Journal of Geophysical Research: Atmospheres最新文献

The microwave sounding instruments have been providing humidity and temperature profiles critical for improving numerical weather prediction (NWP). In recent years, advanced microwave sounders have been proposed to be onboard future CubeSats that are less expensive than traditional satellites, providing unprecedented rapid-update and low-earth-orbit microwave observations that can follow the evolution of the typhoon and convective weather systems. In this study, the potential values of assimilating future CubeSat constellation microwave radiances in clear-sky condition for regional NWP are preliminarily assessed with observing system simulation experiments using the convection-permitting 4DEnVar for the prediction of typhoon “Infa” and its associated “7.20” heavy rainfall event that occurred in China in July 2021. This constellation is assumed to include 12 CubeSats equipped with microwave sounders obtaining radiances sensitive to water vapor and temperature. The results show the CubeSat constellation radiances demonstrate positive impacts on the analyses and forecasts of typhoon's track, intensity, precipitation, and dynamical conditions. The forecast errors of typhoon's maximum wind speed, minimum sea level pressure, and track are reduced by 17.9%, 14.3%, and 27.9%, respectively. The wind, temperature, and humidity of 6-hr forecasts are improved by 4.95%, 7.89%, and 5.17%, respectively. The rainfall forecast scores in terms of fraction skill score and equitable threat score are also improved by 7% and 5.9% with the ameliorative prediction of magnitude and pattern of rainfall maximum centers for both the landfall typhoon and “7.20” heavy rainfall event. This study indicates the potential benefits of microwave radiances from a low-earth-orbit CubeSat constellation with rapid revisits using advanced data assimilation on the regional NWP in the future.

Glacier mass in the Karakoram region has increased since the mid-1990s in contrast to Himalayan glaciers. The scarcity and brevity of observations, including in situ observations and weather station data, limit our understanding of the Karakoram anomaly. Here, we present the stable water isotopes and snow accumulation records of Karakoram's first shallow ice core, covering 1998 to 2018. The decreased δ¹⁸O suggests an overall cooling trend in the Karakoram region for the early 21st century. In contrast, snow accumulation shows a significant increasing trend, which reveals that the Karakoram glacier has been getting more and more precipitation supplies recently. Using correlation field analysis and a backward trajectory model, we find that the decreased temperature and increased precipitation in the Karakoram are related to weakening westerlies. The northward shift of westerlies will transport colder water vapor from the North Atlantic to Karakoram, decreasing temperature and increasing precipitation in Karakoram. Our results indicated a trend of temperature cooling and precipitation increase trend in the Karakoram related to the weakened westerlies and the North Atlantic, which might explain the Karakoram anomaly that occurred in the Pamir-Karakoram-Kunlun region under the present climate change background. These results can provide new insights for predicting the changes in glacier mass in Karakoram and evaluating the variations in Asian water towers.

The quasi-biennial oscillation (QBO) is the dominant mode of interannual variability in the tropical stratosphere and influences climate worldwide. This study investigates the seasonal predictability of QBO in eight dynamical forecasting models from international operational centers and organizations. Results show that current dynamical models have high skills in predicting the QBO evolution benefiting from their capability of characterizing the typical downward propagation of QBO. Further analyses show that most models can reasonably reproduce the QBO teleconnection patterns, but with weaker amplitude than observation, which may be due to the underestimation of the Holton-Tan effect in models. Prediction skills of the QBO teleconnections to winter surface climate patterns are not yet satisfactory in some models though they have quite high QBO skills. These results provide a comprehensive evaluation of current status of the QBO dynamical seasonal prediction and thus a deeper understanding of the QBO seasonal predictability, which would shed light on predicting QBO and its teleconnection.

Tropical cyclones (TCs) frequently encounter and interact with mesoscale eddies when moving over the ocean, affecting both of their subsequent evolutions. The South China Sea (SCS) suffers the most frequent TCs among global oceans and contains active eddies. On average, one TC encounters and interacts with at least one eddy during its lifespan in the SCS. Using 27-year satellite data and numerical simulations, we examined the response and feedback of mesoscale eddies to TCs over the SCS, based on TCs' interaction with 183 cyclonic ocean eddies (COEs) and 152 anticyclonic ocean eddies (AOEs). TCs induce a symmetric sea surface height reduction with maximum reduction (∼5 cm on average) appearing at the TC center. Pre-existing COEs (AOEs) are enhanced (weakened) by TCs with an average 24% increase (17% decrease) in amplitude and enlarged (shrunk) with an average 19% increase (13% decrease) in radius. Stronger or slower-moving TCs enhance COEs more significantly. Pre-TC existing COEs (AOEs) enhance (suppress) TC-induced sea surface cooling, and thus suppress (fuel) TC intensification, with the effect of COEs statistically stronger than AOEs. This modulating effect is more pronounced under stronger or slower-moving TCs. Eddy's location also influences the modulating effect, with pre-TC COEs located to the left (right) of TC track shifting the largest cooling leftwards (rightwards). Furthermore, the left-located COEs increase cooling amplitude by 61%, stronger than right-located COEs (26%). These results suggest that the combination of mesoscale eddies and TC attributes complicates TC-induced sea surface cooling, which potentially affects TC intensification, wind structure, and rainfall.

Atmospheric rivers (ARs) are filamentary structures within the atmosphere that account for a substantial portion of poleward moisture transport and play an important role in Earth's hydroclimate. However, there is no one quantitative definition for what constitutes an atmospheric river, leading to uncertainty in quantifying how these systems respond to global change. This study seeks to better understand how different AR detection tools (ARDTs) respond to changes in climate states utilizing single-forcing climate model experiments under the aegis of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP). We compare a simulation with an early Holocene orbital configuration and another with CO₂ levels of the Last Glacial Maximum to a preindustrial control simulation to test how the ARDTs respond to changes in seasonality and mean climate state, respectively. We find good agreement among the algorithms in the AR response to the changing orbital configuration, with a poleward shift in AR frequency that tracks seasonal poleward shifts in atmospheric water vapor and zonal winds. In the low CO₂ simulation, the algorithms generally agree on the sign of AR changes, but there is substantial spread in their magnitude, indicating that mean-state changes lead to larger uncertainty. This disagreement likely arises primarily from differences between algorithms in their thresholds for water vapor and its transport used for identifying ARs. These findings warrant caution in ARDT selection for paleoclimate and climate change studies in which there is a change to the mean climate state, as ARDT selection contributes substantial uncertainty in such cases.

In March 2019, a strong atmospheric river (AR) originating from the Gulf of Mexico transported abundant moisture inland and fueled a record-breaking bomb cyclone in Colorado, resulting in widespread winter weather hazards across several states. Experimental model simulations and trajectory analysis indicate that mid-tropospheric latent heat release (LHR) coincided with the strength of the warm conveyor belt and played a key role in the deepening of the cyclone. The LHR promoted the generation of a lower tropospheric positive potential vorticity (PV) anomaly and a stronger low-level cyclonic circulation, enhancing the cyclone, low-level jet stream, and associated water vapor transport. Additionally, it generated an upper tropospheric negative PV anomaly and strong upper-level anticyclonic circulation, influencing the structure of the trough-ridge couplet and the associated Rossby wave. Reductions in the initial intensity of the AR and disallowing LHR both weakened the cyclone. However, disallowing LHR significantly disturbed the synoptic-scale structure of the storm and embedded Rossby wave, resulting in a stronger impact. Thus, the reduction of diabatic PV generation, under the influence of AR activities, was crucial in the explosive intensification of the cyclone. Additionally, the reductions in AR intensity and the magnitude of cyclone weakening did not show a linear relationship. Few studies have explored interactions between ARs and continental cyclones, and this paper highlights the need for further research on AR-associated extreme weather events inland.

We propose the first unified objective framework (SyCLoPS) for detecting and classifying all types of low-pressure systems (LPSs) in a given data set. We use the state-of-the-art automated feature tracking software TempestExtremes (TE) to detect and track LPS features globally in ERA5 and compute 16 parameters from commonly found atmospheric variables for classification. A Python classifier is implemented to classify all LPSs at once. The framework assigns 16 different labels (classes) to each LPS data point and designates four different types of high-impact LPS tracks, including tracks of tropical cyclone (TC), monsoonal system, subtropical storm and polar low. The classification process involves disentangling high-altitude and drier LPSs, differentiating tropical and non-tropical LPSs using novel criteria, and optimizing for the detection of the four types of high-impact LPS. A comparison of our labels with those in the International Best Track Archive for Climate Stewardship (IBTrACS) revealed an overall accuracy of 95% in distinguishing between tropical systems, extratropical cyclones, and disturbances. SyCLoPS produces a better TC detection skill compared to the previous algorithms, highlighted by an approximately 6% reduction in the false alarm rate compared to the previous TE algorithm. The vertical cross section composite of the four types of high-impact LPS we detect each shows distinct structural characteristics. Finally, we demonstrate that SyCLoPS is valuable for investigating various aspects of LPSs in climate data, such as the evolution of a single LPS track, patterns of LPS frequencies, and precipitation or wind influence associated with a particular LPS class.

While the variability of West Antarctic sea ice, represented by the Antarctic Dipole (ADP), has been extensively investigated, the sea ice variability around East Antarctica has received relatively less attention. In this study, we identify significant dipole-like sea ice variability over the Bellingshausen, Weddell, and King Hakon VII Seas during austral winter, which is comparable in magnitude to the ADP and is termed the East Antarctic sea ice Dipole (EAD). Our analysis shows that the EAD pattern is driven by local thermodynamic and dynamic processes associated with an anomalous low-pressure system over the Weddell Sea, which is linked to a Rossby wave train triggered by tropical sea surface temperature variabilities, particularly those over the Indian Ocean. This teleconnection between the Indian Ocean and East Antarctica is further confirmed by numerical model experiments, with Indian Ocean warming identified as the main forcing. Our findings highlight the importance of this teleconnection in improving sea ice prediction models, especially in light of the projected acceleration of Indian Ocean warming in climate models.

Oceanic whitecaps, as indicators of increased air–sea exchange, can be effectively captured by Landsat-8 Operational Land Imager (OLI) images with a 30-m spatial resolution. This study used a new adaptive iterative triangle algorithm, which was applied to 400 OLI images synchronized with National Data Buoy Center-measured wind speeds, covering the different offshore areas of the United States over the last ten years to automatically extract whitecap-affected pixels. By integrating radiative transfer equations, we calculated the whitecap coverage (W), determined that a 4 km window size is optimal for calculating W, and established a whitecap coverage-sea surface wind speed model. A sliding window method was used to achieve high-resolution (100 m) large-area sea surface wind speed estimations. More importantly, this study provides new insights into the impact of different wind and wave conditions on W and regional variations in the whitecap coverage-sea surface wind speed model from an optical satellite perspective, which can effectively quantify the wind-wave breaking and distinguish the regional variations of them in different offshore seas. By comparing with 10-m resolution data from the Sentinel-2 Multi-Spectral Instrument, we further clarified the impact of spatial resolution on the selection of methods for calculating W, revealing scale effects in optical remote sensing of whitecap detection and proposing corresponding W calculation strategies. These results demonstrate the potential of high-resolution optical remote sensing for monitoring whitecaps, estimating sea surface winds and studying air–sea interactions.

Snowmelt and related extreme events can have profound natural and societal impacts. However, the studies on projected changes in snow-related extremes across the Tianshan Mountains (TS) and Pamir regions have been underexplored. Utilizing regional climate model downscaling and bias-corrected CMIP6 data, this study examined the changes in snowmelt and water available for runoff (SM_ROS, rainfall plus snowmelt) during the cold seasons across these regions for historical (1994–2014) and future (2040–2060) periods under shared socioeconomic pathway (SSP) scenarios (SSP245 and SSP585). The results demonstrated that accumulated snowmelt was projected to rise by 17.98% and 20.36%, whereas SM_ROS could increase by 26.97% and 28.95%, respectively, under SSP245 and SSP585 scenarios. Despite relatively minimal changes in extreme snowmelt, the magnitude of the historical daily maximum extreme SM_ROS (10-year return level) was 28.04 mm and was expected to increase by 15.32% and 15.31% under the SSP245 and SSP585 scenarios, respectively, especially in western TS exceeding 26%. Meanwhile, areas with a daily extreme SM_ROS exceeding 50 mm could rise by over 13.5%. A notable rise in daily extreme SM_ROS and its area occupation in high intensity highlighted an increased risk of rainfall-driven extreme snowmelt events. The absolute increase in snowfall and frequent snow-rain phase transitions during the cold season under climate warming (SSP245: 2.19°C and SSP585: 2.22°C) benefits the increase in high-intensity rain-on-snow events, leading to extreme SM_ROS augmentation. The findings emphasize the significant role of rainfall-trigger snowmelt events in exacerbating extreme snowmelt in a warming climate.