Journal of Geophysical Research: Atmospheres最新文献

Hyper-resolution land surface modeling provides an unprecedented opportunity to simulate locally relevant water and energy cycles. However, the available meteorological forcing data is often insufficient to fulfill the requirements of hyper-resolution modeling. Here, we developed a comprehensive downscaling framework based on topography-adjusted methods and automated machine learning (AutoML). With this framework, a 90 m and hourly atmospheric forcing data set was developed from ERA5 data at a 0.25° resolution, and the Common Land Model (CoLM) was then forced with the developed forcing data over two complex terrain regions (the Heihe River Basin and Upper Colorado River Basin). We systematically evaluated the downscaled forcing and the CoLM outputs against both in situ observations and gridded data. The ground-based validation results suggested consistent improvements for all downscaled forcing variables with mean RMSE improved by 6.362%–95.86%. The downscaled forcings, which incorporated detailed topographic features, offered improved magnitude estimates, achieving a comparable level of performance to that of regional reanalysis forcing data. The downscaled forcing driving the CoLM model showed comparable or better skills in simulating water and energy fluxes, as verified by in situ validations. The hyper-resolution simulations provided a detailed and more reasonable description of land surface processes and attained similar spatial patterns and magnitudes with high-resolution land surface data, especially over highly elevated areas. Additionally, this study highlighted the benefits of using mountain radiation theory-based shortwave radiation downscaling models and AutoML-assisted precipitation downscaling models. These findings emphasized the significance of integrating topography-based downscaling methods for hillslope-scale simulations.

Soil freeze-thaw cycles play a critical role in ecosystem, hydrological and biogeochemical processes, and climate. The Tibetan Plateau (TP) has the largest area of frozen soil that undergoes freeze-thaw cycles in the low-mid latitudes. Evidence suggests ongoing changes in seasonal freeze-thaw cycles during the past several decades on the TP. However, the status of diurnal freeze-thaw cycles (DFTC) of shallow soil and their response to climate change largely remain unknown. In this study, using in-situ observations, the latest reanalysis, machine learning, and physics-based modeling, we conducted a comprehensive assessment of the spatiotemporal variations of DFTC and their response to climate change in the upper Brahmaputra (UB) basin. About 24 ± 8% of the basin is subjected to DFTC with a mean frequency of 87 ± 55 days during 1980–2018. The area and frequency of DFTC show small long-term changes during 1980–2018. Air temperature impacts on the frequency of DFTC changes center mainly around the freezing point (0°C). The spatial variations in the response of DFTC to air temperature can primarily be explained by three factors: precipitation (30.4%), snow depth (22.6%) and seasonal warming/cooling rates (14.9%). Both rainfall and snow events reduce diurnal fluctuations of soil temperature, subsequently reducing DFTC frequency, primarily by decreasing daytime temperature through evaporation-cooling and albedo-cooling effects, respectively. These results provide an in-depth understanding of diurnal soil freeze-thaw status and its response to climate change.

During processing in deep convective cloud systems, highly viscous or glassy secondary organic aerosol (SOA) particles can develop a porous structure through a process known as atmospheric freeze-drying. This structural modification may enhance their heterogeneous ice nucleation ability under cirrus conditions through the pore condensation and freezing mechanism. Pristine, compact SOA particles, on the other hand, are recommended to be treated as ice-inactive in models. This recommendation also applies to internally mixed particles, where a coating layer of secondary organic matter (SOM) deactivates the intrinsic ice nucleation ability of the core, which may be a mineral dust grain. Ice cloud-processing may also improve the ice nucleation ability of such a composite particle by inducing structural changes in the coating layer, which can release active sites on the mineral surface. In this work, we investigated the change in the ice nucleation ability of pure SOA particles from the ozonolysis of α-pinene and two types of internally mixed particles (zeolite and coal fly ash particles coated with SOM) after being subjected to the atmospheric freeze-drying process simulated in an expansion cloud chamber. For pure α-pinene SOA, we found only a slight improvement in the ice nucleation ability of the ice cloud-processed, porous particles compared to their pristine, compact counterparts at 221 and 217 K. In contrast, the zeolite and coal fly ash particles, which were initially deactivated by the organic coating, became significantly more ice-active after atmospheric freeze-drying, emphasizing that such composite particles cannot be excluded from model simulations of heterogeneous ice formation.

Global Storm Resolving Models (GSRMs) provide a way to understand weather and climate events across scales for better-informed climate impacts. In this work, we apply the recently developed and validated CAM (Community Atmosphere Model)—MPAS (Model for Prediction Across Scales) modeling framework, based on the open-source Community Earth System Model (CESM2), to examine the tropical convection features at the storm resolving scale over the Maritime Continent region at 3 km horizontal spacing. We target two global numerical experiments during the winter season of 2018 for comparison with observation in the region. We focus on the investigation of the representations of the convective systems, precipitation statistics, and tropical cyclone behaviors. We found that regional-refined experiments show more accurate precipitation distributions, diurnal cycles, and better agreement with observations for tropical cyclone features in terms of intensity and strength statistics. We expect the exploration of this work will further advance the development and use of the storm-resolving model in precipitation predictions across scales.

The Lorenz Energy Cycle (LEC) in the Taiwan Earth System Model Version 1 (TaiESM1) historical simulation is calculated and compared with ERA5 to evaluate the model performance from the thermodynamic aspect. The future change of LEC is accessed by comparing the SSP5-8.5 and historical simulations in TaiESM1. TaiESM1 reasonably simulates the global mean, seasonal cycle, and spatial patterns of the energy reservoirs with larger values in the mean energy components and smaller in the eddy energy components. The energy cycle in TaiESM1 is about 35%–45% stronger than ERA5, except from December to February. The impact of global warming on the LEC is different at the vertical levels. The influence of meridional temperature gradient change is the dominant factor in the intensity of the energy cycle, and the change in static stability only contributes to the lower troposphere. Lifting the tropopause in the tropics increases the meridional temperature gradient and produces more zonal mean potential energy (P_M) in the upper troposphere. P_M is the primary driver of the LEC and leads to a more active energy cycle in the upper troposphere. As the tropical tropospheric depth increases and the mid-latitude eddy activities become more active, more (less) energy is stored in the upper (lower) troposphere, and the energy conversion processes tend to become stronger (weaker) in the upper (lower) troposphere.

The atmospheric circulation in the northern hemisphere interacts with that in the southern hemisphere mainly via lateral coupling and atmospheric mass exchanges across equator. Using the NCEP-NCAR daily reanalysis over period 1979–2019, we examine the Inter-Hemispheric Oscillation (IHO) on the synoptic timescale during boreal winter and its associations with Rossby waves by employing a 9-day high-pass filter and the time-lag regression method. Our results demonstrate that the IHO exist in the surface air pressure anomalies on synoptic timescale which links to both the quasi-stationary and migratory Rossby waves with large amplitudes in the mid- and high- latitudes from the troposphere up to the stratosphere. The synoptic-IHO related quasi-stationary waves appear mainly in zonal, roughly dominated by waves with wave-number 1–7 at different latitudes. These waves disperse the wave energy eastward along westerly jet streams. On the other hand, the synoptic-IHO related migratory Rossby waves are mainly observed in mid latitudes between 30°N–65°N and 30°S–60°S with stronger intensity in northern than in southern hemisphere. The phase speed of these waves is roughly estimated at about 9.5lon/d. The energy of intrinsic Rossby waves also propagates mainly eastward. The synoptic-IHO has important impacts on surface air temperature via both the quasi-stationary and migratory Rossby waves, inducing significant temperature changes over both Eurasian continent and North America as estimated from 40 winters. All the results are beneficial for us to better understand the IHO along with its interaction with Rossby waves and the formation mechanisms of cold weather and climate extremes during boreal winter.

Nitrogen (N) isotopic fractionation during nitrogen oxides (NO_x) cycling and conversion into atmospheric nitrate alters the original N isotopic composition (δ¹⁵N) of NO_x emissions. Limited quantification of these isotopic effects in urban settings hampers the δ¹⁵N-based identification and apportionment of NO_x sources. δ¹⁵N of nitrogen dioxide (NO₂) measured during winter in downtown Fairbanks, Alaska, displayed a large temporal variability, from −10.2 to 24.1‰. δ¹⁵N(NO₂) records are found to be driven by equilibrium isotopic fractionation, at a rate in very close agreement with theoretical predictions. This result confirms that N isotopic partitioning between NO and NO₂ can be accurately predicted over a wide range of conditions. This represents an important step for inferring NO_x emission sources from isotopic composition measurement of reactive nitrogen species. After correcting our δ¹⁵N(NO₂) measurements for N fractionation effects, a δ¹⁵N-based source apportionment analysis identifies vehicle and space heating oil emissions as the dominant sources of breathing-level NO_x at this urban site. Despite their large NO_x emissions, coal-fired power plants with elevated chimney stacks (>26 m) appear to make a small contribution to surface NO_x levels in downtown Fairbanks (likely less than 18% on average). The combined uncertainties of the δ¹⁵N of NO_x from heating oil combustion and of the influence of low temperatures on the δ¹⁵N of NO_x emitted by vehicle exhaust prevent a more detailed partitioning of surface NO_x sources in Fairbanks.

Changes in Arctic sea ice have exerted remarkably effects on the Eurasian climates, but it is unclear whether Arctic sea ice also contributes to Northwest China's ongoing summer drought. This study investigates the influence of the interannual variability of Arctic sea ice on the June drought in Northwest China from 1979 to 2021. It reveals that the early-autumn sea ice in the East Siberian Sea is correlated with drought conditions in June in Northwest China, with a more pronounced connection during the period of 2000/2001–2020/2021 (P2) compared to 1979/1980–1999/2000 (P1). Mitigated drought in Northwest China is associated with anomalously high sea ice concentration (SIC) in the East Siberian Sea. Further analysis suggests that the strengthened link may be due to greater SIC variability in the East Siberian Sea during P2 than P1. In P2, positive early-autumn SIC anomaly is linked to anomalous northeasterly winds, promoting drier soil and widespread cooling in the East European Plain. This dry soil signal may persist into the ensuing spring and early summer, inducing an anticyclonic circulation anomaly over Siberia, which could facilitate the water vapor convergence in Northwest China, thereby enhancing humidity conditions in the region. The insights from this study could offer valuable information for improved prediction of droughts in Northwest China.

In atmospheric models, stochastic generation of subgrid-scale profiles or “subcolumns” has been used for a variety of purposes. Such subcolumns can be generated from subgrid probability density functions (PDFs) at different vertical levels, when such PDFs are available. To do so, the generator needs to decide how strongly points should be correlated in the vertical, that is, how much the values should be overlapped. This is sometimes called “PDF overlap.” To assess vertical correlation in a simplified, observable setting, here the vertical correlation of vertical velocity in subcloud layers is examined. Doppler lidar is used to evaluate the vertical profiles of vertical velocity produced by a large-eddy simulation (LES) model and the Subgrid Importance Latin Hypercube Sampler (SILHS) subcolumn generator. In order to diagnose unrealistic features in subcolumn profiles, various statistical diagnostics are examined here, including the bivariate PDF of vertical velocity at two separated points (i.e., altitudes), the two-point velocity correlation, the integral correlation length, the PDF of two-point velocity differences, and the skewness and kurtosis of two-point velocity differences. The profiles produced by LES match lidar well, except that they are too smooth at small scales. The profiles produced by SILHS exhibit sharp jumps from updraft to downdraft that are not observed in the lidar data. To reduce the generation of these unrealistically sharp jumps, the SILHS sampling method is revised. The diagnostics confirm that the revised sampling method reduces the overprediction of sharp jumps.