International Journal of Climatology最新文献

Drought significantly affects agriculture and ecology in the Horn of Africa (HOA), whereby livelihoods largely depend on rainfed farming. This study aims to analyze drought propagation and its impacts on vegetation and crop productivity, with a specific focus on recovery dynamics. Over the period 2000–2022, we developed and integrated a suite of physically based remote sensing indices, including the Drought Propagation Index (DPI), Crop Stress Index (CSI), Soil Moisture Deficit (SMD), Water Deficit Index (WDI), and Drought Recovery and Rate Index (DRRI), into a novel framework. The performance of this integrated framework was further evaluated against the conventional Standardised Precipitation Index (SPI) to validate its ability for capturing drought propagation and agricultural impacts. The findings identify the eastern and southeastern HOA as major drought hotspots, experiencing severe droughts 70%–100% of the time and exhibiting worsening temporal trends. SPI was strongly correlated with DPI (r = 0.67, p < 0.05), thus proving to be reliable. Vegetation indices showed significant reductions during droughts, while DPI positively correlated with NDVI at 0.56 and EVI at 0.54. Crop production was reduced by 30%–35% in Somalia, especially for maize and sorghum, whereas Ethiopia showed more resistance because of irrigation. The mean recovery time exceeded 2 months during the 2010 and 2016 droughts in southeastern HOA, indicating low resilience, whereas northern areas recovered faster. This framework offers practical recommendations for drought mitigation, drought-resistant crops, and adaptive resource management to deal with vulnerabilities.

The impact of the amplitude characteristic of the intra-seasonal oscillation (ISO) on water vapour transport is examined for the March–May (MAM) and September–November (SON) seasons. The ISO temporal indices are constructed from daily anomalies of the first two principal components of the empirical orthogonal function (EOF) applied on outgoing longwave radiation anomalies, filtered for 25–70 days. A threshold method subsequently applied to the normalised ISO amplitude using the first two EOF components is used to identify extreme events associated with ISO peaks. This allows identifying 129 (119) strong ISO events (sISOs) and 39 (40) weak ISO events (wISOs) during the MAM (SON) season. The findings revealed in MAM that, during sISO events, the southern part of the domain experiences a strengthening of the moisture convergence on the Atlantic coast, to the south-east, associated with a westerly moisture transport. In addition, sISO induces negative anomalies in the horizontal component of moist static energy (MSE) advection. wISO are characterised by a predominance of significant moisture divergence to the east of the domain and in the centre of the Congo Basin, accompanied by moisture transport from the Indian Ocean. This event generates positive moisture advection anomalies, fuelled by a zonal influx of MSE. In contrast, the two examined ISO events exhibit opposing patterns of moisture transport during the SON season. Specifically, a dipole structure emerges with divergence anomalies in the north coinciding with convergence anomalies in the south during the sISO. Analysis of the zonal components of moisture transport indicates a reinforcement during wISO events in both seasons, due to the reinforcement of the African Easterly Jet (AEJ). During wISOs, stronger low-level westerlies facilitate increased moisture import from the Atlantic Ocean. Furthermore, compared to sISO events, wISOs are associated with more intense northern and southern components of the AEJ. Finally, uncentred pattern correlation coefficients between strong and weak ISO events and the different phases of the Madden-Julian Oscillation (MJO) vary depending on the season and the specific MJO phase.

The Loess Plateau Region (LPR) in China, identified as one of the world's most severely affected areas by soil erosion, contains fragile ecosystems highly susceptible to climate variability. This study examines the long-term trends and abrupt shifts in temperature and precipitation extremes across the LPR from 1982 to 2023, offering a comprehensive assessment of climate change in this sensitive region. Homogenised daily data for the mean (T _mean), maximum (T _max) and minimum (T _min) temperatures, along with precipitation (PPT), were collected from 160 meteorological stations. Data quality control and homogenisation were performed using RHtestsV3. The datasets were interpolated onto a 0.75° × 0.75° grid via ordinary Kriging in ArcGIS. Monotonic trends were analysed using the Mann–Kendall test and Sen's slope, with iterative pre-whitening to mitigate autocorrelation bias. Abrupt shifts were detected using the Mann–Whitney U test, with significance assessed via Z-values and Monte Carlo simulations. We found a significant regional warming of annual T _mean at 0.0177°C/year (≈0.71°C since 1982). Annual T _max changed little (0.002°C/year, non-significant), whereas T _min decreased overall (−0.051°C/year), despite spring–summer T _min increases and winter T _min cooling (−0.018°C/year). Consequently, the diurnal temperature range widened, especially in winter. The strongest warming occurred in winter and spring, with over 83% of grid cells exhibiting significant trends (p < 0.05). Precipitation increased significantly (4.81 mm/year; 200 mm over the study period), predominantly during the rainy season (+3.02 mm/year). Spatial variability was notable, where 41% of grid cells showed precipitation increases, while 13% exhibited declines, particularly in the southeastern LPR. Abrupt shifts affected 27% of temperature and 18.5% of precipitation grid cells, predominantly around 1993–1997 and 2009–2012; however, these shifts were localised and not statistically significant at the regional scale. Persistent warming and moderately increasing precipitation characterise the LPR's recent climate, underscoring the necessity for continued monitoring and adaptive strategies to address the impacts of temperature and precipitation extremes in this vulnerable region.

With the emergence of the ‘Arctic amplification’ phenomenon in recent years, vegetation in the circum-Arctic region has exhibited significant greening trends, while summer Arctic cyclones have also shown a notable increase in both frequency and intensity. To explore the potential relationship between Arctic vegetation changes—particularly in the northern margin of the Eurasian continent (NME)—and the intensification of Arctic cyclone activity under the backdrop of accelerated Arctic warming, this study investigates the impact of vegetation greening on the intensity of summer Arctic cyclones in this region. Using GIMSS 3G+ NDVI data from 1982 to 2022, combined with the WRF numerical weather model, the analysis reveals that, compared to the 1980s, tundra regions such as the central and eastern parts of the NME experienced the most pronounced increase in vegetation, corresponding to significant warming in these areas. As temperatures rose markedly, the north–south land-sea temperature gradient in the NME intensified, enhancing atmospheric baroclinicity in coastal regions. Consequently, cyclone intensity increased accordingly. The rise in the Green Vegetation Fraction (GVF) led to an increase in the Leaf Area Index (LAI) while reducing surface albedo, allowing the surface to absorb more shortwave solar radiation. This process plays a critical role in driving the rise in near-surface temperatures and the development of cyclones.

Drought-flood abrupt alternation (DFAA)—characterised by rapid transitions between drought and flood events—is an extreme compound natural hazard that threatens ecosystems, particularly in fragile environments such as karst regions. Soil moisture is a key indicator of surface hydrological processes, directly regulating vegetation growth and recovery. This study utilises the Standardised Soil Moisture Index (SSMI) to identify DFAA events and applies Granger causality analysis to reveal soil moisture-vegetation interactions. The impact of DFAA on vegetation productivity is quantified, with a comparative analysis between karst and non-karst regions in southern China. The results indicate that: (1) From 2000 to 2020, SSMI showed a significant upward trend. Drought-to-flood events were more frequent, intense, and longer in duration than flood-to-drought events. Both types of events also exhibited substantial spatial heterogeneity. (2) SSMI and NDVI were positively correlated, with the strongest correlation reaching 0.32 at a 60-day lag. Bidirectional causality between SSMI and NDVI was predominant, affecting 49.24% of the study area. (3) Flood-to-drought events synergistically intensified drought–flood stress, whilst drought-to-flood events mitigated stress effects. Consequently, vegetation recovery was slower after flood-to-drought events, particularly in hydrologically vulnerable karst regions.

Spatiotemporal variations in extreme precipitation and their impacts on population exposure remain poorly understood due to biases in climate model projections and assumptions inherent in emission scenarios. In this study, we evaluate the bias-correction performance of three deep learning techniques—Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and hybrid CNN-LSTM—using extreme precipitation indices derived from 10 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. Building on this, we assess exposure for China's nine river basins under Shared Socioeconomic Pathway (SSP)1–2.6 and SSP3–7.0 in the near future (2031–2060) and far future (2071–2100) and quantify the potential benefits of achieving carbon neutrality on reducing population exposure to extreme precipitation. Our findings reveal that CNN outperforms both LSTM and CNN-LSTM in correcting biases. Compared to the historical period (1985–2014), both R95p and Rx1day exhibit increasing trends across China at the scenario and temporal levels. The SSP3–7.0 scenario shows more pronounced increases than SSP1–2.6, and the long-term trends (2071–2100) are greater than those observed in the short-term period (2031–2060). Spatially, the strongest responses are observed in the southwest and southeast coastal regions, while changes in inland areas are more moderate. On the exposure side, SSP3–7.0 results in a substantial national increase, with far-future exposure projected to reach approximately 2.7 times the near-future levels for Rx1day and 1.2 times for R95p. Hotspot areas of exposure are found along the Yangtze–Pearl River Delta–Southeast coastal belt, the southwestern uplands, and the Beijing–Tianjin–Hebei–Bohai corridor. In contrast, the carbon-neutral pathway (SSP1–2.6) leads to approximately 80% reductions in national exposure in the far future, with reductions greater than 70% in all nine basins. We suggest that climate extreme changes, rather than population dynamics or extreme-population interactions, are anticipated to dominate these reductions in the future. These results provide important scientific support for ongoing efforts aimed at achieving carbon neutrality by the 2060s to reduce the potential risk of extreme precipitation in China and its nine major river basins.

Compound drought and heatwave (CDHW) events are complex climate extremes driven by global climate change, making it challenging to estimate their comprehensive risk using univariate statistics. To address this challenge, we construct a two-dimensional joint function based on the duration and severity of CDHWs using China as a case study, enabling assessment of joint occurrence probability under diverse scenarios. Whilst previous studies have conducted multi-dimensional risk assessments, most have focused on individual variables or examined specific aspects of drought-heatwave relationships. By simultaneously integrating duration and severity through a bivariate joint function, our approach advances this line of research and provides a comprehensive multi-dimensional framework for assessing the compound characteristics of CDHWs. The results revealed that for China as a whole, changes in the severity threshold had a greater impact on extreme CDHWs, and the joint occurrence probability of severe CDHWs (events with a duration exceeding 7 days and a severity surpassing the 80th, 90th, or 95th percentile) was more sensitive to the severity. The conditional probability rose more rapidly for short-term events (3 days) than for long-term events (5–7 days). When the duration exceeded 7 days and the severity exceeded different thresholds, the joint occurrence probability of CDHWs was within the range of 2%–6%. Amongst the different regions, the duration and severity had varying impacts on the joint occurrence probability. The extreme CDHWs in North China, Northeast China, and western Northwest China were more strongly influenced by the duration. Jianghuai, South China, Southwest China, eastern Northwest China, and the Tibetan Plateau were more prone to longer-lasting extreme CDHWs. In these areas, when CDHWs lasting more than 7 days occurred, changing the severity threshold had a greater impact on their extremity. In North China in 1997, the longest CDHW had joint return periods of over 1000 years when both duration and severity exceeded thresholds, and over 500 years when either exceeded thresholds. The findings demonstrate the variations in the impacts of duration and severity on the joint probability of CDHWs in different regions of China.