Abstract Wave energy (WE) flux aligns with the direction of group velocity, in contrast to the conventional Eliassen‐Palm (EP) flux, which describes wave activity transfers. This study focuses on the climatological characteristics of WE flux within the North Pacific storm track using 6‐hourly output from the Japanese 55‐year Reanalysis (JRA‐55). In January, pronounced downward WE flux dominates the lower troposphere, while upward WE flux prevails in the upper troposphere, with a distinct divergence maximum around 500 hPa. The dynamical mechanism of this vertical structure is explained with the ascent‐descent configuration of air parcels in extratropical cyclones. Over the ocean, warm air plays a primary role in driving the WE flux, whereas over the continent, cold air motion becomes more influential. Two major baroclinic sources are identified near the Liao River basin and the eastern North Pacific, contributing to the growth of winter storms. Enhanced downward WE flux originating from 200 hPa above the Rocky Mountains is associated with the upper tropospheric advection of wave energy by the westerly jets. During midwinter, the upward WE flux in the upper troposphere over the central North Pacific is substantially suppressed, whereas the lower‐tropospheric downward WE flux does not exhibit a comparable minimum. These results highlight the importance of examining the vertical structure of WE flux for understanding storm‐track energetics and the maintenance of midlatitude winter circulation.
Abstract Ozone (O 3 ) pollution in coastal China exhibits a widespread increasing trend in autumn, coinciding with the active period of typhoons. Yet, the role of typhoons in autumn O 3 variations remains unclear. Here, we analyze autumn O 3 evolution during 63 typhoon episodes over 2014–2023, and illustrate diverse impacts of typhoons on O 3 variations over different coastal regions. We find that sideswiping typhoons, which move northward along the coasts, are more likely to trigger O 3 pollution, enhancing surface O 3 by 14.6∼39.5%. Model simulations show that O 3 enhancements over northern coastal regions are primarily resulted from chemical production and vertical transport (VTRA), while horizontal transport becomes the dominant factor responsible for elevated O 3 in southern regions during Typhoon “Muifa.” Impacts of typhoons on autumn O 3 under global warming are further investigated using a pseudo‐global‐warming simulation technique. If the typhoon occurred under a warmer background, substantial O 3 increases during typhoon days would be found, particularly in the northern regions, driven by stronger chemical processes and VTRA. Sensitivity simulations suggest that counteracting this warming‐induced O 3 amplification would require a joint reduction of NO x and VOCs emissions by 30%–75%. This study offers new insights on autumn O 3 variations impacted by typhoons based on observations and model projections, providing a scientific basis for surface O 3 formation and possible control strategy in the warming future.
Abstract Long‐term changes in aerosol optical depth (AOD) and its vertical structure are important for understanding aerosol climate effects. However, how aerosols from different sources contribute to AOD changes across tropospheric layers remains unclear. Using CALIPSO observations from 2006 to 2021, together with time‐series decomposition, aerosol‐type analysis, and concentration‐weighted trajectory analysis, this study investigates AOD changes in the lower (0–4), middle (4–8), and upper (8–12 km) troposphere, cross‐validated by MODIS and MERRA‐2 data sets. Results show that global AOD changes exhibit pronounced vertical and regional differences. Lower‐tropospheric AOD decreased significantly over densely populated regions such as China and eastern U.S., mainly because of reductions in local anthropogenic‐influenced aerosols. Over West Asia, lower‐ and middle‐tropospheric dust weakened markedly, and the seasonal peak of lower‐tropospheric AOD declined by 41% after 2017, associated with weaker Arabian Peninsula dust emissions and suppressed dust transport under anomalous spring–summer circulation patterns. Across 30°N–60°N, dust and polluted dust declined widely in the middle and upper troposphere. After 2015, the trend components of middle‐ and upper‐tropospheric AOD over the northwestern Pacific decreased by 40% and 27%, respectively, consistent with weakened dust uplift and reduced eastward long‐range transport. Intensified wildfire activity after 2012 altered lower‐tropospheric AOD seasonality over central Russia from bimodal to trimodal and increased the frequency of middle‐ and upper‐tropospheric AOD anomalies over North America and western Europe. These major variations are generally consistent with MODIS and MERRA‐2 observations, highlighting the importance of vertical aerosol structures and transport processes in understanding global aerosol evolution.
Abstract Based on lightning data from 84 tropical cyclones (TCs) that made landfall in China from 2010 to 2022, this study investigates the spatial patterns of intense lightning in TCs and their associations with large‐scale environmental conditions. Individual time periods ranking in the top 10% of hourly lightning frequency within 0–100 km and 200–500 km radii from TC center were defined as intense inner‐core (IIC) and intense outer‐rainbands (IOR) samples, respectively. The differences in key environmental parameters—including relative humidity (RH), convective available potential energy (CAPE), convective inhibition, vertical wind shear (VWS), vorticity and divergence—of the above two sample‐sets were examined. The results show that IICs are concentrated over offshore waters, whereas IORs both offshore and inland. The highest CAPE is shown in the outer‐rainbands region in IOR, while IIC is characterized by a much smaller CAPE gradient between the inner core and outer rainbands. The vertically averaged RH at all layers is higher for IIC than IOR. The vorticity and vertical velocity of IOR in the lower and middle troposphere (500–850 hPa) are greater than those of IIC, indicating the intense lightning in the outer rainbands is associate with stronger dynamic conditions, particularly enhanced vertical motion and VWS. The VWS dominates the spatial distribution of intense lightning—specifically, downshear‐left quadrant in IIC and downshear‐right quadrant in IOR. Moderate VWS (5–10 m s −1 ) promotes lightning in the outer rainbands, but under strong VWS conditions (>10 m s −1 ) the fractions of IOR drops markedly with a substantial proportion (35%) of IIC still exists.
Abstract Measuring vertical velocity is crucial for improving our understanding of deep convection. The Convective Core Observations through MicrOwave Derivatives in the trOpics (C2OMODO) mission aims to retrieve vertical velocity using ice‐sensitive microwave measurements acquired at two closely spaced time intervals. In preparation for the mission, it is essential to better understand the relationship between vertical velocity and satellite‐observed ice‐sensitive microwave brightness temperatures. However, vertical velocity within convective cores is rarely measured, and no comprehensive data set currently exists. To address this gap, we developed MesoNHforC2OMODO RASTA‐km (RAdar SysTem Airborne), a data set of kilometer‐scale Meso‐NH simulations created for the C2OMODO project. It is based on five airborne field campaigns during which the RASTA radar was deployed: CADDIWA, EXAEDRE, HAIC–Cayenne, HAIC–Darwin, and MAESTRO. The data set comprises approximately 1.2 billion atmospheric columns and 2.1 million updrafts, of which 717 000 are in the growth phase, derived from more than 50 simulations. It has been validated against RASTA radar and satellite observations. Using this data set, we confirm results from previous idealized studies. First, temporal variations in C2OMODO brightness temperatures are linearly related to temporal variations in ice water path. Second, the vertical velocity of growing convective cells can be inferred from C2OMODO measurements. We further demonstrate that vertical velocity can be inferred in deep convection from C2OMODO brightness temperatures with a gradient boosting method regressor, an ensemble‐based machine learning model.
