Journal of Geophysical Research: Atmospheres最新文献

Katabatic storms in southeastern Greenland are fierce, density-driven, downslope wind events with substantial implications for the local and downstream weather conditions and climate. This study presents a detailed assessment of their representation across three generations of global reanalysis products (ERA5, ERA-Interim, and ERA40) from the European Centre for Medium-Range Weather Forecasts, paired with a hierarchy of simulations at different grid resolutions with the Community Earth System Model version 2 (CESM2). Using the high-resolution (2.5-km resolution) Copernicus Arctic Regional Reanalysis (CARRA) as a benchmark, we find that the global reanalysis data sets systematically underestimate wind speeds (around 30% in ERA5 and 50% in ERA-Interim and ERA40) and fail to capture key structural features of these regional storms. Similar deficiencies are observed in CESM2 simulations when using standard latitude-longitude grids at 1–2$��°�$ horizontal resolutions, which is a common model configuration used in recent iterations of the Coupled Model Intercomparison Projects. Variable-resolution configurations in CESM2 with enhanced representation of the Greenland topography demonstrate a marked improvement in capturing the strength and structure of these regional storms. Sensitivity simulations further confirm that steeper ice-sheet margins (better resolved at higher spatial resolution) are crucial for accurately representing katabatic acceleration. These findings underscore the importance of spatial resolution and realistic topographic representation in simulating local climate extremes. Accurately capturing such events is vital not only for understanding modern climate dynamics on and around the polar ice sheets, but likely also for simulating realistic ice sheet/Earth system interactions in glacial climates of the past.

Launched in April 2023, the Tropospheric Emissions: Monitoring of Pollution (TEMPO), instrument provides for the first time hourly measurements of atmospheric pollutants over most of North America at high spatial resolution (∼2 × 4.75 km²). This evaluation of TEMPO's first year demonstrates the capability of total formaldehyde column retrievals (ΩHCHO, version 3) at different locations, seasons, and meteorological conditions. The ΩHCHO product is assessed using 36 ground-based Pandora direct-sun measurements from Pandonia Global Network (PGN) as a reference data set. The 36 PGN sites were chosen for consistency in direct-sun and sky-scan measurement modes. In the first year of operation (Aug 2023–Sep 2024), TEMPO ΩHCHO exhibits moderate to strong agreement at PGN sites in both measurement modes (R² = 0.63 to 0.85). TEMPO shows a negligible bias of −2 ± 20% at lower ΩHCHO (<1.0 × 10¹⁶ molecule cm⁻²) and a larger underestimation of −22 ± 5% at higher ΩHCHO (>1.5 × 10¹⁶ molecule cm⁻²). TEMPO clearly captures the seasonal variability of ΩHCHO, with summer values being greatest and winter, spring, and fall values being lower by − 62%, − 45%, and − 29%, respectively. TEMPO shows no consistent bias at any time of day with excellent agreement with Pandora for different meteorological conditions. For all hourly differences between TEMPO and Pandora, 96% fall within 1 × 10¹⁶ molecules cm⁻². TEMPO provides almost 50% more days with at least one observation compared to observations taken only at 1 p.m., from typical polar-orbiting satellites. These findings confirm the high quality of TEMPO's ΩHCHO measurements under a wide variety of conditions and show great promise for future scientific applications.

The subtropical westerly jet (SWJ), a fundamental element of the midlatitude atmospheric circulation that greatly impacts global weather and climate, exhibits multiple centers with relatively high wind speed on the hemispheric scale. It manifests pronounced spatial heterogeneity when these jet centers experience asynchronous variations. However, previous researches about this heterogeneity are still limited. This study examines the long-term variability of the spatial heterogeneity of SWJ during boreal summer (July–August) over recent 40 years, particular for the decadal changes. Three centers with high wind speed along SWJ, respectively located over Atlantic, West Asia and East Asia (short for ATJ, WAJ and EAJ) exhibit marked spatial heterogeneity in their temporal evolution. Both of ATJ and WAJ demonstrate the equatorward shifts, while EAJ shows poleward shift. This zonal heterogeneity is particularly pronounced on the decadal time scale, with all three centers have phase transitions in their meridional displacements around 1998. The decadal changes in the atmospheric circulation before and after 1998 encompass the circulation anomalies caused by the individual meridional displacements of the three jet centers. Meanwhile, the combined circulation anomalies caused by these jet centers closely match the decadal change of the atmospheric circulation. This provides compelling evidence for a strong dynamical connection between the atmospheric circulation and the heterogeneous variation of SWJ. The phase transitions of the Interdecadal Pacific Oscillation and Atlantic Multidecadal Oscillation, combined with the rapid retreat of Arctic sea ice, influence the decadal changes in the spatial heterogeneity of SWJ by modulating the meridional temperature gradient.

Atmospheric rivers (ARs) are long bands of strong horizontal water vapor transport responsible for over 90% of total integrated vapor transport (IVT) in extratropical and polar regions. Using a 12-year record (2010–2022) of ground-based remote sensing, radiosonde, snow stake, and reanalysis data from Summit Station, Greenland, we quantify the impacts of 41 AR events on snowfall, clouds, and the atmospheric state. Although ARs occur 0.97% of all times and 2.68% of snowing times, they contribute 5.8% to total snowfall, enhance snowfall rates by 80%, and double daily snowfall accumulation relative to general snowing conditions. AR events increase near-surface and atmospheric profile temperatures by over 7°C up to 350 hPa and increase specific humidity by 66%, deepen clouds and increase radar reflectivity. While ARs contribute only a modest fraction to total accumulation in central Greenland, they consistently produce clouds and snowfall and create an environment that enables enhanced snow particle growth processes typically not observed in an area characterized by cold, dry conditions.

Ammonia (NH₃) is an important alkaline gas, mainly emitted from agricultural activities, playing an important role in global nitrogen cycle and surface ecosystems. Chemical transport models and emission inventories are widely used to study the emission, transport, and chemical transformation of NH₃. However, traditional static inventories consider emissions as unidirectional, overlooking interactions between NH₃ emissions and other ecosystems, especially land surface processes linked to emissions. In this study, we achieved bidirectional NH₃ exchange between land surface and atmospheric chemistry models by developing WRF-CN-Chem, a model integrating the Noah-MP land surface model with carbon-nitrogen dynamics (Noah-MP-CN) and the Weather Research and Forecasting model with atmospheric chemistry (WRF-Chem). Compared with the static Multi-resolution Emission Inventory for China, the dynamic bidirectional model exhibits higher spatiotemporal resolution and demonstrated a stronger temporal correlation with satellite observations. WRF-CN-Chem model estimated 7.88 TgN NH₃ emission in year 2020 in eastern China. Additionally, we incorporated the atmospheric nitrogen deposition, simulated by the “Online” experiment, into the soil ammonium pool. Our findings revealed an increase of 2.25 TgC yr⁻¹ in land net primary productivity (NPP) in eastern China attributable to the increased nitrogen deposition. By incorporating bidirectional NH₃ exchange between land surface and atmosphere chemistry models, this study enhances the simulation of dynamic ammonia emissions and improves understanding of atmospheric nitrogen deposition processes. Furthermore, linking these processes to land NPP provides valuable insights for sustainable land management and pollution mitigation strategies, helping address the environmental impacts of excessive fertilization.

Floods often cause substantial losses worldwide, and skillful flood predictions are critical to water management and disaster relief. However, the overlooking of surface water flow in the land surface models (LSMs) leads to the defect of flood simulation and prediction. In this study, a quasi-3D LSM, incorporated with the overland flow, has been driven by downscaled numerical weather prediction (NWP) to establish a high-resolution flood prediction system. Compared to the Sentinel-1 imagery, the quasi-3D LSM reasonably depicts the distributions of deluged regions and surface runoff for an unprecedented flood event over North China in July-August 2023. The surface lateral flow redistributes soil moisture, resulting in wetter valleys and drier ridgelines. In addition, the results show that the downscaled precipitation prediction is skillful at a lead time of 3.5 days, while the reliable flood prediction can be expected with a lead time of up to 6 days, especially in low-lying regions. Our work highlights that reliable flood prediction can be achieved through integrating the high-resolution quasi-3D LSM and the NWP, which is crucial for disaster prevention and reduction.

Variable falling-snow deposition caused by near-surface turbulence in complex terrain is an important factor contributing to snow cover heterogeneity. A simple falling-snow deposition model is often needed for hydrological, climatic, and land surface studies. Here, we use the Large Eddy Simulation Model S-ARPS (Snow Advanced Regional Prediction System) to simulate falling-snow deposition over single three-dimensional (3D) hills with different obstacle Reynolds numbers, and over a real complex terrain area at Namtso under different wind conditions. An EOF (Empirical Orthogonal Function) method is applied to the LES data to establish a simple prediction model for snow deposition. For single 3D hills, the accuracy of the EOF-based falling-snow deposition model reaches as high as 78%, and for the Namtso terrain 80%. The EOF-based model presented in this study is mathematically simple and practically easy to implement in comparison to machine-learning and large-eddy simulation models for application to climatic and hydrological studies, which universality can be expanded with further vorticity to spatial mode studies.

Based on 3 years of summertime radar observations in East China, this study quantifies the relationship between polarimetric radar signatures (PRSs) and retrieved raindrop size distributions (RSDs) in heavy-rainfall-producing convection. Multiple PRSs, including the 30-dBZ and 40-dBZ echo-tops, the integrated intensities of $��Z_{\text{DR}}�$ and $��K_{\text{DP}}�$ columns, the maximum graupel and hail height, as well as the changes in $��Z_H�$ and $��Z_{\text{DR}}�$ within the warm-cloud layer, can to some extent indicate the mean raindrop size, number concentration, and rain rate at the low level (1-km level). A Multi-Layer Perceptron model is designed and trained to preliminarily predict these RSD parameters using the PRSs as inputs. These RSD parameters are generally reproduced and the correlations between predicted and observed values are above 0.8. The study clearly demonstrates the quantitative constraints of PRSs on low-level RSDs in heavy-rainfall-producing convection. Such microphysical constraints have potential applications for polarimetric radar in the prediction of RSDs and rainfall intensity.

Several studies have presented methods for estimating daytime or nighttime atomic oxygen (O) from measurements made by the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on the Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics satellite. In the present study, we describe algebraic formulas adapted from the standard SABER Version 2 method for estimating daytime and nighttime O based on the assumption that ozone in the upper mesosphere is in equilibrium. We highlight uncertainties in the calculations that lead to global mean differences ranging up to a factor of two or more in the nighttime O concentration. Important considerations are the choice of which reactions are included in the ozone budget and the values of uncertain parameters used in the model of emissions from vibrationally excited hydroxyl. SABER observations cover almost all local times. Since O is long-lived in the upper mesosphere, the dominant source of diurnal variation is transport by tidal winds. Even when accounting for this transport, there are appreciable differences between daytime and nighttime O. Daytime O is higher than is consistent with the nighttime O that is estimated from several methods. The estimated daytime O has a strong and nearly linear dependence on ozone while the nighttime O is sensitive to multiple parameters in the model of OH airglow emissions. The day/night discrepancy supports other evidence that the SABER daytime mesospheric ozone has large uncertainties and needs to be updated.

China is the largest methane (CH₄) emitter globally, with the Yangtze River Delta (YRD) region recognized as a major emission hotspot. However, due to the scarcity of in situ observations and the complex spatiotemporal variability of these sources, significant uncertainties remain in regional CH₄ emission estimates. To address this, we conducted continuous atmospheric CH₄ concentration measurements from 1 June 2023, to 31 May 2024, at a central YRD site. Using an atmospheric transport model and a Bayesian inversion framework, we quantified monthly and sub-monthly CH₄ emissions from different source categories, with a focus on waste treatment (including both landfill and wastewater). The results reveal the following key findings: (a) Substantial discrepancies were found between prior and posterior emissions across all categories. At the city scale, posterior annual CH₄ emissions were estimated to be 87.4%, 64.6%, 109.5%, and 91.9% of prior emissions for all categories, waste treatment, rice paddy + wetland, and other sources, respectively, with waste treatment contributing the largest uncertainty. (b) Strong seasonal biases were observed, with waste treatment emissions peaking in August and reaching a minimum in March (a 2.6-fold variation), while rice paddy emissions were overestimated in May and underestimated in August by a factor of two. (c) CH₄ emissions from waste treatment exhibited high temperature sensitivity, increasing by 29%–31% per 10°C rise. Under future warming scenarios, waste treatment CH₄ emission factors (EFs) will increase by up to 121.3% under SSP5-8.5 by the end of the century (2091–2100), relative to 2023–2024 levels. In contrast, atmospheric pressure showed negligible influence (3.2% per 1 hPa) on waste treatment CH₄ emissions. (d) More additional observations (i.e., satellite or multiple sites) are strongly suggested to resolve the prior spatial pattern of emissions, especially for fossil fuel-related sources.