International Journal of Climatology最新文献

The Amazon rainforest is a critical component of the global climate system, with its vast vegetation driving the regional water cycle through evapotranspiration. However, deforestation is severely disrupting this balance, and most studies have focused primarily on surface-level impacts. This study examines how deforestation affects the vertical structure of humidity in Amazonia, using high-vertical-resolution specific humidity profiles from GPS radio occultation (GPS-RO) observations between 2007 and 2023. Gid cells across the region were classified by the percentage of accumulated forest loss (low deforestation, LD < 10%; high deforestation, HD > 50%) using data from the PRODES/INPE system. Results show consistent and significant drying in highly deforested areas, especially in the lower troposphere (below ~3–4 km), with the strongest reductions in specific humidity (up to −7.8 g kg⁻¹) found below 1.5 km. This drying intensifies during the dry season (May–September), when forest-driven evapotranspiration plays its greatest role. Humidity differences persist into the mid-troposphere, suggesting possible changes in convective development or broader-scale compensatory subsidence. The robustness of the signal was validated through integrated humidity profiles at different vertical layers, confirming that the observed drying is not a methodological artefact but a direct physical consequence of vegetation loss. These findings highlight the deep coupling between land cover and moisture dynamics in the tropics, with major implications for regional climate modelling and conservation strategies in the Amazon.

Human populations face significant challenges due to the increasing frequency and intensity of severe precipitation events mainly caused by climate change. In this regard, this study provides a comprehensive analysis of the changes in extreme precipitation and their relationships with large-scale atmospheric circulations (ACs) and greenhouse gases (GHGs) in Northern Cyprus. The analysis used daily precipitation records from 33 precipitation stations covering the period 1979–2014. To detect and quantify trends in extreme precipitation indices (EPIs), the Mann–Kendall and Modified Mann–Kendall tests were employed, alongside Sen's slope estimator for trend magnitudes. Additionally, the Spearman correlation test and multiple linear regression (MLR) modelling were used to evaluate the relationships between EPIs, ACs and GHGs. The results suggested significant increasing trends in extreme precipitation and a decrease in the dry periods on an annual scale. Seasonal analysis revealed a decline in consecutive dry days during autumn, while the intensity of precipitation extremes showed upward trends in both winter and spring. Furthermore, both correlation and MLR analyses revealed that the ACs, consisting of the Western Mediterranean Oscillation Index (WEMOI), North Atlantic Oscillation (NAO) and Polar/Eurasia pattern (POLEUR), were the most influential circulation patterns affecting EPIs on annual and seasonal scales. Additionally, significant positive correlations were found between GHGs and EPIs, particularly in winter and spring. CH₄ and N₂O were more influential during the cooler seasons, whereas CO₂ became dominant mainly in spring.

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.