Atmospheric Circulation Indices Research Articles

Spatial Heterogeneity in the Response of Winter Wheat Yield to Meteorological Dryness/Wetness Variations in Henan Province, China

Knowledge of the responses of winter wheat yield to meteorological dryness/wetness variations is crucial for reducing yield losses in Henan province, China’s largest winter wheat production region, under the background of climate change. Data on climate, yield and atmospheric circulation indices were collected from 1987 to 2017, and monthly self-calibrating Palmer drought severity index (sc-PDSI) values were calculated during the winter wheat growing season. The main results were as follows: (1) Henan could be partitioned into four sub-regions, namely, western, central-western, central-northern and eastern regions, based on the evolution characteristics of the time series of winter wheat yield in 17 cities during the period of 1988–2017. Among them, winter wheat yield was high and stable in the central-northern and eastern regions, with a remarkable increasing trend (p < 0.05). (2) The sc-PDSI in February had significantly positive impacts on climate-driven winter wheat yield in the western and central-western regions (p < 0.05), while the sc-PDSI in December and the sc-PDSI in May had significantly negative impacts on climate-driven winter wheat yield in the central-northern and eastern regions, respectively (p < 0.05). (3) There were time-lag relationships between the sc-PDSI for a specific month and the atmospheric circulation indices in the four sub-regions. Furthermore, we constructed multifactorial models based on selected atmospheric circulation indices, and they had the ability to simulate the sc-PDSI for a specific month in the four sub-regions. These findings will provide scientific references for meteorological dryness/wetness monitoring and risk assessments of winter wheat production.

Seasonal patterns and interannual variations of atmospheric circulation over the Bering Sea in 1995–2022

Mean patterns of atmospheric circulation over the Bering Sea in 4 seasons: winter (January-March), spring (April-June), summer (July-September) and autumn (OctoberDecember) are described using the author’s typification of synoptic situations. Frequency of all 6 types of synoptic situations is calculated and the predominant types are determined, by season. Mean values of the meridional and zonal indices of atmospheric circulation and the number and intensity of cyclones in the Bering Sea area are calculated for each season in the period 1995–2022. Cyclonic activity and direction and intensity of general wind transfer, particularly intensity of winter and summer monsoons, are considered separately for the western and eastern parts of the Bering Sea. Interannual variations of all these parameters are traced. For all seasons, the wind transfer over the entire area is determined by cyclonic activity in the western Bering Sea: the higher activity leads to weaker monsoon in winter (northeasterly) and summer (southwesterly) but strengthening in spring (southeasterly) and autumn (northwesterly). Since the middle 1990s, autumns and winters in the Bering Sea became warmer (with the warmest period in the late 2010s when southeasterlies prevailed in winter), but springs became colder (southeastern wind transfer in early 2000s changed to the northeastern one in late 2010s), with no definite tendency for summer, when the meridional index of atmospheric circulation was rather stable and the zonal index had a negative trend (weakening of westerlies). Trends and cycles of oceanographic conditions in the northwestern Bering Sea generally coincided with the changes in wind transfer in any season.

Added value of seasonal hindcasts to create UK hydrological drought storylines

Abstract. The UK has experienced recurring periods of hydrological droughts in the past, including the drought declared in summer 2022. Seasonal hindcasts, consisting of a large sample of plausible weather sequences, can be used to create drought storylines and add value to existing approaches to water resources planning. In this study, the drivers of winter rainfall in the Anglian region in England are investigated using the ECMWF SEAS5 hindcast dataset, which includes 2850 plausible winters across 25 ensemble members and 3 lead times. Four winter clusters are defined using the hindcast winters based on possible combinations of various atmospheric circulation indices (such as the North Atlantic Oscillation, NAO; East Atlantic, EA, pattern; and El Niño–Southern Oscillation). Using the 2022 drought as a case study, we demonstrate how storylines representing alternative ways the event could have unfolded can be used to explore plausible worst-case scenarios over winter 2022/23 and beyond. The winter clusters span a range of temperature and rainfall response in the study region and represent circulation storylines that could have happened over winter 2022/23. River flow and groundwater level simulations with the large sample of plausible hindcast winters show that drier-than-average winters characterised by predominantly NAO−/EA− and NAO+/EA− circulation patterns could have resulted in the continuation of the drought with a high likelihood of below-normal to low river flows across all selected catchments and boreholes by spring and summer 2023. Catchments in Norfolk were particularly vulnerable to a dry summer in 2023 as river flows were not estimated to recover to normal levels even with wet winters characterised predominantly by NAO−/EA+ and NAO+/EA+ circulation patterns, due to insufficient rainfall to overcome previous dry conditions and the slow response nature of groundwater-dominated catchments. Through this analysis, we aim to demonstrate the added value of this approach to create drought storylines during an ongoing event. Storylines constructed in this way supplement traditional weather forecasts and hydrological outlooks, in order to explore a wider range of plausible outcomes.

Spatiotemporal variations of extreme weather events and climate drivers in the Three Gorges Reservoir Area and its surrounding regions from 1960 to 2020

The Three Gorges Reservoir Area and its Surrounding Region (TGRASR) play a crucial role in the Yangtze River Basin. Over the past years, there has been growing concern about the effects of extreme weather events in this area, mainly attributed to climate change. To effectively address the implications of climate change, a thorough understanding of the temporal and spatial variability of these events is necessary. However, some of the previous studies have been limited to analyzing trend changes in events and have not sufficiently considered the connection with atmospheric circulation indices. In this study, we analyzed the spatio-temporal variations of extreme weather events using the Mann-Kendall and Sen's slope trend methods. Then, the correlation between extreme weather events and climate factors is explored using the Mantel test, Geo-Detector method (GDM), and Partial wavelet coherency (PWC) in terms of linear, hierarchical heterogeneity, and nonlinear, respectively. Finally, we investigated the potential climate factors that affect the temporal and spatial variability of extreme weather occurrences. The results showed that(1) among the 12 extreme indices analyzed in the TGRASR region from 1960 to 2020, there was a decreasing trend observed in Consecutive dry days (CDD), Consecutive wet days (CWD), and Annual count of days when precipitation is ≥10 mm (R10), while the remaining indices exhibited an increasing trend. Subsequent analysis employing the M-K mutation revealed that the mutation point for CWD occurred around 1975, resulting in a significant decrease in its trend. Additionally, the Simple daily intensity index (SDII), Annual count of days when the daily maximum temperature (TX) is >25 °C (SU25), and R10 displayed a decreasing trend. Mutations in the trends SU25, Annual count of days when the daily minimum temperature (TN) is >20 °C (TR20), and Annual count of days with at least 6 consecutive days when TX > 90th percentile (WSDI) were observed in 1997, 1999, 2003, and 2005, respectively. Following these mutations, all four indices displayed a substantial increase. (2) CDD, CWD, and R10 exhibit a decreasing trend across most stations, whereas the remaining indices demonstrate an increasing trend. Regarding spatial distribution, indices characterizing precipitation frequency and intensity exhibit higher mean values in the southeastern part of the TGRASR. (3) Mantel test: North Pacific pattern (NP), North Tropical Atlantic (NTA), and Global Mean Land/Ocean Temperature (GMLOT) have significant linear correlations with extreme precipitation and extreme temperature events; GDM: GMLOT and NTA are the main contributors to extreme precipitation and extreme temperature; PWC: 12 extreme indices have some nonlinear associations with all five climate factors. The study's findings can assist decision-makers in formulating sustainable water resource management strategies and measures for mitigating drought and flood risks. Additionally, the study contributes to uncovering climatic factors that influence changes in extreme weather events.

Meteorological Characteristics of a Continuous Ice-Covered Event on Ultra-High Voltage Transmission Lines in Yunnan Region in 2021

Yunnan plays a pivotal role in transmitting electricity from west to east within China’s Southern Power Grid. During 7–13 January 2021, a large-scale continuous ice-covering event of ultra-high voltage (UHV) transmission lines occurred in the Qujing area of eastern Yunnan Province. Based on ERA5 reanalysis data and meteorological observation data of UHV transmission line icing in China’s Southern Power Grid, the synoptic causes of the icing are comprehensively analyzed from various perspectives, including weather situations, vertical stratification of temperature and humidity, local meteorological elements, and atmospheric circulation indices. The results indicate a strong East Asian trough and a blocking high directing northern airflow southward ahead of the ridge. Cold air enters the Qujing area and combines with warm and moist air from the subtropical high pressure of 50–110° E. As warm and cold air masses form a quasi-stationary front over the northern mountainous area of Qujing due to topographic uplift, the mechanism of “supercooling and warm rain” caused by the “warm–cold” temperature profile structure leads to freezing rain events. Large-scale circulation indices in the Siberian High, East Asian Trough, and 50–110° E Subtropical High regions provided clear precursor signals within 0–2 days before the icing events.

North Atlantic atmospheric circulation indices: Links with summer and winter temperature and precipitation in north‐west Europe, including persistence and variability

AbstractVariability in seasonal weather in north‐west Europe is substantially determined by jet stream variability. The North Atlantic Oscillation (NAO) has been well studied as a key representation of this jet stream variability, but other circulation indices are also important. Here the first three principal component empirical orthogonal functions (EOFs) of 500 hPa geopotential height (GPH), which broadly correspond to the NAO, the East Atlantic pattern (EA) and Scandinavian pattern (SCA), as well as jet speed and latitude, are correlated with temperature and precipitation anomalies over Europe with a focus on north‐west Europe, as well as measures of persistence and variability. In high summer (July and August), all three of the principal EOFs are significantly correlated with extreme temperatures in large areas of northern Europe. In winter, for much of north‐west Europe, both temperatures and precipitation are positively correlated with the jet speed, and precipitation is negatively correlated with EOF3. There is some non‐stationarity in some of the relationships, notably between winter precipitation and EOF1, and between July/August precipitation and EOF2. In addition to single variate correlations, multiple correlation coefficients are also used to determine areas of significant correlation when combining two or three of the circulation indices. The multiple correlation analyses show that combining the three EOFs produces significant correlations with temperature and precipitation over much of Europe. These analyses provide scope for using seasonal forecasts to predict likely temperature and precipitation anomalies based on predicting the atmospheric circulation anomalies and downscaling them. Improved seasonal forecasts of temperature and precipitation, including persistence and variability, will be useful to a number of users, such as agrifood, transport, energy supply and insurance.

Spatiotemporal Variability in Rainfall Erosivity and Its Teleconnection with Atmospheric Circulation Indices in China

Rainfall erosivity (RE) is a critical factor influencing soil erosion, and soil erosion is closely related to land ecosystem health and long-term sustainable utilization. To ensure regional stable food supply and ecological balance, it is crucial to study the spatiotemporal distribution and influencing factors of RE. This study focuses on China and its three natural regions using daily precipitation data from 611 stations from 1960 to 2020. The study analyses the spatiotemporal changes in RE. Rainfall events were classified as moderate, large, and heavy based on rainfall intensity. The RE contribution from different rainfall levels to the total RE was analyzed, and the key climatic drivers closely linked to RE were identified using random forest. The results demonstrate that (1) on a national scale, RE shows a significant increasing trend, marked by an 81.67 MJ·mm·ha−1·h−1/decade. In the subregions, the Eastern Monsoon Region (EMR) and Qinghai–Tibet Plateau Region (QTR) show a significant increasing trend, with a greater change rate in EMR of 108.54 MJ·mm·ha−1·h−1/decade, and the Northwest Arid Region (NAR) shows a nonsignificant upwards trend. (2) The average RE increases northwest–southeast nationwide, ranging from 60.15 MJ·mm·ha−1·h−1 to 31,418.52 MJ·mm·ha−1·h−1. The RE contribution generated by different rainfall levels to the total RE exhibits spatial variations. The dominant types show that EMR is influenced by heavy RE, NAR is dominated by large RE, and QTR is affected by moderate RE. (3) The REs are associated with teleconnection indices, but the impact of these indices varies in different regions. The Western Hemisphere Warm Pool has the greatest impact on the EMR, while the North Atlantic Oscillation and Atlantic Multidecadal Oscillation are the factors influencing RE in NAR and QTR, respectively. (4) On a national scale, for every 1 mm increase in annual total rainfall, the RE increased by 8.54 MJ·mm·ha−1·h−1, a sensitivity of 8.54 MJ·mm·ha−1·h−1/mm. For the three subregions, there are differences in the sensitivity of RE to changes in annual precipitation. The highest sensitivity is found in EMR, at 8.71 MJ·mm·ha−1·h−1/mm, which is greater than the sensitivity indices in NAR (6.19 MJ·mm·ha−1·h−1/mm) and QTR (3.60 MJ·mm·ha−1·h−1/mm). This study can provide theoretical references for future regional soil erosion prediction and sustainable agricultural development in China.

Extreme rainfall events variation during 1860–1909 in the Korean Peninsula: Investigation of the possible circulation mechanism by a method of analogue

In the present paper, we made an investigation of extreme rainfall events for 1860―1909 in the Korean Peninsula, and the possible atmospheric circulation state by comparing recent rainfall variability with atmospheric circulation indices. In the present study, very significant historical rainfall records and flood damage records since the mid-19th century, modern rainfall records and global climate reanalysis data were applied. Results show: (1) Pentad extreme rainfall events had undergone a clear decrease, with an abrupt falling after shifting point of 1879―1880. (2) The monthly frequencies of extreme rainfall events revealed remarkable descent trends for June–October, which changed from 29 time/pentad to 7.3 time/pentad on average, especially for July–September. (3) Almost all climate zones except for northern land displayed significant decreasing trends, nevertheless no clear incidence of extreme rainfall event was found over the typical pluvial regions. (4) Through an analogue method by comparing the extreme precipitation weather and the atmospheric mechanism for recent decades (1961―2016), we suggested that West Pacific Subtropical High had experienced a strengthen change for the past period and the typhoon-related torrential rain processes had been suppressed for the study area, and the upper air jet stream might have been displaced poleward during 1860―1909. This study made a first investigation of the extreme rainfall events since the mid-19th century applying both instrumental and documental records in the study area, and some significant findings were obtained, which will help in understanding extreme climate variability and its mechanism of late 19th century and early 20th century in the Korean Peninsula.

Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models

Runoff from the high-cold mountains area (HCMA) is the most important water resource in the arid zone, and its accurate forecasting is key to the scientific management of water resources downstream of the basin. Constrained by the scarcity of meteorological and hydrological stations in the HCMA and the inconsistency of the observed time series, the simulation and reconstruction of mountain runoff have always been a focus of cold region hydrological research. Based on the runoff observations of the Yurungkash and Kalakash Rivers, the upstream tributaries of the Hotan River on the northern slope of the Kunlun Mountains at different time periods, and the meteorological and atmospheric circulation indices, we used feature analysis and machine learning methods to select the input elements, train, simulate, and select the preferences of the machine learning models of the runoffs of the two watersheds, and reconstruct the missing time series runoff of the Kalakash River. The results show the following. (1) Air temperature is the most important driver of runoff variability in mountainous areas upstream of the Hotan River, and had the strongest performance in terms of the Pearson correlation coefficient (ρXY) and random forest feature importance (FI) (ρXY = 0.63, FI = 0.723), followed by soil temperature (ρXY = 0.63, FI = 0.043), precipitation, hours of sunshine, wind speed, relative humidity, and atmospheric circulation were weakly correlated. A total of 12 elements were selected as the machine learning input data. (2) Comparing the results of the Yurungkash River runoff simulated by eight machine learning methods, we found that the gradient boosting and random forest methods performed best, followed by the AdaBoost and Bagging methods, with Nash–Sutcliffe efficiency coefficients (NSE) of 0.84, 0.82, 0.78, and 0.78, while the support vector regression (NSE = 0.68), ridge (NSE = 0.53), K-nearest neighbor (NSE = 0.56), and linear regression (NSE = 0.51) were simulated poorly. (3) The application of four machine learning methods, gradient boosting, random forest, AdaBoost, and bagging, to simulate the runoff of the Kalakash River for 1978–1998 was generally outstanding, with the NSE exceeding 0.75, and the results of reconstructing the runoff data for the missing period (1999–2019) could well reflect the characteristics of the intra-annual and inter-annual changes in runoff.

Hymenaea stignocarpa Mart. ex Hayne growth–climate relationships are regulated by soil water saturation in Cerrado-Pantanal ecotone

The expected intensification of the dry season, and concentration of rainfall during the wet season, can disrupt tree growth and regional biodiversity in the Cerrado-Pantanal transition zone. Thus, this study aims to assess the climatic responses of Hymenaea stigonocarpa tree growth, a common tree species in this region. Incremental cores were collected at breast height (ca. 1.3 m) from 67 trees to construct a dendrochronological series to correlate annual growth with local meteorological variables and large-scale atmospheric circulation indices from the Pacific and Atlantic Oceans. Significant positive correlations were observed between tree growth and the Pacific Ocean indices. Maximum and average air temperatures of the previous dry season negatively influenced growth, while precipitation at the beginning of the growth season (November) positively influenced growth. Tree growth was not correlated with temperature or rainfall during the wet season. However, at the end of the wet season (February), tree growth was negatively correlated with air temperature and positively correlated with rainfall, but the relationship shifted in the next month (March) suggesting that soil water saturation reduced growth. Our results indicate that dendrochronological studies of H. stignocarpa are useful for assessing the environmental change on the Cerrado-Pantanal ecotone. Furthermore, variations in water availability and temperature associated with changes in large-scale oceanic circulation and local meteorological conditions will impact the growth dynamics of this important Cerrado tree species.

Modeling of Precipitation Prediction Based on Causal Analysis and Machine Learning

The factors influencing precipitation in western China are quite complex, which increases the difficulty in determining accurate predictors. Hence, this paper models the monthly measured precipitation data from 240 meteorological stations in mainland China and the precipitation data from the European Centre for Medium-Range Weather Forecasts and the National Climate Centre and employs 88 atmospheric circulation indices to develop a precipitation prediction scheme. Specifically, a high-quality grid-point field is created by fusing and revising the precipitation data from multiple sources. This field is combined with the Empirical Orthogonal Function decomposition and the causal information flow. Next, the best predictors are screened through Empirical Orthogonal Function decomposition and causal information flow, and a data-driven precipitation prediction model is established using a Back Propagation Neural Network and a Random Forest algorithm to conduct the 1-month, 3-month, and 6-month precipitation predictions. The results show that: The machine learning-based precipitation prediction model has high accuracy and is generally able to predict the precipitation trend in the western region better. The Random Forest algorithm significantly outperforms the Back Propagation Neural Network algorithm in the prediction of the three starting times, and the prediction ability of both models gradually decreases as the starting time increases. Compared with the 2022 flood season prediction scores of the Institute of Atmospheric Sciences of the Chinese Academy of Sciences, the model improves the prediction of 1-month and 3-month precipitation in the western region and provides a new idea for the short-term climate prediction of precipitation in western China.

ИЗМЕНЕНИЕ КЛИМАТА И ЕГО ВЛИЯНИЕ НА СЕЛЬСКОЕ ХОЗЯЙСТВО

The overview of papers dealing with the analysis of current and future climate change on the territory of Russia and their impact on the crop productivity is presented. Using the reanalysis data for 1950-2020, trends in air temperature and precipitation are estimated for different regions of Russia. A correlation was found between changes in temperature and atmospheric circulation indices.

Air Temperature Change at the End of the Late Holocene and in the Anthropocene in the Middle Volga Region, European Russia

The temporal variability of air temperature in the Middle Volga region from 1828 to 2021 is considered according to instrumental observations at the oldest meteorological station in the east of the East European Plain (Kazan University) and throughout the Asian part of Russia against the background of long-term climate fluctuations in the Northern Hemisphere of Earth. A general trend toward an increase in air temperature was revealed. It was found that climate change in Kazan was consistent with the climatic processes that occurred in the Middle Volga region as a whole. The greatest warming for the entire observation period was observed in the winter and spring seasons of the year. In December, warming occurred at a maximum rate of 0.28 °C/10 years. At the same time, the most intense warming process was observed from 1991 to 2021. The analysis of low-frequency fluctuations in the series of monthly average air temperatures made it possible to identify different periods of change, both in type (direction) and intensity. It is shown that in the Middle Volga region, positive anomalies of air temperature have occurred more often than negative ones in recent decades. Statistical data processing was also carried out for 30-year periods, starting from the first period, i.e., 1841–1870. This made it possible to reveal long-term changes in air temperature. Comparisons of climatic parameters in two periods, i.e., 1828–1945 and 1946–2021, allowed us to reliably detect the climatic beginning of the increasingly identifiable Anthropocene epoch (since 1946), characterized by a sharp increase in air temperature, increased interannual variability of the air temperature regime, and a significant increase (by about three times) in the rate of warming in the Middle Volga region. A correlation was made between atmospheric circulation indices and air temperature fluctuations in Kazan over different periods. The closest relationship was found for the 1990–2020 period. It is shown that the contribution of global factors to air temperature variability in the Middle Volga region during the Anthropocene reached 37% in winter and 32% in summer; in annual terms, this contribution amounted to 54%.

КЛИМАТИЧЕСКИЕ ИЗМЕНЕНИЯ НА ТЕРРИТОРИИ ПРИВОЛЖСКОГО ФЕДЕРАЛЬНОГО ОКРУГА В XX-XXI ВЕКАХ И ИХ ПОСЛЕДСТВИЯ ДЛЯ АГРОСФЕРЫ

The spatial and temporal variability of the main climatic indicators on the territory of the Volga Federal District in the 20th-21st centuries is considered. A general increasing trend in air temperature is revealed, and a heterogeneous pattern of precipitation changes is shown. The temperature variations until the end of the 21st century are analyzed on the example of Kazan using 40 CMIP6 climate models for four anthropogenic scenarios. In the paper, there is an assessment of the dynamics of agroclimatic resources on the territory of the Volga Federal District: the duration of the growing season, the sum of positive temperatures, total precipitation, and photosynthetic radiation. A correlation has been found between the temperature fluctuations in the region and the atmospheric circulation indices (NAO, AO, SCAND, and EAWR). For the territory of Tatarstan, a degree of aridity and waterlogging is evaluated using the agroclimatic indices: the Budyko’s dryness index, the Selyaninov’s hydrothermal coefficient, the Sapozhnikova’s moisture index. It is shown that there is a tendency toward an increase in the duration of the growing season, its heat supply and aridity in the region in summer. A statistical estimation of the dependence of spring wheat yield on the agroclimatic indices on the territory of the Republic of Tatarstan is given.

Nonstationary frequency analysis and uncertainty quantification for extreme low lake levels in a large river-lake-catchment system

Extreme hydrological events have become increasingly frequent on a global scale. The middle Yangtze River also faces a substantial challenge in dealing with extreme flooding and drought. However, the long-term characteristics of the extreme hydrological regime have not yet been adequately recognized. Moreover, there is uncertainty in the extreme value estimation, and this uncertainty needs to be distinguished and quantified. In this study, we investigated the nonstationary frequency characteristics of extreme low lake levels (ELLLs), taking the Poyang Lake as an example. Daily lake levels from 1960 to 2022 were utilized to estimate the return level using the generalized Pareto distribution (GPD). The uncertainty from three sources, i.e., the parameter estimator, threshold selection, and covariate, was quantified via variance decomposition. The results indicate that (1) the parameter estimator is the predominant source of uncertainty, with a contribution rate of approximately 87 %. The total uncertainty of the covariate, threshold, and interaction term is only 13 %. (2) Two indexes, namely the annual minimum water level (WLmin) and the days with peak over the 90 % threshold per year (DPOT90), decreased (0.01–0.03 m/year) and increased (0.17–1.39 days/year), respectively, indicating a progressively severe drought trend for Poyang Lake. (3) The return level with return period of 5 to 100 years significantly decreased after the early 21st century. A large spatial heterogeneity was identified for the variation in the return level, and the change rate of the return level with a 100-year return period ranged from 5 % to 40 % for the whole lake. (4) The ELLLs had a stronger correlation with the catchment discharge than with the Yangtze River discharge and the large-scale atmospheric circulation indices. This study provides a methodology with reduced uncertainty for nonstationary frequency analysis (NFA) of ELLLs exemplified in large river–lake systems.

Meteorological drought assessment and prediction in association with combination of atmospheric circulations and meteorological parameters via rule based models

The development of data-driven models along with the data that are recorded from satellites has made the study of water engineering more attractive. In this study, SPEI values were calculated with the meteorological variables and then a combination of the meteorological variables as well as atmospheric circulation values was considered as a data mining model input for estimating the droughts. The series of the SPEI values for 3-, 6-, 9-, 12-, 24- and 48-month time scales were obtained during 1988-2016. In this study, both M5 Tree model and Associate Rules were used to predict and analyze the meteorological drought at 6 synoptic stations in the basin considering both the atmospheric circulations (NAO, SO, MOgi, MOac, WeMO, Mediterranean, Red, Black, Caspian, and Persian Gulf SSTs) and the meteorological variables (lagged relative humidity, evapotranspiration, average temperature, minimum-maximum temperature and pressure). The results showed that making use of a combination of the atmospheric circulation indices and meteorological variables in the models increases the accuracy of the model and improves the results in a long term period. The best result in the study of drought in Urmia Lake basin is related to SPEI48 (R = 0.85, RMSE = 0.08, MAE = 0.11) and in the association rules the value of the lift index of best rule is 1.32. Although both approaches provided acceptable results, M5 Tree model had a comparative advantage due to simple and practical linear relationships.

Количество штормов в некоторых морях России: тренды и связь с крупномасштабными атмосферными индексами

Changes in the recurrence of extreme wind waves in the World Ocean are connected with the global climate change. The end of the 20th and the beginning of the 21st centuries are characterized by significant climate warming, the reduction of the Arctic sea ice and changes in the recurrence of various extreme meteorological events. The main motivation of this research is to assess the trends of storm recurrence for the time period from 1979 up to 2020 and to analyze the connection between storminess and large-scale atmospheric circulation indexes. This research contains information about the number of storms that occurred in seven Russian Seas, including the Black, Caspian, Barents, Kara, Bering Seas, the Sea of Okhotsk and the Sea of Japan/East Sea. These seas are located in different climate conditions determined by the Atlantic, Pacific and Arctic oceans. The analysis of wave climate and storm activity is based on the results of wave modelling by WAVEWATCH III with input NCEP/CFSR wind and ice data. The mean plots, maximum, and 95% percentile sig-nificant wave heights are also presented in the research. Significant linear uptrends in the number of storms were found in the Kara, Caspian, Bering, Okhotsk Seas, and in the Sea of Japan. The relationship between the inter-annual variability of the number of storms and large-scale at-mospheric indexes is considered.

Some patterns of formation of extreme surface air temperature in the area of the Spitzbergen (Svalbard) archipelago during the cold period

The article presents research findings of meteorological conditions during the formation of extreme surface air temperature (SAT) in the cold period from October to May in the Barentsburg area, the Spitzbergen (Svalbard) archipelago. Six extremely cold periods and six extremely warm periods for the weather sequence from 1912 to 2022 are considered. Correlation coefficients of extreme SAT in Barentsburg with extreme SAT in the points of Longyearbyen, Hornsund, Isfjord Radio, Pyramiden, Nu-Alesund, located in the west of Spitzbergen (Svalbard), were calculated. Also, characteristics of atmospheric circulation at the surface level, the level of the geopotential surface 500 hPa and 700 hPa and air temperature anomalies are analyzed, the results of comparing atmospheric circulation indices NAO and AO in the formation of extreme SAT are presented. Characteristic localizations of the polar vortex were found during the formation of negative and positive anomalies of SAT. For cold periods with extremely low temperatures, the localization of the polar vortex in the northeast of the Kara Sea and Severnaya Zemlya archipelago is observed. At the surface level the baric trough from the Atlantic depression is shifted to the coast of Scandinavia and the southern part of the Barents Sea. The Svalbard region is under the influence of the periphery of the anticyclonic field over the Arctic basin, Greenland, over the western Arctic Seas, conditions are formed for the deviation of SAT by 3–4 degrees below normal. In the case of extremely high SAT, the polar vortex is shifted to the eastern part of the Canadian Arctic Archipelago and the Baffin Sea. At the surface level, the baric trough from the Icelandic depression develops over the Greenland, Norwegian and Barents Seas, which leads to the moving of warm air masses from the Atlantic to polar latitudes and the formation of average SAT in the Spitzbergen (Svalbard) region above long-term values by 1–2 degrees. The estimates of macroprocesses obtained can be used for preparing long-term forecasts for the northern polar region and detailing them for the Spitzbergen (Svalbard) region.

Estimating prediction horizon of reservoir computer on L63 system when observed variables are incomplete

Reservoir computer (RC) is an attractive neural computing framework that can well predict the dynamics of chaotic systems. Previous knowledge of the RC performance is established on the case that all variables in a chaotic system are completely observed. However, in practical circumstances the observed variables from a dynamical system are usually incomplete, among which there is a lack of understanding of the RC performance. Here we utilize mean error growth curve to estimate the RC prediction horizon on the Lorenz63 system (L63), and particularly we investigate the scenario of univariate time series. Our results demonstrate that the prediction horizon of RC outperforms that of local dynamical analogs of L63, and the state-space embedding technique can improve the RC prediction in case of incomplete observations. We then test the conclusion on the more complicated systems, and extend the method to estimate the intraseasonal predictability of atmospheric circulation indices. These results could provide indications for future developments and applications of the RC.

An abrupt change of winter surface air temperature over the northern part of Korean Peninsula in the late 1980s and related atmospheric circulation variability

The aim of this study is to detect the variation of winter surface air temperature (SAT) over the northern part of Korean Peninsula (NPKP) for the period of 1949–2020 under global warming, as well as to investigate its possible mechanisms. All temperature data used for the study were obtained from representative 37 weather stations over NPKP. Results are as follows. 1) Winter temperature anomaly series manifested a clear shifting point in the late 1980s, with upward rate of about 0.19 °C per decade for the period of 1949–2020, and those were generally smaller than normal year prior to the shifting point, but were greater after 1988. 2) Spatial patterns of first mode of empirical orthogonal function before or after the shifting point in the Northern Hemisphere had reversely alternated, as an evidence describing the possible linkage between negative (positive) phase of Arctic oscilliation and cold (warm) winter over the study area. 3) A schematic diagram shows the connections of atmospheric circulation indices with the winter temperature over the study area: the East Asian trough had the best relationship with winter SAT over the study area among the East Asian winter monsoon components affecting East Asia. However, the possible causes of the winter temperature variation in the study area need to be further investigated.