npj Climate and Atmospheric Science最新文献

Shallow, sparse, non-precipitating convective clouds forming over the ocean are considered among the least organized cloud fields. The formation mechanism of these clouds is associated with random, local perturbations that create buoyant parcels. Their sparseness suggests no or very weak interactions between clouds. Here, we show that such clouds form within a well-organized, stable, dense mesh of convective cells that operate continuously, independent of the presence of visible clouds.

A national-level afforestation plan has been announced by the Chinese government to combat global warming through carbon sequestration. However, the biophysical feedback of afforestation under future climate scenarios has not yet been assessed. Here, using the Weather Research and Forecast model (WRF) nested by the bias-corrected MPI-ESM1-2-HR model, we simulated how future afforestation regulated the land surface temperature (LST) in China. The results show that afforestation induces a significant cooling effect over the period 2041–2060 under the SSP2-4.5 scenario, in particular in the cold season. The additional cooling effect offsets about 3.69% of the projected LST increase due to global warming and even overcompensates the LST increase in southwestern China. On the diurnal cycles, afforestation induces daytime cooling effects of −0.21 °C caused by increased latent heat fluxes, while nighttime warming effects of 0.05 °C induced mainly by cloud feedback. Our findings highlight the importance of the scientific identification of afforestation areas when developing land-management strategies and biophysical feedback for climate change mitigation.

The influence of the thermodynamic forcing of the Tibetan Plateau (TP) on the Asian summer monsoon remains controversial because the role of elevated heating across the TP remains unclear at multiple time scales. At the extended-range scale, the boundary forcing is more important than the initial field in the forecast process. In this study, we investigated the role of subdaily thermodynamic forcing across the TP in generating 30-day predictions of precipitation in East Asia by conducting a series of hindcast experiments. The surface potential vorticity forcing was used to identify typical years when the TP forcings were extremely strong or weak. The results indicated that the subdaily thermal forcing of the TP was very important for improving the East Asian precipitation forecast accuracy, especially for predictions longer than 14 days in June 2022, when diffusion heating is very strong and can develop over the TP. In such a case, the corrected TP heating could not only correct for low-level water vapor transport but also modular uplevel circulation, which could propagate downstream, thus favoring the correct prediction of precipitation over East Asia. However, in the other cases, the individual influences of thermal perturbations across the TP are not the only important factors. These findings reveal ways to improve the extended-range forecast skill over East Asia.

Over the period 1979-2022, European surface air temperatures warmed around three times as fast as the global mean temperatures in both winter and summer. Here we define “excess” European warming as the difference between the rate of European regional warming and the rate of global warming and investigate the causes. Using a simple observation-based method, we estimate that around 40% ± 39% (in winter) and 29% ± 10% (in summer) of excess European warming is “dynamical” - attributable to changes in atmospheric circulation. We show that the rate of European warming simulated in CMIP6 models compares well with the observations, but only because these models warm too fast in the global mean; excess European warming is underestimated, particularly in winter. The CMIP6 models simulate well the magnitude of the thermodynamic component of excess European warming since 1979 in both winter and summer, they suggest only a weak dynamical contribution in the multi-model mean. The models suggest greenhouse gas-induced warming made the largest contribution to excess thermodynamic warming in winter, whereas changes in anthropogenic aerosols made the largest contribution in summer. They also imply a substantially reduced future rate of excess European warming in summer. However, the failure of current models to simulate observed circulation trends (either as a forced response or as a combination of forced response and internal variability) also implies large uncertainty in future rates of European warming.

In recent years, numerous studies have explored the relationship between atmospheric conditions and respiratory viral infections. However, these investigations have faced certain limitations, such as the use of modestly sized datasets, a restricted geographical focus, and an emphasis on a limited number of respiratory pathogens. This study aimed to develop a nationwide respiratory virus infection risk prediction model through machine learning approach. We utilized the CRFC algorithm, a random forest-based method for multi-label classification, to predict the presence of various respiratory viruses. The model integrated binary classification outcomes for each virus category and incorporated air quality and meteorological data to enhance its accuracy. The data was collected from 31 regions in China between 2016 and 2021, encompassing pathogen detection, air quality indices, and meteorological measurements. The model’s performance was evaluated using ROC curves, AUC scores, and precision-recall curves. Our model demonstrated robust performance across various metrics, with an average overall accuracy of 0.76, macro sensitivity of 0.75, macro precision of 0.77, and an average AUC score of 0.9. The SHAP framework was employed to interpret the model’s predictions, revealing significant contributions from parameters such as age, NO₂ levels, and meteorological conditions. Our model provides a reliable tool for predicting respiratory virus risks, with a comprehensive integration of environmental and clinical data. The model’s performance metrics indicate its potential utility in clinical decision-making and public health planning. Future work will focus on refining the model and expanding its applicability to diverse populations and settings.

Recent advancements in artificial intelligence (AI) have notably enhanced global weather forecasting, yet accurately predicting typhoon intensity remains challenging. This is largely due to constraints inherent in regression algorithm properties including deep neural networks and inability of coarse resolution to capture the finer-scale weather processes. To address these insufficiencies in typhoon intensity forecasting, we propose an attractive approach by initiating regional Weather Research and Forecasting (WRF) model with Pangu-weather, a state-of-the-art AI weather forecasting system (AI-Driven WRF), whose forecasting power can be further augmented by the implementation of dynamic vortex initialization. The results highlight limitations in Pangu-Weather’s capability to accurately forecast typhoon intensity. In contrast, the AI-Driven WRF model demonstrated notable advancements over Pangu-Weather, achieving more reliable and accurate predictions of typhoon intensity. Furthermore, the AI-Driven WRF model demonstrated promising results in predicting typhoon intensity and wind details, showing commendable performance to traditional global numerical model-driven WRF models. Our analysis underscores the potential of AI weather forecasting models as a viable alternative for driving regional models, suggesting a promising avenue for future research in meteorology.

The hurricane, with maximum wind speed over 64 kts, is among the most terrible calamities over the northern Atlantic (NATL). Previous studies identified a poleward migration of tropical cyclone (TC) genesis over the Pacific Ocean, but the shift over the NATL is statistically insignificant. The present study detects a robust southward migration in the genesis latitude of NATL TCs that later reach hurricane strength after 1979, which is consistent with a growth in hurricane frequency in the southern part (10°-20°N) of NATL. This increasing trend of hurricane frequency is intimately attributable to the decreasing vertical shear of zonal wind, resulting from a decreasing north-south temperature gradient. The reduced north-south temperature gradient is primarily caused by greater warming trend in tropospheric temperature in the subtropics, driven by intensified static stability. The present research suggests a potential increase in the hazards confronted by low-latitude islands and coastal nations in Northern America.

As a result of the high volume of maritime traffic along the coasts of Namibia and western South Africa, mariners recorded numerous in-situ wind observations since early times. Many of these historical data are currently available through the International Comprehensive Ocean-Atmosphere Data Set (ICOADS). Here, we make use of these historical data to develop an instrumental index for the characterization of the upwelling-favorable winds over the southern Benguela Upwelling System from 1833 to 2014. Our results suggest that upwelling in this region has increased since the mid-1980s, in good agreement with previous research. However, when the entire period is considered, our index does not evidence a long-term trend but a multidecadal variability with an oscillation period between 20 and 30 years. We found a significant influence of ENSO exerted through the modulation of the position of the South Atlantic high-pressure system. However, this teleconnection may be highly non-stationary.

The tropical cyclone (TC) generated orographic precipitation frequently causes severe floods and landslides over coastal and land areas, but its underlying processes remain largely unresolved. This study explored this issue using a high-density rain gauge network and Doppler radar observations to investigate an intense orographic precipitation event over Da-Tun Mountain (DT) in northern Taiwan associated with Typhoon Meari (2011). Detailed examination of observations and the quantification of precipitation enhancement showed that the seeder–feeder mechanism, rather than the widely known upslope lifting mechanism, was a primary contributor to heavy precipitation. Smaller-scale, landfalling convective elements embedded within TC background precipitation and their interactions with DT also influenced the degree of orographic enhancement of precipitation. These rapidly evolving scenarios represent a secondary contributor to the modulation of precipitation intensities. The results from the study provide important insights into the relative importance of the different processes of orographically enhanced precipitation for TCs.

The 2023/24 El Niño, emerging after a rare triple-dip La Niña, garnered global attention due to its potential to evolve into an extreme event, given the largest accumulation of warm water in the equatorial western Pacific since 1980. Despite initial expectations, its growth rate unexpectedly decelerated in mid-2023, preventing it from reaching the anticipated intensity. Here, we show through observational analyses that unusual easterly anomalies over the tropical western-central Pacific, persisting after the end of the preceding La Niña, significantly contributed to this slowdown. A prominent east‒west sea surface temperature gradient in the region has been identified as the crucial factor associated with these unusual and persistent easterly anomalies. This temperature gradient is directly attributed to a negative North Pacific Meridional Mode and a deepened thermocline over the Philippine Sea. These findings offer a deeper understanding of the atypical transition from a prolonged multi-year La Niña to an El Niño.