Climate Field Research Articles

Determination of ecological conditions and geographical distribution of vegetation in the Goksu basin

On the ecological conditions and distribution of vegetation in any geographical area; The mutual interaction of factors such as climate (temperature-precipitation), topography (altitude-mountain extent), soil plays an important role. In addition, these factors also determine the ecological and geographical distribution of vegetation at micro and macro levels. In this study, geographic information systems (GIS) are used as a method and here; Digital elevation model of the basin (30x30), multi-year climate data (precipitation, temperature), Erinc climate type results, soil distribution, stand distribution, plant profiles and field photographs are the materials used in the study. By processing these data, the type and distribution of vegetation in the Goksu basin were determined. According to these findings, physical factors such as altitude and the extent of the mountains have created significant differences in the precipitation and temperature distribution of the basin. This difference was clearly observed in the Erinc climate classification results, and the south of the basin presented humid and semihumid climate characteristics, and the north presented semi-arid climate characteristics. These climatic conditions also affected the soil formation and type,causing a wide distribution of non-calcareous brown soils and non-calcareous brown forest soils in the field. As a result of all these conditions, plant species showed different vertical and spatial distribution. In the part from the south of the basin to Saimbeyli, plant species such maquis, pinus brutia, pinus nigra, Cedrus libani, Abies, Juniperus are distributed, while in the north, oak species such as oak, Bromus torhentallus, Astragalus, Thymus have been distributed. Thus, factors such as climate, topography and soil played an important role in the spread of vegetation and species in the Goksu Basin.

Design and development of mobile application for millets

India is an agriculture leading country. Millets are indigenous to many parts of the world. Millets may have been consumed by humans for about 7,000 years and potentially had a pivotal role in the rise of multi-crop agriculture and settled farming societies. Every day new technology are introduced for increasing the yield and processing technology. Hence, the dissemination of information to the farmers at the time when it is required becomes very important. With the widespread availability of smartphones and Internet, there is a huge potential for supplying essential information via this means. This paper presents a mobile-apps directed millets to the agricultural sector designed in Android Operating System. The Millets AMPP App include the information about Origin and History, Area and Distribution, Botanical Description, Season And Varieties, Morphological Description, Crop Management as Climatic Requirements, Soil, Field Preparation, Seed and Sowing, Manures and Fertilizers, Water Management, Weed Control, Crop Protection as Pests and Diseases, Harvesting and Threshing, Yield, Nutrient Information, Health benefits, Machinery and value added products for the millets Maize, Sorghum, Kambu, Ragi, Pannivaragu, Samai, Thinai, Varagu, Banyard Millet. The detailed information are designed and presented in the form of an Android application-AMPP Millet App. The paper also discusses the steps to develop applications and the content available.

Research Transmutation of Agenda Setting Theory (2010-2022)—A Meta-Analysis Based on Journalism Studies

To celebrate the 50th anniversary of the birth of Agenda Setting Theory, this paper takes the international flagship journal “Journalism Studies” as the object, and uses the Meta-Analysis method to re-count a large number of the existing empirical literature, and analyze the transmutation in Agenda Setting theory in terms of research focus, subject areas and research methods over the past 12 years by statistical indicators in the relevant literature and corresponding statistical formulas. The results show that Agenda Setting research cannot escape from seeking to establish a link between the salience of media issues and the public’s perception of the importance of these issues; the thematic focus of this theoretical research goes beyond the traditional concern for public affairs and continues to spill over to the military, climate, finance, and other fields, which expand the scope of application of the theory; with the development of network science and artificial intelligence, network analysis and machine learning methods are gradually being used in Agenda Setting research, injecting new perspectives and more possibilities into this theoretical research.

A pseudoproxy assessment of why climate field reconstruction methods perform the way they do in time and space

Abstract. Spatiotemporal paleoclimate reconstructions that seek to estimate climate conditions over the last several millennia are derived from multiple climate proxy records (e.g., tree rings, ice cores, corals, and cave formations) that are heterogeneously distributed across land and marine environments. Assessing the skill of the methods used for these reconstructions is critical as a means of understanding the spatiotemporal uncertainties in the derived reconstruction products. Traditional statistical measures of skill have been applied in past applications, but they often lack formal null hypotheses that incorporate the spatiotemporal characteristics of the fields and allow for formal significance testing. More recent attempts have developed assessment metrics to evaluate the difference of the characteristics between two spatiotemporal fields. We apply these assessment metrics to results from synthetic reconstruction experiments based on multiple climate model simulations to assess the skill of four reconstruction methods. We further interpret the comparisons using analysis of empirical orthogonal functions (EOFs) that represent the noise-filtered climate field. We demonstrate that the underlying features of a targeted temperature field that can affect the performance of CFRs include the following: (i) the characteristics of the eigenvalue spectrum, namely the amount of variance captured in the leading EOFs; (ii) the temporal stability of the leading EOFs; (iii) the representation of the climate over the sampling network with respect to the global climate; and (iv) the strength of spatial covariance, i.e., the dominance of teleconnections, in the targeted temperature field. The features of climate models and reconstruction methods identified in this paper demonstrate more detailed assessments of reconstruction methods and point to important areas of testing and improving real-world reconstruction methods.

Determining water use and crop coefficients of drip-irrigated cotton in south Xinjiang of China under various irrigation amounts

Crop evapotranspiration (ETc) and crop coefficient (Kc) can vary among various geographical locations and cultivated species. Therefore, determining regional- and species-specific crop coefficients is fundamental for rational irrigation water management. A two-year field study was conducted in south Xinjiang of northwest China to estimate periodic ETc and reference evapotranspiration (ET0), and thus develop the regional crop coefficient (Kc) at various growth stages of fertigated cotton (Gossypium hirstum L. Xinluzhong No 67) under various irrigation amounts. ETc was determined by the soil water balance method, while ET0 was obtained using the FAO (Food and Agriculture Organization of the United Nations) Penman-Monteith model. The results indicated that increasing irrigation amounts resulted in increased seed cotton yields, but reduced crop water productivity (WP). The water consumption at flowering and boll setting stages accounted approximately for 60% of the seasonal water use of cotton under various irrigation amounts. The average locally developed Kc (Kc-local) values over two seasons were 0.38, 1.30 and 1.09 for initial, mid-season, and end-season stages, respectively. The locally developed initial Kc (Kc-local-ini) (0.38) was higher than the optimized initial-stage Kc (Kc-opt-ini) (0.23) and similar to the FAO recommended Kc (Kc-FAO) (0.35). The optimized Kc (Kc-opt) did not significantly differ from Kc-FAO at mid-season and end-season stages. The locally developed middle-stage Kc (Kc-local-mid) was 10% higher than the optimized middle-stage Kc (Kc-opt-mid) and the FAO recommended middle-stage Kc (Kc-FAO-mid), and the locally developed end-stage Kc (Kc-local-end) was approximately 82% higher than the optimized end-stage Kc (Kc-opt-end) and the FAO recommended end-stage Kc (Kc-FAO-end). The difference between Kc-local, Kc-opt and Kc-FAO was attributed to the variations of climate condition, cultivated variety and field management. Kc-local determined by the soil water balance method in the present study fully considered the regional conditions including weather, species and management practices, which can be adopted to guide the irrigation management in this region. This study provided credible cotton periodic Kc in the arid and semiarid regions of northwest China. A second-order polynomial relationship between cotton Kc and t/T (the days after emergence/the days of total cotton growth period) was established, which can provide a reference for the determination of local cotton Kc and irrigation management.

EUREC<sup>4</sup>A's <i>HALO</i>

Abstract. As part of the EUREC4A (Elucidating the role of cloud–circulation coupling in climate) field campaign, the German research aircraft HALO (High Altitude and Long Range Research Aircraft), configured as a cloud observatory, conducted 15 research flights in the trade-wind region east of Barbados in January and February 2020. Narrative text, aircraft state data, and metadata describing HALO's operation during the campaign are provided. Each HALO research flight is segmented by timestamp intervals into standard elements to aid the consistent analysis of the flight data. Photographs from HALO's cabin and animated satellite images synchronized with flight tracks are provided to visually document flight conditions. As a comprehensive product from the remote sensing observations, a multi-sensor cloud mask product is derived and quantifies the incidence of clouds observed during the flights. In addition, to lower the threshold for new users of HALO's data, a collection of use cases is compiled into an online book, How to EUREC4A, included as an asset with this paper. This online book provides easy access to most of EUREC4A's HALO data through an intake catalogue. Code and data are freely available at the locations specified in Table 6.

EDUKASI BENCANA KEKERINGAN DAN KESIAPSIAGAAN WARGA MASYARAKAT DALAM MENANGGULANGI BENCANA KEKERINGAN DI KABUPATEN LOMBOK TENGAH PROVINSI NUSA TENGGARA BARAT

Geologically, Indonesia is located at the confluence of three of the world's main plates, namely the Indo-Australian Plate, the Eurasian Plate, and the Pacific Plate, which often cause disasters, for example drought. Central Lombok Regency is one of the regencies in West Nusa Tenggara, with these two seasons, most often hit by drought disasters, considering that the rainfall in Central Lombok is very small. So that disaster education and preparedness are needed for residents. This article uses qualitative research methods. The results of the study show that climate field schools have an important role in disaster education. Climate field school is a form of education for farmers that is very useful especially in understanding the weather and climate combined with new agricultural techniques that will allow farmers to plant different crops at different times of the year. Meanwhile, for preparedness, it is carried out through several programs in the form of urging and asking farmers to be disciplined in carrying out cropping patterns according to the zoning in which they are located (rice-padi-palawija or padi-palawija-palawija), mapping of land related to cropping patterns, planting rice parity that requires little water, namely the Situpagendit variety, and urges farmers to insure their rice plants, which is Rp. 36.000/ Ha for one planting season at PT. JASINDI.
 
 Keywords: Indonesia's geological location, disaster, disaster education.

Ancient Metallurgical Center in Georgia

Georgia is situated in Caucasia, between the Black Sea and the Caspian Sea. Its terrain is created by the Greater and the Lesser Caucasus, rivers with great volume of water flowing between them and coastal plains. The territory of the whole Caucasus and, specifically, Georgia, is one of the main communication areas linking ancient Steppe empires with Near Eastern civilizations (pl. 1), which, alongside rich natural conditions, significantly determined formation of human society from ancient times and proved favourable to its development. Formation and evolution of the human society in any corner of the earth, from the earliest times to ancient civilizations, largely depends on the geographic environment and reasonable exploitation of natural resources by humans. With its mild climate, fertile lands, flora, fauna, alpine pastures and low-lying fields, diverse natural resources, extensive river network, etc. Georgia has always provided convenient conditions for human habitation and development of economic activities. Southern slopes of the Caucasus Mountain Range and the highlands of the Lesser Caucasus are especially rich in metallogenic provinces. The present article deals with when production of different metals started here and whether it became one of the main bases for development of the society.

MSG-GAN-SD: A Multi-Scale Gradients GAN for Statistical Downscaling of 2-Meter Temperature over the EURO-CORDEX Domain

One of the most important open challenges in climate science is downscaling. It is a procedure that allows making predictions at local scales, starting from climatic field information available at large scale. Recent advances in deep learning provide new insights and modeling solutions to tackle downscaling-related tasks by automatically learning the coarse-to-fine grained resolution mapping. In particular, deep learning models designed for super-resolution problems in computer vision can be exploited because of the similarity between images and climatic fields maps. For this reason, a new architecture tailored for statistical downscaling (SD), named MSG-GAN-SD, has been developed, allowing interpretability and good stability during training, due to multi-scale gradient information. The proposed architecture, based on a Generative Adversarial Network (GAN), was applied to downscale ERA-Interim 2-m temperature fields, from 83.25 to 13.87 km resolution, covering the EURO-CORDEX domain within the 1979–2018 period. The training process involves seasonal and monthly dataset arrangements, in addition to different training strategies, leading to several models. Furthermore, a model selection framework is introduced in order to mathematically select the best models during the training. The selected models were then tested on the 2015–2018 period using several metrics to identify the best training strategy and dataset arrangement, which finally produced several evaluation maps. This work is the first attempt to use the MSG-GAN architecture for statistical downscaling. The achieved results demonstrate that the models trained on seasonal datasets performed better than those trained on monthly datasets. This study presents an accurate and cost-effective solution that is able to perform downscaling of 2 m temperature climatic maps.

Retrospection of heatwave and heat index

The frequency and intensity of extreme events especially heat waves (HW) are growing all around the world which ultimately poses a serious threat to the health of individuals. To quantify the effects of extreme temperature, appropriate information, and the importance of HW and heat index (HI) are carefully discussed for different parts of the world. Varied definitions of the HW and HI formula proposed and used by different countries are carried out systematically continent-wise. Different studies highlighted the number of definitions of HW; however, mostly used Steadman’s formulae, which was developed in the late 1970s, for the calculation of HI that uses surface air temperature and relative humidity as climatic fields. Since then, dramatic changes in climatic conditions have been observed as evident from the ERA5 datasets which need to be addressed; likewise, the definition of HW, which is modified by the researchers as per the geographic conditions. It is evident from the ERA5 data that the temperature has increased by 1–2 °C as compared to the 1980s. There is a threefold increase in the number of heatwave days over most of the continents in the last 40 years. This study will help the researcher community to understand the importance of HW and HI. Furthermore, it opens the scope to develop an equation based on the present scenario keeping in mind the basics of an index as considered by Steadman.

Climatic Fields of Sea Currents and Wind Waves in the Sea of Azov

Climatic Fields of Sea Currents and Wind Waves in the Sea of Azov

Dynamic Hillslope Soil Moisture in a Mediterranean Montane Watershed

AbstractVariations in hillslope soil moisture control forest hydrologic fluxes and storage pools, yet sparse observations combined with the complexity and heterogeneity of water movement and storage in the vadose zone can make temporal and spatial patterns and processes difficult to predict. We used two years of field observations of volumetric soil moisture at three depths (15, 30, and 100 cm) across five topographic positions (riparian, toeslope, sideslope, shoulder, and ridge) along three hillslope transects to better understand how soil moisture changes with hillslope position and through time. As expected, we found higher values of soil moisture at all depths at the riparian and toeslope positions. Unexpectedly, we found that ridges were particularly wet during the wet winter months and dried quickly during the summer months, indicating that topography alone cannot account for mean wet season soil moisture in our Mediterranean climate field site. The variability in soil moisture across all soil depths and topographic positions was greatest when soils were dry and decreased under wet soil conditions; this variability remained high in the deeper soil horizons, regardless of season. Lastly, event analysis suggests that the response to early season rainfall was highly variable along the hillslopes and was likely dominated by localized controls such as microtopography and vegetation as well as soil texture, antecedent moisture conditions, and rainfall characteristics. Our results suggest that the drivers of wet and dry season soil moisture dynamics can vary across topographic positions along a hillslope and do not always follow topographic controls.

Impact of merging of historical and future climate data sets on land carbon cycle projections for South America

AbstractEarth System Models (ESMs) project climate change, but they often contain biases in their estimates of contemporary climate that propagate into simulated futures. Land models translate climate projections into surface impacts, but these will be inaccurate if ESMs have substantial errors. Bias concerns are relevant for terrestrial physiological processes which often respond non‐linearly (i.e. contain threshold responses) and are therefore sensitive to absolute environmental conditions as well as changes. We bias‐correct the UK Met Office ESM, HadGEM2‐ES, against the CRU–JRA observation‐based gridded estimates of recent climate. We apply the derived bias corrections to future projections by HadGEM2‐ES for the RCP8.5 scenario of future greenhouse gas concentrations. Focusing on South America, the bias correction includes adjusting for ESM estimates that, annually, are approximately 1 degree too cold, for comparison against 21st Century warming of around 4 degrees. Locally, these values can be much higher. The ESM is also too wet on average, by approximately 1 mm·day−1, which is substantially larger than the mean predicted change. The corrected climate fields force the Joint UK Land Environment Simulator (JULES) dynamic global vegetation model to estimate land surface changes, with an emphasis on the carbon cycle. Results show land carbon sink reductions across South America, and in some locations, the net land–atmosphere CO2 flux becomes a source to the atmosphere by the end of this century. Transitions to a CO2 source is where increases in plant net primary productivity are offset by larger enhancements in soil respiration. Bias‐corrected simulations estimate the rise in South American land carbon stocks between pre‐industrial times and the end of the 2080s is ∼12 GtC lower than that without climate bias removal, demonstrating the importance of merging historical observational meteorological forcing with ESM diagnostics. We present evidence for a substantial climate‐induced role of greater soil decomposition in the fate of the Amazon carbon sink.

DWNet: Dual-Window Deep Neural Network for Time Series Prediction

Multivariate time series prediction is a very important task, which plays a huge role in climate, economy, and other fields. We usually use an Attention-based Encoder-Decoder network to deal with multivariate time series prediction because the attention mechanism makes it easier for the model to focus on the really important attributes. However, the Encoder-Decoder network has the problem that the longer the length of the sequence is, the worse the prediction accuracy is, which means that the Encoder-Decoder network cannot process long series and therefore cannot obtain detailed historical information. In this paper, we propose a dual-window deep neural network (DWNet) to predict time series. The dual-window mechanism allows the model to mine multigranularity dependencies of time series, such as local information obtained from a short sequence and global information obtained from a long sequence. Our model outperforms nine baseline methods in four different datasets.

GeoClimate: a Geospatial processing toolbox for environmental and climate studies

Human activities induce changes on land use and land cover. These changes are most significant in urban areas where topographic features (e.g., building, road) affect the density of impervious surface areas and introduce a range of urban morphological patterns. Those characteristics impact the energy balance and modify the climate locally (e.g., inducing the so-called Urban Heat Island phenomenon). Therefore, there is a need for georeferenced morphological indicators as well as urban classifications (such as Local Climate Zones) that can be directly used for planning or as inputs of climate models. GeoClimate is dedicated to this purpose: it converts raw geographical data (OpenStreetMap and French BDTopo) into indicators useful for climate applications (sky view factor, vegetation fraction, etc.) However, its application is not limited to the climate field. The indicators calculated in GeoClimate can also be used for other diagnostic or planning purposes: studying the territory fragmentation, the influence of the urban fabric on pollution (noise or air chemical transport), the energy consumption, etc. GeoClimate is available as free and open source geospatial software.

Cover crops promote primary crop yield in China: A meta-regression of factors affecting yield gain

• We analyse the effects of cover crops (CCs) on subsequent crop yield in China. • On average over whole China, CCs significantly increased subsequent crop yield by ca. 10 % compared to fallow. • CC type, CC season and N input were key factors influencing the effect of CCs on crop yield. • Results provide a basis for advising farmers on the use of CCs for increasing crop yields. Inclusion of cover crops (CCs) in cropping systems can improve soil quality, reduce agricultural inputs, and improve environmental sustainability. While CCs have been widely promoted in China in recent years, a quantitative assessment of CC effect on crop yield across China is lacking. There is also no overarching analysis on factors explaining variation in yield effects. Here, we carried out a meta-analysis to evaluate the effects of CCs on subsequent primary crop yield in China, based on 53 published studies reporting 442 yield ratios with and without CCs. We found that CCs increased subsequent primary crop yields by 9.7 % on average when compared with fallow across China. The effects of CC varied with different factors related to climatic zone, field management and soil properties. CC type (legume or not), CC season (growing in winter or summer) and nitrogen input were factors substantially influencing primary crop yield. We identified high yield benefits in subtropical rice systems with winter CCs (12.9 ± 3.9 %) but no significant yield benefits in temperate wheat systems with summer CCs (1.9 ± 6.6 %). Within subtropical rice systems, both legume and non-legume CCs were tested widely, and the effects of legume CCs (14.6 ± 4.0 %) on primary crop yield were greater than those of non-legume CCs (7.9 ± 3.7 %). The yield benefits of CCs decreased with increasing fertilizer inputs. These results may be used for developing policy recommendations to improve primary crop yield by integrating targeted CCs associated with nitrogen management into cropping systems in China.

Trends in regional climate change and field crop productivity in Orenburg steppe region of Russia

Over the past four decades, climate in Orenburg region changed towards a significant increase in average annual air temperature from 4.5 ° C to 6.2 ° C and a significant decrease in the annual precipitation from 380 to 320 mm. For winter and early grain crops, these changes led to a sharp decrease in the productivity of crops about 2 times compared to the maximum values in the early 90s. Sunflower and sorghum cereals being the most thermophilic and drought-resistant crops adapted to the changed climate conditions better than other crops. The relationships that we have established between the temperature anomaly in the lower troposphere of the Northern Hemisphere and crop productivity can be used in models for long-term forecasting of weather changes and the productivity of field crops in Orenburg region.

Simulated Variation Characteristics of Oceanic CO2 Uptake, Surface Temperature, and Acidification in Zhejiang Province, China

Since preindustrial times, atmospheric CO2 content increased continuously, leading to global warming through the greenhouse effect. Oceanic carbon sequestration mitigates global warming; on the other hand, oceanic CO2 uptake would reduce seawater pH, which is termed ocean acidification. We perform Earth system model simulations to assess oceanic CO2 uptake, surface temperature, and acidification for Zhejiang offshore, one of the most vulnerable areas to marine disasters. In the last 40 years, atmospheric CO2 concentration increased by 71 ppm, and sea surface temperature (SST) in Zhejiang offshore increased at a rate of 0.16°C/10a. Cumulative oceanic CO2 uptake in Zhejiang offshore is 0.3 Pg C, resulting in an increase of 20% in sea surface hydrogen ion concentration, and the acidification rate becomes faster in the last decade. During 2020–2040, under four RCP scenarios, SST in Zhejiang offshore increases by 0.3–0.5°C, whereas cumulative ocean carbon sequestration is 0.150–0.165 Pg C. Relative to RCP2.6, the decrease of surface pH in Zhejiang offshore is doubled under RCP8.5. Furthermore, simulated results show that the relationship between CO2 scenario and oceanic carbon cycle is nonlinear, which hints that deeper reduction of anthropogenic CO2 emission may be needed if we aim to mitigate ocean acidification in Zhejiang offshore under a higher CO2 concentration scenario. Our study quantifies the variation characteristics of oceanic climate and carbon cycle fields in Zhejiang offshore, and provides new insight into the responses of oceanic carbon cycle and the climate system to oceanic carbon sequestration.

Assessment of desertification using modified MEDALUS model in the north Nile Delta, Egypt

Understanding spatial patterns of environmentally sensitive areas (ESAs) to desertification is essential for sustainable land use in the drylands. This work is a novel trial for integrating the erosion quality index (EQI) with a modified Mediterranean Desertification and Land Use (MEDALUS) method and factor analysis to define ESAs to desertification. The model was then applied within 8607.13 km2 (860713 ha) in the north Nile Delta of Egypt as a case study. Climate data, satellite imageries, field observations, and samples collected from 105 soil profiles were analyzed to characterize five thematic quality indices of soil, climate, vegetation, management, and erosion. These indices were weighted based on the factor analysis (FA) and superimposed under the GIS environment in a single map using the weighted sum model. The proposed model showed that 72% of the total area was classified as critical-sensitive to desertification where 70% was classified as highly-critical, while 2% as moderately-critical. No significant difference (p < 0.001) was observed between the results of the proposed model and those calculated by the geometric mean algorithm (MEDALUS model) with R2 and root mean square error of 0.88 and 0.11, respectively. The average linear sensitivity analysis showed that using weights derived from the FA could assimilate the results of the proposed model closer to reality rather than equal weights used in the MEDALUS model. The developed model would improve the insight into desertification processes in the studied area, thereby suitable conservation practices would be adopted in time.

Assessment of the Black Sea Temperature and Salinity Climatic Fields for the Recent Climatological Period (1991–2020)

Purpose. The purpose of the study is to assess the coefficient of vertical turbulent exchange for different layers of the Black Sea basin based on the experimental data on microstructure of the physical fields obtained for the period 2004–2019 in the Black Sea and using the semi-empirical models. Methods and Results. New array of the temperature and salinity climatic fields was assessed by the results of numerical experiments. In the experiment, annual variation of the Black Sea hydrophysical parameters was reconstructed by the numerical model. Modeling included the scheme of assimilating the data of the climatic temperature and salinity array assessed. In contrast to the averaged data of the field observations, the modeled fields are compliant with equations of motion. Besides the temperature and salinity three-dimensional fields, the three-dimensional climatic fields of the Black Sea currents were also reconstructed for each day of a climatic year that is quite impossible using the observational data only. Spatial-temporal variability of the modeled three-dimensional fields was analyzed. The integral characteristics of the Black Sea water dynamics for the recent 30-year climatic period were studied and compared with the analogous ones for the previous century. Simulation was carried out by three-dimensional non-linear model of the Black Sea dynamics developed in Marine Hydrophysical Institute. The horizontal resolution of the model was 5 km, and the EMODNet bathymetry was used. The performed calculations showed that the increased spatial resolution of the temperature and salinity climatic array for the recent period made it possible to reconstruct the dynamics of the Black Sea in all layers in more detail. At the same time, significant small-scale variability of salinity fields was revealed. It was most pronounced at the deep-water horizons. Conclusions. Modeling using a new array of thermohaline fields revealed an increase in the integral temperature of the upper mixed layer in comparison with the experiment with assimilation of the previous version of the climatic array. At that, thinning and «break» of the cold intermediate layer found in the central part of the sea, indicates warming of the sea upper layer during the last 30 years. The highest noise detected at the deep-water horizons in the modeled salinity fields is related to quantity and quality of the salinity data resulted from the field observations. Taking into account insufficient calibration facilities for measuring seawater electrical conductivity, the next version of climatic TS-array requires a more strict procedure for verifying and processing the observation data obtained in the deep-sea layers.