International Journal of Climatology最新文献

The Arabian Peninsula (AP) is an arid region characterised by scarce precipitation with high temporal and spatial variability. Considerable research has predominantly focused on investigating precipitation in the AP from an Eulerian perspective. However, our understanding of the development and lifecycle of precipitation systems from a Lagrangian viewpoint over the AP remains limited. This study seeks to fill the gap by implementing an object-tracking algorithm to the Integrated Multi-satellitE Retrievals for GPM (IMERG; version 07) data to identify convective precipitation systems. The tracking algorithm utilises particle image velocimetry and overlap techniques. Furthermore, we apply hierarchical clustering to the system properties to reveal hidden types and underlying physical mechanisms. Our results indicate three distinct types of convective precipitation systems. Summer systems are found over the southern AP with the lowest propagation speed; they include systems modulated by the Indian Summer Monsoon and topography. Spring systems extend across mid to southern regions with the longest lifetime, greatest rain intensity and volume, and furthest travel distances. The interaction between moist tropical air masses and extratropical cyclones modulates this type. Lastly, winter systems are confined to the northern AP with the highest propagation speed; these are predominantly influenced by midlatitude cyclones. Long-lived systems have higher intensities, cover larger areas, and experience more merging and splitting than short-lived systems. Systems lasting up to 24 h show a typical convective lifecycle wherein the order of peaks is precipitation, volume, and area. Summer and spring systems typically develop in the early afternoon, while winter systems often initiate in the late evening. Summer systems usually cease by late afternoon, whereas winter and spring systems tend to dissipate around midnight. These findings enhance our understanding of convective systems over the AP and open new avenues for weather forecasting, flash-flood modelling, and examining these systems' response to climate change.

The validation of aerosol optical depth (AOD) data is crucial for understanding the impact of wildfires on climate, particularly in regions such as South America, where this phenomenon is recurrent. This study assesses the performance of two global reanalysis products (MERRA-2 and CAMS) against the AERONET observation network for 2003–2023 and, for the overlapping period 2003–2014, also compares both reanalyses and CMIP6 global climate models. Results show that both reanalyses capture the spatiotemporal variability of AOD, with CAMS performing better in representing fire-derived aerosol types, including Black Carbon AOD (BC AOD) and Organic Carbon AOD (OC AOD). Seasonal patterns indicate peak AOD during the dry season (August–October), coinciding with increased fire activity, while systematic overestimation occurs in regions with complex topography. CMIP6 models reproduce the seasonal cycle of total AOD but underestimate BC AOD and OC AOD concentrations when compared with CAMS. These results underline the need for improved model parameterizations and for expanding and maintaining ground-based observation networks such as AERONET to enhance the reliability of aerosol simulations over South America.

This study investigates the historical influence of individual land-use types—forest, grassland, barren, cropland, and urban—on global dryness and wetness trends using CMIP6-LUMIP simulations. By comparing experiments with (historical) and without (hist-noLu) land-use forcing and applying ridge regression, we attribute land-use-induced changes in temperature, precipitation, potential evapotranspiration (PET), and the Standardised Precipitation Evapotranspiration Index (SPEI) to individual land types. CMIP6 models reasonably reproduce observed PET and SPEI patterns, supporting their use in attribution analysis. Over 1901–2014, land-use change induced widespread surface cooling, particularly in the Northern Hemisphere, leading to reduced PET and a coherent midlatitude wetting trend in SPEI. In contrast, precipitation responses were weaker and more heterogeneous. Ridge regression results reveal that cropland and grassland expansion contribute to cooling and PET suppression, while barren land intensifies surface warming and drying, especially in arid zones. Forest and urban land show more localised effects, with urban areas in Southeast Asia and North America exhibiting warming and drying tendencies. Regional assessments across 12 hydroclimatic zones reveal strong spatial heterogeneity: forest dominates wet tropics, grassland regulates PET and SPEI trends in semi-arid regions, barren land drives arid-region drying, and cropland shapes temperate hydroclimate. These findings highlight PET as a key mediator of land–climate interactions and underscore the importance of land-use planning for hydroclimatic resilience.

Extreme precipitation is generally expected to intensify in a warmer climate, posing growing risks to ecosystems and human societies. In this study, we aim to examine the precipitation-temperature relationships in the history and future over eastern China. We first assessed the performance of CMIP6 Multi-Model Ensemble (MME) and ERA5 reanalysis datasets in simulating mean and extreme precipitation across temperature bins during the historical period (1971–2000) in eastern China. Distinct regional biases are identified: ERA5 overestimates precipitation at moderate temperatures (−10°C to 27°C) and underestimates it at low and high temperatures, while the CMIP6 MME consistently underestimates mean and extreme precipitation across all temperature bins. Both datasets reproduce the observed increasing peak structures, where precipitation intensities increase with temperature up to a peak temperature, and then decline at higher temperatures, closely associated with reduced relative humidity. Observed scaling rates range from 3.90%/°C to 5.89%/°C in northeast China and 3.64%/°C–5.63%/°C in southeast China. ERA5 overestimates these rates, approaching the CC rate (7%/°C), while MME underestimates them, aligning with sub-CC rates. Scaling rates for extreme precipitation exceed those for mean precipitation, with higher sensitivity for more extreme events. Future projections (2071–2100) under SSP2-4.5 and SSP5-8.5 scenarios show similar increasing peak structures, with scaling rates exceeding MME historical values. In northeast China, rates range from 5.12%/°C to 7.03%/°C, significantly higher than northeast China's 2.15%/°C–3.96%/°C. Scaling rates for extreme precipitation are projected to be even higher, indicating increased sensitivity to climate change. These findings underscore the need for improved climate models and reanalysis datasets to capture precipitation dynamics and address rising flood risks in eastern China.

This study investigates the changing characteristics of extreme precipitation indices in the Missouri River Basin (MORB) from 1981 to 2022, utilizing three well-known gridded daily precipitation datasets: CHIRPS, CPC, and ERA-5. Employing the Expert Team on Climate Change Detection and Indices (ETCCDI) climate indices and the Mann-Kendall test, the study analyzes MORB mean and extreme precipitation characteristics. Across the three datasets, ERA-5 and CPC exhibited closer agreement in capturing basin-wide mean precipitation, as compared to CHIRPS data. Results indicate a consistent increase in extreme precipitation, particularly in the southern MORB, with total precipitation showing an upward trend across MORB. Annual maximum 1-day precipitation (Rx1 day) exhibits both positive and negative trends, but positive trends dominate, while the Annual maximum consecutive 3-day precipitation (Rx3 day) and Annual maximum consecutive 5-day precipitation (Rx5 day) indices show positive trends, suggesting an enhanced risk of large floods. Return Level (RL) values are obtained by employing the Gumbel distribution to model annual daily maximum precipitation considering two different historical time periods. The analysis of differences in daily annual maximum RLs showed a significant increase in precipitation magnitudes between recent and historic periods. This indicates that extreme precipitation events of a given frequency are now associated with larger precipitation totals, implying that storms of a given magnitude are occurring more frequently in recent decades. A larger positive trend is observed in the average intensity of precipitation as assessed by the Simple Daily Intensity Index (SDII), and the annual total precipitation on days with precipitation exceeding the 95th percentile (R95 pTOT) in the southern MORB. The findings of this study are useful for informing flood risk management, water resource planning, agricultural sustainability, and climate change adaptation in the Missouri River Basin.