Climate Change Assessment Using Negative Extreme Deviations of Surface Temperature Field (Case Study: Tbilisi)

To conduct regional climate change assessment for the selected location (Tbilisi) more than century period hydrometeorological observation data of day-night minimal temperature is used. In the presented paper only temperature negative values of whole winter season are used. The temperature field change is characterized by the following four parameters: the minimal temperature sum (winter-day temperature is less than zero) of winter frost days;frost day number;season minimal temperature and the occurrence date of season minimal temperature. The modular structure of the above listed parameters has been studied and the mathematical expressions for temporal changes of those parameters have been obtained considering dynamical norms and cyclical changes. The intensity of this change in terms of global warming has been determined. One of the main parameters determining climate change is the variations of temperature field. Despite the fact that this parameter has been modified in the large range due to numerous impacts, they kept stable and provide sustainable equilibrium of the Earth’s energy potential during long-term (several decades) period. This was the reason why the Earth climate remained unchained. As it is known, the anthropogenic impact on the environment has breached the sustainable balance of the Earth energy potential, the potential has been gradually grown almost for more than a century and is known by the name of global warming. The above-mentioned process has already created the greatest threat to the existence of Earth’s biosphere and if it still continues the catastrophic results eventually have to be expected. This led that the problem solving has been set as an urgent task. The numerous fundamental studies for all regions of the world have been carried out to assess the intensity of this process.

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.

Experiments in numerical modelling of the Pacific sea surface temperature anomalies

By using the atmosphere-ocean coupled model (CGCM) which is composed of a 2-level global atmospheric general circulation model and a 4-layer Pacific oceanic general circulation model developed in the Institute of Atmospheric Physics of Chinese Academy of Sciences, and two model climatological fields got from the two independent models' numerical integrations respectively, the Pacific sea surface temperature anomalies (SSTA) from 1988 to 1989 are simulated in this paper with observed atmospheric general circulation data and sea surface temperature fields as initial conditions and monthly coupling scheme. In order to remove systematic biases of the model climatological fields, interaction variables between atmosphere and ocean are also corrected simultaneously. The experiments show that the simulation results can be improved effectively if these interaction variables are corrected in spite of the fact that there always exist systematic biases in independent numerical simulations of atmospheric part and oceanic part within CGCM. The basic characteristics of the observed Pacific SSTA in September and October 1988 have been simulated by using the correction scheme, such as the negative SSTA domain in the whole E-quatorial Pacific east to 150°E and the positive SSTA domain in the Western Pacific, the northern subtropical Pacific and nearly the whole Southern Pacific. Further numerical simulations show that the model can simulate not only the SSTA in the Pacific and its seasonal variations but also its interannual changes (for example, La Nino event in the Equatorial Pacific terminated after May 1989) to a certain degree. Furthermore, some problems existing in experiment processes and what we shoud do in the following stage are also discussed and analysed in this paper.

Is the global sea surface temperature rise accelerating?

This is an exploratory investigation to search for the presence of an acceleration in global sea surface temperature rise, which is essential to identify anthropogenic contributions to the climate change during the 20 th century. A weighted statistical model with an acceleration parameter was built progressively to reconstruct the variations in the global sea surface temperature data considering statistically significant confounders and autoregressive disturbances in the process. From the preliminary residual analysis of a weighted regression model, emerged a parsimonious model with first order autoregressive disturbances with a deterministic trend, acceleration and periodicity of 69 yr and its 138 yr subharmonic. The final model solution, selected from 29 alternative combinations of the model parameters using Mallows' s Cp metric, revealed a statistically significant deterministic trend, 0.40 ± 0.03C/c(p < 0.01), and acceleration, 0.67 ± 0.11C/c^2(p < 0.01) explaining 33% of the global sea surface temperature variations. The combined yearly trend and acceleration in global sea surface temperature as predicted by the model,exhibit a strong correlation with the yearly increase in the global CO^2 concentrations observed during the 20th century.

Contribution of the tropical sea surface temperature in the Arctic tropospheric warming during 1979-2013

本文通过CAM6-Nor大气环流模式模拟实验研究热带海表温度在1979-2013年北极对流层增温中的贡献.分析结果表明热带海表温度变化可以解释历史模拟实验中的秋季和1月增温的30%-40%.这意味着热带海表温度可能是1979年至2013年间北极冬季对流层变暖的主要驱动因素之一.除了1月份850hPa以下的北极增温,热带海表温度的影响通常来自热带中东部太平洋,热带印度-西太平洋和热带大西洋的联合作用.对1月份850hPa以下的北极增温的影响主要来自热带印度-西太平洋.

Prediction of Climate Change in the 21^(st) Century in Dalian Area under the Various Emission Scenarios

By using the simulative results of more than 20 climate system models which were provided by the fourth assessment report of the Intergovernmental Panel on Climate Change(IPCC),the climate change in Dalian area in the 21st century under the different scenarios(SRES A2,SRES A1B and SRES B1) were analyzed and predicted with the multi-model's aggregative simulative results via the interpolation downscaling calculation.The results showed that the climate in Dalian would have the obvious warming and wetting tendency in the 21st century as a whole.The annual average warming tendency of air temperature would be 2.45-3.46 ℃/100 years,and the annual precipitation increase trend would be 5.8%-16.3% per 100 years.The warming in winter would be the most obvious,and the precipitation increase would be comparatively obvious in winter and spring.The precipitation decrease would be comparatively obvious in autumn in the previous period of 21st century.In A2,A1B and B1 scenarios,the air temperatures in the late period of 21st century would respectively be 3.46,3.44 and 2.45 ℃ higher than in the ordinary years,and the annual precipitation would respectively be 16.3%,11.8% and 5.79% more than in the ordinary years.

Spatial and temporal variation of daytime and nighttime MODIS land surface temperature across Nepal

Land surface temperature(LST)is an important variable for assessing climate change and related environmental impacts observed in recent decades.Regular monitoring of LST using satellite sensors such as MODIS has the advantage of global coverage,including topographically complex regions such as Nepal.In order to assess the climatic and environmental changes,daytime and nighttime LST trend analysis from 2000 to 2017 using Terra-MODIS monthly daytime and nighttime LST datasets at seasonal and annual scales over the territory of Nepal was performed.The magnitude of the trend was quantified using ordinary linear regression,while the statistical significance of the trend was identified by the Modified Mann—Kendall test.Our findings suggest that the nighttime LST in Nepal increased more prominently compared to the daytime LST,with more pronounced warming in the pre-monsoon and monsoon seasons.The annual nighttime LST increased at a rate of 0.05 K yr-1(p<0.01),while the daytime LST change was statistically insignificant.Spatial heterogeneity of the LST and LST change was observed both during the day and the night.The daytime LST remained fairly unchanged in large parts of Nepal,while a nighttime LST rise was dominant all across Nepal in the pre-monsoon and monsoon seasons.Our results on LST trends and their spatial distribution can facilitate a better understanding of regional climate changes.

Impact of large-scale vegetation restoration project on summer land surface temperature on the Loess Plateau, China

A large-scale afforestation project has been carried out since 1999 in the Loess Plateau of China. However, vegetation-induced changes in land surface temperature (LST) through the changing land surface energy balance have not been well documented. Using satellite measurements, this study quantified the contribution of vegetation restoration to the changes in summer LST and analyzed the effects of different vegetation restoration patterns on LST during both daytime and nighttime. The results show that the average daytime LST decreased by 4.3°C in the vegetation restoration area while the average nighttime LST increased by 1.4°C. The contributions of the vegetation restoration project to the changes in daytime LST and nighttime LST are 58% and 60%, respectively, which are far greater than the impact of climate change. The vegetation restoration pattern of cropland (CR) converting into artificial forest (AF) has a cooling effect during daytime and a warming effect at nighttime, while the conversion of CR to grassland has an opposite effect compared with the conversion of CR to AF. Our results indicate that increasing evapotranspiration caused by the vegetation restoration on the Loess Plateau is the controlling factor of daytime LST change, while the nighttime LST change is affected by soil humidity and air humidity.

Record Arctic Ozone Loss in Spring 2020 is Likely Caused by North Pacific Warm Sea Surface Temperature Anomalies

Record ozone loss was observed in the Arctic stratosphere in spring 2020.This study aims to determine what caused the extreme Arctic ozone loss.Observations and simulation results are examined in order to show that the extreme Arctic ozone loss was likely caused by record-high sea surface temperatures(SSTs)in the North Pacific.It is found that the record Arctic ozone loss was associated with the extremely cold and persistent stratospheric polar vortex over February-April,and the extremely cold vortex was a result of anomalously weak planetary wave activity.Further analysis reveals that the weak wave activity can be traced to anomalously warm SSTs in the North Pacific.Both observations and simulations show that warm SST anomalies in the North Pacific could have caused the weakening of wavenumber-1 wave activity,colder Arctic vortex,and lower Arctic ozone.These results suggest that for the present-day level of ozone-depleting substances,severe Arctic ozone loss could form again,as long as certain dynamic conditions are satisfied.

Sea Surface Temperature Extremes of Different Intensity in the China Seas During the Global Warming Acceleration and Hiatus Periods

Based on the daily OISST V2 with 0.25ohorizontal resolutions, the present study looks into the variations of sea surface temperature (SST) extremes in the China Seas for different segments of the period 1982-2013. The two segments include the warming acceleration period from 1982 to 1997 and the hiatus period from 1998 to 2013 when the global mean surface temperature (GMST) did not significantly increase as expected, or even decreased in some areas.First, we construct the regional average time series over the entire China Seas (15°-45°N, 105°-130°E) for these SST extremes. During the hiatus period, the regionally averaged 10th, 1th and 0.1th percentile of SSTs in each year decreased significantly by 0.40℃, 0.56℃ and 0.58℃ per decade, respectively. The regionally averaged 90th, 99th and 99.9th percentile of SSTs in each year decreased slightly or insignificantly. Our work confirm that the regional hiatus was primarily reflected by wintertime cold extremes. Spatially, the trends of cold extremes in different intensity were nonuniformly distributed. Cold extremes in the near-shore areas were much more sensitive to the global warming hiatus. Hot extremes exhibited non-significant trend in the China Seas during the hiatus period. In short, the variations of the SST extremes in the two periods were non-uniform spatially and asymmetric seasonally. It is unexpected that the hot and cold extremes of each year during 1998-2013 were still higher than those extremes during 1982-1997. It is obvious that compared with the warming acceleration period, hot extremes were far more likely to occur in the recent hiatus as a result of a 0.3℃ warmer shift in the mean temperature distribution. Moreover, hot extremes in the China Seas will be sustained or amplified with the end of warming hiatus and the continuous anthropogenic warming.

Analysis of Climate Change in the Area of Vojvodina-Republic of Serbia and Possible Consequences

Climate change conditions a wide range of impacts such as the impact on weather,but also on ecosystems and biodiversity,agriculture and forestry,human health,hydrological regime and energy.In addition to global warming,local factors affecting climate change are being considered.Presentation and analysis of the situation was carried out using geoinformation technologies(radar recording,remote detection,digital terrain modeling,cartographic visualization and geostatistics).This paper describes methods and use of statistical indicators such as LST,NDVI and linear correlations from which it can be concluded that accelerated construction and global warming had an impact on climate change in period from 1987 to 2018 in the area of Vojvodina–Republic of Serbia.Also,using the global SRTM DEM,it is shown how the temperature behaves based on altitude change.Conclusions and possible consequences in nature and society were derived.

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.

Overview of global monthly surface temperature data in the past century and preliminary integration

This paper analyzes the status of existing resources through extensive research and international cooperation on the basis of four typical global monthly surface temperature datasets including the climate research dataset of the University of East Anglia(CRUTEM3), the dataset of the U.S. National Climatic Data Center(GHCN-V3), the dataset of the U.S. National Aeronautics and Space Administration(GISSTMP), and the Berkeley Earth surface temperature dataset(Berkeley). China's first global monthly temperature dataset over land was developed by integrating the four aforementioned global temperature datasets and several regional datasets from major countries or regions. This dataset contains information from 9,519 stations worldwide of at least 20 years for monthly mean temperature, 7,073 for maximum temperature, and 6,587 for minimum temperature. Compared with CRUTEM3 and GHCN-V3, the station density is much higher particularly for South America, Africa,and Asia. Moreover, data from significantly more stations were available after the year 1990 which dramatically reduced the uncertainty of the estimated global temperature trend during 1990e2011. The integrated dataset can serve as a reliable data source for global climate change research.

A COMPARISON AND EVALUATION OF TWO CENTENNIAL-SCALE SEA SURFACE TEMPERATURE DATASETS IN THE CHINA SEAS AND THEIR ADJACENT SEA AREAS

Two reconstructed sea surface temperature(SST) datasets(HadISST1 and COBE SST2) with centennial-scale are compared on the SST climate change over the China Seas and their adjacent sea areas. Two independent datasets show consistency in statistically significant trends, with a warming trend of 0.07—0.08 ℃ per decade from 1890 to2013. However, in shorter epochs(such as 1961—2013 and 1981—2013), HadISST1 exhibits stronger warming rates than those of COBE SST2. Both datasets experienced a sudden decrease in the global hiatus period(1998—2013), but the cooling rate of HadISST1 is lower than that of COBE SST2. These differences are possibly caused by the different observations sources which are incorporated to fill with data-sparse regions since 1982. Different data sources may lead to higher values in HadISST1 from 1981 to 2013 than that in COBE SST2. Meanwhile, the different data sources and bias adjustment before the World War II may also cause the large divergence between COBE SST2 and HadISST1,leading to lower SST from 1891 to 1930. These findings illustrate that the long-term linear trends are broadly similar in the centennial-scale in the China Seas using different datasets. However, there are large uncertainties in the estimate of warming or cooling tendency in the shorter epochs, because there are different data sources, different bias adjustment and interpolation method in different datasets.

NUMERICAL STUDY ON THE EFFECTS OF SEA SURFACE TEMPERATURE ON TROPICAL CYCLONE INTENSITY-PART I:NUMERICAL EXPERIMENT OF THE TROPICAL CYCLONE INTENSITY RELATED TO SST

In the context of a model of tropical cyclone intensity based on an improved meso-scale atmospheric model, numerical simulation is performed of the track and intensity variation of tropical cyclones (TC) arising from sea surface temperature (SST) variation over a specified sea region. Evidence suggests that the model is capable of modeling quite welt the track and intensity of TC: SST variation leads to an abrupt change in the cyclone intensity: the response of the cyclone to the abrupt SST change lasts 8—12 h.

NUMERICAL STUDY ON THE EFFECTS OF SEA SURFACE TEMPERATURE ON TROPICAL CYCLONE INTENSITY—PART Ⅱ:COUPLING MODEL AND EXPERIMENT

A tropical cyclone-marine mixed layer model including air-sea interaction is established to conduct numerical experiment with the effects of SST on the cyclone's intensity,Evidence suggests that with air-sea interaction involved,SST rise causes a drop of central pressure of the storm and SST impact on the intensity is weaker in the coupling case.Moreover,study is undertaken of the intensity variation of another tropical cyclone moving in the cyclone's cold-tail sector,with the results in good agreement with the observational fact.

NUMERICAL EXPERIMENTS OF THE EFFECTS OF SEA SURFACE TEMPERATURE ANOMALIES OVER THE PACIFIC ON PRECIPITATION IN 1991

A zonal domain,primitive equation model is used in this paper to study the influences of the main sea surface tem- perature anomaly(SSTA)areas over the Pacific on precipitation in 1991.Some numerical experiments are made and the mechanisms of the influences are discussed.The results show that the influences of the SSTA are mainly confined within the tropical and the subtropical regions.The direct effect of the SSTA is to change the exchanges of the sensible heat and the water vapour between the air and the sea,through the consequent changes of temperature and the flow fields and the feedback process of condensation,the SSTA finally affects precipitation.

NUMERICAL EXPERIMENTS FOR THE INFLUENCE OF ANOMALOUS SEA SURFACE TEMPERATURE ON THE SOUTH CHINA SEA SUMMER MONSOON

Several sensitivity experiments are done by using the T42L9 global spectral model developed by IAP for investigating the influence of sea surface temperature anomaly (SSTA) in different regions on the South China Sea Summer Monsoon (SCSM).It shows that when SSTA presents a La Nina pattern,the onset date of SCSM will be earlier and the convection in the South China Sea region will be consistently stronger,and vice versa.Specially,SSTA in the central and eastern Pacific plays a main role in the variation of the onset and the strength of SCSM.When SSTA of this area is lower,the onset of SCSM comes earlier,the strength of SCSM becomes stronger, otherwise,the conclusion is contrary.The influence of SSTA in the tropical West Pacific on the onset date of SCSM is not clear,but it strongly affects the strength of the monsoon.The warmer SST in this region will bring about a stronger SCSM,and vice versa.The relationship between SSTA in the tropical western Indian Ocean and SCSM has been investigated.It is found that the SSTA in this region can influence the onset of SCSM,and plays a role similar to the one in the eastern Pacific.The above results also reflect that the activity of SCSM has a close relationship with the El Nino or La Nina events.The onset and the strength change of the SCSM are obviously influenced by the heating status anomaly on the tropic Pacific through the Walker circulation.

Feedback and contribution of vegetation, air temperature and precipitation to land surface temperature in the Yangtze River Basin considering statistical analysis

Land surface temperature(LST),especially day-night LST difference(LSTd-LSTn),is a key variable for the stability of terrestrial ecosystems,affected by vegetation and climate change.Quantifying the contribution and feedback of vegetation and climate to LST changes is critical to developing mitigation strategies.Based on LST,Normalized vegetation index(NDVI),land use(LU),air temperature(AT)and precipitation(Pre)from 2003 to 2021,partial correlation was used to analyze the response of LST to vegetation and climate.The feedback and contribution of both to LST were further quantifed by using spatial linear relationships and partial derivatives analysis.The results showed that both interannual LST(LSTy)and LSTd-LSTn responded negatively to vegetation,and vegetation had a negative feedback effect in areas with significantly altered.Vegetation was also a major contributor to the decline of LSTd-LSTn.With the advantage of positive partial correlation area of 94.99%,AT became the main driving factor and contributor to LSTy change trend.Pre contributed negatively to both LSTy and LSTd-LSTn,with contributions of-0.004℃/y and-0.022℃/y,respectively.AT played a decisive role in LST warming of YRB,which was partially mitigated by vegetation and Pre.The present research contributed'to,the,detection,of LST changes and improved understanding of the driving mechanism.

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.