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Chao Liu
,
Yang Yang
,
Hailong Wang
,
Lili Ren
,
Jiangfeng Wei
,
Pinya Wang
, and
Hong Liao

Abstract

Since China implemented the Air Pollution Prevention and Control Action Plan in 2013, the aerosol emissions in East Asia have been greatly reduced, while emissions in South Asia have continued to increase. This has led to a dipole pattern of aerosol emissions between South Asia and East Asia. Here, the East Asian summer monsoon (EASM) responses to the dipole changes in aerosol emissions during 2013–17 are investigated using the atmosphere model of Community Earth System Model version 2 (CESM2). We show that decreases in East Asian emissions alone lead to a positive aerosol effective radiative forcing (ERF) of 1.59 (±0.97) W m−2 over central-eastern China (25°–40°N, 105°–122.5°E), along with a 0.09 (±0.07)°C warming in summer during 2013–17. The warming intensified the land–sea thermal contrast and increased the rainfall by 0.32 (±0.16) mm day−1. When considering both the emission reductions in East Asia and increases in South Asia, the ERF is increased to 3.39 (±0.89) W m−2, along with an enhanced warming of 0.20 (±0.08)°C over central-eastern China, while the rainfall insignificant decreased by 0.07 (±0.16) mm day−1. It is due to the westward shift of the strengthened western Pacific subtropical high, linked to the increase in black carbon in South Asia. Based on multiple EASM indices, the reductions in aerosol emissions from East Asia alone increased the EASM strength by almost 5%. Considering the effect of the westward shift of WPSH, the dipole changes in emissions together increased the EASM by 5%–15% during 2013–17, revealing an important role of South Asian aerosols in changing the East Asian climate.

Free access
Fa Liu
,
Xunming Wang
,
Fubao Sun
,
Hong Wang
,
Lifeng Wu
,
Xuanze Zhang
,
Wenbin Liu
, and
Huizheng Che

Abstract

Land surface temperature (LST) is an essential variable for high-temperature prediction, drought monitoring, climate, and ecological environment research. Several recent studies reported that LST observations in China warmed much faster than surface air temperature (SAT), especially after 2002. Here we found that the abrupt change in daily LST was mainly due to the overestimation of LST values from the automatic recording thermometer under snow cover conditions. These inhomogeneity issues in LST data could result in wrong conclusions without appropriate correction. To address these issues, we proposed three machine learning models—multivariate adaptive regression spline (MARS), random forest (RF), and a novel simple tree-based method named extreme gradient boosting (XGBoost)—for accurate prediction of daily LST using conventional meteorological data. Daily air temperature (maximum, minimum, mean), sunshine duration, precipitation, wind speed, relative humidity, daily solar radiation, and diurnal temperature range of 2185 stations over 1971–2002 from four regions of China were used to train and test the models. The results showed that the machine learning models, particularly XGBoost, outperformed other models in estimating daily LST. Based on LST data corrected by the XGBoost model, the dramatic increase in LST disappeared. The long-term trend for the new LST was estimated to be 0.32° ± 0.03°C decade−1 over 1971–2019, which is close to the trend in SAT (0.30° ± 0.03°C decade−1). This study corrected the inhomogeneities of daily LST in China, indicating the strong potential of machine learning models for improving estimation of LST and other surface climatic factors.

Full access
Chaoming Huang
,
Hailong Liu
,
Xidong Wang
,
Hong Li
,
Zhaoru Zhang
,
Juncheng Zuo
, and
Ruyun Wang

Abstract

This study explores the role of the Pacific decadal oscillation (PDO) in modulating the relationship between El Niño–Southern Oscillation (ENSO) and typhoon tracks. Tropical cyclone (TC) trajectories in the western North Pacific (WNP) in 1950–2017 are clustered into seven clusters, including three recurved trajectories and four straight-moving tracks. These clusters are distinguished well by number of TCs, intensity, lifetime, genesis position/month, landing, and track. The sea surface temperature (SST) anomaly in the composite analysis and accumulated cyclone energy (ACE) of each cluster demonstrate that there are four clusters dominated by ENSO. The associated ENSO effects on these clusters are manifested by steering flow and vertical wind shear (VWS) in the composite differences between El Niño and La Niña years. However, such ENSO effects on TC quantity, genesis location, and track of these corresponding clusters are significantly enhanced during the PDO positive phases only for two clusters that are formed in the southeastern part of the WNP and undergo a long lifetime and track, because the PDO explains little local environmental variance where the other two clusters are located in the northern part of the WNP. This conclusion is also supported by TC track density analysis. The two leading modes of empirical orthogonal functions (EOF) analysis of TC track density are significantly correlated with ENSO. The enhancement of ENSO effects during the PDO positive phase exhibits by the second mode through local SST, VWS, and steering flow.

Significance Statement

Accurate prediction of tropical cyclone (TC) activity can help preparedness and therefore reduce the losses of life and property. Long-term track prediction relies on our understanding how TC tracks are associated with interannual and longer climate variability. This study uses historical data of 1950–2017 in the western North Pacific and reveals that only for two of four track clusters that are affected by El Niño–Southern Oscillation (ENSO), the associated ENSO effects are enhanced during the Pacific decadal oscillation positive phases because the oscillation has significant influence on vertical wind shear and steering flow where these two clusters are located. The findings enrich the mechanisms of TC track variabilities and will help improve long-term prediction of TC tracks.

Free access
Tingting Wang
,
Fubao Sun
,
Wee Ho Lim
,
Hong Wang
,
Wenbin Liu
, and
Changming Liu

Abstract

Climate change and its potential threats on water security call for reliable predictions of evapotranspiration (ET) and runoff Q at different time scales, but current knowledge of the differences in their predictability between humid and nonhumid regions is limited. Based on spatially distributed catchments in China, the authors characterized their predictability and provided plausible explanations. Using the Budyko framework, it was confirmed that annual ET is predictable in nonhumid regions but less predictable in humid regions, and annual Q is predictable in humid regions but less reliable in nonhumid regions. The main cause of the varied predictability lies in the variation of water storage change ΔS in the water balance equation. It affects both the estimation and the variability of Q in nonhumid catchments more than that in humid catchments, which increases the challenge of predicting annual Q in nonhumid regions, while the opposite effect occurs in annual ET prediction between humid and nonhumid catchments. Moreover, the differences between the controlling factors of ET variability in different regions add more differences in their predictability. The dominant control of precipitation makes it easy to predict annual ET in nonhumid regions. By contrast, precipitation, potential evaporation, and their covariance take considerable effort to determine annual ET variations, which leads to less reliable ET estimation and predictability in humid catchments. Therefore, one can accurately predict annual ET in nonhumid catchments and Q in humid catchments based on commonly used hydrological models. With proper consideration of ΔS, the predictability of annual ET and Q in both humid and nonhumid catchments can be improved.

Full access
Yu Wang
,
Hong-Qing Wang
,
Lei Han
,
Yin-Jing Lin
, and
Yan Zhang

Abstract

This study was designed to provide basic information for the improvement of storm nowcasting. According to the mean direction deviation of storm movement, storms were classified into three types: 1) steady storms (S storms, extrapolated efficiently), 2) unsteady storms (U storms, extrapolated poorly), and 3) transitional storms (T storms). The U storms do not fit the linear extrapolation processes because of their unsteady movements. A 6-yr warm-season radar observation dataset was used to highlight and analyze the differences between U storms and S storms. The analysis included geometric features, dynamic factors, and environmental parameters. The results showed that storms with the following characteristics changed movement direction most easily in the Beijing–Tianjin region: 1) smaller storm area, 2) lower thickness (echo-top height minus base height), 3) lower movement speed, 4) weaker updrafts and the maximum value located in the mid- and upper troposphere, 5) storm-relative vertical wind profiles dominated by directional shear instead of speed shear, 6) lower relative humidity in the mid- and upper troposphere, and 7) higher surface evaporation and ground roughness.

Full access
Yuntao Wei
,
Hong-Li Ren
,
Baoqiang Xiang
,
Yan Wang
,
Jie Wu
, and
Shuguang Wang

Abstract

The Madden–Julian oscillation (MJO) is the dominant intraseasonal wave phenomenon influencing extreme weather and climate worldwide. Realistic simulations and accurate predictions of MJO genesis are the cornerstones for successfully monitoring, forecasting, and managing meteorological disasters 3–4 weeks in advance. Nevertheless, the genesis processes and emerging precursor signals of an eastward-propagating MJO event remain largely uncertain. Here, we find that the MJO genesis processes observed in the past four decades exhibit remarkable diversity with different seasonality and can be classified objectively into four types, namely, a novel downstream origin from the westward-propagating intraseasonal oscillation (WPISO; 20.4%), localized breeding from the Indian Ocean suppressed convection (IOSC; 15.4%), an upstream succession of the preceding weakly dispersive (WD; 25.9%), and strongly dispersive (SD; 38.3%) MJO. These four types are associated with different oceanic background states, characterized by central Pacific cooling, southern Maritime Continent warming, eastern Pacific cooling, and central Pacific warming for the WPISO, IOSC, WD, and SD types, respectively. The SD type is also favored during the easterly phase of the stratospheric quasi-biennial oscillation. Diverse convective initiations possibly imply various kinds of propagations of MJO. The subseasonal reforecasts indicate robustly distinct prediction skills for the diverse MJO genesis. A window of opportunity for skillful week 3–4 prediction probably opens with the aid of the WPISO-type MJO precursor, which has increased the predictability of primary MJO onset by 1 week. These findings suggest that the diversified MJO genesis can be skillfully foreseen by monitoring unique precursor signals and can also serve as benchmarks for evaluating contemporary models’ modeling and predicting capabilities.

Open access
Bingrong Sun
,
Shengpeng Wang
,
Man Yuan
,
Hong Wang
,
Zhao Jing
,
Zhaohui Chen
, and
Lixin Wu

Abstract

Near-inertial internal waves (NIWs) are thought to play an important role in powering the turbulent diapycnal mixing in the ocean interior. Nevertheless, the energy flux into NIWs below the surface boundary layer (SBL) in the global ocean is still poorly understood. This key problem is addressed in this study based on a Community Earth System Model (CESM) simulation with a horizontal resolution of ~0.1° for its oceanic component and ~0.25° for its atmospheric component. The CESM shows good skill in simulating NIWs globally, reproducing the observed magnitude and spatial pattern of surface NIW currents and wind power on NIWs (W I ). The simulated downward flux of NIW energy (F SBL) at the SBL base is positive everywhere. Its quasi-global integral (excluding the region within 5°S–5°N) is 0.13 TW, about one-third the value of W I . The ratio of local F SBL to W I varies substantially over the space. It exhibits an increasing trend with the enstrophy of balanced motions (BMs) and a decreasing trend with W I . The kinetic energy transfer from model-resolved BMs to NIWs is positive from the SBL base to 600 m but becomes negative farther downward. The quasi-global integral of energy transfer below the SBL base is two orders of magnitude smaller than that of F SBL, suggesting the resolved BMs in the CESM simulations making negligible contributions to power NIWs in the ocean interior.

Free access
Pi-Huan Wang
,
Adarsh Deepak
, and
Siu-Shung Hong

Abstract

Formulas that can be used to determine the optical path between two points along an atmospheric ray path are derived for the case when the local zenith angle of the ray path is larger than 70°. For angles less than 70°, these formulas reduce to the airmass function; viz., the secant of the zenith angle. The formulation presented in this paper is genera] enough to be applicable to a wide variety of atmospheric conditions, such as spherical and nonspherical atmospheres, and vertically and horizontally homogeneous as well as inhomogeneous atmospheres. Formulation for the case when atmospheric refraction is important also is presented here.

Full access
Chengzu Bai
,
Mei Hong
,
Dong Wang
,
Ren Zhang
, and
Longxia Qian

Abstract

The identification of the rainfall–runoff relationship is a significant precondition for surface–atmosphere process research and operational flood forecasting, especially in inadequately monitored basins. Based on an information diffusion model (IDM) improved by a genetic algorithm, a new algorithm (GIDM) is established for interpolating and forecasting monthly discharge time series; the input variables are the rainfall and runoff values observed during the previous time period. The genetic operators are carefully designed to avoid premature convergence and “local optima” problems while searching for the optimal window width (a parameter of the IDM). In combination with fuzzy inference, the effectiveness of the GIDM is validated using long-term observations. Conventional IDMs are also included for comparison. On the Yellow River or Yangtze River, twelve gauging stations are discussed, and the results show that the new method can simulate the observations more accurately than traditional IDMs, using only 50% or 33.33% of the total data for training. The low density of observations and the difficulties in information extraction are key problems for hydrometeorological research. Therefore, the GIDM may be a valuable tool for improving water management and providing the acceptable input data for hydrological models when available measurements are insufficient.

Full access
Mei Hong
,
Ren Zhang
,
Dong Wang
,
Min Wang
,
Kefeng Liu
, and
Vijay P. Singh

Abstract

A new dynamical–statistical forecasting model of the western Pacific subtropical high (WPSH) area index (AI) was developed, based on dynamical model reconstruction and improved self-memorization, in order to address the inaccuracy of long-term WPSH forecasts. To overcome the problem of single initial prediction values, the self-memorization function was introduced to improve the traditional reconstruction model, thereby making it more effective for describing chaotic systems, such as WPSH. Processing actual data, the reconstruction equation was used as a dynamical core to overcome the problem of employing a simple core. The resulting dynamical–statistical forecasting model for AI was used to predict the strength of long-term WPSH forecasting. Based on 17 experiments with the WPSH during normal and abnormal years, forecast results for a period of 25 days were found to be good, with a correlation coefficient of ~0.80 and a mean absolute percentage error of <8%, showing that the improved model produced satisfactory long-term forecasting results. Additional experiments for predicting the ridgeline index (RI) and the west ridge-point index (WI) were also performed to demonstrate that the developed model was effective for the complete prediction of the WPSH. Compared with the authors’ previous models and other established models of reasonable complexity, the current model shows better long-term WPSH forecasting ability than do other models, meaning that the aberrations of the subtropical high could be defined and forecast by the model.

Full access