 34 days, respectively. In autumn, transition periods exist in only the southern Chukchi Sea, with a multiyear average duration of only 16 days. Moreover, in the Kara Sea, positive correlation events can be found in some years, which are caused by weather time scale processes.

Diversity on the Interannual Variations of Spring Monthly Precipitation in Southern China and the Associated Tropical Sea Surface Temperature Anomalies

There is a continuous and relatively stable rainy period every spring in southern China(SC).This spring precipitation process is a unique weather and climate phenomenon in East Asia.Previously,the variation characteristics and associated mechanisms of this precipitation process have been mostly discussed from the perspective of seasonal mean.Based on the observed and reanalysis datasets from 1982 to 2021,this study investigates the diversity of the interannual variations of monthly precipitation in spring in SC,and focuses on the potential influence of the tropical sea surface temperature(SST)anomalies.The results show that the interannual variations of monthly precipitation in spring in SC have significant differences,and the correlations between each two months are very weak.All the interannual variations of precipitation in three months are related to a similar western North Pacific anomalous anticyclone(WNPAC),and the southwesterlies at the western flank of WNPAC bring abundant water vapor for the precipitation in SC.However,the WNPAC is influenced by tropical SST anomalies in different regions each month.The interannual variation of precipitation in March in SC is mainly influenced by the signal of El Nino-Southern Oscillation,and the associated SST anomalies in the equatorial central-eastern Pacific regulate the WNPAC through the Pacific-East Asia(PEA)teleconnection.In contrast,the WNPAC associated with the interannual variation of precipitation in April can be affected by the SST anomalies in the northwestern equatorial Pacific through a thermally induced Rossby wave response.The interannual variation of precipitation in May is regulated by the SST anomalies around the western Maritime Continent,which stimulates the development of low-level anomalous anticyclones over the South China Sea and east of the Philippine Sea by driving anomalous meridional vertical circulation.

Corresponding Relation between Warm Season Precipitation Extremes and Surface Air Temperature in South China

Hourly data of 42 rain gauges over South China during 1966–2005 were used to analyze the corresponding relation between precipitation extremes and surface air temperature in the warm season(May to October).The results show that below 25℃,both daily and hourly precipitation extremes in South China increase with rising temperature.More extreme events transit to the two-time Clausius-Clapeyron(CC)relationship at lower temperatures.Daily as well as hourly precipitation extremes have a decreasing tendency nearly above 25℃,among which the decrease of hourly extremes is much more significant.In order to investigate the efects of rainfall durations,hourly precipitation extremes are presented by short duration and long duration precipitation,respectively.Results show that the dramatic decrease of hourly rainfall intensities above 25℃ is mainly caused by short duration precipitation,and long duration precipitation extremes rarely occur in South China when surface air temperature surpasses 28℃.

Spatiotemporal detection of land use/land cover changes and land surface temperature using Landsat and MODIS data across the coastal Kanyakumari district, India

This study assesses the changes in land use/land cover(LULC) and land surface temperature(LST) to identify their impacts from 2000 to 2020 along the coast of Kanyakumari district, India using remote sensing techniques. Landsat images are used to estimate the LULC changes and the MODIS data for LST.The Maximum Likelihood Classification(MLC) method is used, and the LULC is classified into six categories: Agriculture Land, Barren Land, Salt Pan, Sandy Beach, Settlement, and Waterbody. Within the two decades of the present change detection study, upheave in the Settlement area of 49.89% is noticed, and the Agriculture Land is exploited by 20.09%. Salt Pan emits a high LST of 31.57°C, and the Waterbodies are noticed with a low LST of 28.9°C. However, the overall rate of LST decreased by 0.56°C during this period. This study will help policymakers make appropriate planning and management to overcome the impact of LULC and LST in the forthcoming years.

The Coordinated Influence of Indian Ocean Sea Surface Temperature and Arctic Sea Ice on Anomalous Northeast China Cold Vortex Activities with Different Paths during Late Summer

The Northeast China cold vortex(NCCV)during late summer(from July to August)is identified and classified into three types in terms of its movement path using machine learning.The relationships of the three types of NCCV intensity with atmospheric circulations in late summer,the sea surface temperature(SST),and Arctic sea ice concentration(SIC)in the preceding months,are analyzed.The sensitivity tests by the Community Atmosphere Model version 5.3(CAM5.3)are used to verify the statistical results.The results show that the coordination pattern of East Asia-Pacific(EAP)and Lake Baikal high pressure forced by SST anomalies in the North Indian Ocean dipole mode(NIOD)during the preceding April and SIC anomalies in the Nansen Basin during the preceding June results in an intensity anomaly for the first type of NCCV.While the pattern of high pressure over the Urals and Okhotsk Sea and low pressure over Lake Baikal during late summer-which is forced by SST anomalies in the South Indian Ocean dipole mode(SIOD)in the preceding June and SIC anomalies in the Barents Sea in the preceding April-causes the intensity anomaly of the second type.The third type is atypical and is not analyzed in detail.Sensitivity tests,jointly forced by the SST and SIC in the preceding period,can well reproduce the observations.In contrast,the results forced separately by the SST and SIC are poor,indicating that the NCCV during late summer is likely influenced by the coordinated effects of both SST and SIC in the preceding months.

CORRELATION BETWEEN PEAK INTENSITY OF EXTREME AFTERNOON SHORT-DURATION RAINFALL AND HUMIDITY AND SURFACE AIR TEMPERATURE IN SOUTHEAST COAST OF CHINA

Using hourly rainfall intensity, daily surface air temperature, humidity and low-level dew point depressions at55 stations in the southeast coast of China, and sea surface temperature from reanalysis in the coastal region, this paper analyzes the connection between peak intensity of extreme afternoon short-duration rainfall(EASR) and humidity as well as surface air temperature. The dependency of extreme peak intensity of EASR on temperature has a significant transition. When daily highest surface temperature is below(above) 29°C, the peak rainfall intensity shows an ascending(descending) tendency with rising temperature. Having investigated the role of moisture condition in the variation of EASR and temperature, this paper discovered that the decrease of peak rainfall intensity with temperature rising is connected with the variation of relative humidity. At higher temperatures, the land surface relative humidity decreases dramatically as temperature further increases. During this process, the sea surface temperature maintains basically unchanged, resulting in indistinct variations of water vapor content at seas. As water vapor over land is mainly contributed by the quantitative moisture transport from adjacent seas, the decline of relative humidity over land will be consequently caused by the further rise of surface air temperature.

Study on the Relationship between the Oceanic Nino Index and Surface Air Temperature and Precipitation Rate over the Kingdom of Saudi Arabia

Abnormal weather conditions and extreme weather existed over the Kingdom Saudi Arabia (KSA) through the last decades. The present paper investigates the relationship between the Oceanic Nino Index (ONI) and variability of surface air temperature and precipitation rate over KSA through the period from 1950 to 2015 year. The NCEP/NCAR Reanalysis of monthly data sets of the mean surface air temperature and precipitation rate for the domain of the KSA is used. In addition, El Nino3.4 monthly data through the period (1950-2015) are used. For that period, the data set of the three months moving average of Nino3.4 anomaly, Oceanic Nino index (ONI), is used and analyzed. The time series, anomaly and correlation coefficient techniques are used to analyze the data sets through the present study. The results revealed that the KSA climate parameters, temperature and precipitation rates are controlled by ONI mainly in the autumn and winter seasons.

Close relationship between the East Asian westerly jet and Russian far East surface air temperature in summer

本文研究了夏季东亚副热带高空西风急流的年际变化与俄罗斯远东地面温度的关系并揭示了二者的相互作用机制。结果表明:副热带西风急流北侧环流与俄罗斯远东地区地面温度关系密切,二者存在着正反馈机制。一方面,急流北侧西风减弱(增强)可以通过关联的反气旋(气旋)式环流引起下沉(上升)运动,使得俄罗斯远东地区地面增温(降温)。另一方面,俄罗斯远东地区地面温度也能作为热源(热汇)在高层激发出反气旋式(气旋式)环流,导致急流北侧西风减弱(增强),进而影响急流的南北移动。这些结果揭示了俄罗斯远东地区地表状况的重要性,有助于进一步认识和提高东亚夏季气候的季度预测水平。