Search

In recent years, more and more record-breaking extreme weather/climate events have been reported from the Asian monsoon region, which have caused tremendous loss of property and lives. In this paper, we analyzed the Asian summer monsoon (ASM) variability during the Holocene and evaluated future climate extremes in monsoonal China from a paleoclimatic view. We found a significant regime transition to more chaotic fluctuations, with enhanced decadal variability of the ASM since 6.6 ka BP. We suggested the gradual intensification of ENSO was responsible for enhancing the ASM variability since the late mid-Holocene. If the observed relationship of monsoon mean intensity, ENSO and decadal variability of the ASM in the past 11.2 ka continue to exist, enhanced decadal variability of ASM in the future warming world will be expected. As a result, the intensification of daily precipitation extremes, superimposed on enhanced decadal variability of ASM, might make the record-breaking extremes more frequent in the future, increasing the risk of climate-related disasters in China. [ABSTRACT FROM AUTHOR]

Agricultural production depends on local agroclimatic conditions to a great extent, affected by ENSO and other ocean-atmospheric climate modes. This paper analyzed the spatio-temporal distributions of climate elements in the Jianghan Plain (JHP), Central China, and explored the impacts from teleconnection patterns, aimed at providing references for dealing with climate change and guiding agricultural activities. Both linear and multifactorial regression models were constructed based on the frequentist quantile regression and Bayesian quantile regression method, with the daily meteorological data sets of 17 national stations in the plain and teleconnection climate characteristic indices. The results showed that precipitation in JHP had stronger spatial variability than evapotranspiration. El Niño probably induced less precipitation in summer while the weakening Arctic Oscillation might lead to more summertime precipitation. The Nash-Sutcliffe efficiency (NSE) of the multifactorial and linear regression model at the median level were 0.42–0.56 and 0.12–0.18, respectively. The mean relative error (MRE) ranged −2.95–−0.26% and −7.83–0.94%, respectively, indicating the much better fitting accuracy of the multiple climatic factors model. Meanwhile it confirmed that the agricultural climate in JHP was under the influence from multiple teleconnection patterns. [ABSTRACT FROM AUTHOR]

This study investigated the combined impacts of the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on streamflow under four scenarios: neutral, pure ENSO, pure IOD, and a combination of ENSO and IOD. The Jinsha River Basin (JRB), at the head of the Yangtze River, was used as a case study. By using statistical methods such as coherent wavelet analysis (WTC), we are committed to studying what kind of impact the IOD will have, the difference in impact between ENSO and IOD at different stages, and the difference in impact between ENSO and IOD on the mean and extreme values of runoff, compared with traditional single ENSO event, to provide support for water resource management, especially for reservoir operation. The key results are as follows. (a) Both ENSO and IOD events affect annual and seasonal streamflow in the JRB. (b) The impact of pure IOD events on annual streamflow in the JRB was twice as great as that of pure ENSO events in developing years, whereas the opposite was true in decaying years. (c) The combined impact of ENSO and IOD led to a higher streamflow maximum than the annual or seasonal average streamflow. Conversely, their impact on the streamflow minima was less than 10% during both developing and decaying years, except at Zhimenda Station. (d) Overall, water shortages could be more serious in developing years than in neutral years, and much more attention should be given to flooding control in decaying years. These results can be used as a reference for water resource management concerning agricultural planning and ecological protection in the JRB. [ABSTRACT FROM AUTHOR]

Based on precipitation data from 60 stations in South China (SC) and NCEP reanalysis data, the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT_4.9) is used to analyze the difference of water vapor transport tracks, water vapor sources, and their precipitation contribution rate to frontal/monsoon precipitation, with four combinations of ENSO and PDO phase for a period of 53 years (1960–2012). The results show that: (1) For the frontal precipitation, in the Pacific Decadal Oscillation positive phase (PDO+), there is a great positive water vapor difference between ENSO+ (the positive ENSO phase) and ENSO− (the negative ENSO phase) over the tropical Indian Ocean (IO), the Bay of Bengal (BOB), the South China Sea (SCS) and the western Pacific (WP), the distribution of the difference is adjusted for PDO− (the negative PDO phase). For monsoon precipitation, when PDO and ENSO are in phase resonance, water vapor gathers over IO-BOB-SCS. (2) For the frontal precipitation, both PDO+ and PDO−, compare with ENSO+, more water vapor from SCS for ENSO−, but the southward water vapor transport anomaly over the western part of BOB-SCS-ocean east of the Philippines, which leads a decline in precipitation contribution rate of SCS water vapor. Both ENSO+ and ENSO−, compare with PDO−, more water vapor comes from IO-BOB for PDO+, but their precipitation contribution rates are lower. (3) For the summer monsoon precipitation, SCS and IO are important rain contributor sources. Regardless of the PDO phase, compared with ENSO+, there is more water vapor from the IO and WP for ENSO−, the easterly anomaly in the south of the stronger subtropical high brings more water vapor from WP to SC, the strong westerly anomalies in the IO-BOB-SCS increases IO water vapor transporting to SC, so water vapor precipitation contribution rates of IO and WP are higher. Both ENSO+ and ENSO−, compare with PDO−, more water vapor comes from SCS and EC for PDO+, but their precipitation contribution rates are lower. (4) The water vapor transport process of precipitation in PFS over SC is jointly affected by ENSO and PDO. [ABSTRACT FROM AUTHOR]

The El Niño Southe58rn Oscillation (ENSO) is a typical oscillation affecting climate change, and its stable periodicity, long-lasting effect, and predictable characteristics have become important indicators for regional climate prediction. In this study, we analyze the Standardized Precipitation Evapotranspiration Index (SPEI), the Niño3.4 index, the Southern Oscillation Index (SOI), and the Multivariate ENSO Index (MEI). Additionally, we explore the spatial and temporal distribution of the correlation coefficients between ENSO and SPEI and the time lag between ENSO events of varying intensities and droughts. The results reveal that the use of Nino3.4, MEI, and SOI produces differences in the occurrence time, end time, and intensity of ENSO events. Nino3.4 and MEI produce similar results for identifying ENSO events, and the Nino3.4 index accurately identifies and describes ENSO events with higher reliability. In China, the drought-sensitive areas vulnerable to ENSO events include southern China, the Jiangnan region, the middle and lower reaches of the Yangtze River, and the arid and semi-arid areas of northwestern China. Droughts in these areas correlate significantly with meteorological drought, and time-series correlations between ENSO events and droughts are significantly stronger in regions close to the ocean. Drought occurrence lags ENSO events: when using the Niño3.4 index to identify ENSO, droughts lag the strongest and weakest El Niño events by 0–12 months. However, when using the MEI as a criterion for ENSO, droughts lag the strongest and weakest El Niño events by 0–7 months. The time lag between the strongest ENSO event and drought is shorter than that for the weakest ENSO event, and droughts have a wider impact. The results of this study can provide a climate-change-compatible basis for drought monitoring and prediction. [ABSTRACT FROM AUTHOR]

The long-term stability and sustainability of offshore wind energy resources are very important for wind energy exploration. In this study, the Cyclostationary Empirical Orthogonal Function (CSEOF) method, which can determine the time varying spatial distributions and long-term fluctuations in the cyclostationary geophysical process, was adopted to investigate the geographical and temporal variability of offshore wind resources in China Seas. The CSEOF analysis was performed on wind speeds at 70 m height above the sea surface from a validated combined Quick Scatterometer (QuikSCAT) and Advanced Scatterometer (ASCAT) wind product (2000–2016) with high spatial resolution of 12.5 km, and Climate Forecast System Reanalysis (CFSR) wind data (1979–2016) with a grid size of 0.5° × 0.5°. The decomposition results of the two datasets indicate that the first CSEOF mode represents the variability of wind annual cycle signal and contributes 77.7% and 76.5% to the wind energy variability, respectively. The principal component time series (PCTS) shows an interannual variability of annual wind cycle with a period of 3–4 years. The second mode accounts for 4.3% and 4.7% of total wind speed variability, respectively, and captures the spatiotemporal contribution of El Niño Southern Oscillation (ENSO) on regional wind energy variability. The correlations between the mode-2 PCTS of scatterometer or CFSR winds and the Southern Oscillation Index (SOI) are greater than 0.7, illustrating that ENSO has a significant impact on China's offshore wind resources. Moreover, the mode-1 or mode-2 spatial pattern of CFSR winds is basically consistent with that of scatterometer data, but CFSR underestimates the temporal variability of annual wind speed cycle and the spatial changes of wind speed related to ENSO. Compared with reanalysis data, scatterometer winds always demonstrate a finer structure of wind energy variability due to their higher spatial resolution. For ENSO events with different intensities, the impact of ENSO on regional wind resources varies with time and space. In general, El Niño has reduced wind energy in most regions of China Seas except for the Bohai Sea and Beibu Bay, while La Niña has strengthened the winds in most areas except for the Bohai Sea and southern South China Sea. [ABSTRACT FROM AUTHOR]

Drought is a severe natural hazard all over the world, resulting in enormous losses in many aspects, especially in agriculture. It is essential to analyze the spatio-temporal variation of drought and its relationships with the El Niño-Southern Oscillation under a background of global climate change for better drought prevention. The Songhua River Basin (SHRB), which is an important food base in northeastern China that suffered a severe drought in 2020, was chosen as the research site. The standardized precipitation evapotranspiration index (SPEI) was chosen as the drought index to analyze the spatio-temporal variation of droughts in the SHRB by linear regression analysis and T-test using the meteorological data from 1960 to 2019. The cross-wavelet analysis was adopted to reveal the relationship between the SPEI and El Niño-Southern Oscillation indexes (the Niño 1+2 SST Index (SST1), Niño 3 SST Index (SST2), Niño 3.4 SST Index (SST3), Niño 4 SST Index (SST4), and Southern Oscillation Index (SOI)). The results reveal that the changing trends of yearly, spring, summer, autumn, and winter precipitation were 0.56, 1.47 (p < 0.05), 0.13, 0.04, and 0.16 (p < 0.05) mm/a, respectively; the precipitations were higher in the southeastern regions and lower in the western regions, with extreme values of 831.62 mm and 381.69 mm, respectively. The SPEI was significantly increased (p < 0.05) with a gradient of 0.01/a on a yearly scale and were increased in all seasons (significant in winter (p < 0.05)). The drought probability on a yearly scale was dominated by summer and autumn. The SPEI was positively correlated with SST1, SST2, SST3, and SST4 in a different period with a different resonant period and was negatively correlated with the SOI with a short-term period for 3–4 years from 1986 to 1990 and a long-term period for 9–12 years from 1992 to 2010. These results could provide a scientific guide for drought prevention in the SHRB. [ABSTRACT FROM AUTHOR]

With the aggravation of the ocean–atmosphere cycle anomaly, understanding the potential teleconnections between climate indices and drought/flood conditions can help us know natural hazards more comprehensively to better cope with them. This study aims at exploring the spatiotemporal patterns of drought and its multi-scale relations with typical climate indices in the Huaihe River Basin. First, the spatial patterns were identified based on the seasonal Standardized Precipitation Index (SPI)-3 during 1956–2020 by means of the Empirical Orthogonal Function (EOF). The two leading sub-regions of spring and winter droughts were determined. Then, we extracted the periodicity of spring and winter SPI-3 series and the corresponding seasonal climate indices (Arctic Oscillation (AO), Bivariate El Niño–Southern Oscillation (ENSO)Timeseries (BEST), North Atlantic Oscillation (NAO), Niño3, and Southern Oscillation Index (SOI)) and the sunspot number by using the Continuous Wavelet Transform (CWT). We further explored the teleconnections between spring drought, winter drought, and climate indices and the sunspot number by using Cross Wavelet Transform (XWT) and Wavelet Coherence (WTC) analyses. The results show that there are in-phase multi-scale relations between spring/winter PC1 and AO, BEST, and Niño3, of which the climate indices lead spring PC1 by 1.5–2 years and the climate indices lag winter PC1 by 1.5–3 years. Anti-phase relations between spring PCs and SOI and the sunspot number were observed. NAO mainly affects the interdecadal variation in spring drought, while AO and Niño3 focus on the interannual variation. In addition, Niño3 and SOI are more related to the winter drought on interdecadal scales. Moreover, there is a positive correlation between the monthly average precipitation/temperature and Niño3 with a lag of 3 months. The results are beneficial for improving the accuracy of drought prediction, considering taking NAO, AO, and Niño3 as predictors for spring drought and Niño3 and SOI for winter drought. Hence, valuable information can be provided for the management of water resources as well as early drought warnings in the basin. [ABSTRACT FROM AUTHOR]

This study examines whether there are significant differences in intensity and destructiveness of landfalling tropical cyclones (TCs) over China in central Pacific warm (CPW), eastern Pacific warm (EPW) and La Niña (LA) years. By analyzing different seasons and locations of TCs making landfall over China, it was found that TCs in LA years generally had a larger power dissipation index (PDI) and may cause more disasters in China, while TCs in EPW years had a larger PDI over South China in autumn. A larger PDI of TCSC (landing location in Southern China) usually occurred in EPW years and a larger PDI of TCEC (landing location in Eastern China) occurred in LA years, compared with CPW years. The TCs in LA years were generally stronger, more frequent, and of longer duration over China, because of the positive relative humidity (RH) anomalies, the significant anomalous cyclone that occupied the South China Sea (SCS), and the easterly wind anomalies providing a beneficial steering flow for TCs making landfall. In EPW years, although TCs were less frequent, they had stronger intensity when making landfall and a longer lifetime over land which was mainly caused by a broad band of anomalous westerlies over the SCS giving rise to a belt of positive relative vorticity anomalies, as well as the slow translation speed of TCs before landfall supplying more energy for TCs to survive over land. Overall, we conclude that greater caution is warranted when TCs occur in LA and EPW years, as they may result in more serious disasters in China. [ABSTRACT FROM AUTHOR]