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Yue Sun, Haishan Chen, Siguang Zhu, Jie Zhang, and Jiangfeng Wei

Abstract

Under the background of global warming, the Eurasian warming features evident spatial heterogeneity, and Northeast Asia (NEA) is one of the regions with the most significant summer warming. Based on reanalysis data and the CESM1.2.2 model, we investigated the possible impacts of spring Eurasian snowmelt on recent NEA summer warming and the relevant mechanisms. Results show that increased (decreased) spring snowmelt over East Europe to West Siberia (EEWS) is closely linked to NEA summer warming (cooling). Increased spring snowmelt can wet the soil, weakening surface sensible heating to the atmosphere and cooling the atmosphere. The persistent anomalous soil moisture and surface sensible heat induce geopotential height decrease over EEWS and strengthen the eastward-propagating wave train. Furthermore, positive geopotential height anomalies appear in downstream NEA in summer via the adjustment of the atmospheric circulation. Controlled by the anomalous high-pressure system, the west part of NEA is affected by the southerly warm advection, while the east is affected by adiabatic warming induced by the dominant descending motion. Meanwhile, decreased cloud and increased incident solar radiation over NEA favor summer land surface warming. Model results suggest that CESM1.2.2 can basically reproduce the positive correlation between NEA summer land surface temperature and EEWS spring snowmelt. With the positive spring snowmelt forcing, the simulated positive soil moisture and negative sensible heat anomalies persist from spring to summer over EEWS. Consequently, negative geopotential height anomalies appear over the snowmelt region while positive anomalies occur around Lake Baikal, resulting in evident NEA land surface warming.

Open access
Qunshu Tang, Zhiyou Jing, Jianmin Lin, and Jie Sun

Abstract

The Mariana Ridge is one of the prominent mixing hotspots of the open ocean. The high-resolution underway marine seismic reflection technique provides an improved understanding of the spatiotemporal continuous map of ocean turbulent mixing. Using this novel technique, this study quantifies the diapycnal diffusivity of the subthermocline (300–1200-m depth) turbulence around the Mariana Ridge. The autotracked wave fields on seismic images allow us to derive the dissipation rate ε and diapycnal diffusivity K ρ based on the Batchelor model, which relates the horizontal slope spectra with +1/3 slope to the inertial convective turbulence regime. Diffusivity is locally intensified around the seamounts exceeding 10−3 m2 s−1 and gradually decreases to 10−5–10−4 m2 s−1 in ~60-km range, a distance that may be associated with the internal tide beam emanating paths. The overall pattern suggests a large portion of the energy dissipates locally and a significant portion dissipates in the far field. Empirical diffusivity models K ρ(x) and K ρ(z), varying with the distance from seamounts and the height above seafloor, respectively, are constructed for potential use in ocean model parameterization. Geographic distributions of both the vertically averaged dissipation rate and diffusivity show tight relationships with the topography. Additionally, a strong agreement of the dissipation results between seismic observation and numerical simulation is found for the first time. Such an agreement confirms the suitability of the seismic method in turbulence quantification and suggests the energy cascade from large-scale tides to small-scale turbulence via possible mechanisms of local direct tidal dissipation, near-local wave–wave interactions, and far-field radiating and breaking.

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Jing Sun, Kun Yang, Weidong Guo, Yan Wang, Jie He, and Hui Lu

Abstract

The Inner Tibetan Plateau (ITP; also called the Qiangtang Plateau) appears to have experienced an overall wetting in summer (June, July, and August) since the mid-1990s, which has caused the rapid expansion of thousands of lakes. In this study, changes in atmospheric circulations associated with the wetting process are analyzed for 1979–2018. These analyses show that the wetting is associated with simultaneously weakened westerlies over the Tibetan Plateau (TP). The latter is further significantly correlated with the Atlantic multidecadal oscillation (AMO) on interdecadal time scales. The AMO has been in a positive phase (warm anomaly of the North Atlantic Ocean sea surface) since the mid-1990s, which has led to both a northward shift and weakening of the subtropical westerly jet stream at 200 hPa near the TP through a wave train of cyclonic and anticyclonic anomalies over Eurasia. These anomalies are characterized by an anomalous anticyclone to the east of the ITP and an anomalous cyclone to the west of the ITP. The former weakens the westerly winds, trapping water vapor over the ITP while the latter facilitates water vapor intruding from the Arabian Sea into the ITP. Accordingly, summer precipitation over the ITP has increased since the mid-1990s.

Open access
Zhangkang Shu, Jianyun Zhang, Junliang Jin, Lin Wang, Guoqing Wang, Jie Wang, Zhouliang Sun, Ji Liu, Yanli Liu, Ruimin He, Cuishan Liu, and Zhenxin Bao

Abstract

We evaluated 24-h control forecast products from The International Grand Global Ensemble center over the 10 first-class water resource regions of Mainland China in 2013–2018 from the perspective of precipitation processes (continuous) and precipitation events (discrete). We evaluated the forecasts from the China Meteorological Administration (CMA), the Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency (JMA), the Korea Meteorological Administration (KMA), the United Kingdom Met Office (UKMO), and the National Centers for Environmental Prediction (NCEP). We analyzed the differences among the numerical weather prediction (NWP) models in predicting various types of precipitation events and showed the spatial variations in the quantitative precipitation forecast efficiency of the NWP models over Mainland China. Meanwhile, we also combined four hydrological models to conduct meteo-hydrological runoff forecasting in three typical basins and used Bayesian model averaging (BMA) method to perform the ensemble forecast of different scenarios. Our results showed that the models generally underestimate and overestimate precipitation in northwestern China and southwestern China, respectively. This tendency became increasingly clear as the lead time rose. Each model has a high reliability for the forecast of no-rain and light rain in the next 10 days, whereas the NWP model only has high reliability on the next day for moderate and heavy rain events. In general, each model showed different capabilities of capturing various precipitation events. For example, the CMA and CMC forecasts had a better prediction performance for heavy rain but greater errors for other events. The CPTEC forecast performed well for long lead times for no-rain and light rain but had poor predictability for moderate and heavy rains. The KMA, UKMO, and NCEP forecasts performed better for no-rain and light rain. However, their forecasting ability was average for moderate and heavy rain. Although the JMA model performed better in terms of errors and accuracy, it seriously underestimated heavy rain events. The extreme rainstorm and flood forecast results of the coupled JMA model should be treated with caution. Overall, the ECMWF had the most robust performance. Discrepancies in the forecasting effects of various models on different precipitation events vary with the lead time and region. When coupled with hydrological models, NWP models not only control the accuracy of runoff prediction directly but also increase the difference among the prediction results of different hydrological models with the increase in NWP error significantly. Among all the single models, ECMWF, JMA, and NCEP have better effects than the other models. Moreover, the ensemble forecast based on BMA is more robust than the single model, which can improve the quality of runoff prediction in terms of accuracy and reliability.

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Jinyuan Xin, Yuesi Wang, Yuepeng Pan, Dongsheng Ji, Zirui Liu, Tianxue Wen, Yinghong Wang, Xingru Li, Yang Sun, Jie Sun, Pucai Wang, Gehui Wang, Xinming Wang, Zhiyuan Cong, Tao Song, Bo Hu, Lili Wang, Guiqian Tang, Wenkang Gao, Yuhong Guo, Hongyan Miao, Shili Tian, and Lu Wang

Abstract

Based on a network of field stations belonging to the Chinese Academy of Sciences (CAS), the Campaign on Atmospheric Aerosol Research network of China (CARE-China) was recently established as the country’s first monitoring network for the study of the spatiotemporal distribution of aerosol physical characteristics, chemical components, and optical properties, as well as aerosol gaseous precursors. The network comprises 36 stations in total and adopts a unified approach in terms of the instrumentation, experimental standards, and data specifications. This ongoing project is intended to provide an integrated research platform to monitor online PM2.5 concentrations, nine-size aerosol concentrations and chemical component distributions, nine-size secondary organic aerosol (SOA) component distributions, gaseous precursor concentrations (including SO2, NOx, CO, O3, and VOCs), and aerosol optical properties. The data will be used to identify the sources of regional aerosols, the relative contributions from nature and anthropogenic emissions, the formation of secondary aerosols, and the effects of aerosol component distributions on aerosol optical properties. The results will reduce the levels of uncertainty involved in the quantitative assessment of aerosol effects on regional climate and environmental changes and ultimately provide insight into how to mitigate anthropogenic aerosol emissions in China. The present paper provides a detailed description of the instrumentation, methodologies, and experimental procedures used across the network, as well as a case study of observations taken from one station and the distribution of main components of aerosol over China during 2012.

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