Atmospheric Science Letters最新文献

This study investigates the impact of a scale-aware convective parameterization scheme (CPS) on the simulation of typhoon track and intensity through a series of experiments using the Global-to-Regional Integrated Forecast SysTem (GRIST) model. The results of four typhoon cases show the scale-aware CPS generally reduces the track error by about 15 km and the intensity error by about 10% compared to the default CPS. By analyzing the budget equation of surface pressure tendency, we found the surface pressure fall due to CPS heating is about 0.6 hPa h⁻¹ weaker when the scale-aware CPS is used. This is, however, compensated by enhanced microphysics heating, which more than offsets the reduction in CPS and yields a net pressure depression of about 1 hPa h⁻¹. In fact, when parameterized convection is suppressed, the microphysics process takes up the convective instability left over by CPS and stimulates even stronger diabatic heating by 13.8%. The increased microphysics precipitation, along with the intensified grid-scale ascending, further validates the assertion. The results of this study demonstrate the benefits of scale-aware CPS on typhoon modeling.

China faces the Northwest Pacific with the world's most active tropical cyclones (TCs). Whether and how the warming of the “Roof of the World”, the Tibetan Plateau (TP), influences the environmental field and precipitation of landfalling TC (LTC) remains unclear. In this study, a data-driven objective classification reveals that the key environmental field influencing the LTC precipitation in China is closely related to the TP-related high pressure. The precipitation rate of LTC in China exhibits an overall increasing trend over the past 43 years under TP warming. However, the trend of the precipitation rate depends on environmental fields. For LTCs affecting southeastern China, the South Asian High (SAH) intensifies and extends eastward, the Western Pacific Subtropical High (WPSH) shifts westward but weakens, stabilizing the atmosphere and reducing precipitation rate. For LTCs impacting southern China, the SAH and WPSH strengthen, increasing atmospheric instability and enhancing precipitation rate.

The most active synoptic-scale disturbances in Eurasia are embedded within the Siberian storm track. This paper investigates the linkage between the winter Siberian storm track (WSST) and the winter precipitation in China and explores the underlying physical processes. The results show that an intensified WSST is associated with a decrease in winter precipitation along the southeast coast of China and in the East China Sea on the interannual scale. The anomalous low-level northerly winds over eastern China and upper-level positive vorticity anomalies over the East China Sea, accompanied by the subsidence, exert an inhibitory effect on precipitation. The anomalous moisture flux divergence related to northerly winds reduces the moisture supply. The interaction between WSST and mean flow may sustain the anomalous large-scale atmospheric circulation and baroclinicity. In addition, synoptic-scale disturbances originating from the WSST region propagate to the East China Sea, forming cyclonic circulation anomalies that are unfavorable for precipitation.

Terrestrial water availability, quantified by precipitation minus evapotranspiration (P−E), is essential in Earth's water cycle, whereas model simulation of P−E is still largely biased and requires a post-processing procedure. This study introduces the grid-by-grid cumulative distribution function (CDF) matching method to correct simulation bias in P−E, based on the ERA5-Land dataset and outputs from 13 selected CMIP6 global climate models. The CDF matching method has a particular advantage in preserving the trends simulated by laws of physics in climate models, and three (additive, multiplicative, and additive–multiplicative mixed) trend preservation strategies are compared in this study. The cross-validation from 1951 to 2014 indicates that all the trend preservation strategies effectively improve the simulated spatial characteristics of P−E with increased spatial correlation, enhanced sign agreement and reduced mean absolute error. Specifically, the additive strategy outperforms in improving the spatial similarity and accuracy of P−E in the humid region and global average, whereas the mixed strategy is the optimal in the hyper-arid, arid, and semi-arid regions. Furthermore, the mixed strategy has a significant advantage in preserving the signs of P−E across the globe. This study exhibits a computationally efficient statistical approach for bias correction of P−E simulation, and validates its flexible correction strategies regarding different terrestrial aridity conditions.

Previous studies on the South China Sea Summer Monsoon (SCSSM) mainly focused on a certain stage of its seasonal march. In this manuscript, we consider the monsoon onset and retreat as a whole and analyze the duration of the SCSSM and the monsoonal rainfall. We first verify the reasonableness of the SCSSM onset and retreat dates derived from the National Climate Center of China Meteorological Administration, and thus obtain the monsoon duration. Direct validation shows that longer SCSSM durations are associated with the anomalous westerly wind and warm–humid airmass, and vice versa, and thus the monsoon duration is trustworthy. The SCSSM duration shows remarkable low-frequency variations after the mid-2000s (increasing from 25.2 pentads to 28.5 pentads), which is mainly associated with the interdecadal delayed monsoon retreat. Corresponding to the extension of SCSSM duration by more than half a month, the monsoonal rainfall across the East Asia–western North Pacific also shows a significant increase. Compared to the traditional summertime rainfall over a fixed period (i.e., from May to September), our newly defined monsoonal rainfall (i.e., total rainfall within the monsoon duration) may be more physically meaningful and reflective of climatic changes.

This study explores the potential of deep learning as a subgrid parameterization for global storm-resolving models (GSRMs) by employing Large-Eddy Simulation (LES) to generate high-resolution cold pools under various convective structures. The high-resolution data is coarsened to 0.8, 1.6, 3.2, and 6.4 km to mimic the horizontal resolutions of GSRMs. U-Net deep learning models are developed to predict the high-resolution distribution of cold pools using coarsened near-surface (at height of 100 m) physical variables, including horizontal winds, potential temperature, and relative humidity. Results show that the U-Net models effectively capture cold pool characteristics, particularly their edges and intensity distribution at coarser scales. Additionally, high-resolution predictions provide enhanced information on horizontal heterogeneity that is not fully captured by low-resolution fields across different convective regimes. Sensitivity experiments indicate that U-Net prediction from input that includes wind fields outperforms those with thermodynamic variables only, highlighting the importance of accurately simulating dynamical variability in GSRMs. These findings can contribute to the advancement of improved subgrid machine-learning based parameterizations for next-generation atmospheric models.

This study evaluated the performance of a high-resolution statistical downscaling (HSD) approach integrating optimal global climate models (GCMs) and quantile delta mapping (QDM) for the source region of the Yangtze and Yellow rivers, and then projected regional precipitation extremes. The GCMs captured the general precipitation pattern, but the results indicated systematic overestimations, particularly in eastern parts of the region, with deviations reaching 304.8% for winter. The HSD approach improved the spatial correlation coefficients (SCCs) and reduced the biases for mean precipitation and precipitation extremes, outperforming the GCMs with SCCs for annual precipitation of up to 0.87 and reduction in bias by 35%–60% in the simulation of extreme indices. Future projections revealed substantial reduction in consecutive dry days and pronounced increase in the annual total precipitation on wet days, annual count of wet days (precipitation ≥ 1 mm), and annual count of days with heavy precipitation (precipitation ≥ 10 mm) over the source region under different emission scenarios. Specifically, the latter demonstrated accelerated growth with enhanced greenhouse gas concentration, increasing by 14.5%, 39.9%, and 57.3% under shared socioeconomic pathway (SSP)126, SSP245, and SSP585, respectively, by the late 21st century. The findings of this study highlight the need for enhanced flood risk management strategies over the source region of the Yangtze and Yellow rivers to address the prospect of increased precipitation, and emphasize the critical role of coupling GCM optimization and QDM downscaling in generating reliable, high-resolution climate projections over regions of complex terrain.

Malaria is a pivotal health concern worldwide and is particularly affecting the population of Africa. This work investigates shifts in precipitation and temperature patterns, which will influence the planning of health activities, especially regarding malaria. The primary goal of this study is to support reducing vulnerability to malaria in the city of Douala in Cameroon. In this study, we assessed the efficacy of the vector-borne disease community model VECTRI developed by the International Center for Theoretical Physics (ICTP) in simulating malaria transmission in Douala, Cameroon. We utilized rainfall data from Climate Hazards Group InfraRed Precipitation with Station version 2 (CHIRPS2) and temperature data from the ERA5 dataset for the historical period from 2005 to 2015. Furthermore, we conducted simulations using nine outputs derived from the dynamical downscaling of the regional climate model from the CORDEX-CORE model ensemble with a resolution of 0.22° over Africa, focusing on two distinct time frames: near future (2031–2060) and far future (2070–2099). We aim to investigate the potential impacts of global warming on malaria transmission in Douala. Evaluated metrics encompassed risk maps for the entomological inoculation rate (EIR) and the parasite ratio (PR). Throughout the historical period using rainfall and temperature, the model adeptly replicates observed EIR and PR. Projections indicate heterogeneous changes across the study area under global warming, with localized increases or decreases in EIR and PR. As radiative forcing levels escalate (from 2.6 to 8.5 W m⁻²), the magnitude of change in EIR and PR gradually intensifies.

In addition to the global mean surface temperature (GMST), accurately predicting regional features associated with GMST changes is essential for effective climate policymaking at the regional level. This study investigates regional patterns of surface temperature and precipitation associated with interannual GMST variability and evaluates their representation in state-of-the-art climate models. Although the influence of the El Niño-Southern Oscillation (ENSO) on GMST variability is well recognized, it accounts for less than 50% of the total interannual variability. A significant portion of GMST warming, independent of ENSO, is closely associated with widespread regional warming—most notably over North America—along with increased atmospheric river activity and enhanced precipitation along its west coast. However, current climate models systematically underestimate the ENSO-independent component of GMST variability, thereby introducing uncertainties into our interpretation of GMST changes.

During the fire season of 2023, extreme continuous wildfires in Canada exported smoke to distant areas. On June 6–8, record-breaking smoke concentrations impacted human health and the environment in New York City (NYC) and its surroundings. In this work, for the first time, we incorporate Lagrangian airmass trajectories with Copernicus Atmospheric Monitoring Service (CAMS) forecasts to trace back the origin of the smoke in NYC and identify the weather systems governing its transport. We locate the main smoke plume which originated from fires in Quebec. The smoke traveled at a height of about 500 hPa southward and descended slantwise to NYC behind a deep cyclone over the east coast. A second peak in smoke concentration in NYC emerged by air that circulated around the cyclone back to the city, collecting smoke again from the fires in Quebec. Smoke from the major fires in western Canada did not contribute significantly to the NYC event but was transported at tropopause level toward Europe. The findings highlight the critical role of synoptic-scale systems in the transport of wildfire smoke.