Search Results (19)

The regularity of rainfall seasonality is very important for vegetation growth, the livelihood of the population, agricultural production, and ecosystem sustainability. Changes in precipitation and its extremes have been widely reported; however, the spatial and temporal variations in rainfall seasonality and their underlying mechanisms are less understood. Here, we analyzed the changes in rainfall seasonality and possible teleconnection mechanisms in the eastern China monsoon region during 1981–2022, with a special focus on the El Niño-Southern Oscillation (ENSO), El Niño Modoki (ENSO_M), and Indian Ocean Dipole (IOD). Our results show that due to the changes in rainfall concentration, rainfall magnitude, or both, rainfall seasonality has developed in the northern China (NC, 0.15 × 10⁻³ yr⁻¹) and central China (CC, 0.07 × 10⁻³ yr⁻¹) monsoon regions, and weakened in the northeastern China (NEC, −0.08 × 10⁻³ yr⁻¹) and southern China (SC, −0.15 × 10⁻³ yr⁻¹) monsoon regions during the recent decades. The large-scale circulation and SST anomalies induced by cold or warm phases of the IOD, ENSO_M, and (or) ENSO can explain the enhanced seasonality in the NC and CC monsoon regions and weakened seasonality in the NEC and SC monsoon regions. The wavelet coherence analysis further shows that the dominated climatic factors for rainfall seasonality changes are different in the CC, NC, SC, and NEC monsoon regions, and that rainfall seasonality is also affected by the coupling of the IOD, ENSO_M, and ENSO. Our results highlight that the IOD, ENSO_M, and ENSO are important climatic causes for rainfall seasonality changes in the eastern China monsoon region. Full article

The Mongolian Plateau grassland (MPG) is critical for ecological conservation and sustainability of regional pastoral economies. Aboveground net primary productivity (ANPP) is a key indicator of grassland health and function, which is highly sensitive to variabilities in large-scale atmospheric circulations, commonly referred to as teleconnections (TCs). In this study, we analyzed the spatial and temporal variations of ANPP and their response to local meteorological and large-scale climatic variabilities across the MPG from 1982 to 2015. Our analysis indicated the following: (1) Throughout the entire study period, ANPP displayed an overall upward trend across nine ecoregions. In the Sayan montane steppe and Sayan alpine meadow ecoregions, ANPP displayed a distinct inflection point in the mid-1990s. In the Ordos Plateau arid steppe ecoregion, ANPP continuously increased without any inflection points. In the six other ecoregions, trends in ANPP exhibited two inflection points, one in the mid-1990s and one in the late-2000s. (2) Precipitation was the principal determinant of ANPP across the entire MPG. Temperature was a secondary yet important factor influencing ANPP variations in the Ordos Plateau arid steppe. Cloud cover affected ANPP in Sukhbaatar and central Dornod, Mongolia. (3) The Atlantic Multidecadal Oscillation affected ANPP by regulating temperature in the Ordos Plateau arid steppe ecoregion, whereas precipitation occurred in the other ecoregions. The Pacific/North America, North Atlantic Oscillation, East Atlantic/Western Russia, and Pacific Decadal Oscillation predominantly affected precipitation patterns in various ecoregions, indicating regional heterogeneities of the effects of TCs on ANPP fluctuations. When considering seasonal variances, winter TCs dominated ANPP variations in the Selenge–Orkhon forest steppe, Daurian forest steppe, and Khangai Mountains alpine meadow ecoregions. Autumn TCs, particularly the Pacific/North America and North Atlantic Oscillation, had a greater impact in arid regions like the Gobi Desert steppe and the Great Lakes Basin desert steppe ecoregions. This study’s findings will enhance the theoretical framework for examining the effects of TCs on grassland ecosystems. Full article

With global warming driving an increase in extreme precipitation, the ensuing disasters present an unsustainable scenario for humanity. Consequently, understanding the characteristics of extreme precipitation has become paramount. Analyzing observational data from 1961 to 2020 across 29 meteorological stations in Heilongjiang Province, China, we employed kriging interpolation, the trend-free pre-whitening Mann–Kendall (TFPW–MK) method, and linear trend analysis. These methods allowed us to effectively assess the spatiotemporal features of extreme precipitation. Furthermore, Pearson’s correlation analysis explored the relationship between extreme precipitation indices (EPIs) and geographic factors, while the geodetector quantified the impacts of climate teleconnections. The results revealed the following: (1) There has been a clear trend in increasing extreme precipitation over the last few decades, particularly in the indices of wet day precipitation (PRCPTOT), very wet day precipitation (R95P), and extremely wet day precipitation (R99P), with regional mean trends of 10.4 mm/decade, 5.7 mm/decade, and 3.4 mm/decade, respectively. This spatial trend showed a decrease from south to north. (2) Significant upward trends were observed in both spring and winter for the maximum 1-day precipitation (RX1day) and the maximum 5-day precipitation (RX5day). (3) The latitude and longitude were significantly correlated with the most extreme precipitation indices, while elevation showed a weaker correlation. (4) Extreme precipitation exhibited a nonlinear response to large-scale climate teleconnections, with the combined influence of factors having a greater impact than individual factors. This research provides critical insights into the spatiotemporal dynamics of extreme precipitation, guiding the development of targeted strategies to mitigate risks and enhance resilience. It offers essential support for addressing regional climate challenges and promoting agricultural development in Heilongjiang Province. Full article

This study leverages the relationships between the Great Plains low-level jet (GP-LLJ) and the circumglobal teleconnection (CGT) to assess the enhancement of 30-day rainfall forecast in the Northern Great Plains (NGP). The assessment of 30-day simulated precipitation using the Climate Forecast System (CFS) is contrasted with the North American Regional Reanalysis, searching for sources of precipitation predictability associated with extended wet and drought events. We analyze the 30-day sources of precipitation predictability using (1) the characterization of dominant statistical modes of variability of 900 mb winds associated with the GP-LLJ, (2) the large-scale atmospheric patterns based on 200 mb geopotential height (HGT), and (3) the use of GP-LLJ and CGT conditional probability distributions using a continuous correlation threshold approach to identify when and where the forecast of NGP precipitation occurs. Two factors contributing to the predictability of precipitation in the NGP are documented. We found that the association between GP-LLJ and CGT occurs at two different scales—the interdiurnal and the sub-seasonal, respectively. The CFS reforecast suggests that the ability to forecast sub-seasonal precipitation improves in response to the enhanced simulation of the GP-LLJ and CGT. Using these modes of climate variability could improve predictive frameworks for water resources management, governance, and water supply for agriculture. Full article

The precipitation on the northern slope of the Kunlun Mountains significantly impacts the green economy of the Tarim Basin’s southern edge. Observations have noted an expansion of the surface water area in this region, though the reasons for this are not yet fully understood. Due to limited instrumental data, this study leverages field measurements from the third Xinjiang comprehensive expedition and multiple gridded datasets. Through trend analysis and a geographical detector model, it examines the precipitation’s decadal, interannual, and seasonal variations across key areas (Hotan River Basin, Keriya River Basin, Qarqan River Basin, and Kumukuli Basin), identifying factors behind the spatial and temporal distribution of regional precipitation. The findings reveal the following: (1) An increase in annual precipitation across the region from 187.41 mm in the 1980s to 221.23 mm in the early 21st century, at a rate of 10.21 mm/decade, with the most significant rise in the eastern Kunlun-Kumukuli Basin. (2) Precipitation exhibits clear seasonal and spatial patterns, predominantly occurring in spring and summer, accounting for 90.27% of the annual total, with a general decrease from the mountains towards downstream areas. (3) Rising average annual temperatures contribute to an unstable atmospheric structure and increased water-holding capacity, facilitating precipitation. Significant influences on precipitation changes include the North Atlantic Oscillation and solar flux, explaining 43.98% and 31.21% of the variation, respectively. Full article

Climate is probably the most important factor affecting river discharge and flow dynamics. Low flows in rivers during the warm period cause stress to aquatic ecosystems and pose a challenge to sustainable water management. Previous research has shown that the average minimum discharge of the 30 driest continuous days, known as Q₃₀, is a suitable measure for ecological flow estimation in Lithuania. This study aims to examine whether large-scale atmospheric processes, so-called teleconnections, can have an impact on Q₃₀ during the warm period. Hydrological data for 1961–2020 from 25 water gauging stations were used to search for hydrological response signals with five selected climate indices (NAO, SCA, POL, EA/WR, and EA). Pearson correlation and Wilcoxon–Mann–Whitney test approaches were applied. The results suggested that the EA/WR and NAO had the strongest influence on Q₃₀ in the studied region during the warm period. The positive phases of the indices tended to cause a greater decrease in Q₃₀ values due to the prevailing easterly edge of the anticyclonic circulation over the studied region determined by the EA/WR and NAO indices, while the negative phases of the mentioned indices caused an increase and greater dispersion of Q₃₀. Full article

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. Full article

Global climate change and human activities have increased the frequency and severity of droughts. This has become a critical factor affecting vegetation growth and diversity, resulting in detrimental effects on agricultural production, ecosystem stability, and socioeconomic development. Therefore, assessing the response of vegetation dynamics to drought can offer valuable insights into the physiological mechanisms of terrestrial ecosystems. Here, we applied long-term datasets (2001–2020) of solar-induced chlorophyll fluorescence (SIF) and normalized difference vegetation index (NDVI) to unveil vegetation dynamics and their relationship to meteorological drought (SPEI) across different vegetation types in the Yangtze River Basin (YRB). Linear correlation analysis was conducted to determine the maximum association of SPEI with SIF and NDVI; we then compared their responses to meteorological drought. The improved partial wavelet coherence (PWC) method was utilized to quantitatively assess the influences of large-scale climate patterns and solar activity on the relationship between vegetation and meteorological drought. The results show that: (1) Droughts were frequent in the YRB from 2001 to 2020, and the summer’s dry and wet conditions exerted a notable influence on the annual climate. (2) SPEI exhibits a more significant correlation with SIF than with NDVI. (3) NDVI has a longer response time (3–6 months) to meteorological drought than SIF (1–4 months). Both SIF and NDVI respond faster in cropland and grassland but slower in evergreen broadleaf and mixed forests. (4) There exists a significant positive correlation between vegetation and meteorological drought during the 4–16 months period. The teleconnection factors of Pacific Decadal Oscillation (PDO), El Niño Southern Oscillation (ENSO), and sunspots are crucial drivers that affect the interaction between meteorological drought and vegetation, with sunspots having the most significant impact. Generally, our study indicates that drought is an essential environmental stressor that disrupts vegetation growth over the YRB. Additionally, SIF demonstrates great potential in monitoring vegetation response to drought. These findings will be meaningful for drought prevention and ecosystem conservation planning in the YRB. Full article

The Tarim Basin is a large inland arid basin in the arid region of northwest China and has been experiencing significant “warming and wetting” since 1987. As a result, the purpose of this paper is to determine whether the climate transition phenomenon occurred in the Tarim Basin as well as the role of atmospheric circulation in this process. We use meteorological data and atmospheric circulation indexes to study the seasonal trends of climate change in this region from 1987 to 2020 to understand how they are affected by atmospheric circulation. The findings show that, from 1987 to 2020, the Tarim Basin experienced significant warming and wetting; with the exception of the winter scale, all other seasonal scales exhibited a clear warming and wetting trend. From the perspective of spatial distribution, most of the areas showed a significant warming trend, and the warming amplitude around the basin is greater than that in the central area of the basin. However, there are significant regional differences in precipitation change rates. Meanwhile, wavelet analysis shows that there is a significant oscillation period of 17–20 years between climate change and the atmospheric circulation index during 1987–2020. The correlation analysis shows that the Pacific decadal oscillation (PDO) and El Niño-Southern Oscillation (ENSO) are the main influencing factors of climate change in the Tarim Basin at different seasonal scales, while the teleconnection of the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) is low and the PDO dominates the summer and autumn temperature changes in the Tarim Basin. The research results of this paper show that, despite the warming and wetting trends since 1987 in the Tarim Basin, the climate type did not change. From 1987 to 2020, the main teleconnection factors of climate change in the Tarim Basin were PDO and ENSO. Full article

Due to the inherent non-stationary and nonlinear characteristics of original streamflow and the complicated relationship between multi-scale predictors and streamflow, accurate and reliable monthly streamflow forecasting is quite difficult. In this paper, a multi-scale-variables-driven streamflow forecasting (MVDSF) framework was proposed to improve the runoff forecasting accuracy and provide more information for decision-making. This framework was realized by integrating random forest (RF) and Gaussian process regression (GPR) with multi-scale variables (hydrometeorological and climate predictors) as inputs and is referred to as RF-GPR-MV. To validate the effectiveness and superiority of the RF-GPR-MV model, it was implemented for multi-step-ahead monthly streamflow forecasts with horizons of 1 to 12 months for two key hydrological stations in the Jinsha River basin, Southwest China. Other MVDSF models based on the Pearson correlation coefficient (PCC) and GPR with/without multi-scale variables or the PCC and a backpropagation neural network (BP) or general regression neural network (GRNN), with only previous streamflow and precipitation, namely, PCC-GPR-MV, PCC-GPR-QP, PCC-BP-QP, and PCC-GRNN-QP, respectively, were selected as benchmarks. Experimental results indicated that the proposed model was superior to the other benchmark models in terms of the Nash–Sutcliffe efficiency (NSE) for almost all forecasting scenarios, especially for forecasting with longer lead times. Additionally, the results also confirmed that the addition of large-scale climate and circulation factors was beneficial for promoting the streamflow forecasting ability, with an average contribution rate of about 15%. The RF in the MVDSF framework improved the forecasting performance, with an average contribution rate of about 25%. This improvement was more pronounced when the lead time exceeded 3 months. Moreover, the proposed model could also provide prediction intervals (PIs) to characterize forecast uncertainty, as supplementary information to further help decision makers in relevant departments to avoid risks in water resources management. Full article

The pine looper Bupalus piniaria (L.) is one of the most common pests feeding on the Scots pine Pinus sylvestris L. Pine looper outbreaks show a feature of periodicity and have significant ecological and economic impacts. Climate and weather factors play an important role in pine looper outbreak occurrence. We tried to determine what weather conditions precede B. piniaria outbreaks in the southeast of the West Siberian Plain and what climate oscillations cause them. Due to the insufficient duration and incompleteness of documented observations on outbreaks, we used the history of pine looper outbreaks reconstructed using dendrochronological data. Using logistic regression, we found that the factor influencing an outbreak the most is the weather four years before it. A combination of warm spring, dry summer, and cool autumn triggers population growth. Summer weather two years before an outbreak is also critical: humidity higher than the average annual value in summer is favorable for the pine looper. The logistic regression model predicted six out of seven outbreaks that occurred during the period for which weather data are available. We discovered a link between outbreaks and climatic oscillations (mainly for the North Atlantic oscillation, Pacific/North America index, East Atlantic/Western Russia, West Pacific, and Scandinavian patterns). However, outbreak predictions based on the teleconnection patterns turned out to be unreliable. We believe that the complexity of the interaction between large-scale atmospheric processes makes the direct influence of individual oscillations on weather conditions relatively small. Furthermore, climate changes in recent decades modulated atmospheric processes changing the pattern predicting pine looper outbreaks: Autumn became warmer four years before an outbreak, and summer two years before became drier. Full article

Drought research is an important aspect of drought disaster mitigation and adaptation. For this purpose, we used the Standardized Precipitation Evapotranspiration Index (SPEI) to investigate the spatial-temporal pattern of drought and its impact on crop production. Using monthly precipitation (Precip) and temperature (Temp) data from 1986–2015 for 39 weather stations, the drought index was obtained for the time scale of 3, 6, and 12 months. The Mann–Kendall test was used to determine trends and rates of change. Precip and Temp anomalies were investigated using the regression analysis and compared with the drought index. The link between drought with large-scale atmospheric circulation anomalies using the Pearson correlation coefficient (R) was explored. Results showed a non-uniform spatial pattern of dryness and wetness which varied across Myanmar agro-ecological zones and under different time scales. Generally, results showed an increasing trend for the SPEI in the three-time scales, signifying a high tendency of decreased drought from 1986–2015. The fluctuations in dryness/wetness might linked to reduction crop production between 1986–1999 and 2005, 2008, 2010, 2013 cropping years. Results show relationship between main crops production and climate (teleconnection) factors. However, the low correlation values (i.e., <0.49) indicate the extent of the relationship within the natural variability. However, readers are urged to interpret this result cautiously as reductions in crop production may also be affected by other factors. We have demonstrated droughts evolution and trends using weather stations, thus providing useful information to aid policymakers in developing spatially relevant climate change adaptation and mitigation management plans for Myanmar. Full article

Glacier mass variations are climate indicators. Therefore, it is essential to examine both winter and summer mass balance variability over a long period of time to address climate-related ice mass fluctuations. In this study, we analyze glacier mass balance components and hypsometric characteristics with respect to their interactions with local meteorological variables and remote large-scale atmospheric and oceanic patterns. The results show that all selected glaciers have lost their equilibrium condition in recent decades, with persistent negative annual mass balance trends and decreasing accumulation area ratios (AARs), accompanied by increasing air temperatures of ≥ +0.45 °C decade⁻¹. The controlling factor of annual mass balance is mainly attributed to summer mass losses, which are correlated with (warming) June to September air temperatures. In addition, the interannual variability of summer and winter mass balances is primarily associated to the Atlantic Multidecadal Oscillation (AMO), Greenland Blocking Index (GBI), and East Atlantic (EA) teleconnections. Although climate parameters are playing a significant role in determining the glacier mass balance in the region, the observed correlations and mass balance trends are in agreement with the hypsometric distribution and morphology of the glaciers. The analysis of decadal frontal retreat using Landsat images from 1984 to 2014 also supports the findings of this research, highlighting the impact of lake formation at terminus areas on rapid glacier retreat and mass loss in the Swiss Alps. Full article

Although much progress has been made in identifying the large-scale drivers of recent summer precipitation variability in North China, the evolution of these drivers over longer time scales remains unclear. We investigate multidecadal and interannual variability in North China summer precipitation in the 110-year Coupled ECMWF Reanalysis of the Twentieth Century (CERA-20C), considering changes in regional moisture and surface energy budgets along with nine circulation indices linked to anomalous precipitation in this region. The CERA-20C record is separated into three distinct periods according to the running climatology of summer precipitation: 1901–1944 (neutral), 1945–1979 (wet), and 1980–2010 (dry). CERA-20C reproduces expected relationships between large-scale drivers and regional summer precipitation anomalies well during 1980–2010, but these relationships generally do not extend to earlier periods. For example, a strong relationship with the Eurasian teleconnection pattern only emerges in the late 1970s, while correlations with the El Niño-Southern Oscillation and the Pacific–Japan pattern change sign in the mid-twentieth century. We evaluate two possible reasons for this nonstationarity: (1) the underlying atmospheric model may require strong data assimilation constraints to capture large-scale circulation influences on North China, or (2) large-scale drivers inferred from recent records may be less general than expected. Our analysis indicates that both factors contribute to the identified nonstationarity in CERA-20C, with implications for the reliability of seasonal forecasts and climate projections based on current models. Full article

Understanding the spatiotemporal regime of summer precipitation at local scales plays a key role in regional prevention and mitigation of floods disasters and water resources management. Previous works focused on spatiotemporal characteristics of a region as a whole but left the influence of associated physical factors on sub-regions unexplored. Based on the precipitation data of 77 meteorological stations in the Poyang Lake basin (PYLB) from 1959 to 2013, we have investigated regional characteristics of summer precipitation in the PYLB by integrating the rotated empirical orthogonal function (REOF) analysis with hierarchical clustering algorithm (HCA). Then the long-term variability of summer precipitation in sub-regions of the PYLB and possible links with large-scale circulations was investigated using multiple trend analyses, wavelet analysis and correlation analysis. The results indicate that summer precipitation variations in the PYLB were of very striking regional characteristics. The PYLB was divided into three independent sub-regions based on two leading REOF modes and silhouette coefficient (SC). These sub-regions were located in northern PYLB (sub-region I), central PYLB (sub-region II), and southern PYLB (sub-region III). The summer precipitation in different sub-regions exhibited distinct variation trends and periodicities, which was associated with different factors. All sub-regions show no trends over the whole period 1959–2013, rather they show trends in different periods. Trends per decade in annual summer precipitation in sub-region I and sub-region II were consistent for all periods with different start and end years. The oscillations periods with 2–3 years were found in summer precipitation of all the three sub-regions. Summer precipitation in sub-region I was significantly positively correlated with the previous Indian Ocean Dipole (IOD) event, but negatively correlated with East Asian Summer Monsoon (EASM). While summer precipitation in sub-region II and sub-region III showed weak teleconnections with climate indices. All of the results of this study are conducive to further understand both the regional climate variations in the PYLB and response to circulation patterns variations. Full article