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Wilbert Weijer
,
Milena Veneziani
,
Jaclyn Clement Kinney
,
Wieslaw Maslowski
,
Jiaxu Zhang
, and
Michael Steele
Open access
Zeen Zhu
,
Fan Yang
,
Pavlos Kollias
,
Katia Lamer
,
Edward Luke
,
James B. Mead
,
Yong Meng Sua
,
Andrew M. Vogelmann
, and
Allison McComiskey

Abstract

Cloud microphysical processes, such as droplet activation, condensational growth, and collisional growth, play a central role in the evolution of clouds and precipitation. Accurate representations of these processes in numerical models are challenging partially due to incomplete understanding of them at the process-level arising from limited systematic observations. Most surface-based active remote sensors, including today’s operational cloud radars and lidars, have a resolution on the order of tens of meters. This resolution is insufficient to resolve cloud microphysical processes that manifest at finer (meter and sub-meter) scales. A new set of ultra-high-resolution ground-based radar and lidar systems have been developed to address this observational gap. The newly developed 94-GHz cloud radar has a range resolution down to 2.8 m, or a factor of 10 finer than typical radars, using a large bandwidth and quadratic phase coding techniques. The lidar has a range resolution down to 10 cm, or a factor of 100 finer than typical lidars, using a time-gated time-correlated single photon counting technique. Such high-resolution observations were previously only achievable through in situ aircraft measurements. Even then, aircraft measurements do not permit continuous long-term cloud observation as is possible with ground-based remote sensing instruments. In this study, the first-light cloud observations from the new radar and lidar systems are shown to reveal detailed cloud structures that conventional sensors could only perceive in a bulk sense, thus providing new avenues to investigate cloud microphysical processes and their impact on weather and climate.

Open access
Masahiro Shiozaki
,
Hiroki Tokinaga
, and
Masato Mori

Abstract

Atmospheric teleconnections from the Pacific El Niño are key to determining the East Asian winter climate. Using the database for policy decision-making for future climate change (d4PDF) large-ensemble simulations, the present study investigates a mechanism for the warm and cold East Asian winters during El Niño with a focus on atmospheric teleconnections triggered by anomalous sea surface temperature (SST) patterns in the tropical Indo-Pacific. Our results show that the western Pacific (WP) teleconnection pattern plays a primary role in the warm winters in East Asia. The WP pattern tends to appear in years when both an early El Niño and the positive phase of the Indian Ocean dipole (IOD) mode develop in boreal autumn. In those years, the tropical Indian Ocean (TIO) strongly warms in the following winter, forming a distinct zonal contrast in precipitation anomalies over the tropical Indo-Pacific through a reduced Walker circulation. The Rossby wave source anomalies indicate that the WP pattern is associated with the weakened Indo-Pacific Walker circulation. By contrast, the WP pattern does not dominate in the cold winters due to the absence of strong TIO warming. The present study proposes a mechanism that promotes the excitation of the WP pattern through the upper-troposphere divergence in East Asia associated with the Walker circulation modulated by the tropical Indo-Pacific interbasin interaction.

Significance Statement

The East Asian winter temperature variability is controlled not only by the strong atmospheric internal variability in the midlatitudes and high latitudes but also by remote forcing from the tropical ocean. Our study investigates how El Niño exerts diverse impacts on the East Asian winter temperature, depending on where atmospheric convection intensifies in the tropical Pacific Ocean and the Indian Ocean. Our results show that an intense warming of the tropical Indian Ocean and the early development of El Niño are the major factors for warm winters in East Asia. Given that a precursor of the intense Indian Ocean warming appears in boreal autumn, our findings should contribute to the improvement of seasonal prediction for the East Asian winter climate.

Open access
Wenhui Chen
,
Huijuan Cui
,
Francis W. Zwiers
,
Chao Li
, and
Jingyun Zheng

Abstract

Based on the observations and the Coupled Model Intercomparison Project phase 6 (CMIP6) multi-model simulations, we conducted a detection and attribution analysis for the observed changes in intensity and frequency indices of extreme precipitation during 1961-2014 over the whole of China and within distinct climate regions across the country. A space-time analysis is simultaneously applied in detection so that spatial structure on the signals is considered. Results show that the CMIP6 models can simulate the observed general increases of extreme precipitation indices during the historical period except for the drying trends from southwestern to northeastern China. The anthropogenic signal (ANT) is detectable and attributable to the observed increase of extreme precipitation over China, with human-induced greenhouse gas (GHG) increases being the dominant contributor. Additionally, we also detected the ANT and GHG signals in China’s Temperate continental, Subtropical-tropical monsoon, and Plateau mountain climate zones, demonstrating the role of human activity in historical extreme precipitation changes on much smaller spatial scales.

Restricted access
Tatsuya Seiki
and
Takashi M. Nagao

Abstract

Aggregation efficiency in the upper troposphere is highly uncertain because of the lack of laboratory experiments and aircraft measurements, especially at atmospheric temperatures below −30°C. Aggregation is physically broken down into collision and sticking. In this study, theory-based parameterizations for the collision efficiency and sticking efficiency are newly implemented into a double moment bulk cloud microphysics scheme. Satellite observations of the global ice cloud distribution are used to evaluate the aggregation efficiency modeling.

Sensitivity experiments of 9-day global simulations using a high-resolution climate model show that the use of collision efficiency parameterization causes a slight increase in the cloud ice amount above the freezing level over the tropics to midlatitudes and that the use of our new sticking efficiency parameterization causes a significant increase in the cloud ice amount and a slight decrease in the snow amount particularly in the upper troposphere over the tropics. The increase/decrease in the cloud ice/snow amount in the upper troposphere over the tropics is consistent with the vertical profile of radar echoes. Moreover, the ice fraction of the cloud optical thickness is still underestimated worldwide. Finally, the cloud radiative forcing increases over the tropics to reduce the bias in the radiation budget. These results indicate that our new aggregation efficiency modeling reasonably functions even at atmospheric temperatures below −30°C; however, further improvements of the ice cloud modeling are needed.

Restricted access
Jonathan Lin
,
Chia-Ying Lee
,
Suzana J. Camargo
, and
Adam Sobel

Abstract

Past studies have shown a significant observed poleward trend in the latitude at which tropical cyclones reach their lifetime maximum intensity (LMI), especially in the Northwest Pacific basin. Given the brevity of the historical record, it remains difficult to separate the forced trend from internal variability of the climate system. A recently developed tropical cyclone downscaling model is used to downscale the Community Earth System Model 2 (CESM2) pre-industrial control simulation. It is found that the observed trend in the latitude at which tropical cyclones reach their LMI in the Northwest Pacific is very unlikely to be caused by internal variability. The same downscaling model is then used to downscale CESM2 simulations under historical forcing. The resulting trend distribution shows significant poleward migration of tropical cyclone LMI, even after regressing out both natural variability and the part of the forced warming pattern that projects onto natural variability. The results indicate that the observed poleward migration of the latitude at which tropical cyclones reach their LMI in the Northwest Pacific basin is likely to be, at least in part, forced. However, the magnitude of the projected poleward trend in climate models can be significantly modulated by the simulated spatial pattern of ocean warming. This highlights how discrepancies between models and observations, with regards to projected changes to the equatorial zonal sea-surface-temperature gradient under anthropogenic forcing, can lead to large uncertainties in projected changes to the LMI latitude of tropical cyclones.

Restricted access
Yukitaka Ohashi
and
Kazuki Hara

Abstract

This study attempted to forecast the morning fog expansion (MFE), commonly referred to as the “sea of clouds,” utilizing an artificial intelligence (AI) algorithm. The radiation fog phenomenon that contributes to the sea of clouds is caused by various weather conditions. Hence, the MFE was predicted using datasets from public meteorological observations and a mesoscale numerical model (MSM). In this study, a machine-learning technique, the gradient boosting method, was adopted as the AI algorithm. The Miyoshi Basin in Japan, renowned for its MFE, was selected as the experimental region. Training models were developed using datasets from October, November, and December 2018–2021. Subsequently, these models were applied to forecast MFE in 2022. The model employing the upper atmospheric prediction data from the MSM demonstrated the highest robustness and accuracy among the proposed models. For untrained data in the fog season during 2022, the model was confirmed to be sufficiently reliable for forecasting MFE, with a high hit rate of 0.935, a low Brier score of 0.119, and a high Area Under the Curve (AUC) of 0.944. Furthermore, the analysis of the importance of the features elucidated that the meteorological factors, such as synoptic-scale weak wind, temperatures close to the dew-point temperature, and the absence of middle-level cloud cover at midnight, strongly contribute to the MFE. Therefore, the incorporation of upper-level meteorological elements improves the forecast accuracy for MFE.

Restricted access
Chen Liu
,
Lei Chen
, and
Stefan Liess

Abstract

The features of large-scale atmospheric circulations, storm tracks, and the mean flow–eddy interaction during winter Pacific–North American (PNA) events are investigated using National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data at subseasonal time scales from 1979 to 2022. The day-to-day variations of storm-track activity and streamfunction reveal that storm-track activity varies along the evolution of mean flow. To better understand storm-track variability with the mean flow–eddy interaction, further exploration is made by analyzing local energy energetics. The changes in horizontal and vertical baroclinic energy conversions from background flow correspond to the storm-track anomalies over the North Pacific, indicating that the anomalies in storm tracks are due to the anomalous mean flow associated with PNA patterns impacting energy conversion through mean flow–eddy interaction. Eddy feedback driven by vorticity and heat fluxes is analyzed. This provides a concrete illustration of how eddy feedback serves as a positive factor for the upper-tropospheric circulation anomalies associated with the PNA pattern.

Significance Statement

The background flow plays a crucial role in governing storm-track dynamics. Our emphasis is on the Pacific storm tracks (PST) and their relation to Pacific–North American (PNA) patterns at subseasonal time scales. We unveil the relationship between anomalies of PST and PNA patterns using local energetics and eddy feedback on a day-by-day basis. It is noteworthy that the evolution of anomalous storm tracks during PNA events is the manifestation of mean flow–eddy interaction. Additionally, we provide detailed confirmation of the impact of anomalous storm tracks on large-scale anomalies associated with the PNA pattern.

Restricted access
Yang Zhao
,
Jianping Li
,
Yuan Tian
, and
Jiao Li

Abstract

This study investigates the disparity in quantitative moisture contribution and synoptic-scale vertical motion in the lower reaches of the Yangtze River basin (LYRB) for different extreme precipitation (EP) types, which are categorized as EP associated with atmospheric river (AR&EP) or EP associated with nonatmospheric river (non-AR&EP). To analyze moisture contribution, backward tracking using the Water Accounting Model-2layers is performed. In general, the remote moisture contribution is 9.7 times greater than the local contribution, with the ocean contribution being 1.67 times stronger than the land contribution. However, terrestrial and oceanic contributions obviously increase in the EP types, especially for oceanic contribution being double in magnitude. Notably, the west Pacific (WP) contribution emerges as the dominant differentia between the EP types, playing a crucial role in the AR formation. By solving the quasigeostrophic omega equation, the upper-level jet (ULJ) stream acts as the primary dynamic forcing for transverse vertical motion in AR&EP, while the baroclinic trough exhibits a relatively weaker influence. However, both systems have a nearly equal impact on vertical velocity in non-AR&EP. The enhanced shearwise elevation in the non-AR&EP type is the response of the stronger upper-level ridge over the Tibetan Plateau (TP), which induces enhanced Q vector, the divergence pointing toward the LYRB. However, the main dynamic difference is the location of ULJ, which serves as the trigger role although weak. Diabatic forcing proves to be the decisive factor for vertical motion development, the difference attributed to the released excessive latent heating with excess moisture contribution from the WP in AR&EP with enhanced precipitation.

Significance Statement

The main objective of this study is to investigate quantitative moisture contribution by applying Water Accounting Model-2layers and vertical motion attribution using the quasigeostrophic omega equation for extreme precipitation types based on the presence or absence of atmospheric river. Our findings reveal excessive moisture from the west Pacific serving not only as key in atmospheric river formation but also as the primary trigger for intensified diabatic vertical motion, inducing enhanced precipitation. The direction of strong winds in the north of the Tibetan Plateau holds crucial forecasting implications, which determine the location of the upper-level jet stream downstream. The transverse vertical motion, induced by the upper-level jet stream, plays the dominant dynamic role in both extreme precipitation (EP) types.

Restricted access
Yu Lin
,
Haishen Lü
,
Karl-Erich Lindenschmidt
,
Zhongbo Yu
,
Yonghua Zhu
,
Mingwen Liu
, and
Tingxing Chen

Abstract

River ice changes due to climate change significantly impact river hydrology, economies, and societies. This study employed the CMIP6 data and a river ice model to predict global river ice changes in response to climate change. Results indicate significant declines in global river ice due to global warming. With each 1°C increase in surface air temperature (SAT) in the future, river ice extent of ice-affected rivers decrease by 2.11 percentage points, and ice duration shorten by 8.4 days. Under the SSP2-4.5 scenario, the long-term mean SAT is 1.2°C to 2.5°C higher than in the near-term. This leads to a 1.9 percentage points to 4.4 percentage points decrease in global river ice extent, a 6.8 to 15.1-day decrease in river ice duration, and ice-free rivers increasing by up to 4.02%. The SSP5-8.5 scenario predicts a warmer long-term mean SAT, leading to greater reductions in river ice. Geographically, river ice loss is most notable in North America, Europe, Siberia, and the Tibetan Plateau (TIB), particularly in peninsular, coastal, and mountainous regions due to the combined effects of initial temperatures and temperature increases. Overall, the E.Europe (EEU) shows the greatest loss of river ice on average. Monthly analyses show the most substantial decreases from October to June, indicating pronounced seasonal variability. This study provides valuable insights for addressing challenges related to river ice changes in high-latitude and high-elevation regions.

Restricted access