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Xue Yi
,
Deqin Li
,
Chunyu Zhao
,
Lidu Shen
, and
Xiaoyu Zhou

Abstract

High-density surface networks have become available in recent years in a number of regions throughout the world, but their utility in high-resolution dynamic downscaling has not been examined. As an attempt to fill such a gap, a suite of high-resolution (4 km) dynamical downscaling simulations is developed in this study with the Weather Research and Forecasting (WRF) Model and observation nudging over Liaoning in northeastern China. Three experiments, including no nudging (CTL), analysis nudging (AN), and combined analysis nudging and observation nudging with surface observations (AON), are conducted to downscale the CFSv2 reanalysis with the WRF Model for the year 2015. The three 1-yr regional climate simulations were compared with the independent surface observations. The results show that observational nudging can improve the simulation of surface variables, including temperature, wind speed, humidity, and pressure, more than nudging large-scale driving data with AN alone. The two nudging simulations can improve the cold bias for the temperature of the WRF Model. For precipitation, both the simulations with AN and observation nudging can capture the pattern of precipitation; however, with the introduction of small-scale information at the surface, AON cannot further improve the simulation of precipitation.

Free access
Ying Gong
,
Sen Yang
,
Jinfang Yin
,
Shu Wang
,
Xiao Pan
,
Deqin Li
, and
Xue Yi

Abstract

Reanalysis datasets have been widely used in meteorological research, including studies of Northeast China cold vortices (NCCVs), where these datasets act as effective substitutes for observations. However, to date, no studies have focused on their performance in reproducing NCCVs. To address this knowledge gap, we adopted an automatic three-step identification algorithm (TIA) and used it to detect NCCVs from ERA5 and MERRA-2 reanalysis datasets spanning 39 warm seasons (May–September) during the period from 1980 to 2018. A comparative method was employed for a rough verification of the characteristics of the reproduced NCCVs. Moreover, a dataset derived from 1370 Chinese ground-based observational stations was used to verify the performance of the reanalysis models in reproducing the precipitation and air temperature associated with NCCVs. The results show that the TIA identified the majority of NCCVs, with an accuracy of approximately 90% from ERA5 or MERRA-2. Both reanalysis models can reproduce the characteristics of NCCVs (including location, strength, and duration), and both replicate air temperature better than precipitation. ERA5 and MERRA-2 showed strong consistency in reproducing the central longitude, central latitude, central height, and range of NCCVs, with correlation coefficients of 0.974, 0.972, 0.996, and 0.919, respectively, at the 99.9% significance level. The daily average 2-m temperatures in both reanalysis datasets were in good agreement with observations; however, overestimations of approximately 7°–8°C arose in steep high-altitude regions. In addition, both models tended to overestimate light rain (≤5 mm day−1) by approximately 1.2 mm and underestimate heavy rain (≥20 mm day−1) by over 6.7 mm.

Free access
Pinya Wang
,
Yang Yang
,
Daokai Xue
,
Yi Qu
,
Jianping Tang
,
L. Ruby Leung
, and
Hong Liao

Abstract

Compound hazards are more destructive than the individual ones. Using observational and reanalysis datasets during 1960–2019, this study shows a remarkable concurrent relationship between extreme heatwaves (HWs) over southeastern coast of China (SECC) and tropical cyclone (TC) activities over western North Pacific (WNP). Overall, 70% of HWs co-occurred with TC activities (TC–HWs) in the past 60 years. Although the total frequency of TCs over WNP exhibited a decreasing trend, the occurrences of TC–HWs over SECC have been increasing, primarily due to the increasing HWs in the warming climate. In addition, TC–HWs are stronger and longer lasting than HWs that occur alone (AHWs). And in the long-term perspective, both AHWs and TC–HWs exhibit increasing trends, especially since the mid-1980s. The enhancement on HWs caused by TC activities is sustained until TCs make their landfalls and then collapse. Based on composite analysis, TC activities enhance HWs by modulating atmospheric circulations and triggering anomalous descending motion over southern China mainland which intensifies the western Pacific subtropical high (WPSH) and favors increased temperatures therein. Given the severe adverse impacts of TC–HWs on coastal populations, more research is needed to assess the future projections of TC–HWs, as HWs are expected to be more frequent and stronger as the climate warms, whereas TCs over WNP may occur less often.

Restricted access
Kun Zhao
,
Mingjun Wang
,
Ming Xue
,
Peiling Fu
,
Zhonglin Yang
,
Xiaomin Chen
,
Yi Zhang
,
Wen-Chau Lee
,
Fuqing Zhang
,
Qing Lin
, and
Zhaohui Li

Abstract

On 4 October 2015, a miniature supercell embedded in an outer rainband of Typhoon Mujigae produced a major tornado in Guangdong province of China, leading to 4 deaths and up to 80 injuries. This study documents the structure and evolution of the tornadic miniature supercell using coastal Doppler radars, a sounding, videos, and a damage survey. This tornado is rated at least EF3 on the enhanced Fujita scale. It is by far the strongest typhoon rainband tornado yet documented in China, and possessed double funnels near its peak intensity.

Radar analysis indicates that this tornadic miniature supercell exhibited characteristics similar to those found in United States landfalling hurricanes, including a hook echo, low-level inf low notches, an echo top below 10 km, a small and shallow mesocyclone, and a long lifespan (3 h). The environmental conditions—which consisted of moderate convective available potential energy (CAPE), a low lifting condensation level, a small surface dewpoint depression, a large veering low-level vertical wind shear, and a large cell-relative helicity—are favorable for producing miniature supercells. The mesocyclone, with its maximum intensity at 2 km above ground level (AGL), formed an hour before tornadogenesis. A tornado vortex signature (TVS) was identified between 1 and 3 km AGL, when the parent mesocyclone reached its peak radar-indicated intensity of 30 m s−1. The TVS was located between the updraft and forward-flank downdraft, near the center of the mesocyclone. Dual-Doppler wind analysis reveals that tilting of the low-level vorticity into the vertical direction and subsequent stretching by a strong updraft were the main contributors to the mesocyclone intensification.

Open access
Sisi Chen
,
Lulin Xue
,
Sarah Tessendorf
,
Thomas Chubb
,
Andrew Peace
,
Luis Ackermann
,
Artur Gevorgyan
,
Yi Huang
,
Steven Siems
,
Roy Rasmussen
,
Suzanne Kenyon
, and
Johanna Speirs

Abstract

This study presents the first numerical simulations of seeded clouds over the Snowy Mountains of Australia. WRF-WxMod, a novel glaciogenic cloud-seeding model, was utilized to simulate the cloud response to winter orographic seeding under various meteorological conditions. Three cases during the 2018 seeding periods were selected for model evaluation, coinciding with an intensive ground-based measurement campaign. The campaign data were used for model validation and evaluation. Comparisons between simulations and observations demonstrate that the model realistically represents cloud structures, liquid water path, and precipitation. Sensitivity tests were performed to pinpoint key uncertainties in simulating natural and seeded clouds and precipitation processes. They also shed light on the complex interplay between various physical parameters/processes and their interaction with large-scale meteorology. Our study found that in unseeded scenarios, the warm and cold biases in different initialization datasets can heavily influence the intensity and phase of natural precipitation. Secondary ice production via Hallett–Mossop processes exerts a secondary influence. On the other hand, the seeding impacts are primarily sensitive to aerosol conditions and the natural ice nucleation process. Both factors alter the supercooled liquid water availability and the precipitation phase, consequently impacting the silver iodide (AgI) nucleation rate. Furthermore, model sensitivities were inconsistent across cases, indicating that no single model configuration optimally represents all three cases. This highlights the necessity of employing an ensemble approach for a more comprehensive and accurate assessment of the seeding impact.

Significance Statement

Winter orographic cloud seeding has been conducted for decades over the Snowy Mountains of Australia for securing water resources. However, this study is the first to perform cloud-seeding simulation for a robust, event-based seeding impact evaluation. A state-of-the-art cloud-seeding model (WRF-WxMod) was used to simulate the cloud seeding and quantified its impact on the region. The Southern Hemisphere, due to low aerosol emissions and highly pristine cloud conditions, has distinctly different cloud microphysical characteristics than the Northern Hemisphere, where WRF-WxMod has been successfully applied in a few regions over the United States. The results showed that WRF-WxMod could accurately capture the clouds and precipitation in both the natural and seeded conditions.

Restricted access
Antonietta Capotondi
,
Andrew T. Wittenberg
,
Matthew Newman
,
Emanuele Di Lorenzo
,
Jin-Yi Yu
,
Pascale Braconnot
,
Julia Cole
,
Boris Dewitte
,
Benjamin Giese
,
Eric Guilyardi
,
Fei-Fei Jin
,
Kristopher Karnauskas
,
Benjamin Kirtman
,
Tong Lee
,
Niklas Schneider
,
Yan Xue
, and
Sang-Wook Yeh

Abstract

El Niño–Southern Oscillation (ENSO) is a naturally occurring mode of tropical Pacific variability, with global impacts on society and natural ecosystems. While it has long been known that El Niño events display a diverse range of amplitudes, triggers, spatial patterns, and life cycles, the realization that ENSO’s impacts can be highly sensitive to this event-to-event diversity is driving a renewed interest in the subject. This paper surveys our current state of knowledge of ENSO diversity, identifies key gaps in understanding, and outlines some promising future research directions.

Full access
Augusto C. V. Getirana
,
Emanuel Dutra
,
Matthieu Guimberteau
,
Jonghun Kam
,
Hong-Yi Li
,
Bertrand Decharme
,
Zhengqiu Zhang
,
Agnes Ducharne
,
Aaron Boone
,
Gianpaolo Balsamo
,
Matthew Rodell
,
Ally M. Toure
,
Yongkang Xue
,
Christa D. Peters-Lidard
,
Sujay V. Kumar
,
Kristi Arsenault
,
Guillaume Drapeau
,
L. Ruby Leung
,
Josyane Ronchail
, and
Justin Sheffield

Abstract

Despite recent advances in land surface modeling and remote sensing, estimates of the global water budget are still fairly uncertain. This study aims to evaluate the water budget of the Amazon basin based on several state-of-the-art land surface model (LSM) outputs. Water budget variables (terrestrial water storage TWS, evapotranspiration ET, surface runoff R, and base flow B) are evaluated at the basin scale using both remote sensing and in situ data. Meteorological forcings at a 3-hourly time step and 1° spatial resolution were used to run 14 LSMs. Precipitation datasets that have been rescaled to match monthly Global Precipitation Climatology Project (GPCP) and Global Precipitation Climatology Centre (GPCC) datasets and the daily Hydrologie du Bassin de l’Amazone (HYBAM) dataset were used to perform three experiments. The Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme was forced with R and B and simulated discharges are compared against observations at 165 gauges. Simulated ET and TWS are compared against FLUXNET and MOD16A2 evapotranspiration datasets and Gravity Recovery and Climate Experiment (GRACE) TWS estimates in two subcatchments of main tributaries (Madeira and Negro Rivers). At the basin scale, simulated ET ranges from 2.39 to 3.26 mm day−1 and a low spatial correlation between ET and precipitation indicates that evapotranspiration does not depend on water availability over most of the basin. Results also show that other simulated water budget components vary significantly as a function of both the LSM and precipitation dataset, but simulated TWS generally agrees with GRACE estimates at the basin scale. The best water budget simulations resulted from experiments using HYBAM, mostly explained by a denser rainfall gauge network and the rescaling at a finer temporal scale.

Full access
Yongkang Xue
,
Ismaila Diallo
,
Aaron A. Boone
,
Tandong Yao
,
Yang Zhang
,
Xubin Zeng
,
J. David Neelin
,
William K. M. Lau
,
Yan Pan
,
Ye Liu
,
Xiaoduo Pan
,
Qi Tang
,
Peter J. van Oevelen
,
Tomonori Sato
,
Myung-Seo Koo
,
Stefano Materia
,
Chunxiang Shi
,
Jing Yang
,
Constantin Ardilouze
,
Zhaohui Lin
,
Xin Qi
,
Tetsu Nakamura
,
Subodh K. Saha
,
Retish Senan
,
Yuhei Takaya
,
Hailan Wang
,
Hongliang Zhang
,
Mei Zhao
,
Hara Prasad Nayak
,
Qiuyu Chen
,
Jinming Feng
,
Michael A. Brunke
,
Tianyi Fan
,
Songyou Hong
,
Paulo Nobre
,
Daniele Peano
,
Yi Qin
,
Frederic Vitart
,
Shaocheng Xie
,
Yanling Zhan
,
Daniel Klocke
,
Ruby Leung
,
Xin Li
,
Michael Ek
,
Weidong Guo
,
Gianpaolo Balsamo
,
Qing Bao
,
Sin Chan Chou
,
Patricia de Rosnay
,
Yanluan Lin
,
Yuejian Zhu
,
Yun Qian
,
Ping Zhao
,
Jianping Tang
,
Xin-Zhong Liang
,
Jinkyu Hong
,
Duoying Ji
,
Zhenming Ji
,
Yuan Qiu
,
Shiori Sugimoto
,
Weicai Wang
,
Kun Yang
, and
Miao Yu

Abstract

Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface ­temperatures show a lag correlation with summer precipitation in several remote regions, but current global land–atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hotspot” regions identified here; the ensemble means in some “hotspots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.

Free access