International Journal of Climatology最新文献

A comprehensive strategy that incorporates trend analysis, machine learning (ML), and climate model review is needed to improve water resource forecasts and evaluate hydroclimatic variability. The present study effectively combined various forms of categorical and continuous performance metrics for the CMIP6 and the reanalysis datasets in the Upper Godavari Sub-basin area (UGSB) (India). MERRA2 reanalysis datasets demonstrated the highest accuracy for precipitation forecasting, achieving a POD of 0.82 and CSI of 0.71, while JRA-55 closely followed with a CSI of 0.69. CMIP6 models exhibited overestimation tendencies, with a mean FAR of 0.34, highlighting their limitations in capturing precipitation extremes. Thereafter, to understand the long-term variability of the best reanalysis product, trend analysis was also performed using the Mann-Kendall test, Pettitt's test, Van Neumann ratio (VNR), and Innovative Trend Analysis (ITA). This analysis revealed properly the spatial variability of the precipitation, showing increasing (1.5–2.3 mm/year) and decreasing rates for various stations inside the UGSB. Thereafter, the temporal frequency and the intensity were captured by the Continuous Wavelet Transform (CWT) analysis, which further identified shifts in hydroclimatic variability towards higher frequencies after 2000. Thereafter, the prediction accuracy of prediction datasets of various ML models, which included Random Forest (RF), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and XGBoost models, were optimised by The Harris Hawks Optimization (HHO) algorithm, and the best optimised model, RF-HHO, showed reducing RMSE to 4.92 at Ambajogai, 4.81 at Bodhegaon, and 5.21 at Ranjni. The study highlights the importance of combining reanalysis products, trend analysis, and optimised ML models to improve future precipitation predictions and support effective water resource management.

Over the last 75 years, the Ohio River Valley has experienced significant changes in snowfall frequency, timing and magnitude that resulted in meaningful societal impacts. Through the application of a synoptic climatology, this study furthers the understanding of the synoptic-scale atmospheric forcings responsible for snowfall and explains how their variations influenced observed snowfall trends from 1948 to 2021. Three dominant atmospheric environments were identified as being responsible for snowfall in this region: (1) types resulting in northwesterly flow conductive to lake-effect snowfall primarily affecting the northeast region, (2) types with mid-latitude cyclones generating wrap-around or frontal snowfall in the central and southern regions and (3) types with high-pressure centers over or adjacent to the region producing lighter snowfall due likely to smaller-scale processes. Analysis revealed significant temporal trends in the seasonal frequencies of specific snowfall types that, in combination with changes to daily snowfall magnitudes over time, result in significant trends to seasonal snowfall totals per type and align with previously reported domain-wide snowfall trends. Further, many of the snowfall-producing weather types' inter-annual frequencies were significantly correlated to the phase of teleconnection indices. When the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) were negatively phased, more than half of the weather types were significantly more frequent due to the larger-scale atmosphere providing suitable conditions for the development of the snowfall-producing types. These findings underscore the importance of understanding the integration of weather systems across scales and how changes manifest across a cascade of physical forcing mechanisms.

The Indian Ocean has been warming rapidly over the last few decades. However, this warming has not been uniform, with the southern subtropical Indian Ocean (STIO, 15°S–35°S) cooling until the late 20th century and then warming abruptly. We show that the Indonesian Throughflow (ITF) into the Indian Ocean exhibits strong decadal variability with a weaker negative trend over the recent decades, and hence cannot explain the continued heat gain by the upper ocean of the STIO at a rate of 0.41 (± 0.02) × 10²² J/decade. An increased ITF during the hiatus in global surface warming initiated the STIO warming, resulting in a weaker Mascarene high and its decoupling from the Southern Ocean atmospheric variability. This enhanced Tasman inflow into the Indian Ocean and weakened Agulhas outflow offsetting the recently weakened ITF, caused a positive feedback that continued to warm the upper ocean of the STIO.

In the tropical Pacific, El Niño (EN) typically causes positive precipitation anomalies further east than La Niña (LN)'s negative anomalies. This study constructs an idealised scenario of symmetrical CO₂ ramp-up (RU) and ramp-down (RD) phases to analyse the changes in asymmetric precipitation response to EN and LN. The results reveal that, during the CO₂ RD phase, tropical Pacific precipitation anomalies in EN and LN both shift more eastward and southward compared to the RU phase, but with more pronounced shifts in EN than LN. These structural changes in precipitation anomalies are primarily driven by the dynamic component associated with changes in circulation anomalies, which cannot be explained by variations in sea surface temperature (SST) anomalies. The spatial pattern of climatological SST changes between the CO₂ RD and RU phases exhibits an EN-like warming structure, particularly extending southward, which enhances low-level moisture and weakens atmospheric stability. Consequently, precipitation during EN responds more strongly to SST anomalies in this region, accompanied by eastward and southward shifts. While LN partially offsets the impact of the climatological warming pattern and results in less pronounced shifts, thereby contributing to the structural changes in the EN–LN asymmetry of precipitation anomalies.

ENSO exerts a strong influence on global mean surface temperature. Using reanalysis data and CMIP6 AMIP simulations, this study examined distinct late winter (February–March) Northern Hemisphere (NH) mid-to-high latitude surface air temperature (SAT) responses to Eastern Pacific (EP) and Central Pacific (CP) ENSO types. EP El Niño induces a “north cold, south warm” SAT dipole pattern over Eurasia, contrasting sharply with the “north warm, south cold” pattern observed during CP El Niño. EP La Niña produces SAT anomalies nearly opposite to EP El Niño, whereas the CP La Niña responses are comparatively weak. Enhanced low pressure and cyclonic circulation over Eurasia during EP El Niño drive meridional temperature advection, establishing the SAT dipole. Conversely, anomalous high pressure and anticyclonic circulation dominate during CP El Niño and both La Niña types. Observational analysis reveals that the divergent Eurasian SAT patterns stem from differences in tropical Pacific convection anomalies. These anomalies excite distinct poleward-propagating Rossby wave trains and modulate the variability of the polar front jet (PFJ), thereby influencing extratropical circulation. CMIP6 AMIP models realistically simulate these contrasting late winter NH SAT responses.

The phytoplankton bloom magnitude (PBM) off the Yangtze River Estuary has important responses to the changes in the east–west position of the northwest Pacific subtropical high (NWPSH). The potential physical mechanism is analyzed through observations and a physical–biogeochemical model. Results reveal that the anomalously high precipitation in June–July increases the extension ranges of low salinity and high NO₃ concentration water in NWPSH westward extension years. However, due to persistent cloudy and rainy weather, the light condition is unfavorable for the explosive growth of phytoplankton. When the NWPSH moves northward in August, the study area is controlled by anomalously anticyclonic circulation; hot and sunny weather prevails, the nutrients carried by the Yangtze River discharge during the previous June–July period continue to extend toward Jeju Island with the diluted water, favoring the explosive growth of phytoplankton, and vice versa. Sensitivity experiments reveal that when the June–July averaged river discharge and nutrients in NWPSH westward extension years are twice those in normal years, the range of phytoplankton blooms in August can extend to 124.2° E. When the June–July averaged river discharge and nutrients in NWPSH westward extension years are only 0.5 times those in normal years, the range of phytoplankton blooms in August only extends to 123° E. Therefore, the changes in river discharge and nutrient expansion range caused by the east–west position changes of the NWPSH in June–July are important reference indicators for phytoplankton bloom variations throughout the summer. The study provides new scientific reference for seasonal phytoplankton bloom forecasting off the Yangtze River Estuary.

The convection over the Tibetan Plateau (TP) is active during the warm season, but the characteristics of the convective initiation (CI) remain less understood. Based on the observation data of FengYun-4A (FY-4A) satellite from 2018 to 2022, the temporal and spatial distribution characteristics of CI during the warm season over the TP have been investigated. It is found that the CI mainly occurs on the windward slopes of the valley plains along the mountain ranges in the warm season over the TP, with a high-frequency area located at 27° N–30° N, 87° E–92° E. CI occurrences are closely linked to monsoon activity, intensifying with the onset of the monsoon and exhibiting a northward expansion during summer. The CI presents a single diurnal peak at 13:00 (local solar time, the same below) and a minimum at 23:00. Daytime CI primarily occurs over the plains, whereas nighttime initiation is mainly concentrated along the hillsides. The study on the CI in the warm season over the TP is beneficial to deepening the understanding of the development of convection.

Heatwaves (HWs) characteristics (number, frequency and duration) in state capitals of the Northern Brazil Region (NBR) and their climate projections throughout the century were analysed using observed data from meteorological stations and Regional Climate Models (RCMs). HWs were identified considering a minimum period of three consecutive days during which the daily maximum temperature exceeds the 90th percentile. Results showed that more than 125 HWs were recorded in each NBR capital between 1980 and 2019. On average, these HWs were observed over a span of 17–21 days per year, with individual events typically lasting between 3 and 5 days. In the last four decades (1980–2019), the number of HWs as well as their frequency and duration have increased over the NBR. Regarding the model's performance, the HWs characteristics could be reasonably reproduced by the ensemble generated by the RCM from the CORDEX-CORE. However, the errors found led us to apply a bias-correction method to produce a fair indication of the future changes. In a future perspective considering the most pessimistic scenario (RCP8.5), HWs are projected to increase in number, frequency, and duration in almost all capitals, with these conditions expected to worsen by the end of the century. The projected intensification suggests that the number of HWs could more than double in most NBR capitals, while the increase in frequency and duration can exceed 600% and 300%, respectively. These regional projected changes reinforce the need for deep, rapid and sustained mitigation and accelerated implementation of adaptation actions to contain and reduce global warming and its related impacts and risks for humans and ecosystems.