Investigation of Aerosol Direct Effect Over China Under El Niño and Its Spatial Distribution Using WRF-Chem

atmosphere

Article Investigation of Aerosol Direct Effect over China under El Niño and Its Spatial Distribution Using WRF-Chem

Fangzhou Li 1,2, Wenshi Lin 1,2,*, Baolin Jiang 3 and Jiangnan Li 1,2,*

1 School of Atmospheric Sciences, and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Guangzhou 510275, China; [email protected] 2 Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519000, China 3 School of Geography and Tourism, Huizhou University, Huizhou 516000, China; [email protected] * Correspondence: [email protected] (W.L.); [email protected] (J.L.)

Abstract: With rapid economic development and urbanization, the air pollution problem over China has drawn great attention. To explore the aerosol direct effect (ADE) over China, two simulations were conducted using WRF-Chem V3.5.1 in the summer of 2015. One was a control run (CTL) including aerosol effect and related physical and chemical processes, and the other one was a sensitivity simulation (SEN), the same as CTL except that aerosol-radiation interactions were turned off. The differences between two tests were analyzed, in particular over regions in South China (SC) and East China (EC). Results showed the following. (1) The large-scale circulation showed a strong El Niño signal, associated with cooling and wet anomalies over EC, while warming and dry anomalies over EC. (2) Due to ADE, there was a significant decrease in precipitation and an increase in AOD over SC and EC, albeit with different mechanisms. (3) In SC, ADE cooled the region reinforcing the El Niño impact and suppressing water vapor fluxes, which led to a more stable atmosphere and weakened water cycle. In EC, ADE caused vertical circulation anomalies opposing the El Niño impact. (4) ADE showed obvious land-sea difference in precipitation and shortwave radiation. Keywords: aerosol direct effect (ADE); WRF-Chem; China; El Niño; summer 2015 Citation: Li, F.; Lin, W.; Jiang, B.; Li, J. Investigation of Aerosol Direct Effect over China under El Niño and Its Spatial Distribution Using 1. Introduction WRF-Chem. Atmosphere 2021, 12, 58. https://doi.org/10.3390/atmos Aerosols play an important role in climate change characterized by influencing the 12010058 atmosphere directly and indirectly. The aerosol direct effect (ADE) refers to cooling at the surface and warming in the atmosphere via absorbing and scattering solar radiation [1–3]. Received: 26 November 2020 The aerosol indirect effect is mainly by changing the microphysical processes in which Accepted: 25 December 2020 aerosols serve to act as cloud condensation nuclei (CCN) or ice nuclei (CN) [4–6]. Published: 31 December 2020 With increasing aerosol emission, the air pollution problems over China have drawn great attention. There are plenty of investigations on the aerosol influences on climate over Publisher’s Note: MDPI stays neu- China. For example, Huang et al. [7] conducted simulations to explore the aerosol direct tral with regard to jurisdictional clai- effect (ADE) on climate over China and found that aerosols significantly contributed to ms in published maps and institutio- warming in the atmosphere. Li et al. [8] investigated the aerosol effect on precipitation over nal affiliations. the Pearl River Delta region which has large aerosol loading due to rapid urbanization. It is indicated that ADE tended to enhance the generation of rainwater while the aerosol indirect effect tended to weaken precipitation. To provide a valuable perspective on emission control policies, Zhou et al. [9] investigated the relationship between PM variability and Copyright: © 2020 by the authors. Li- 2.5 censee MDPI, Basel, Switzerland. aerosol-radiation interactions. The study pointed out that emission control was much more This article is an open access article effective under heavy polluted conditions due to aerosol-radiation interactions. distributed under the terms and con- As one of the most important drivers in the climate over East Asia, ENSO(El Niño– ditions of the Creative Commons At- Southern Oscillation) has certain influence on aerosol-meteorology interactions. In Abish tribution (CC BY) license (https:// and Mohanakumar’s study [10], it was implied that during La Nina, aerosol loading was creativecommons.org/licenses/by/ suppressed due to a weaker zonal circulation. Chang et al. [11] showed that El Niño events 4.0/). were in favor of exaggerating the pollution in winter over eastern China. Kim et al. [12]

Atmosphere 2021, 12, 58. https://doi.org/10.3390/atmos12010058 https://www.mdpi.com/journal/atmosphere Atmosphere 2021, 12, 58 2 of 17

suggested that ADE can drastically modulate the ENSO impact especially over the Asia monsoon area as a response to climate change. Moreover, Bellouin et al. [13] indicated that with the decreasing trend of aerosol emission atmospheric warming due to ADE might be weakened. Although plenty of research has demonstrated the important role of ADE on the regional climate over China, few studies describe the spatial distribution of ADE and the underlying mechanism, particularly under the background of ENSO. The spatial heterogeneity of ADE can provide beneﬁcial perspective for making effective pollution control policies. Therefore, a strong El Niño event was selected here to explore ADE over China during the summer of 2015. In this paper, the descriptions of datasets, methods and experimental design are ﬁrst presented in Section2. Main results are presented in Section3. In Section 3.1, the simulation results are comprehensively evaluated. In Section 3.2, a composite analysis is implemented to describe the spatial characteristics of the meteorological anomalies due to ENSO. Moreover, a variation analysis is conducted to indicate the unique anomalies in the summer 2015. After that, the differences between control (CTL) and sensitivity simulation (SEN) tests are presented to demonstrate ADE over China and the associated mechanism. Finally, the conclusion and discussion follow in Section4.

2. Data and Methodology 2.1. Model Settings Two experiments were conducted using a WRF(Weather Research and Forecasting) model version 3.5.1, coupled with a chemistry module (WRF-Chem V3.5.1; [14,15]). One was a control test (CTL), and the other one a sensitivity test (SEN). The simulated domain (Figure1) covered the region, 60 ◦ W–150◦ E, 5◦ S–45◦ N, consisting of 180 × 290 grid cells, and no nesting was applied. The horizontal resolution was 30 km × 30 km along with 42 layers vertically. The physical and chemical schemes selected for the simulation were the Yonsei University planetary boundary layer scheme [16], the Kain–Fritsch cumulus parameterization scheme [17], and the longwave and shortwave radiation scheme of the Rapid Radiative Transfer Model for general circulation models [18] (RRTMG). The gas-phase chemistry model was based on the Regional Acid Deposition Model version 2 [19], while the Modal Aerosol Dynamics Model for Europe/Secondary Organic Aerosol Model [20,21] was chosen as the driver for aerosols [14]. As microphysical processes are critical for aerosol indirect effect, a double-moment scheme, the Morrison aerosol microphysics scheme [22,23] was applied. With the Kain-Fritsch cumulus scheme, there is no feedback from the pa- rameterized convection to the atmospheric radiation and the photolysis schemes [24]. In previous studies on simulating the aerosol effect over China [25–29], the selected schemes were widely adopted, and their capabilities were sufficiently demonstrated. The simulated time period covered from 0000 UTC on 26 May to 0000 UTC on 31 August 2020, with the first five days as a spin-up time. The analysis was mainly focused on the time period from June to August (JJA). Sea surface temperature (SST), sea ice, vegetation fraction and albedo were updated every 6 h (sst_update = 1). Upper-air nudging was turned on to reduce the deviation between simulation and observation. Nudging was applied to the horizontal wind and temperature with a coefficient of 0.0003 s−1 and a frequency of 60 s. The model settings are summarized in Table1. Atmosphere 20202121, 1212, 58x FOR PEER REVIEW 3 of 17

Figure 1 1.. TheThe simulated simulated region region (60° (60◦ WW–150–150°◦ E,E, 5° 5◦ SS–45–45°◦ N).N).

Table 1. WRFWRF-Chem-Chem configuration conﬁguration options options for for the the experiment. experiment. YS YSU,U, Yonsei Yonsei University University;; RADM2 RADM2,, Regional Acid Deposition Model v versionersion 2 2. Atmospheric Process WRF-Chem Option Atmospheric Process WRF-Chem Option Cloud microphysics Morrison scheme LongwaveCloud and shortwave microphysics radiation Rapid Radiative Morrison Transfer scheme Model Longwave and shortwave radiation Rapid Radiative Transfer Model Surface layer Monin-Obukhov scheme Surface layer Monin-Obukhov scheme Land surface model Noah Land-Surface model Land surface model Noah Land-Surface model PlanetaryPlanetary boundary boundary layer layer YSU YSU scheme scheme CumulusCumulus Kain Kain–Fritsch–Fritsch scheme scheme PhotolysisPhotolysis Fast Fast-J-J photolysis photolysis GasGas phase phase chemistry chemistry RADM2 RADM2 AerosolAerosol chemistry chemistry MADE/SORGAM MADE/SORGAM including including some some aqueous aqueous reactions reactions AnthropogenicAnthropogenic emissions emissions RETRO RETRO

InIn the SEN test,test, thethe modelmodel settings settings were were the the same same as as in in CTL CTL test. test. To To exclude exclude the the aerosol aer- osolradiation radiation impact, impact, the aerosolthe aerosol direct direct feedback feedback was wa turneds turned off. off. The The experimental experimental design design is ispresented presented in in Table Table2. 2.

Table 2. Experimental design.

TestTest Description Description Namelist Namelist Options Options CTLCTL Control Control experiment experiment including including aerosol aerosol effect. effect. aer_ra_feedback aer_ra_feedback = 1 = 1 SENSEN Same Sameas CTL as CTLexcept except excluding excluding ADE. ADE. aer_ra_feedback aer_ra_feedback = 0 = 0

2.2.2.2. Datasets Datasets and and M Methodsethods TheThe National National Centers Centers for for Environmental Environmental Prediction Prediction (NCEP) Final Operational Global Analysis data ((https://rda.ucar.edu/datasets/ds083.2/https://rda.ucar.edu/datasets/ds083.2/) was) was used used as as the the initial initial and and bound- bound- aryary conditions ofof thethe simulation.simulation. The The “Reanalysis “Reanalysis of of the the Tropospheric Tropospheric chemical chemical composition composi- tionover over the pastthe past 40 yr” 40 (RETRO,yr” (RETRO, http://retro.enes.org http://retro.enes.org)) was was used us fored anthropogenicfor anthropogenic emissions. emissions.To evaluate the control simulation results, comparison was made with ERA5, ﬁfth generationTo evaluate of ECMWF the control atmospheric simulation reanalysis results, dataset comparison [30] on was fundamental made with metrological ERA5, fifth generationvariables such of ECMWF as temperature, atmospheric geopotential reanalysis height, dataset wind, [30 precipitation] on fundamental and speciﬁc metrological humid- variablesity. The ERA5 such datasetas temperature, is consistent geopotential with the inputheight, dataset wind, usedprecipitation for simulation, and specific NCEP, hu- in midity.observation The ERA5 and data dataset assimilation is consistent methods. with the It has input a spatial dataset resolution used for simulation, of 0.25◦ × 0.25 NCEP,◦.A in3-hourly observation reanalysis and datasetdata assimilation is applied formethods. model evaluationIt has a spatial in Section resolution 3.1 while of 0.25° the monthly × 0.25°. Amean 3-hourly reanalysis reana datasetlysis dataset is used is for applied composite for model analysis evaluation in Section in 3.2 Section. The evaluation 3.1 while was the

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based on widely used statistical methods, abnormal correlation coefﬁcient (ACC) and normalized root mean square error (NRMSE)[31]. The signiﬁcance mentioned in the following text is calculated according to the Student’s t-test. The maximum value of ACC is 1.0. The simulation performance was considered robust when ACC reached 0.6 [32]. The calculation of ACC and NRMSE are as follows:

N ∑i (yis − yis)(yio − yio) ACC = q q (1) 1 N 2 1 N 2 N N ∑i=1(yis − yis) N ∑i=1(yio − yio) q 1 N 2 N ∑i=1(yis − yio) NRMSE = 1 N (2) N ∑i=1 yio

where yis and yio represent the simulation value and reanalysis value on grid cell i, respectively. yis and yio represent the mean values from simulation and reanalysis data on grid cell i, respectively. While calculating ACC, N is the total number of effective times on every grid, which results in a spatial distribution of ACC. While calculating NRMSE, N is the total grid number spatially. The classiﬁcation of El Niño years and La Nina years were based on the average multivariate ENSO Index from June to August [33] (MEI). From 1979–2015, the seven highest indexes were selected as El Niño years (1982, 1983, 1987, 1993, 1997, 2009, and 2015), and the seven lowest were selected as La Nina years (1988, 1989, 1999, 2007, 2010, 2011, and 2013).

3. Results 3.1. Model Evaluation The simulation performance was evaluated based on the comparison between CTL test result and ERA5 datasets on temperature at 2 m, specific humidity at 700 hPa, precipitation rate, geopotential height and wind fields at 850 hPa and 500 hPa. The pattern correlations and NRMSEs of the JJA average fields between simulation and reanalysis data were calculated and are illustrated in Table3. All the pattern correlations passed the significant test at a level of 99% and were higher than 0.7, indicating good performance of the simulation in reproducing the distributions of these meteorological variables, especially specific humidity, temperature and wind. The NRMSEs were generally small, with those of specific humidity, temperature and geopotential height less than 0.001, while those of precipitation rate and wind were relatively larger.

Table 3. Evaluation of the simulated meteorological ﬁelds (average ﬁelds from June to August 2015).

Variables (Unit) Pattern Correlation 1 NRMSE 2 Temperature at 2 m (K) 0.998 0.0002 Speciﬁc humidity (kg/kg) 0.999 0.0002 Precipitation rate (mm/day) 0.792 0.0032 Geopotential height at 850 hPa (gpm) 0.782 0.00006 Geopotential height at 500 hPa (gpm) 0.987 0.00002 U wind at 850 hPa (m/s) 0.993 0.0012 V wind at 850 hPa (m/s) 0.971 0.0022 U wind at 500 hPa (m/s) 0.991 0.0012 V wind at 500 hPa (m/s) 0.962 0.0180 1 The pattern correlations passed the signiﬁcant test at a level of 99%. 2 NRMSE stands for the normalized root mean square error. Atmosphere 2021, 12, x FOR PEER REVIEW 5 of 17 Atmosphere 2021, 12, 58 5 of 17

In Figure 2, the distributions of the deviations between simulation and reanalysis In Figure2, the distributions of the deviations between simulation and reanalysis dataset are illustrated. The first row (Figure 2a–c) indicates the differences in the JJA av- dataset are illustrated. The first row (Figure2a–c) indicates the differences in the JJA aver- erage specific humidity at 700 hPa, precipitation rate and temperature at 2 m, respectively. age specific humidity at 700 hPa, precipitation rate and temperature at 2 m, respectively. TheThe simulated simulated specific specific humidity humidity errors errors we werere relatively relatively larger larger over over China, China, especially especially over over −4 SouthSouth China. China. However, However, the magnitude waswas almostalmost negligiblenegligible (around(around 1010−4 kg/kg).kg/kg). The The prprecipitationecipitation rate rate was was generally generally smaller in the model than in reanalysis data.data. The tempera-temper- atureture deviateddeviated slightly,slightly, inin aa rangerange fromfrom −−11 K to 1 K, over most China, China, except except the the Tibetan Tibetan PlateauPlateau region. region. The The second second row row indicates indicates the the deviations deviations on on the the geopotential geopotential hei heightght and and windwind at at 850 850 hPa hPa and and 500 500 hPa, hPa, respectively. respectively. The The geopotential geopotential heights heights at at both both layers layers we werere generallygenerally overestimated overestimated in in the the model model,, with with regional regional averaged averaged extents extents of of around around 0.299% 0.299% andand 0.336% 0.336% at at 850 850 hPa hPa and and 500 500 hPa, hPa, respectively. respectively. It It can can be be seen seen from from Figur Figuree 22d,d,ee thatthat in thethe simulation, simulation, there there we werere northeasterly northeasterly wind wind anomal anomaliesies at at low low layer layer and and northwesterly northwesterly windwind anomal anomaliesies at at high high layer layer over over South South China. China. The The wind wind deviation deviation wa wass relatively relatively large large overover the the Tibetan Tibetan Plateau Plateau region, region, which which to to a a certain certain extent extent explains explains the the specific specific humidity humidity deviationdeviation over over there there as as well. well. This This is is consistent consistent with with the the negative negative deviation deviation of of precipitation precipitation,, sincesince the the major major water water vapor vapor sources sources over over South South China China originate originate mainly mainly from from the the Indian Indian OceanOcean and and the the South South China China Sea Sea (SCS) (SCS) [3 [344]]..

FigureFigure 2. 2. DifferencesDifferences between between simulation simulation from from CTL CTL test test and and ERA5 ERA5 reanalysis reanalysis datasets datasets averaged averaged from from June June to to August August 2015 2015 onon ( (aa)) specific speciﬁc humidity at 700 hPahPa (unit:(unit: 1010−−33 kg/kg),kg/kg), (b (b) )precipitation precipitation rate rate (unit: (unit: mm/day), mm/day), (c ()c )temperature temperature at at 2 2 m m (unit: (unit: K),K), ( (dd)) th thee geopotential geopotential height height (contour; (contour; unit: unit: gpm) gpm) and and wind wind (vector; (vector; unit: unit: m/s) m/s) at at 850 850 hPa, hPa, and and (e (e) )500 500 hPa. hPa.

ToTo further further evaluate evaluate the the simulation simulation performance performance on on a a temporal temporal scale scale,, the the ACCACC waswas computedcomputed based based on on the the CTL CTL test test result result and and ERA5 ERA5 reanalysis reanalysis dataset dataset at a at time a time interval interval of 3of hours. 3 h. Figure Figure3 3presents presents the the distributions distributions of of ACCACC onon the same variables as illustrated illustrated in in FigureFigure 2.2 . The The dotted dotted area area passed the signiﬁcantsignificant testtest at at a a level level of of 90%. 90%. For For the the convenience convenience of ofdiscussion, discussion, the the conﬁdence confidence level level was was chosen chosen in considerationin consideration of the of the spatial spatial consistence consistence and sample number of the results. The simulation performance on precipitation rate (Figure3b )

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Atmosphere 2021, 12, 58 and sample number of the results. The simulation performance on precipitation rate 6(Fig- of 17 ure 3b) was relatively poorer than other variables, while those of specific humidity at 700 hPa (Figure 3a) and temperature at 2 m (Figure 3c) had the best performance among all. The simulations of geopotential height and wind were relatively consistent with reanaly- siswas data relatively temporally poorer over than South other China variables, (above while0.6). those of speciﬁc humidity at 700 hPa (FigureOverall,3a) and the temperature model reproduced at 2 m (Figurewell the3c) major had thecharacteristics best performance of the fun amongdamental all. The me- teorologicalsimulations offields geopotential both spatially height and and temporally, wind were which relatively provides consistent certain with reliability reanalysis for data the followingtemporally analysis. over South China (above 0.6).

Figure 3. The temporal ACC (contour)(contour) ofof simulationsimulation fromfrom CTL CTL test test and and ERA5 ERA5 reanalysis reanalysis datasets datasets on on (a ()a speciﬁc) specific humidity humidity at −3 at700 700 hPa hPa (unit: (unit: 10 10−3kg/kg), kg/kg), ((bb)) precipitationprecipitation raterate (unit:(unit: mm/day),mm/day), ( (c) temperature at 2 m (unit: K), ( d,g) the geopotential height (unit: gpm), gpm), ( (ee,,hh)) zonal zonal wind, wind, and and ( (ff,,ii)) meridional meridional wind wind at at 850 850 hPa hPa (the (the second second row) row) and and 500 500 hPa hPa (the (the third third row). row). Dotted area ind indicateicate that the values passed the signiﬁcantsignificant test at a level of 90%.

3.2. CharacteristericsOverall, the model of the reproducedLarge-scale Circulation well the majorin Summer characteristics 2015 Under ofEl theNiño fundamental meteorologicalAs one of the ﬁelds main both drivers spatially of the and climate temporally, over East which Asia, provides ENSO certain events reliability could cause for enormousthe following anomalies analysis. in large-scale circulation. In summer 2015, China was also strongly influenced3.2. Characteristerics by the El ofNiño the Large-Scaleevent. Therefore, Circulation to depict in Summer the characteristics 2015 under El of Niño the large-circulation changes in summer 2015, this section presents the composite analysis of El Niño As one of the main drivers of the climate over East Asia, ENSO events could cause years and La Nina years, and the variance analysis in 2015. enormous anomalies in large-scale circulation. In summer 2015, China was also strongly In Figure 4a–d, the composite analysis on JJA average temperature at 2m and precip- inﬂuenced by the El Niño event. Therefore, to depict the characteristics of the large- itation rate in El Niño years and La Nina years are presented. An obvious inverse pattern circulation changes in summer 2015, this section presents the composite analysis of El Niño between El Niño and La Nina was shown over China. Under El Niño (La Nina), the tem- years and La Nina years, and the variance analysis in 2015. In Figure4a–d, the composite analysis on JJA average temperature at 2m and precipitation rate in El Niño years and La Nina years are presented. An obvious inverse pattern between El Niño and La Nina was shown over China. Under El Niño (La Nina), the temperature over China was relatively lower (higher) while the precipitation rate was Atmosphere 2021, 12, x FOR PEER REVIEW 7 of 17

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perature over China was relatively lower (higher) while the precipitation rate was rela- tivelyrelatively higher higher (lower), (lower), particularly particularly over overEast China East China.. In Figure In Figure 4e,f, anomalies4e,f, anomalies in 2015 in 2015were similarwere similar to those to in those Figure in Figure4a,b, the4a,b, mean the state mean of state El Niño of El years Niño, but years, with but a larger with a magni- larger tudemagnitude.. The precipitation The precipitation rate was rate higher was higher over East over China East China and lower and lower over overSouth South China. China. As twoAs two regions regions incurring incurring strong strong El ElNiño Niño signal, signal, they they will will be be further further discussed discussed separately separately in in thethe following following section sections.s.

FigureFigure 4 4.. AverageAverage temperature temperature at at 2m 2m (left; (left; unit: unit: K) K)and and precipitation precipitation rate rate (right; (right; unit: unit: mm/day) mm/day) anomaliesanomalies of of JJA JJA in in (a (a,b,b) )El El Niño Niño years, years, (c (,dc,)d La) La Nina Nina years, years, and and (e,f ()e ,2015f) 2015 from from ERA5 ERA5 reanalysis reanalysis datasets.datasets.

FigureFigure 55 isis samesame asas FigureFigure4 4but but for for anomalies anomalies of of SST SST and and 700-hPa 700-hPa specific specific humidity. humidity. In InEl El Niño Niño years, years, SST SST is relatively is relatively warmer warmer over over the Baythe Bay of Bengal, of Bengal, the westthe west of SCS of andSCS theand West the WPacificest Pacific Ocean, Ocean and cooler, and cooler over the over East the Pacific East Ocean.Pacific Ocean. In La Nina In La years, Nina the years, Pacific the Ocean Pacific is Oceanmainly is dominated mainly dominated with positive with SST positive anomalies, SST anomalies, especially overespecially the east over of SCS.the east In 2015, of SCS. the Inpositive 2015, the anomalies positive overanomalies SCS were over stronger. SCS were Compared stronger. Compared with the mean with state the mean of El Niño,state of it Elwas Niño, warmer it was over warmer South over China Sou andth coolerChina overand Eastcooler China. over InfluencedEast China. by Influenced the positive by SSTthe positiveanomalies SST and anomalies the western and Pacificthe western subtropical Pacific high subtropical over the high east, over strong the positive east, strong anomalies pos- itiveof specific anomalies humidity of specific were seenhumidity over thesewere twoseen regions over these as well. two It regions implies as that well. the It relatively implies thathigher the temperaturerelatively higher over temperature SCS led to larger over SCS water led vapor to larger transport water tovapor East transport China, resulting to East in positive specific humidity anomalies. It can be seen from Figure6a that there were China, resulting in positive specific humidity◦ ◦ anomalies. It can be seen from Figure◦ 6a that◦ thereabnormal were upwardabnormal motions upward over motions 105 over–110 105E° and–110 the °E and West the Pacific West OceanPacific (120Ocean–125 (120°E)– while downward motions over 110◦–120◦ E under El Niño condition. By contrast, under 125 °E) while downward motions over 110°–120 °E under El Niño condition. By contrast, La Nina condition, the area from 105◦ E to 120◦ E was dominated by downward motion under La Nina condition, the area from 105 °E to 120 °E was dominated by downward anomalies, especially over South China (105◦–110◦ E). Figure6c shows that South China (105◦–120◦ E) was dominated by the downward motion anomaly. However, East China

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motion anomalies, especially over South China (105°–110 °E). Figure 6c shows that South China (105°–120 °E) was dominated by the downward motion anomaly. However, East China (110°–125 °E) was influenced by both downward motion and upward motion anomalies, which was in favor of circulation changes transporting water vapor from the Pacific Ocean and further enhancing the precipitation. This is consistent with the positive anomalies of precipitation rate over East China illustrated in Figure 4f. Overall, the summertime anomalies in El Niño years and La Nina years exhibited distinct inverse patterns. El Niño caused cooling on land, especially over South China. Its impact on temperature showed a land-sea difference. Meanwhile, El Niño induced a de- Atmosphere 2021, 12, 58 crease over South China and an increase over East China on precipitation. The distribu-8 of 17 tions of the anomalies in 2015 resembled those in El Niño years but the magnitudes were larger. Stronger specific humidity anomalies in 2015 over East China and the Pacific Ocean indicated that positive SST anomalies over SCS and vertical circulation anomalies en- (110◦–125◦ E) was inﬂuenced by both downward motion and upward motion anomalies, whichhanced was water in favor vapor of transport circulation and changes subsequent transporting convergence water vapor, especially from theover Paciﬁc East China Ocean. andIn short further, summer enhancing 2015 theoverall precipitation. showed a Thisstrong iser consistent signal than with the the mean positive states anomalies of El Niño of precipitationevents. rate over East China illustrated in Figure4f.

Figure 5. AverageAverage sea sea surface surface temperature temperature (left; (left; unit: unit: K) K) and and specific speciﬁc humidity humidity at 700 at 700 hPa hPa (right; (right; unit: kg/kg) anomalies anomalies of of JJA JJA in in (a (a,b,b) )El El Niño Niño years, years, (c (c,d,d) )La La Nina Nina years, years, and and (e (e,f,)f )2015 2015 from from ERA5 ERA5 reanalysisreanalysis datasets.datasets.

Overall, the summertime anomalies in El Niño years and La Nina years exhibited distinct inverse patterns. El Niño caused cooling on land, especially over South China. Its impact on temperature showed a land-sea difference. Meanwhile, El Niño induced a decrease over South China and an increase over East China on precipitation. The distributions of the anomalies in 2015 resembled those in El Niño years but the magnitudes were larger. Stronger speciﬁc humidity anomalies in 2015 over East China and the Paciﬁc Ocean indicated that positive SST anomalies over SCS and vertical circulation anomalies

enhanced water vapor transport and subsequent convergence, especially over East China. In short, summer 2015 overall showed a stronger signal than the mean states of El Niño events. AtmosphereAtmosphere2021 2021, 12, 1,2 58, x FOR PEER REVIEW 99 of of 17 17

FigureFigure 6.6. JJAJJA average average anomalies anomalies of of the the zonal zonal mean mean vertical vertical velocity velocity (unit: (unit: Pa/s; Pa/s; the values the values are in- are ◦ ◦ increasedcreased by by −5−050 times) times) from from 20° 20 NN–30–30° NN in in(a ()a El) El Niño Niño years, years, (b ()b La) La Nina Nina years, years, and and (c ()c )2015 2015 from from ERA5ERA5 reanalysis reanalysis datasets. datasets. Left Left axis axis stands stands for for pressure pressure levels levels (unit: (unit: hPa) hPa) and and right right axis axis stands stands for for the heightthe height (unit: (unit: km). km).

3.3.3.3. AerosolAerosol DirectDirect EffectEffect ImpactImpact onon PrecipitationPrecipitation ToTo examineexamine the ADE ADE,, the the difference differencess between between CTL CTL and and SEN SEN test test are are presented presented in this in thissection section.. Figure Figure 7 show7 showss the themonthly monthly average average differences differences (CTL (CTL–SEN)–SEN) in precipit in precipitationation and andwater water vapor vapor fluxes. fluxes. The anomalies The anomalies of precipitation of precipitation rate show rate obvious show obviouscontrasts contrasts between betweenland and land sea, andcharacterized sea, characterized by negative by negative anomalies anomalies on land onand land relatively and relatively small positive small positiveanomalies anomalies over the over sea. the The sea. domain The domain average average of precipitation of precipitation rate reduct rate reductionion was–0.236 was − mm/day0.236 mm/day due to ADE due to. South ADE. China South was China one was of the one areas of the showing areas showing large anomalies large anomalies,, which whichpassed passed the significant the significant test at testa level at a of level 90%. of From 90%. June From to June August, to August, the distribution the distribution of pre- of precipitation rate differences remained similar with a decreasing trend over South cipitation rate differences remained similar with a decreasing trend over South China. In China. In June, slight positive precipitation rate anomalies were seen over East China. June, slight positive precipitation rate anomalies were seen over East China. In Figure In Figure7b,d,f , water vapor transport anomalies were significantly different over South 7b,d,f, water vapor transport anomalies were significantly different over South China and China and East China. Over South China, the water vapor flux anomalies were mainly East China. Over South China, the water vapor flux anomalies were mainly transported transported by anomalous dry northerly winds, opposing the climatological water vapor by anomalous dry northerly winds, opposing the climatological water vapor originating originating from the SCS, the Indian Ocean and the Pacific Ocean [28]. Therefore, there from the SCS, the Indian Ocean and the Pacific Ocean [28]. Therefore, there were water were water vapor divergence anomalies leading to a precipitation decrease. By contrast, vapor divergence anomalies leading to a precipitation decrease. By contrast, southwest- southwesterly water vapor flux anomalies prevailed over East China, which were especially erly water vapor flux anomalies prevailed over East China, which were especially strong strong in June and decreased in July and August. in June and decreased in July and August. Summer is the season with relatively large aerosol optical depth (AOD), especially over areas with high urbanization and population such as South China and East China [35]. Figure 8 exhibits the difference in AOD (550 nm) due to ADE. Positive values indicate that AOD was higher in the CTL test than in the SEN test, which means the ADE increased AOD. In most of China, the ADE caused an increase in AOD, especially over the south

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showing a positive feedback [36]. The ADE induced an increase in AOD by 0.0285 on domain average. However, over North China, there existed a slight weakening effect. This

Atmosphere 2021, 12, 58 implies that, the ADE was more significant over the populated areas with high aerosol10 of 17 emission. The distribution of the AOD difference is consistent with precipitation rate difference and its variation from June to August (Figure 7a,c,e).

FigureFigure 7. 7. MonthlyMonthly average average differences differences due due to ADE to ADE (CTL (CTL-SEN-SEN test) in test) precipitation in precipitation rate (unit: rate (unit: mm/day;mm/day; dotted dotted area area indicate indicate that that the the values values passed passed the the significant signiﬁcant test test at a at level a level of 90%.) of 90%.) on ( ona) (a) June,June, ( (cc)) July, July, and ( e) August; and in the vertical integral of moisture flux ﬂux (vector; unit: kg m −1− 1s−1s−) 1) andand its its absolute absolute value value (contour; (contour; unit: unit: kg kg m m−1− s1−1s)− on1) on(b) ( June,b) June, (d) ( July,d) July, and and (f) (Augustf) August..

Summer is the season with relatively large aerosol optical depth (AOD), especially over areas with high urbanization and population such as South China and East China [35]. Figure8 exhibits the difference in AOD (550 nm) due to ADE. Positive values indicate that AOD was higher in the CTL test than in the SEN test, which means the ADE increased AOD. In most of China, the ADE caused an increase in AOD, especially over the south showing a positive feedback [36]. The ADE induced an increase in AOD by 0.0285 on domain average. However, over North China, there existed a slight weakening effect. This implies that, the ADE was more signiﬁcant over the populated areas with high aerosol emission. The distribution of the AOD difference is consistent with precipitation rate difference and its variation from June to August (Figure7a,c,e).

Atmosphere 2021, 12, x FOR PEER REVIEW 11 of 17 Atmosphere 2021, 12, 58 11 of 17

Figure 8. Monthly average difference in aerosol optical depth (AOD, 550 nm) due to ADE (CTL-SEN Figure 8. Monthly average difference in aerosol optical depth (AOD, 550 nm) due to ADE (CTL- SEN test)test) on on(a) (June,a) June, (b) ( bJuly,) July, and and (c) ( cAugust.) August. As ADE occurs mainly via the interactions of aerosol with radiation, the differences in As ADE occurs mainly via the interactions of aerosol with radiation, the differences latent heat flux (LH), downward shortwave radiation flux (SW) and downward longwave in latent heat flux (LH), downward shortwave radiation flux (SW) and downward flux (LW) are summarized in Figure9. ADE induced decreases in LH and SW and increases longwave flux (LW) are summarized in Figure 9. ADE induced decreases in LH and SW in LW. The scattering and absorbing effect of aerosol led to weakened SW, and further and increases in LW. The scattering and absorbing effect of aerosol led to weakened SW, decreased LH. The domain average variations due to ADE were −2.855, −11.606, and and further decreased LH. The domain average variations due to ADE were −2.855, 0.290 W/m2 for LH, SW, and LW, respectively. Most of the solar radiation absorbed −11.606, and 0.290 W/m2 for LH, SW, and LW, respectively. Most of the solar radiation by the ground surface (60–70%) was consumed via evaporation. The distributions of absorbedthe by changes the ground in LH surface and SW (60 showed–70%) was land-sea consumed difference via evaporation. similar to thatThe ofdistribu- precipitation tions ofchanges the changes (Figure in 7LHa,c,e and), mainly SW showed over South land- andsea difference West China, similar which to means that of ADE precipi- influenced tation changesthe precipitation (Figure 7a,c,e) mainly, mainly through over aerosol-radiation South and West interaction. China, which Considering means ADE the in- changes fluencedin the temperature precipitation in Figure mainly4a,e, through ADE aerosol was in- phaseradiation with interaction. the El Niño Consider impacting over the South changeChina.s in temperature The decrease in Figure in LHmeans 4a,e, ADE weakened was in evaporation phase with processes, the El Niño which impact might over suppress South China.precipitation. The decrease The enhancing in LH means impact weakened on LW dueevaporation to ADE wasprocesses, relatively which small might compared suppresswith precipitation. that on SW. However,The enhancing ADE was,impact overall, on LW in favordue to of ADE a more was stable relatively atmosphere. small compared with that on SW. However, ADE was, overall, in favor of a more stable atmosphere.

AtmosphereAtmosphere 2021, 12,2021 x FOR, 12 PEER, 58 REVIEW 12 of 17 12 of 17

Figure 9. JJA average differences (unit: W/m2) due to ADE (CTL-SEN test) in (a) latent heat flux, (b) Figure 9. JJA average differences (unit: W/m2) due to ADE (CTL-SEN test) in (a) latent heat flux, downward shortwave radiation flux, and (c) downward longwave radiation flux. (b) downward shortwave radiation flux, and (c) downward longwave radiation flux. To better investigate the spatial variation of ADE, we select EC (East China; 28–36◦ N, To better investigate the spatial variation of ADE, we select EC (East China; 28–36° 110–124◦ E) and SC (South China; 18–27◦ N, 105–122◦ E) as shown in Figure 10. Figures 11 and 12 N, 110–124° E) and SC (South China; 18–27° N, 105–122° E) as shown in Figure 10. Figures show the difference in vertical velocity due to ADE. Positive (negative) values indicate 11 and 12 show the difference in vertical velocity due to ADE. Positive (negative) values upward (downward) motion. In SC region, the vertical velocity anomalies induced by indicateADE upward were mostly(downward) downward motion. motion, In SC with region, stronger the vertical intensity v inelocity June. anomalies Circulation in- anomaly duced inby favorADE ofwe waterre mostly vapor downward transport occurredmotion, with in July strong ander August, intensity which in June. was consistentCircula- with tion anomalythe weakening in favor inof thewater ADE-induced vapor transport precipitation occurred rate in July decrease and August, (Figure7 whichc,e). However, was it consistentwas with relatively the weakening weak compared in the to ADE effect-induced on the radiation precipitation change. rate decrease (Figure 7c,e). However, it was relatively weak compared to effect on the radiation change. In summary, ADE caused a reduction in precipitation rate and an increase in AOD over most of China. Interestingly, we found that the mechanism of ADE may differ over SC and EC regions. In SC, ADE caused stronger precipitation rate decrease (–0.646 mm/day) than that in EC (–0.240 mm/day). The water vapor flux was suppressed by ADE (–0.151 kg m−1 s−1) rather than enhanced (0.832 kg m−1 s−1) in EC. The radiation forcing was stronger than that in EC. SW was reduced by 16.659 W/m2 and 9.312 W/m2 averaged over

Atmosphere 2021, 12, x FOR PEER REVIEW 13 of 17

SC and EC in JJA, respectively. LH was reduced by 7.678 W/m2 and 4.166 W/m2 averaged over SC and EC in JJA, respectively. LW changes were relatively insignificant over both regions. In SC, ADE decreased the shortwave radiation via scattering and absorbing effects, which cooled the region in phase with El Niño impact. This might result in increases in the pressure over land and a pressure gradient pointing from land to ocean. As a consequence, the water vapor transport towards SC was weakened in spite of the positive specific humidity anomalies in summer of 2015. This resulted in a more stable atmosphere that suppressed precipitation. In EC, besides the absorbing effect, ADE also enhanced the Atmosphere 2021, 12, 58 vertical circulation anomaly in contrast to that caused by the El Niño effect, which further13 of 17 suppressed precipitation. The suppressing effect was enhanced from June to August.

Atmosphere 2021, 12, x FOR PEER REVIEW ◦ ◦ 14 of 17 FigureFigure 10. 10. SelectedSelected regions. regions. Red Red box box (18 (18–27–27° N,N, 105 105–122–122° E) E)indicates indicates SC, SC, South South China; China; blue blue box box ◦ ◦ indicatesindicates EC (28 (28–36–36° N,N, 110 110–124–124° E),E), East East China China..

FigureFigure 11 11.. MonthlyMonthly average difference duedue toto ADEADE (CTL-SEN (CTL-SEN test) test) in in the the zonal zonal mean mean vertical vertical velocity velocity(unit: (unit: m/s) overm/s) over SC region SC region on (a on) June, (a) June, (b) July, (b) July, and (andc) August. (c) August. Left axisLeft standsaxis stands for pressure for pressure levels

levels(unit: (unit: hPa) and hPa) right and axisright stands axis stands for the for height the height (unit: (unit: km). km).

Atmosphere 2021, 12, x FOR PEER REVIEW 14 of 17

Atmosphere 2021, 12, 58 Figure 11. Monthly average difference due to ADE (CTL-SEN test) in the zonal mean vertical14 ve- of 17 locity (unit: m/s) over SC region on (a) June, (b) July, and (c) August. Left axis stands for pressure levels (unit: hPa) and right axis stands for the height (unit: km).

Figure 12. Same as Figure 11 but over EC region.

In summary, ADE caused a reduction in precipitation rate and an increase in AOD over most of China. Interestingly, we found that the mechanism of ADE may differ over SC and EC regions. In SC, ADE caused stronger precipitation rate decrease (−0.646 mm/day) than that in EC (−0.240 mm/day). The water vapor flux was suppressed by ADE (−0.151 kg m−1 s−1) rather than enhanced (0.832 kg m−1 s−1) in EC. The radiation forcing was stronger than that in EC. SW was reduced by 16.659 W/m2 and 9.312 W/m2 averaged over SC and EC in JJA, respectively. LH was reduced by 7.678 W/m2 and 4.166 W/m2 averaged over SC and EC in JJA, respectively. LW changes were relatively insigniﬁcant over both regions. In SC, ADE decreased the shortwave radiation via scattering and absorbing effects, which cooled the region in phase with El Niño impact. This might result in increases in the pressure over land and a pressure gradient pointing from land to ocean. As a consequence, the water vapor transport towards SC was weakened in spite of the positive speciﬁc humidity anomalies in summer of 2015. This resulted in a more stable atmosphere that suppressed precipitation. In EC (Figure 12), besides the absorbing effect, ADE also enhanced the vertical circulation anomaly in contrast to that caused by the El Niño effect, which further suppressed precipitation. The suppressing effect was enhanced from June to August.

4. Conclusions and Discussion In this study, two simulations were conducted using the WRF-Chem V3.5.1 model, with (CTL) and without (SEN) aerosol-radiation interaction. The differences between two tests were used to estimate ADE. We focused on two regions, South China and East China, which have high aerosol aerosols due to rapid urbanization and large populations. Although several publications have investigated ADE over China, few studies discuss the joint ENSO effect and ADE. With consideration of the specialty of the El Niño condition in summer 2015, ADE over China was studied and discussed above. Results of certain referential values are presented and can be summarized as follows: Atmosphere 2021, 12, 58 15 of 17

1. As a strong El Niño event, summer 2015 showed similar signals as the mean state of El Niño years based on the composite analysis. The large-scale circulation changes of summer 2015 tended to induce cooling (warming) and wet (dry) conditions over East China (South China). Moreover, summer 2015 also showed additional features. For example, positive specific humidity anomalies were found over East China and the Pacific Ocean. This is because that the positive SST anomalies over SCS enhanced the water vapor transport eastwards. In addition, the upward motion anomalies over the West Pacific Ocean and downward motion anomalies over East China enhanced the circulation and water vapor transport from the ocean. However, the upward motion anomalies were relatively small over South China. 2. Including aerosol effect and their related physical and chemical processes, the CTL test reproduced well the main characteristics of the fundamental meteorological fields based on comparing with reanalysis. 3. ADE on precipitation rate showed obvious land-sea differences to those on SW. Over China, they were mainly negative, especially over SC. Aerosol scattering and absorbing effects led to decreases in SW and increases in LW, which further led to a weakened LH, a more stable atmosphere and a weakened water cycle. 4. The spatial heterogeneity of ADE was discussed. The water vapor transport was suppressed due to ADE over SC (−0.151 kg m−1 s−1) while strongly enhanced over EC (0.832 kg m−1 s−1). SW reduction due to ADE was more significant over SC (16.659 W/m2) than over EC (9.312 W/m2). This implies that aerosol radiative forcing was relatively stronger over SC than over EC. 5. In SC, ADE-induced precipitation reduction was mainly caused by radiative forcing and weakened water vapor transport, which enhanced atmospheric stability. The cooling effect of aerosol was consistent with the El Niño effect over SC. In EC, ADE- induced precipitation reduction was related to the vertical circulation anomalies that were contradictory with those caused by El Niño effect. The investigation was mainly focused on China and Southeast Asia. The universality of the results needs to be further tested. This study only discusses a case occurring in summer 2015. However, ADE during La Nina and the boreal winter is also of great significance. Moreover, aerosol indirect effect over China and its interaction with ENSO background could be another meaningful topic in our future study.

Author Contributions: Conceptualization, J.L.; methodology, J.L. and B.J.; software, F.L. and B.J.; validation, F.L.; formal analysis, F.L.; investigation, F.L.; resources, J.L. and W.L.; data curation, F.L.; writing—original draft preparation, F.L.; writing—review and editing, J.L. and W.L.; visualization, F.L.; supervision, J.L. and W.L.; project administration, J.L. and W.L.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript. Funding: The study is funded by the National Key Research and Development Program of China [2018YFC1507402], National Natural Science Foundation of China (Grant No. 41875168 and No. 42075064), and Guangzhou Science and Technology Plan (201707010088). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data available in a publicly accessible repository that does not issue DOIs. Publicly available da-tasets were analyzed in this study. This data can be found here: https://rda.ucar.edu/datasets/ds083.2/; http://retro.enes.org; https://cds.climate.copernicus.eu/ cdsapp#!/home. Acknowledgments: We sincerely appreciate the free reanalysis datasets provided by National Cen- ters for Environmental Prediction (NCEP), European Centre for Medium-Range Weather Forecasts (ECMWF), and National Center for Atmospheric Research (NCAR). Conﬂicts of Interest: The authors declare no conﬂict of interest. Atmosphere 2021, 12, 58 16 of 17

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