International Journal of Climatology最新文献

Based on observational monthly precipitation data at 21 stations in the eastern part of the arid region of Northwest China (ARNWC), the NCEP/NCAR reanalysis atmospheric circulation data, and ERA5-land reanalysis surface soil moisture and sensible heat flux data during 1961–2022, we examine the interdecadal change in the relationship between spring Asia-Pacific Oscillation (APO) and summer precipitation in the eastern part of the ARNWC. Results show the relationship between spring APO and summer precipitation in the eastern part of the ARNWC has weakened significantly since 2000. The southward (northward) shift of the subtropical westerly jet over East Asia and anomalous anticyclone (cyclone) over the Mongolia Plateau play an important role in modulating the dynamical and water vapour conditions responsible for the summer precipitation in the eastern part of the ARNWC during 1961–1999 and 2000–2022. The soil moisture can “memorise” the anomalous signal of APO from spring to summer and feedback to the atmosphere via influencing the summer surface sensible heat in the Tibetan Plateau during 1961–1999. Therefore, the spring APO-related atmospheric circulation can contribute to favourable dynamical and water vapour conditions responsible for the summer precipitation in the eastern part of the ARNWC. However, the land-atmosphere interaction in the Tibetan Plateau cannot retain the signal of spring APO and release it in summer during 2000–2022. Therefore, the APO loses its close relationship with summer precipitation in the eastern part of the ARNWC in this period.

Long-term sunshine duration (SSD) data are critical for solar energy assessments, climate modelling, and ecosystem studies. This study develops a promising systematic quality control (QC) framework to enhance the reliability of historical hourly SSD data across China during 1951~2023, considering the historical data status and characteristics of SSD and related meteorological elements observed from surface stations. The four-step QC framework integrates (1) consistency checks with daily SSD, (2) plausible range verification, (3) temporal alignment with theoretical sunrise and sunset times and (4) integrated spatial and cross-element consistency assessments using cloud cover, ground surface temperature variation and precipitation duration. Analysis reveals 4467 station-months with erroneous zero SSD values (primarily between 1951 and 2000) and 357,500 days exceeding theoretical sunlight hours (predominantly pre-1980), highlighting critical data gaps linked to historical digitisation limitations. Spatial consistency checks identified regional anomalies, with over 90% data coverage post-1960, while cross-element checks demonstrated strong correlations between sunshine percentage (SSP) and cloud cover. The framework assigns QC flags to classify data quality, achieving a robust validation of the dataset. Results underscore the necessity of multi-element verification, particularly in addressing sparse station coverage in early decades. Furthermore, the study suggests that QC procedures can be further refined with the availability of high-resolution hourly cloud cover data, enabling more precise assessments at finer temporal scales. This study not only addresses data quality issues in Chinese SSD records but also provides a transferable framework for improving solar radiation datasets in regions with similar observational challenges.

Climate simulations and projections play a critical role in the research of climate change. Their estimated values and ranges are often calculated by the multi-model ensemble. Therefore, in order to enrich research methods and reduce uncertainty, this paper simulates and projects global temperature anomalies based on a non-stationary probability model, using greenhouse gases as covariates and calculates the time profile of the exceedance probabilities for each temperature under various emission scenarios. The results show that the simulated interval includes the majority of the actual temperatures, and the projected future temperatures are close to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. The very likely (likely) ranges of uncertainty are 0.38°C (0.20°C), which reduce the uncertainty. The probability of global temperature anomalies exceeding 3°C is very small, and the 1.5°C global warming level is unlikely (very low scenario), about as likely as not (low scenarios) and very likely (intermediate scenarios) to be exceeded. Furthermore, the time profiles can accurately represent the probability of exceeding a certain temperature for each year.

This study investigates the dynamics of drought characteristics in Iran under historical (1966–2019) and future (2020–2050) conditions using the SSP2-4.5 scenario. Four drought characteristics—severity, duration, magnitude, and peak intensity—were analysed through the Standardised Precipitation Index (SPI) and copula-based multivariate models. Data from 39 synoptic stations were utilised, with projections downscaled from the HadGEM3-GC31-LL model using LARS-WG. The results show a general reduction in drought severity, duration, and magnitude under the SSP2-4.5 scenario. The copula-based analysis demonstrated that the T-copula was the optimal fit for 96% of stations in the future period, compared to 54% in the historical period. This underscores its ability to capture the dependencies among drought characteristics, particularly under changing climatic conditions. Conditional probabilities and risks for 20-year return periods showed an increase under SSP2-4.5, indicating a higher likelihood of less severe but more frequent drought events within shorter timeframes. Conversely, for 50-year return periods, severe droughts remain rare but show a moderate increase in probability and risk compared to the historical period. These findings emphasise the need for region-specific adaptation strategies to address increasing drought risks and disparities under future climate scenarios.

Arctic Sea ice variability arises from both anthropogenic forcing and natural climate modes such as the El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO) and Arctic Oscillation (AO). While these modes are known to influence sea ice concentration (SIC) and thickness (SIT), their impacts on seasonal extremes remain less understood. In this study, the extreme SIC and SIT are investigated using ERA5 and CMEMS reanalysis products, applying a non-stationary generalised extreme value (GEV) framework with climate indices as covariates. Results indicate that winter and spring sea-ice variability is most pronounced in the Barents and Greenland Seas, where strong Atlantic inflows and dynamic atmospheric conditions make the marginal ice zone highly sensitive to even minor perturbations. Conversely, in the central and peripheral Arctic, variability maximises in summer and autumn, when melt processes, ice-albedo feedback and delayed freeze-up intensify interannual fluctuations. ENSO exerts notable seasonal effects: El Niño events enhance extreme SIC in the Laptev Sea but reduce it in the East Siberian Sea during summer, while SIT extremes increase in the Canadian Arctic Archipelago (CAA) and decline in the East Siberian Sea across all seasons. NAO-related anomalies include stronger SIC and SIT extremes in the Beaufort Sea and CAA and reductions in the Chukchi Sea during autumn. AO effects include increased SIC in the Chukchi Sea during summer and autumn, but decreases in the Beaufort and CAA in summer; SIT extremes rise in the CAA during spring but fall in the Beaufort Sea in summer. Composite analysis further reveals that out-of-phase NAO-AO states intensify autumn sea ice extremes, whereas in-phase conditions exert weaker influences. These results emphasise the distinct and seasonally varying roles of climate modes in shaping Arctic Sea ice extremes, offering insights into future Arctic climate variability.

In hydrological research, the development of accurate prediction models necessitates the establishment of intricate rain gauge networks. This prerequisite is especially pronounced in geographically challenging regions, such as the Northwestern Himalayas (NWH). In such topographically complex areas, the configuration and spatial density of rain gauges play a pivotal role in shaping the reliability and accuracy of the collected data. Over the past few decades, this region has witnessed an unprecedented surge in extreme localised weather events, like cloudburst, resulting in catastrophic loss of lives and livelihoods. We designed an optimal rain gauge network that would provide significantly better sampling of rainfall patterns in the study region. In this work, we utilise advanced techniques and methodologies, such as dynamic time warping (DTW) and neural networks, to extract complex underlying patterns from the historical rainfall data. Leveraging historical meteorological data spanning the past four decades, we employed data-driven analyses to unveil discernible rainfall patterns. These patterns were instrumental in identifying geographical regions characterised by analogous rainfall trends. Subsequently, we engineered tailored rain gauge networks in these identified areas to acquire rainfall data of the highest precision. For Himachal Pradesh and adjoining areas, we designed a network consisting of 50 strategically identified rain gauge locations. This network was carefully evaluated to ensure the optimal number of rain gauges, placement, and overall quality of the rain gauges. This innovative approach holds significant promise in laying the groundwork for advanced and reliable rainfall monitoring systems. In doing so, it has the potential to yield substantial benefits for a multitude of stakeholders, including local communities, agriculture, infrastructure development, and the overarching goals of environmental sustainability.