RESEARCH ARTICLE
10.1029/2018JD029528
Key Points:• A pronounced increase of offshore
rainfall was observed at southern
Hainan in the summer of 2010
• Coastal upwelling was suppressedby southeasterly wind anomalies inthe summer of 2003, 2010, and 2016
• Warm SST anomalies due to weak
upwelling produces a stronger land
breeze and convergence over the sea,
ultimately leading to more rainfall

Response of the Diurnal Cycle of Summer Rainfall toLarge‐Scale Circulation and Coastal Upwellingat Hainan, South China
Rui Shi1, Qinbo Cai2,3, Lingyu Dong4, Xinyu Guo5

, and Dongxiao Wang1,61State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences,
Guangzhou, China, 2Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan
Provincee, Haikou, China, 3Hainan Meteorological Service, Haikou, China, 4Hainan Meteorological Observatory, Haikou,
China, 5Center for Marine Environmental Studies, Ehime University, Matsuyama, Japan, 6Guangzhou Branch, Universityof Chinese Academy of Sciences, Beijing, China

Abstract The modulation of coastal rainfall at Hainan by large‐scale circulation and coastal upwelling isCorrespondence to:D. Wang,
dxwang@scsio.ac.cn

Citation:
Shi, R., Cai, Q., Dong, L., Guo, X., &Wang, D. (2019). Response of thediurnal cycle of summer rainfall tolarge‐scale circulation and coastal
upwelling at Hainan, South China.
Journal of Geophysical Research:
Atmospheres, 124, 3702–3725. https://doi.org/10.1029/2018JD029528
Received 21 AUG 2018
Accepted 21 FEB 2019
Accepted article online 1 MAR 2019
Published online 3 APR 2019
Author Contributions:Conceptualization: Rui Shi
Data curation: Qinbo Cai, Lingyu
DongSupervision: Dongxiao Wang
Writing ‐ original draft: Rui Shi
Writing – review & editing: Rui Shi,
Xinyu Guo©2019. American Geophysical Union.
All Rights Reserved.

SHI ET AL.

studied using observations and numerical modeling. Tropical Rainfall Measuring Mission data show thatthe monthly mean rainfall off southern Hainan was considerably higher in August 2010 than in August
2011. The main cause of this difference is an intensiﬁcation of offshore rainfall from midnight to earlymorning. Compared with the multiyear (2000–2017) average ﬁeld, reanalysis data show that there is anapparent increase in atmospheric humidity in August 2010. During this time, a moderate El Niño wasdecaying and a new La Niña event was developing, so a signiﬁcant adjustment of the large‐scale circulation
was observed in the western Paciﬁc. The resulting anomalous onshore wind depresses the developmentof upwelling, leading to a relatively warm sea surface temperature off the south coast of Hainan, which inturn enhances the land‐sea thermal contrast and land breeze at night. Decreases in upwelling intensity
and asymmetric intensiﬁcation of nocturnal and daytime offshore rainfall are conﬁrmed not only in thesummer of 2010 but also in the summer of 2003. Numerical simulations using the Weather Research andForecasting model verify that the enhanced land breeze ultimately leads to pronounced coastal rainfall off
southern Hainan at night. The results indicate that localized surface convergence associated with the sea andland breezes may be more important than the atmospheric humidity and convective instability inmodulating the diurnal cycle of rainfall for tropical islands.

1. Introduction
Precipitation on tropical and subtropical islands has a pronounced diurnal cycle that appears as a systematic phaseshift between the island and its surrounding sea. The rainfall over the island starts near its coastline in the afternoon and quickly reaches its peak in the early night. Around midnight, the rainfall moves from the land to thesurrounding water, reaching a peak in the early morning before dissipating around noon. This phenomenon
has been frequently documented for the Maritime Continent region, via surface observations (Houze et al.,
1981; Johnson & Priegnitz, 1981; Kamimera et al., 2012; Kanamori et al., 2013), satellite observations (Ichikawa& Yasunari, 2006; Keenan & Carbone, 2007; Kikuchi & Wang, 2008; Nesbitt & Zipser, 2003; Williams & Houze,
1987; Yamamoto et al., 2008; Yang & Slingo, 2001), and numerical simulations (Love et al., 2011; Sato et al., 2009).
In the tropics, the diurnal cycle of rainfall is considered as a key process in the variability of rainfall not only
on the diurnal scale itself but also on the synoptic, intraseasonal, and seasonal scales (Birch et al., 2016;
Ichikawa & Yasunari, 2006; Koh & Teo, 2009; Peatman et al., 2014; Seo et al., 2014; Slingo et al., 2003;
Vincent & Lane, 2016). However, the processes regulating the diurnal cycle of precipitating systems over tropical islands are complicated and poorly understood. Factors to consider, both individually and together,include sea and land breezes (Houze et al., 1981; Love et al., 2011; Miller et al., 2003; Sato et al., 2009;
Wapler & Lane, 2012), propagating gravity waves generated by convective instability (Kilpatrick et al.,2017; Mapes et al., 2003; Yokoi et al., 2017), and upslope ﬂows due to obstacles. Crucially, these processes
are believed to be signiﬁcantly inﬂuenced by the characteristics of the coastal ocean. Thus, greater knowledge of the inﬂuence of oceanic processes on the diurnal cycle of rainfall will improve our understandingof multiscale interactions in tropical and subtropical regions.

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The mesoscale structures of sea surface temperature (SST) associated with upwelling or fronts potentiallyinﬂuence the diurnal cycle of rainfall. Observational and numerical studies have shown that mesoscale
SST variations associated with fronts and eddies result in mesoscale variations in sea surface winds, cloudformation, and rainfall (Chelton, 2013; Czaja & Blunt, 2011; Kuwano‐Yoshida & Minobe, 2017; Minobe
et al., 2008; Nakamura et al., 2012; O'Neill et al., 2010; Perlin et al., 2014; Seo, 2017; Small et al., 2008;
Tanimoto et al., 2009; Xu et al., 2011). This is because the response of surface wind to mesoscale SST structures can modify the surface convergence ﬁeld, which is considered to play a fundamental role in convective
systems and rainfall (Kilpatrick & Xie, 2015; Mapes et al., 2009; Zipser, 1977). Previous studies have carefully
examined this air‐sea interaction between coastal upwelling and surface winds (Boé et al., 2011; Perlin et al.,2007), whereas related research on the diurnal cycle of rainfall has been limited.
The South China Sea (SCS) is an epicontinental marginal sea linking the western Paciﬁc Ocean and eastern
Indian Ocean (Figure 1a). The interaction between the basin gyre, monsoon, bathymetry, and tide generates
mesoscale oceanic processes, such as upwelling, fronts, and eddies, in the northern SCS (NSCS) throughout
the year (Chu & Wang, 2003; Hu et al., 2003; Shi et al., 2015; Wang et al., 2001). The inﬂuence of ocean fronts
and eddies on wind has been investigated in the NSCS region, and the major conclusions from these studies
are consistent with the positive correlation between SST and wind in the open ocean (Chow & Liu, 2012; Shiet al., 2017; Wang et al., 2012). A connection between ocean fronts and rainfall was also reported by Toy andJohnson (2014), who found that modiﬁcation of the convergence and water vapor transport by SST fronts
can signiﬁcantly change the spatial distribution of rainfall.
In summer, several coastal upwelling systems are identiﬁable in the northwestern SCS through observationsof low SST and high chlorophyll concentrations, associated with the dominant southwesterly monsoon. The
dynamical mechanism and biogeochemical effects of these upwelling systems have been described for thecoastal regions of Guangdong Province, the Taiwan Strait, and eastern Vietnam (Gan et al., 2009; Huet al., 2001; Shu et al., 2011; Wang et al., 2014; Xie et al., 2003). Su and Pohlmann (2009) and Jing et al.
(2015) studied the upwelling and upwelling‐induced thermal fronts around Hainan (Figure 1b) using satellite measurements and hydrological surveys. They concluded that the spatiotemporal characteristics of the
upwelling are strongly regulated by wind forcing, meaning that the annual variations in upwelling arehighly sensitive to changes in the large‐scale circulation. Their studies documented the atmospheric inﬂuences on the upwelling system at Hainan; however, the feedback of this upwelling to local rainfall, especiallyits diurnal variation, needs further study.
The diurnal cycle of rainfall in the SCS and southern China regions has been documented previously (Chenet al., 2013; Mao & Wu, 2012) in studies that noted the apparent diurnal variation of rainfall in coastal areas,
although their major conclusions emphasized the inﬂuence of large‐scale topography and monsoonal variability. In the present study, the local effect of coastal upwelling on the diurnal cycle of rainfall at Hainan isinvestigated using observations and numerical simulations. The analysis focuses on 2 months of data for
August 2010 and 2011 because an anomalous increase in offshore rainfall was observed at southern
Hainan in 2010, in sharp contrast to the reduced rainfall in 2011. A detailed investigation is required to determine whether the signiﬁcant change in rainfall intensity was caused by a change in atmospheric circulation
or by the regulation of coastal oceanic processes. The remainder of the paper is organized as follows.
Section 2 introduces the data and model, and the observed changes in the diurnal rainfall cycle are presented
in section 3. The change in synoptic conditions and coastal upwelling in 2010–2011 are analyzed in sections
4. As 2010 was the decaying summer of an El Niño event, we also look at three other El Niño events after
2000 in section 5. The results from the numerical simulation are presented in section 6. Finally, a summaryof the key ﬁndings is provided in section 7.

2. Data and Methods
2.1. Radar and Rainfall Data
We use monthly radar reﬂectivity data from the Hainan Meteorology Administration, China. The meteorological radar operates at 2700–3000 MHz and the accuracy of observation is 1 dBZ. The spatial resolution of
the observed reﬂectivity is 1 (2) km within a detecting radius of 230 (460) km. To further diagnose the activityof rainfall systems, we use the real‐time rainfall product (3B42RT, version 7) of the Tropical Rainfall
Measuring Mission (TRMM). This product is a combination of data observed by multiple passive
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Figure 1. (a) Map of the South China Sea and surrounding area, shading shows bathymetry; (b) monthly mean sea surface temperature (SST; color shading, °C) andsea surface wind (vectors, m/s) averaged for August from 2000 to 2017. The entire area in (a) is the outer domain of the numerical simulation with a resolution of
15 km, and the black rectangle in (a) denotes the inner domain with a resolution of 3 km. The red triangle in (b) is the location of Sanya station for radiosonde
observations. Blue dots indicate the 11 automatic weather stations.

microwave sensors, including the TRMM Microwave Imager, the Special Sensor Microwave Imager, theAdvanced Microwave Scanning Radiometer, and the infrared data observed by geosynchronous satellite.
The 3B42RT data cover the period from 2000 to the present. The 3B42RT data have a high spatial (0.25°)
and temporal (3‐hr) resolution and so are widely used to study the ﬁne‐scale features and diurnal cycle ofrainfall in tropical areas (Kikuchi & Wang, 2008; Sahany et al., 2010).

2.2. SST and Wind Data
Data from the Operational SST and Sea Ice Analysis (OSTIA) are used to investigate the variation in upwelling at Hainan. These data are produced by the U.K. Met Ofﬁce with support from the My Ocean project. The
OSTIA system makes extensive use of multiple satellite retrievals and assimilates in situ observations fromthe Global Telecommunications System to generate a global daily SST map with (1/20)° (~6‐km) resolution
since 2006. It is currently used operationally as the boundary condition for all weather forecast models at the
U.K. Met Ofﬁce and European Centre for Medium‐Range Weather Forecasts (Donlon et al., 2012). The highaccuracy and resolution of OSTIA data can be helpful in simulating the inﬂuence of mesoscale SST perturbations on the sea surface wind (O'Neil et al. 2010; Song et al., 2009), so it is used as the bottom boundary
condition for the atmospheric model in this study. The sea surface wind data observed by Quick
Scatterometer (QuikSCAT) and Advanced Scatterometer (ASCAT) are used in this study. QuikSCAT provides a more extensive geographical and temporal coverage and higher spatial resolution of the ocean vector
winds since 1999. After QuikSCAT ﬁnished its mission in October 2009, ASCAT continues to provide global
sea surface wind data. These two kinds of wind data provide a continuous observation of sea surface windover the SCS.

2.3. Radiosonde and Automatic Weather Station Data
The radiosonde data and automatic weather station (AWS) data are provided by the Hainan Meteorology
Administration, China. Seven years of data from 2008 to 2014 are used in this study. The radiosonde observations include pressure, geopotential height, temperature and dew point temperature, and wind direction
and speed. They are available at standard pressure levels from 1,000 to 200 hPa. In this study, the radiosonde
observations at Sanya station are analyzed because this is the closest station to the southern coastline ofHainan. In addition, the surface observations at 11 AWSs (Figure 1b) close to the coastline are used to validate the numerical simulation results, especially for the simulation of sea and land breezes. The AWSs arewithin 30 km of the coast, so the climatological mean wind hodographs of these AWSs show an apparent
diurnal cycle of sea and land breezes in August.
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2.4. Model Conﬁguration
The numerical simulations are carried out using version 3.9 of the Advanced Research version of the
Weather Research and Forecasting (WRF) model (Skamarock, 2008) under a two‐way nested conﬁguration
with horizontal resolutions of 15 and 3 km. The ﬁne‐resolution domain focuses on Hainan in the northwestern SCS (Figure 1a). Topography is interpolated from the Global Multi‐resolution Terrain Elevation Data
(2010) distributed by the U.S. Geological Survey. The vegetation ﬁeld is interpolated from the Combined
Fraction of Photosynthetically Active Radiation and the land category from the 21‐class category land use
data derived from Moderate Resolution Imaging Spectroradiometer data. Both domains are initialized by
European Centre for Medium‐Range Weather Forecasts interim reanalysis (ERA‐Interim) data at a resolution of 0.75° × 0.75° (Dee et al., 2011). The lateral boundary conditions for the large domain are updated by
ERA‐Interim data every 6 hr to allow large‐scale synoptic weather systems outside of the domain to propagate through the domain. The soil temperature and moisture data used in the simulation are from the
National Centers for Environmental Prediction FNL (Final) Operational Global Analysis data (1° × 1°).
The simulations are performed using 60 vertical sigma levels, including 25 levels below 1,000 m. A ﬁne vertical resolution is speciﬁed in the boundary layer to accurately simulate the vertical turbulent momentum
exchange in this layer. The Mellor‐Yamada‐Nakanishi‐Niino planetary boundary layer scheme (Nakanishi& Niino, 2006), the Noah land surface model (Chen & Dudhia, 2001), the Dudhia shortwave radiation
scheme (Dudhia, 1989), and a rapid radiative transfer longwave radiation scheme (Mlawer et al., 1997)
are set for both domains. The new Tiedtke (Tiedtke, 1989; Zhang et al., 2011) cumulus scheme and the
WRF Single‐Moment 6‐Class Microphysics Scheme (Hong & Lim, 2006) are set for the coarse domain(15 km). In the ﬁne‐resolution domain (3 km), the cumulus scheme is turned off and only the microphysics
scheme is retained, for better representation of organized convection at a ﬁner resolution. A 1‐month
simulation is carried out as a sequence of 3‐day integrations with an update of the initial conditions by
ERA‐Interim data after 3 days and then a restart for the following 3‐day integration. The SST and soil data
are reinitialized at 0000 UTC for each 3‐day integration.

3. Rainfall Difference Between 2010 and 2011
3.1. Monthly Mean Reﬂectivity and Rainfall
The radar observations show an apparent change in the spatial pattern of reﬂectivity at Hainan between
August 2010 and 2011. In August 2010, the meteorological radar observed two high‐reﬂectivity bands(Figure 2a): one over the northern part of the island with reﬂectivity around 300 dBZ and the other off
the southern coastline of Hainan with reﬂectivity around 200 dBZ. In contrast, only one main high‐reﬂectivity band is observed in the central area of Hainan in August 2011, and the reﬂectivity is higher over
the western (550 dBZ) than the eastern (300 dBZ) area (Figure 2b). The high‐reﬂectivity band off thesouthern coast of Hainan observed in 2010 almost disappears in 2011.
The radar echo maps indicate where convective systems occur, frequently in association with rainfall. As
presented in Figure 2, the merged satellite rainfall data (3B42RT) and radar data match closely, providing
a reliable snapshot of the convective activity in August 2010 and 2011 (Figures 2c and 2d). In August
2010, two major rainfall bands are apparent over the northern land area and southern ocean area off
Hainan (Figure 2c; 17.5–18.3°N, 108.5–110.5°E), coinciding almost exactly with the areas showing strong
radar echoes. However, the width of the rainfall band off the southern coast seems wider than the high‐reﬂectivity band. The maximum rainfall rates of these two major rainfall bands are almost the same, at about
0.6 mm/hr. The rainfall rate over most of the ocean region exceeds 0.25 mm/hr, except to the northeast of
Hainan. In August 2011, there is an abrupt weakening of reﬂectivity and rainfall, especially off the southerncoast of Hainan (Figure 2d). The intensity of rainfall over the sea falls below 0.15 mm/hr and the rainfall
band over land shifts to the central area of the island.
The signiﬁcance level of the rainfall anomaly in 2010 and 2011 is then examined using the climatologicalmean over 18 years from 2000 to 2017. The climatological mean rainfall in August is mostly concentrated
over land, with the rainfall near the southern coastline being relatively weak (Figure 3a). The rainfall
rate off the southern coast of the island in August 2010 is almost twice the 18‐year mean (0.50 vs.
0.25 mm/hr), making to apparent positive anomalies that pass the 95% signiﬁcance test (black dashedcontour in Figure 3b). Negative anomalies in rainfall rate occur over the whole study area in 2011,
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Figure 2. (a) Monthly mean reﬂectivity (dBZ) observed by meteorological radar in August 2010. (b) Observed reﬂectivity in August 2011. (c) Monthly mean rainfall
rate (mm/hr) derived from the Tropical Rainfall Measuring Mission 3B42RT dataset in August 2010. (d) The same as (c) but in August 2011.

but their magnitude does not pass the signiﬁcance test (Figure 3c). Thus, the positive anomaly in rainfall
off the southern coastline of Hainan in August 2010 is rare, warranting further study.
3.2. Diurnal Cycle of Rainfall
Since the monthly mean rainfall shows a signiﬁcant enhancement off the southern coast of Hainan inAugust 2010, this variation might also be imprinted on the diurnal cycle of rainfall. Thus, the diurnal evolutions of the monthly mean of the 3‐hourly interval rainfall rate are shown in Figure 4. A shift in the diurnal
cycle of rainfall between the land and the surrounding sea of Hainan is evident in 2010 (Figure 4a).
Nocturnal rainfall appears over the southern part of the island and in the offshore area after 2300 LST (15UTC). The extent and strength of the offshore rainfall reach their maximum values in the early morningand the rainfall gradually dissipates before 1100 LST. Conversely, rainfall over land starts over the northernpart of the island in the early afternoon (1400 LST). It moves slowly southward to the central and southernareas of the island and reaches its maximum before 1700 LST. The rainfall over land dissipates before 2000
LST and a clear sky is observed over land throughout the night. In August 2011, there is a signiﬁcant reduction in the extent and magnitude of the offshore rainfall compared with the diurnal cycle of rainfall inAugust 2010 (Figure 4b). The nocturnal rainfall band disappears off the southern coast of Hainan, and onlya small area of rainfall is observed before 0800 LST. The rainfall over land is also weaker in 2011 than in 2010
before 17 LST, especially in the northern Hainan. In addition, the rainfall over land persists until 2000 LST in2011 that is a longer than in 2010 (Figure 4c).
To quantify the contribution of the change in the diurnal cycle of rainfall to the monthly mean anomaly, theoccurrence frequency and intensity of rainfall are calculated over the area of signiﬁcant anomalies shown in
Figure 3b. Compared with the August climatological mean, the frequency and intensity of nocturnal rainfall
both increased in 2010 (Figures 5a and 5b). The average number of offshore rainy days is less than 12 days,
but it sharply increased to 22 days in 2010 (Figure 5a). In addition, the nocturnal rainfall rate increased to
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Figure 3. (a) Climatological rainfall rate (mm/hr) averaged in August from 2000 to 2017 from the Tropical Rainfall Measuring Mission 3B42RT data set. (b)Anomalies (mm/hr) for August 2010. (c) The same as (b) but for August 2011. (d) The difference between August 2010 and 2011. The black dashed contour in(b) indicates the signiﬁcance level (α = 0.05) of anomalies from the climatological mean.

1.8 mm/hr in 2010 compared with a climatological mean nocturnal rainfall rate of about 1.0 mm/hr(Figure 5b). The peak nocturnal rainfall rate occurred before 0500 LST in 2010, which is 3 hr earlier thanthe peak of the climatological mean. The average offshore rainfall rate is about 0.5 mm/hr in theafternoon and is not very different in 2010. The total number of rainy days varies a little from theclimatological mean in 2011, but the intensity of rainfall is weaker than the average over the whole day.

4. Analysis of the Synoptic Background
The radar and satellite observations show an apparent change in monthly mean rainfall at Hainan in August
2010, especially in the nearshore area off the southern coastline. Moreover, this change seems to be related tothe markedly higher offshore rainfall in the night and early morning. Previous studies have reported thatseveral processes are closely related to the occurrence and migration of nocturnal offshore rainfall near tropical islands, such as large‐scale circulation, and sea and land breezes (Houze et al., 1981; Wapler & Lane,2012). In this section, the inﬂuence of the synoptic background variation is analyzed.
4.1. Variation in Large‐Scale Circulation
According to the Oceanic Niño Index (ONI) distributed by NOAA (http://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php), an El Niño event occurred from 2009 to 2010 and was
followed by a strong La Niña event. The previous studies have documented El Niño–Southern Oscillation
events have a lagged impact on the SST and regional climate at SCS in the summer of the following year
(Du et al., 2009; Hu et al., 2017; Huang et al., 2000; Liu et al., 2014; Wang et al., 2000; Wu et al., 2014; Xie
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Figure 4. (a) Diurnal cycle of the monthly mean rainfall rate (mm/hr) derived from the Tropical Rainfall Measuring Mission 3B42RT data set at intervals of 3 hr inAugust 2010. (b) The same as (a) but in August 2011. (c) The differences between 2010 and 2011 (i.e., 2010 minus 2011). The numbers in the bottom right of thesubplots show the time (LST).

et al., 2009, 2010; Zhang et al., 2017). The change in atmospheric moisture induced by the variation in large‐
scale circulation in the SCS may be one possible reason for the anomalous intensiﬁcation of offshore rainfall
in 2010. Therefore, the moisture and thermal structure of the atmosphere are examined using ERA‐Interim
data on 850 and 500 hPa (Figure 6).
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The climatological (2000–2017) average for August has horizontally uniform speciﬁc humidity over the SCS, while the northwestern area ishigher than the southern area during August (Figures 6a and 6b). The
positive anomalies in speciﬁc humidity during August 2010 suggest anabundant water vapor supply in the troposphere over the SCS(Figures 6c and 6d), which may have produced more offshore rainfall
at Hainan. In contrast, negative anomalies appear over most of the
NSCS in August 2011, which suggests reduced humidity and rainfall at
Hainan (Figures 6e and 6f). The different wind anomalies at 850 and
500 hPa show the change of atmospheric circulation in August 2010
and 2011. In August 2010, there are easterly wind anomalies in the SCS
at 850 and 500 hPa, and the easterly wind anomalies change to southeasterly near Hainan (Figures 6c and 6d). In 2011, the 850‐hPa wind anomalies become very weak and even disappear near Hainan (Figure 6e). At
500 hPa, an anomalous anticyclone located over the Indochina
Peninsula and NSCS produces southeasterly wind anomalies over southern Hainan (Figure 6f).

Figure 5. (a) Total rainy days and b intensity of rainfall events in each phaseof the diurnal cycle. The rainy days are counted where the rainfall rate isnonzero over the area of signiﬁcant change shown in Figure 3b, while theintensity of rainfall is a spatial average. The black dashed lines denote theclimatological mean with ±1 standard deviation (vertical bars) from 2000 to2017. The red and blue lines denote the years 2010 and 2011, respectively.

As a supplement to the ERA‐Interim reanalysis data, we also examineradiosonde observations from Sanya station (red triangle in Figure 1b)
located on the southern coast of Hainan, where atmospheric conditions
are expected to be similar to the offshore area. The monthly mean proﬁles show that the speciﬁc humidity varies little (Figures 7a and 7b),but the relative humidity varies substantially (Figures 7c and 7d). The
diurnal variation in humidity is also small, although the relative
humidity is slightly higher in the early evening than in the morning(Figures 7c and 7d). In 2010, the relative humidity anomaly withrespect to the multiyear average is positive from the surface to about
450 hPa but negative in the upper air. In 2011, both speciﬁc humidity
and relative humidity are lower than the multiyear average from thesurface all the way through to the upper air. Therefore, the radiosonde
observations are further evidence for the signiﬁcant variation in atmospheric humidity near Hainan in 2010 and 2011. However, the temperature proﬁles for the 2 years show little difference from the multiyear
average (Figure 8).
4.2. Variation in Convective Instability

The anomalous wet conditions in 2010 seen in the reanalysis and radiosonde data require further discussion of the processes that release water
vapor and produce more convective rainfall. The convective instability of the environment is an importantfactor when investigating the potential occurrence frequency of rainfall. Therefore, we use the radiosonde
observations at Sanya to estimate the height of the lifting condensation level (LCL; hlcl), the convective available potential energy (CAPE), and convective inhibition (CIN). The hlcl is determined from the temperatureof the LCL (Tlcl), which is calculated following Bolton (1980):
T lcl ¼ ð1=ðT D −56Þ þ lnðT 0 =T D Þ=800Þ−1 þ 56;(1)

where T0 and TD are the temperature and dew point temperature at 1,000 hPa in kelvins, respectively. Then,the height can be simply estimated as follows:
hlcl ¼ ðT lcl −T 0 Þ=γ þ hT 0 ;(2)where γ = 9.8 × 10−3 K/m is the dry adiabatic lapse rate and hT 0 is the height of 1,000 hPa. CAPE is calculated
by integrating the positive buoyancy for a lifting saturated air parcel from the free convection height hlfc to
the equilibrium level hel:
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Figure 6. Monthly mean speciﬁc humidity (color shading, g/kg) and wind (vectors, m/s) at 850 hPa (left column) and
500 hPa (right column) from European Centre for Medium‐Range Weather Forecasts interim reanalysis data. The top
panels (a, b) show the climatological mean from 2000 to 2017; the middle panels (c, d) show the anomalies in August 2010;
and the bottom panels (e, f) show the anomalies in August 2011. The black dashed contour in (e) and (f) indicates thesigniﬁcance level (α = 0.05) of anomalies. Note the different color bars for (a) and (b).

hel

CAPE ¼ ∫hlfc gT v;parcel −T v;env;
T v;env(3)

where Tv, parcel and Tv, env are the virtual temperatures of a speciﬁc air parcel and the environment, respectively, and g is the gravitational constant.
Estimates of the convective parameters are given in Table 1. In general, the atmosphere is nearly convectively unstable at Hainan in summer, and the CAPE is larger in the early night (2000 LST) than in themorning (0800 LST; Table 1). The LCL is below 500 m in the early morning but increases by about 200 m
at night. Since the atmosphere is warm and wet near the sea surface (Figures 7 and 8), the value of CIN issmall over the whole day (Table 1). In general, the LCL height is lower in 2010 than in 2011 (Table 1), whichmeans a lifted air parcel will reach saturation more easily in 2010 than in 2011. The monthly mean CAPE,
which is determined by the area enclosed between the temperature and saturation‐adiabatic lifting trajectories upon LCL is around 2.0 × 103 J/kg, indicating that the coastal atmosphere is convectively unstable
most of the time. The CAPE is not very different in August 2010 and 2011, though it is even lower in 2010
than in 2011. This suggests that the atmosphere is more convectively unstable in 2011 than 2010 at the timeof observation, possibly because of the drier and colder upper air in 2011 (Figures 7 and 8), which led to
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(a)

(b)

(c)

(d)

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Figure 7. (a, b) Monthly mean speciﬁc humidity (g/kg) and (c, d) relative humidity (%) proﬁles observed at Sanya station.
The black dashed lines are the multiyear average from 2008 to 2014. The orange and green heavy lines denote thevalues for 2010 and 2011, respectively. The observation time is 0800 LST for the left‐hand column and 2000 LST for theright‐hand column.

larger CAPE in 2011 than in 2010. Another reason is that heavier rainfall will release more CAPE, so the
CAPE is smaller in 2010 than in 2011, especially in the early morning (0800 LST 2010, 1.82 × 103 J/kg in
Table 1).
The signiﬁcant enhancement in atmospheric moisture in 2010, as shown by the ERA‐Interim data, is clearlyshown on the Skew‐T map. Thus, the dew point depression in 2010 is much smaller than in 2011.

Figure 8. (a) Skew‐T map derived from radiosonde observations at Sanya station at 0800 LST; (b) Skew‐T map at 2000
LST. The solid lines indicate temperature and the dashed lines indicate dew point temperature. Red and blue colors
denote observations in August 2010 and 2011, respectively. Black solid and dashed lines are the mathematical mean of thetemperature and dew point temperature proﬁles in August from 2008 to 2014. The magenta lines denote the dry andsaturation‐adiabatic lifting trajectories of an idealized air parcel starting from the average proﬁle.

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Table 1Mathematical Mean of the LCL Temperature (Tlcl), LCL Height (hlcl), CAPE, and CIN Calculated From the Twice‐Daily Radiosonde Observations in August 2010 and2011 at Sanya
Tlcl (K)3hlcl (m)CAPE (×10 J kg

−1)

CIN (J kg−1)

Year\Time

08 LST

20 LST

08 LST

20 LST

08 LST

20 LST

08 LST

20 LST

2010
2011
Mean

296.48296.37297.05296.26295.50296.91451.68501.01484.80667.72742.66671.63

1.82 ± 0.57
2.18 ± 0.82
2.00 ± 0.78

2.14 ± 0.57
2.36 ± 0.69
2.26 ± 0.76

8.44 ± 15.78
10.68 ± 0.82
15.46 ± 0.78

9.25 ± 12.00
10.74 ± 9.80
17.09 ± 13.10

Note. The standard deviation (numbers after “±”) is also shown for CAPE and CIN. The mean values are calculated over 7 years from 2008 to 2014. Note thedifferent units for CAPE and CIN. CAPE = convective available potential energy; CIN = convective inhibition; LCL = lifting condensation level.

Nonetheless, the difference in atmospheric moisture does not produce a very large change in CAPE between
2010 and 2011. This may be because the positive CAPE is integrated upward from hlcl, which is mainly set by
the surface air temperature and humidity (equations (1) and (2)). These two parameters are not very different in 2010 and 2011, so the CAPE is also similar. The monthly mean CAPE is higher at 2000 LST than at
0800 LST, while there is more offshore rainfall at 0800 LST (Table 1). This suggests that the energy available
from convective instability does not turn into rainfall immediately but a daily alternative process triggers thereleasing of the unstable energy. In other words, lifting processes associated with sea or land breeze may be
more important than convective instability in modulating the diurnal cycle of rainfall near the island.

5. Atmospheric Circulation and Coastal Upwelling
5.1. Variation in Surface Wind and Upwelling
The anomalous atmospheric circulation in the summer of 2010 not only changed the humidity of the upper
air but also the surface wind and signiﬁcantly modulated the coastal upwelling near Hainan in 2010. The
variation in coastal upwelling appears to have been associated with a change in SST (Figure 9). In 2010,
the prevailing southeasterly monsoon dominated the northwestern SCS. The weak alongshore wind is unfavorable for the development of upwelling off southern Hainan, and only weak upwelling is observed along
the eastern and western coastal sea (Figure 9a). Since the upwelling of cold water is suppressed, the coastal
SST off the southern coastline of the island exceeded 29 °C in 2010 (Figure 9a). In contrast, the upwellingsystem developed fully to the south and east of Hainan in 2011 (Figure 9b). The prevailing wind changed
to southerly and southwesterly nearshore, which favored upwelling. Thus, the nearshore SST fell to about
28 °C because of the upwelling of cold water (Figure 9b).
The variation in SST due to upwelling resulted in a marked change in the thermal contrast between land andocean. This led to a corresponding adjustment of sea and land breezes, which is closely related to the diurnal

Figure 9. (a) Monthly mean sea surface temperature (color shading, °C) and wind ﬁeld (vectors, m/s) in August 2010. (b)The same as (a) but in August 2011. The black dashed line in (a) encloses the region over which the upwelling index
UIΔSST, and total offshore rainfall are calculated.

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cycle of rainfall on tropical islands. Warmer water near the southerncoastline of the island may produce a stronger land breeze at night, leading to the enhanced rainfall in August 2010. Likewise, colder water off thecoast of the island may maintain sea breeze in a longer time, leading tomore rainfall in the late afternoon. To examine this hypothesis, the correlation between atmospheric circulation and upwelling intensity is further
examined for other weak upwelling years.
5.2. Correlation between Upwelling Intensity and Diurnal Cycleof Rainfall
To quantify the variability in upwelling intensity after 2000, an upwelling
index UIΔSST is deﬁned as the difference in SST between the coast and theocean, as follows:
UIΔSST ¼ −ðSSTcoast −SSTocean Þ;(4)where SSTcoast is the average SST of grid cells that are closest to the coastline (offshore distance of <5 km) and SSTocean is the average SST of gridcells that are about 100 ± 5 km from the coastline. Therefore, an increase
in UIΔSST corresponds to an increase in upwelling intensity.
The upwelling index for the coastal region south of Hainan shows anapparent year‐to‐year variation in upwelling intensity from 2000 to
2018. The upwelling index is relatively small in the summers of 2003,
2010, and 2016, when the El Niño event is decaying (Figure 10a). The
upwelling index UIΔSST decreases to about 0.6 in August 2003, whichshows a similar depressed upwelling as August 2010 (UIΔSST ≈ 0.66).
The upwelling index is 0.9 in August 2016, which is slightly larger than
the index values in 2003 and 2010 but is still smaller than the indexes ofpeak years. Summer 2005 is a different case: The upwelling intensity iscomparable to that of normal years, although it is the decaying summerof the 2004/2005 El Niño. In general, the decreases in the upwelling index
in 2003, 2010, and 2016 are caused mainly by the increase in coastal SST(~29.5 °C), because the correlation coefﬁcient between SSTcoast andUIΔSST is −0.58, while the correlation coefﬁcient between SSTocean and
UIΔSST is only 0.10 (Figure 10b).

Figure 10. (a) Monthly Oceanic Niño Index (ONI) from National Oceanicand Atmospheric Administration (color shading) and upwelling index
UIΔSST (gray dashed line) from 2000 to 2017. (b) Upwelling index UIΔSST(gray dashed line), sea surface temperature (SST) near coast (blue line),and SST of open ocean (magenta line) from 2000 to 2017. (c) Anomalies intotal nocturnal rainfall (0200–0800 LST) for August from 2000 to 2017.
(d) The same in (c) but for daytime rainfall (1400–2000 LST). The deﬁnitionof UIΔSST is given in section 6. UIΔSST and total rainfall are calculated forthe region shown in Figure 9a. Anomalies are calculated as the monthly
mean minus the multiyear average from 2000 to 2017.

Then, the correlation between upwelling intensity and the diurnal cycle ofrainfall is investigated in 2003, 2006, and 2010. There is clearly an asymmetric modulation of the nocturnal and daytime rainfall by the upwellingin the 2003 and 2010 cases (Figures 10c and 10d). The decrease in upwelling intensity coincides well with a signiﬁcant increase of nocturnal offshore rainfall, and the strongest effect appears in 2010 (Figures 10a and10c). At the same time, the weak upwelling does not lead to an apparent
increase in daytime rainfall (Figure 10d). This suggests that the land‐sea
thermal contrast, modiﬁed by the upwelling, has different effects on thesea and land breeze circulation, which may then regulate the convective
rainfall during daytime and night. The modulation of nocturnal rainfall
by upwelling intensity is not signiﬁcant in 2016, may because UIΔSST issmaller than the cases of 2005 and 2010.

The variability in upwelling intensity is closely related with the pattern ofatmospheric circulation in the decaying summer of El Niño years, butthere are still some uncertainties due to the complexity of El Niño itself. The location of the region of warm
SST anomalies in the Paciﬁc deﬁnes two types of El Niño: the eastern‐Paciﬁc (EP) El Niño and central‐
Paciﬁc (CP) El Niño (Ashok et al., 2007). The CP El Niño can be further classiﬁed into two cases by the

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(a)

(b)

(c)

(d)

10.1029/2018JD029528

Figure 11. Sea level pressure (SLP) anomalies (Pa, color shading) and 850‐hPa wind anomalies (vectors, m/s) in the decaying summer (June–August [JJA]) of El
Niño events in (a) 2002/2003, (b) 2004/2005, (c) 2009/2010, and (d) 2015/2016. Anomalies are calculated as the monthly mean minus the multiyear summer average
from 2000 to 2017. The magenta contours indicate where the value of anomalies exceeds the 95% signiﬁcance level (t test).

different origins and patterns of SST anomalies (Wang & Wang, 2013). Accordingly, the 2002/2003 El Niño isclassiﬁed as CP El Niño I, while 2004/2005 and 2009/2010 are classiﬁed as CP El Niño II, and 2015/2016 is
the traditional EP El Niño. In the decaying summers of these four El Niño events, the atmosphericcirculation in the western Paciﬁc and SCS shows different patterns (Figure 11). For CP El Niño I, anoblate anticyclone resides over the NSCS and the Philippine Sea. This anomalous anticyclone produced
southerly or southeasterly wind anomalies in 2003 and thus inhibited upwelling (Figure 11a). For CP El
Niño II, the anomalous anticyclone shifted to the east of the Philippines (Figure 11b), leading to southwesterly wind anomalies that maintained normal upwelling at Hainan in 2005. In 2010, two strong anticyclones
are identiﬁed in the NSCS and east of Japan (Figure 11c). The anomalous anticyclone over the NSCS produced strong southeasterly wind anomalies that suppressed the development of coastal upwelling. In the traditional EP El Niño, although an anomalous cyclone appears southeast of Japan, there are southeasterly
wind anomalies at south Hainan that weaken the upwelling intensity (Figure 11d). Thus, there is a decrease
in upwelling intensity when southeasterly wind anomalies occurs near southern Hainan, which could
potentially change the diurnal cycle of rainfall at this area.

6. Numerical Simulations of the Modulation of Offshore Rainfall by Upwelling6.1. Results of Control Run
The inﬂuence of upwelling on the island's rainfall is examined further through numerical simulations.
Control runs using realistic SSTs and initial and boundary conditions were carried out in August 2010
and 2011. The simulated winds are ﬁrst validated against the observations at the 11 AWS stations(Figure 1b) close to the southern coastline of Hainan. Sea and land breezes are clearly shown on the multiyear mean wind hodograph for August at these AWSs. Therefore, it is appropriate to validate the simulated
sea and land breezes against the observations (Figure 12). In general, the WRF simulations overestimate themean wind speed by 0.8–1.2 m/s and mean wind direction by 11–26° (Table 2). The observations show anorthwesterly land breeze (WNW‐NNW) and southwesterly sea breeze (SW‐WSW) in 2010. The positive
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Figure 12. (a) Monthly mean winds (m/s) from automatic weather stations (AWSs; blue arrows) and the Weather
Research and Forecasting (WRF) simulations (red arrows) at 0200 LST and 1400 LST (b) in August 2010, and the samein August 2011 (c, d).

differences in the wind directions at 0200 LST indicate that the simulated land breeze is blowing more
southward (offshore, Figure 12a), and the negative differences at 1400 LST indicate that the simulated sea
breeze is blowing more northward (onshore, Figure 12b). In addition, the observations show a stronger
land breeze but weaker sea breeze in 2010 than in 2011 (Table 2). The WRF simulations produce thesame change in the magnitudes of sea and land breezes in 2010 and 2011. Therefore, the WRF simulation
could capture the major features of the sea and land breezes over southern Hainan and may be used forthe further analysis of the modulation of coastal rainfall by the sea and land breezes.
The simulated rainfall is validated against 3B42RT data. The WRF simulation in August 2010 shows a similar spatial pattern of monthly mean rainfall to that derived from the 3B42RT dataset (Figure 13a). Heavy
rainfall over Hainan and its surrounding area is simulated by the WRF model. The rainfall band over thesouthern and southeastern coastal sea is identiﬁable in the WRF simulation. However, compared with themerged satellite observations, the WRF simulation shows much stronger rainfall, over both land and sea.
The intensity of rainfall in the WRF simulation is generally about 0.2–0.4 mm/hr higher than that in the
3B42RT data over the sea. There also seems to be a southward shift in the center of rainfall over land in
the WRF simulation.
The WRF simulation gives a signiﬁcant weakening in rainfall intensity over the sea in August 2011
(Figure 13b). The rainfall rate falls down from more than 0.8 mm/hr in 2010 to about 0.4 mm/hr in 2011 overthe sea. The rainfall over land is also weakened in most areas, except in the southwest of the island(Figures 13b and 13c). Although the WRF simulations produce stronger rainfall than observed, theSHI ET AL.

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Table 2
Validation of Simulated Sea and Land Breezes in the Control Runs for 2010 and 2011
02 LST Aug. 2010

02 LST Aug. 2011

AWS
no.

W0 (m/s)

W0_dif (m/s)

Wdir

Wdir_diff (°)

W0 (m/s)

W0_dif (m/s)

Wdir

Wdir_diff (°)

M01
M02
M03
M04
M05
M06
M07
M08
M09
M10
M11
Average

1.65
1.85
1.67
0.74
1.01
1.37
1.1
0.43
1.16
0.55
2.02
1.23

1.17
−0.05
0.18
1.75
0.48
1.18
0.39
1.05
0.8
1.46
0.23
0.78

NNW
WNW
NNW
NW
NW
NNW
WNW
NNW
WSW
WSW
NW
—

23.29
39.12
32.62
27.81
40.92
17
40.46
25.92
−12.42
−21.7
80.67
26.70

0.73
0.94
1.56
0.59
0.71
1.04
0.7
0.15
1.04
0.39
1.37
0.83

1.92
0.5
0.19
1.7
0.56
1.34
0.83
1.34
0.94
1.75
0.7
1.07

NW
WNW
NW
W
WNW
NW
W
WNW
SW
WSW
NNW
—

21.25
7.08
31.52
48.06
12.87
23.53
44.32
37.27
−24.69−92.2813.4
11.12

Wdir_diff (°)

W0 (m/s)−12.67−11.99−15.57
−43.2
17.67
2.22
−9.84
−0.09
−64.08−12.73−32.65−16.63

2.85
2.98
1.69
2.68
4.23
2.02
2.92
2.78
3.17
2.64
2.97
2.82

W0 (m/s)
M01
M02
M03
M04
M05
M06
M07
M08
M09
M10
M11
Average

2.97
3.09
1.59
2.75
3.34
2.07
3.15
2.39
2.99
2.67
2.82
2.71

14 LST Aug. 2010
W0_dif (m/s)
Wdir
1.14
0.68
2.23
1.16
0.3
1.89
0.42
1.44
1.34
1.28
1.42
1.20

WSW
SW
WSW
WSW
SW
WSW
SW
SW
WNW
WSW
WSW
—

14 LST Aug. 2011
W0_dif (m/s)
Wdir
1.38
1.02
2.44
1.23
−0.38
2.11
0.53
1.17
1.24
1.64
1.34
1.24

SSW
WSW
SSW
WSW
SSW
SW
SSW
SSW
WSW
WSW
SSW
—

Wdir_diff (°)
16.41−63.630.49−53.76
11.39−17.85−28.01−30.04−25.05−30.32−0.47−20.07Note. WRF simulations are validated by the observations of 11 AWSs close to the southern coastline of Hainan (Figure 1b). W0 and Wdir denote the observed
monthly mean wind speed and meteorological wind direction, respectively. W0_dif and Wdir_diff are the differences between the simulation results and observations (i.e., WRF minus observation). The simulated wind speed and directions are validated at 0200 LST and 1400 LST to examine the strength of land and sea
breezes, respectively. AWS = automatic weather station; WRF = Weather Research and Forecasting.

differences in rainfall intensity and spatial pattern between August 2010 and 2011 are mostly captured by the
WRF simulations. The variation in the diurnal cycle of rainfall is also simulated by the WRF model. The
diurnal cycle in the control runs not only shows an apparent enhancement in the nocturnal offshore
rainfall southeast of Hainan in 2010 (Figure 14) but also a stronger increase in offshore rainfall in theafternoon than seen in the TRMM data. This means that the increase in rainfall off southern Hainan
shown in Figure 13c may be overestimated. Note that the diurnal cycle of the WRF simulation shows asoutheastward shift in the offshore rainfall band from 0500 to 0800 LST (Figure 14), which seems to be a
gravity‐wave propagation process associated with the mesoscale convective systems that occurred in 2010;
this will be studied further in future work.

6.2. Sensitivity Experiments
Since the control runs simulate the major feature of the variation in rainfall around Hainan in August 2010
and 2011, three additional experiments are carried out to examine the inﬂuence of upwelling on the rainfall
over Hainan, especially its diurnal cycle. The ﬁrst experiment uses the SST in August 2011 (normal upwelling) to carry out the simulation for August 2010 (hereafter the 2010S run). The second experiment uses the
SST in August 2010 (weak upwelling) to carry out the simulation for August 2011 (2011S run), and the thirdis also run for 2011 but with the SST being 1 K higher than in the 2011S run (2011SP1 run). In the sensitivity
experiments, only the SST in the inner domain is changed; the other model parameters, as well as the initialand boundary conditions, remain the same as in the control run.
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(a)

(b)

10.1029/2018JD029528(c)Figure 13. (a) Monthly mean rainfall rate (mm/hr) from the control run of the Weather Research and Forecasting (WRF) simulation in August 2010. (b) The sameas (a) but in August 2011. (c) The difference between 2010 and 2011 (i.e., 2010 minus 2011).

The 2011S results are discussed ﬁrst because the inﬂuence of upwelling intensity on the monthly mean rainfall can be identiﬁed very clearly in this case (Figure 15a). Compared with the control run in August 2011
(Figure 13b), the warm coastal SST due to weak upwelling leads to an apparent increase in the rainfall off
the southern coast (Figure 15b). The intensity of rainfall over land is generally reduced, except over someareas in the north of the island (Figure 15b). The response of the diurnal cycle of rainfall to the weak upwelling is also simulated well in 2011S. Nocturnal rainfall intensity is signiﬁcantly enhanced off the southerncoast from 0200 LST to 0800 LST, while the change in offshore rainfall intensity at other times is not signiﬁcantly larger than the background value over the sea (0.2 mm/hr, Figure 16). The southern offshore rainfall
is weakened at 1100 LST and the rainfall over land in the north of the island is weakened at 1700 LST. The
variation in offshore rainfall and rainfall over land in 2011S suggests that the variation in upwelling intensity
could inﬂuence the diurnal cycle of rainfall at south Hainan, and this process may be closely related to thesea and land breeze circulation.
The modulation of the land breeze by the upwelling is further investigated in the 2011S case. The results ofthe control run show that the surface wind is generally northward over the sea in the middle of the night(black vectors in Figures 17a and 17b). The surface wind turns to blow along the coastline as it approaches
the nearshore area off the island because of the effect of the land breeze. The land breeze does not reverse thedirection of the onshore wind but rotates the nearshore wind direction clockwise off the southeastern coast
and anticlockwise off the southwestern coast (red vectors in Figures 17a and 17b). The sensitivity run 2011S

Figure 14. Diurnal cycle of rainfall rate difference (mm/hr) between the control runs of August 2010 and 2011 (i.e., 2010 minus 2011).

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Figure 15. (a) Monthly mean rainfall rate (mm/hr) from the sensitivity simulation of 2011 with 2010 SST (2011S). (b) The difference between the sensitivity simulation and the control run results. Contour lines in (b) denote the sea surface temperature difference in the sensitivity simulations from the values in the control run.
WRF = Weather Research and Forecasting.

simulates a much stronger land breeze (red vectors in Figures 17c and 17d) due to the increase in SSTcoast
associated with weak upwelling. The wind is blowing offshore and almost perpendicular to the coastline
as it approaches the southern coastal sea. The land breeze gradually strengthens from the coastline andreaches its maximum magnitude over the sea a few kilometers away (Figure 17e and 17f), where theenhanced land breeze—due to weak upwelling—produces stronger convergence at the surface that may
eventually lead to a synchronous intensiﬁcation of rainfall shown in Figure 16.
The warm SSTcoast due to weak upwelling enhances the land‐ocean thermal contrast at night but reduces it
during the daytime. As a result, the land breeze is intensiﬁed but the sea breeze is weakened on the land. The
simulated surface winds in the 2011 control run generally blow northeastward over the sea in the afternoon,
and a landward rotation in wind direction occurs near the coastline of the island due to the onshore sea
breeze (black vectors in Figures 18a and 18b). It can be seen that the landward rotation of the prevailingwind is weakened in the 2011S case (red vectors in Figures 18a and 18b). Warm SSTcoast in the 2011S case

Figure 16. Diurnal cycle of rainfall rate difference (mm/hr) calculated for the sensitivity experiment minus the control run for August 2011. Black contours indicate
the sea surface temperature difference between the sensitivity and control runs; solid contours indicate an increase in sea surface temperature and weakening of
upwelling in the sensitivity experiment.

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Figure 17. (a, b) Monthly mean surface wind (m/s, vectors) and convergence ﬁeld (s , color shading) from the Weather Research and Forecasting simulations of
the control run and sensitivity run for August 2011 at 0200 LST and 0800 LST. (c, d) Monthly mean land breeze (m/s, calculated by removing the daily meanfrom the hourly wind). (e, f) The difference of land breeze and convergence (i.e., sensitivity run minus control run). The black vectors in (a–d) are the results of thecontrol run and the red vectors denote the sensitivity run with weak upwelling in 2011 (2011S). SST = sea surface temperature.

reduces the land‐ocean thermal contrast nearshore, so the sea breeze is weakened in the area near the coastline (Figures 18c and 18d). The weakened sea breeze reduces the strength of surface wind convergence in theinland area (Figures 18e and 18f), which leads to a reduction in rainfall over land during the afternoon(Figure 16). There is an increase in the sea breeze over the sea away from the coastline in the 2011S case.

Figure 18. As in Figure 17 but for 1700 LST and 2000 LST. WRF = Weather Research and Forecasting.

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Figure 19. (a) Diurnal cycle of the spatially averaged rainfall rate in the coastal region delineated by the dashed line in
Figure 9a. (b) Spatially binned nocturnal (0200–0800 LST) rainfall rate versus the distance from the southern coastline
of Hainan. Red solid (dashed) lines in (a) denote the results of the control (sensitivity) run for 2010, while the blue lines
show the results for 2011. The brown dashed line represents the results of the sensitivity experiment 2011SP for 2011that uses the sea surface temperature of August 2010 increased by 1 K. Colors in (b) refer to the same control and sensitivity simulations.

Figure 20. Schematic summary of this study. SST = sea surface temperature.

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It is caused by the modulation of local SST on the hourly wind and daily mean wind both and has little
inﬂuence on the convergence on the land.
The sensitivity experiment with normal upwelling in 2010 (2010S) simulates stronger nocturnal rainfall thanthe control run over the coastal region of south Hainan (Figure 19a). This seems to contradict the previous
result. However, there is a signiﬁcant difference in the maximum rainfall area simulated by the control and2010S runs. The maximum rainfall simulated by the control run of 2010 occurs in the region from 10 to
20 km offshore (orange bars in Figure 19b). In contrast, the 2010S run simulates maximum rainfall in theregion that is nearly 30 km from the coastline (green bars in Figure 19b). Therefore, the rainfall processes
in these two cases may be different. The main reason for the “intensiﬁed rainfall” in the 2010S case is theadjustment of the wind ﬁeld to the artiﬁcial upwelling. In the 2010S case, the southeasterly sea surface wind
still blows onshore from warm water to cold water across the upwelling zone. The wind speed is reduced at
the cold side of the SST front associated with upwelling (ﬁgures not shown) because of the positive SST‐windcoupling process mentioned in the introduction part. Thus, there is a convergence zone that is suppressed byenhanced vertical motion and rainfall over the frontal area (Chelton et al., 2004; Minobe et al., 2008; Small
et al., 2008; Toy & Johnson, 2014), and this process may confound the effects of the land breeze in the
2010S run.
The 2011SP1 run simulates the most intense rainfall of the ﬁve simulations (Figure 19a). Note that the intensiﬁcation of rainfall is much stronger in the nighttime than the daytime. The nocturnal rainfall rate in
2011SP1 is almost double that in 2011S whereas the daytime rainfall rate is only slightly different(Figure 19a). This asymmetric intensiﬁcation of rainfall further illustrates the importance of the land breeze
in modulating the nocturnal rainfall. In addition, the positive effect of warming SST on rainfall decays with
distance offshore. The most evident enhancement of rainfall simulated in 2011SP1 is restricted to an area
within 70 km of the coastline. At offshore distances greater than 70 km, the rainfall in the 2011SP1 case iscomparable to that in the control run for 2010 (Figure 19b). This shows that the inﬂuence of the local SST
change is limited, although it may signiﬁcantly change the surface moisture ﬂux and coastal rainfall. Thechange in local SST can regulate the diurnal cycle of rainfall over an island according to its inﬂuence on
mesoscale processes such as sea or land breezes, but it does not have a similar impact on the regional climate
as the midlatitude SST front does (Kuwano‐Yoshida & Minobe, 2017; Small et al., 2014).

7. Discussion and Conclusions
The modulation of coastal rainfall at Hainan, South China, by the large‐scale circulation and coastal upwelling has been studied using observations and numerical simulations. Both radar observations and the
TRMM 3B42RT data show a pronounced difference in the monthly mean rainfall off the southern coast of
Hainan between August 2010 and 2011, with much greater rainfall in the former case than the latter.
With respect to an 18‐year average of TRMM 3B42RT data, there are signiﬁcant positive anomalies in offshore rainfall during August 2010 but negative anomalies in 2011. Furthermore, the main contributor to
the anomalously high offshore rainfall in August 2010 is the intensiﬁcation of rainfall at night.
ERA‐Interim and radiosonde data have been used to investigate the relationship between the atmosphericcirculation and the change of rainfall intensity in August 2010 and 2011. Compared with the multiyear(2000–2017) average ﬁeld, there is an apparent increase in the water vapor concentration from the surface
to 500 hPa in 2010. There were easterly monsoon anomalies over the SCS and western equatorial Paciﬁcin August 2010, and these anomalies were much stronger in 2010 than in other years when a decaying El
Niño was conﬁrmed. The anomalous atmospheric circulation leads to wet anomalies over the SCS in 2010
and produces a general intensiﬁcation of total rainfall over Hainan and its surrounding area. This result
differs from previous ﬁndings that there are dry anomalies over the SCS in the decaying summer of
El Niño events because of the anomalous anticyclone over the western Paciﬁc (Tao et al., 2016).
The analysis of convective instability shows that the variation in large‐scale circulation results in littlechange in the CAPE during August 2010 and 2011 at Hainan. However, the change in atmospheric circulation does modulate the coastal upwelling system at Hainan. An upwelling index UIΔSST is deﬁned to quantify the annual variation of upwelling intensity since 2000. Decreases in upwelling intensity were conﬁrmed
in the decaying summers of the 2002/2003, 2009/2010, and 2015/2016 El Niño but not in the 2004/2005
El Niño. The anomalous onshore‐blowing wind associated with southeasterly wind anomalies depresses
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the development of upwelling and a warm SST appears off southern Hainan. Meanwhile, the warm SST near
the coast increases the land‐sea thermal contrast and changes the strength of the sea and land breezes, which
results in an increasing in nocturnal offshore rainfall but little change in daytime offshore rainfall in thesummers of 2003 and 2010. The increase in nocturnal rainfall during the other two El Niño events is not
signiﬁcant, possibly because upwelling intensity is not apparently weakened.
The modulation of the surface wind convergence by the upwelling may be the major reason for the variationin the diurnal cycle of rainfall, and this modulation has been examined using numerical simulations. The
sensitivity experiment of the WRF simulation for August 2011 shows that the warm sea surface due to weak
upwelling produces a stronger land breeze and surface convergence over the sea, ultimately leading to an
intensiﬁcation of rainfall off the southern coast of Hainan at night (Figure 20d). Weak upwelling also
reduced the strength and extent of the sea breeze, causing weak convergence and less inland rainfall(Figure 20c). In contrast, well‐developed upwelling suppressed (enhanced) the land (sea) breeze, producing
less offshore rainfall but more inland rainfall for a normal year (Figures 20a and 20b). Although directconvection forcing by warm SST anomalies could increase rainfall intensity, the surface convergenceinduced by sea or land breeze is a more important process to be considered, especially for the nocturnal rain
off the coastline of a tropical island.
Therefore, our ﬁnal conclusion is that the anomalous atmospheric circulation in the decaying summer of
El Niño events is usually unfavorable for the developing of upwelling off southern Hainan. As a result,the diurnal cycle of rainfall, which is signiﬁcantly regulated by the upwelling intensity, is also changed
around Hainan. It is also noticed that there are still some uncertainties due to the complexity of El Niño itself
and the processes in the coastal sea (Li et al., 2018; Su et al., 2013), which needs more studies in the future.
Acknowledgments
All data used in this study are available
at https://pmm.nasa.gov/data‐access/downloads/trmm for TRMM
precipitation, https://apps.ecmwf.int/
datasets/data/interim‐full‐daily/
levtype=sfc/ for ERA‐Interim
reanalysis data, http://ghrsst‐pp.
metofﬁce.com/pages/latest_analysis/
ostia.html for OSTIA SST, http://apdrc.
soest.hawaii.edu/data/data.php?
discipline_index=3 for QuikSCAT &ASCAT wind, https://rda.ucar.edu/
datasets/ds083.2/index.html#!
description for soil temperature andmoisture data, and https://doi.org/
10.6084/m9.ﬁgshare.7447166.v4 forradiosonde and numerical simulation
results. This research was supported by
the Frontier Science Research Project ofChinese Academy of Sciences (Grant
QYZDJ‐SSW‐DQC022), the National
Natural Science Foundation of China(Grants 41676018, 41476014, and41576002), the Science and Technology
Planning Project of Guangdong
Province, China (2016A020224003),
and the Ministry of Education, Culture,
Sports, Science and Technology, Japan(MEXT) to a project on Joint
Usage/Research Center‐Leading
Academia in Marine and Environment
Pollution Research (LaMer). The
numerical simulation was run with thesupport of the High‐Performance
Computing Division of the South China
Sea Institute of Oceanology and Ms.
Dandan Sui.

SHI ET AL.

References
Ashok, K., Behera, S. K., Rao, S. A., Weng, H., & Yamagata, T. (2007). El Niño Modoki and its possible teleconnection. Journal ofGeophysical Research, 112, C11007. https://doi.org/10.1029/2006JC003798
Birch, C. E., Webster, S., Peatman, S. C., Parker, D. J., Matthews, A. J., Li, Y., & Hassim, M. E. E. (2016). Scale interactions between the MJO
and the western maritime continent. Journal of Climate, 29(7), 2471–2492. https://doi.org/10.1175/JCLI‐D‐15‐0557.1
Boé, J., Hall, A., Colas, F., McWilliams, J. C., Qu, X., Kurian, J., & Kapnick, S. B. (2011). What shapes mesoscale wind anomalies in coastal
upwelling zones? Climate Dynamics, 36(11–12), 2037–2049. https://doi.org/10.1007/s00382‐011‐1058‐5
Bolton, D. (1980). The computation of equivalent potential temperature. Monthly Weather Review, 108(7), 1046–1053. https://doi.org/
10.1175/1520‐0493(1980)108<1046:TCOEPT>2.0.CO;2Chelton, D. B. (2013). Ocean‐atmosphere coupling: Mesoscale eddy effects. Nature Geoscience, 6(8), 594–595. https://doi.org/10.1038/
ngeo1906
Chelton, D. B., Schlax, M. G., Freilich, M. H., & Milliff, M. H. (2004). Satellite measurements reveal persistent small‐scale features in ocean
winds. Science, 303(5660), 978–983. https://doi.org/10.1126/science.1091901
Chen, F., & Dudhia, J. (2001). Coupling an advanced land surface hydrology model with the penn state ncar mm5 modeling system. Part I:
Model implementation and sensitivity. Monthly Weather Review, 129(4), 569–585. https://doi.org/10.1175/1520‐0493(2001)129<0569:
CAALSH>2.0.CO;2Chen, G., Sha, W., Sawada, M., & Iwasaki, T. (2013). Inﬂuence of summer monsoon diurnal cycle on moisture transport and precipitation
over eastern China. Journal of Geophysical Research: Atmospheres, 118, 3163–3177. https://doi.org/10.1002/jgrd.50337
Chow, C. H., & Liu, Q. (2012). Eddy effects on sea surface temperature and sea surface wind in the continental slope region of the northern
South China Sea. Geophysical Research Letters, 39, L02601. https://doi.org/10.1029/2011GL050230
Chu, P. C., & Wang, G. H. (2003). Seasonal variability of thermohaline front in the central South China Sea. Journal of Oceanography, 59(1),65–78. https://doi.org/10.1023/A:1022868407012
Czaja, A., & Blunt, N. (2011). A new mechanism for ocean–atmosphere coupling in midlatitudes. Quarterly Journal of the Royal
Meteorological Society, 137(657), 1095–1101. https://doi.org/10.1002/qj.814
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., et al. (2011). The era‐interim reanalysis: Conﬁguration andperformance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656), 553–597. https://doi.org/
10.1002/qj.828
Donlon, C. J., Martin, M., Stark, J., Roberts‐Jones, J., Fiedler, E., & Wimmer, W. (2012). The Operational sea surface temperature and sea
ice analysis (OSTIA) system. Remote Sensing of Environment, 116(2), 140–158. https://doi.org/10.1016/j.rse.2010.10.017
Du, Y., Xie, S., Huang, G., & Hu, K. (2009). Role of air–sea interaction in the long persistence of El Niño–induced North Indian Ocean
warming. Journal of Climate, 22(8), 2023–2038. https://doi.org/10.1175/2008JCLI2590.1
Dudhia, J. (1989). Numerical study of convection observed during the winter monsoon experiment using a mesoscale two‐dimensionalmodel. Journal of the Atmospheric Sciences, 46(20), 3077–3107. https://doi.org/10.1175/1520‐0469(1989)046<3077:NSOCOD>2.0.CO;2Gan, J. P., Li, L., Wang, D. X., & Guo, X. G. (2009). Interaction of a river plume with coastal upwelling in the northeastern South China Sea.
Continental Shelf Research, 29(4), 728–740. https://doi.org/10.1016/j.csr.2008.12.002
Hong, S. Y., & Lim, J. O. J. (2006). The WRF Single‐Moment 6‐class Microphysics Scheme (WSM6). Journal of the Korean MeteorologicalSociety, 42, 129–151.
Houze, R. A. J., Geotis, S. G., Marks, F. D. Jr., & West, A. K. (1981). Winter monsoon convection in the vicinity of North Borneo. Part I:
Structure and time variation of the clouds and precipitation. Monthly Weather Review, 109(8), 1595–1614. https://doi.org/10.1175/1520‐
0493(1981)109<1595:WMCITV>2.0.CO;2

3722

Journal of Geophysical Research: Atmospheres

10.1029/2018JD029528

Hu, J. Y., Kawamura, H., Hong, H. S., Suetsugu, M., & Lin, M. S. (2001). Hydrographic and satellite observations of summertime upwellingin the Taiwan Strait: A preliminary description. Terrestrial, Atmospheric and Oceanic Sciences, 12(2), 415–430. https://doi.org/10.3319/
TAO.2001.12.2.415(O)Hu, J. Y., Kawamura, H., & Tang, D.‐L. (2003). Tidal front around the Hainan Island, northwest of the South China Sea. Journal ofGeophysical Research, 108(C11), 3342. https://doi.org/10.1029/2003JC001883
Hu, K., Xie, S.‐P., Huang, G., Hu, K., Xie, S. P., & Huang, G. (2017). Orographically anchored El Niño effect on summer rainfall in Central
China. Journal of Climate, 30(24), 10,037–10,045, https://doi.org/10.1175/JCLI‐D‐17‐0312.1
Huang, R. H., Zhang, R.‐H., & Zhang, Q.‐Y. (2000). The 1997/98 ENSO cycle and its impact on summer climate anomalies in East Asia.
Advances in Atmospheric Sciences, 17(3), 348–362. https://doi.org/10.1007/s00376‐000‐0028‐3
Ichikawa, H., & Yasunari, T. (2006). Time–space characteristics of diurnal rainfall over Borneo and surrounding oceans as observed by
TRMM‐PR. Journal of Climate, 19(7), 1238–1260. https://doi.org/10.1175/JCLI3714.1
Jing, Z., Qi, Y., Du, Y., Zhang, S., & Xie, L. (2015). Summer upwelling and thermal fronts in the northwestern South China Sea:
Observational analysis of two mesoscale mapping surveys. Journal of Geophysical Research: Oceans, 120, 1993–2006. https://doi.org/
10.1002/2014JC010601
Johnson, R. H., & Priegnitz, D. L. (1981). Winter monsoon convection in the vicinity of North Borneo. Part II: Effects on large‐scale ﬁelds.
Monthly Weather Review, 109(8), 1615–1628. https://doi.org/10.1175/1520‐0493(1981)109<1615:WMCITV>2.0.CO;2
Kamimera, H., Mori, S., Yamanaka, M. D., & Syamsudin, F. (2012). Modulation of diurnal rainfall cycle by the madden‐Julian oscillation
based on one‐year continuous observations with a meteorological radar in West Sumatera. Scientiﬁc Online Letters on the Atmosphere:
Sola, 5(1), 111–114. https://doi.org/10.1186/s40645‐018‐0202‐9
Kanamori, H., Yasunari, T., & Kuraji, K. (2013). Modulation of the diurnal cycle of rainfall associated with the MJO observed by adense hourly rain gauge network at Sarawak, Borneo. Journal of Climate, 26(13), 4858–4875. https://doi.org/10.1175/JCLI‐D‐12‐00158.1
Keenan, T. D., & Carbone, R. E. (2007). Propagation and diurnal evolution of warm season cloudiness in the Australian and maritime
continent region. Monthly Weather Review, 136(3), 973–994. https://doi.org/10.1175/2007MWR2152.1
Kikuchi, K., & Wang, B. (2008). Diurnal precipitation regimes in the global tropics. Journal of Climate, 21(11), 2680–2696. https://doi.org/
10.1175/2007JCLI2051.1
Kilpatrick, T., & Xie, S.‐P. (2015). ASCAT observations of downdrafts from mesoscale convective systems. Geophysical Research Letters, 42,
1951–1958. https://doi.org/10.1002/2015GL063025
Kilpatrick, T., Xie, S.‐P., & Nasuno, T. (2017). Diurnal convection‐wind coupling in the Bay of Bengal. Journal of Geophysical Research:
Atmospheres, 122, 9705–9720. https://doi.org/10.1002/2017JD027271
Koh, T. Y., & Teo, C. K. (2009). Toward a mesoscale observation network in Southeast Asia. Bulletin of the American Meteorological Society,90(4), 481–488. https://doi.org/10.1175/2008BAMS2561.1
Kuwano‐Yoshida, A., & Minobe, S. (2017). Storm‐track response to SST fronts in the northwestern paciﬁc region in an AGCM. Journal of
Climate, 30(3), 1081–1102. https://doi.org/10.1175/JCLI‐D‐16‐0331.1
Li, Y., Peng, S., Wang, J., Yan, J., & Huang, H. (2018). On the mechanism of the generation and interannual variations of the summer
upwellings west and southwest off the Hainan Island. Journal of Geophysical Research: Oceans, 123, 8247–8263. https://doi.org/10.1029/
2018JC014226
Liu, Q.‐Y., Wang, D., Wang, X., Shu, Y., Xie, Q., & Chen, J. (2014). Thermal variations in the South China Sea associated with the eastern
and Central Paciﬁc El Niño events and their mechanisms. Journal of Geophysical Research: Oceans, 119, 8955–8972. https://doi.org/
10.1002/2014JC010429
Love, B. S., Matthews, A. J., & Lister, G. M. S. (2011). The diurnal cycle of precipitation over the Maritime Continent in a high‐resolution
atmospheric model. Quarterly Journal of the Royal Meteorological Society, 137(657), 934–947. https://doi.org/10.1002/qj.809
Mao, J. Y., & Wu, G. X. (2012). Diurnal variations of summer precipitation over the Asian monsoon region as revealed by TRMM satellite
data. Science China Earth Sciences, 55(4), 554–566. https://doi.org/10.1007/s11430‐011‐4315‐x
Mapes, B., Milliff, R., & Morzel, J. (2009). Composite life cycle of maritime tropical mesoscale convective systems in scatterometer andmicrowave satellite observations. Journal of the Atmospheric Sciences, 66(1), 199–208. https://doi.org/10.1175/2008JAS2746.1
Mapes, B. E., Warner, T. T., & Xu, M. (2003). Diurnal patterns of rainfall in northwestern South America. Part III: Diurnal gravity waves
and nocturnal convection offshore. Monthly Weather Review, 131(5), 830–844. https://doi.org/10.1175/1520‐0493(2003)131<0830:
DPORIN>2.0.CO;2
Miller, S. T. K., Keim, B. D., Talbot, R. W., & Mao, H. (2003). Sea breeze: Structure, forecasting, and impacts. Reviews of Geophysics, 41(3),1011. https://doi.org/10.1029/2003RG000124
Minobe, S., Kuwanoyoshida, A., Komori, N., Xie, S. P., & Small, R. J. (2008). Inﬂuence of the Gulf Stream on the troposphere. Nature,452(7184), U206–U251.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., & Clough, S. A. (1997). Radiative transfer for inhomogeneous atmospheres:
RRTM, a validated correlated‐k model for the longwave. Journal of Geophysical Research, 102(D14), 16,663–16,682. https://doi.org/
10.1029/97JD00237
Nakamura, H., Nishina, A., & Minobe, S. (2012). Response of storm tracks to bimodal Kuroshio path states south of Japan. Journal of
Climate, 25(21), 7772–7779. https://doi.org/10.1175/JCLI‐D‐12‐00326.1
Nakanishi, M., & Niino, H. (2006). An improved Mellor–Yamada level‐3 model: Its numerical stability and application to a regional prediction of advection fog. Boundary‐Layer Meteorology, 119(2), 397–407. https://doi.org/10.1007/s10546‐005‐9030‐8
Nesbitt, S. W., & Zipser, E. J. (2003). The diurnal cycle of rainfall and convective intensity according to three years of TRMM measurements.
Journal of Climate, 16(10), 1456–1475. https://doi.org/10.1175/1520‐0442‐16.10.1456
O'Neill, L. W., Esbensen, S. K., Thum, N., Samelson, R. M., & Chelton, D. B. (2010). Dynamical analysis of the boundary layer and surface
wind responses to mesoscale SST perturbations. Journal of Climate, 23(3), 559–581. https://doi.org/10.1175/2009JCLI2662.1
Peatman, S. C., Matthews, A. J., & Stevens, D. P. (2014). Propagation of the Madden‐Julian Oscillation through the Maritime Continent andscale interaction with the diurnal cycle of precipitation. Quarterly Journal of the Royal Meteorological Society, 140(680), 814–825. https://doi.org/10.1002/qj.2161
Perlin, N., Skyllingstad, E. D., Samelson, R. M., & Barbour, P. L. (2007). Numerical simulation of air sea coupling during coastal upwelling.
Journal of Physical Oceanography, 37(8), 2081–2093. https://doi.org/10.1175/JPO3104.1
Perlin, N., Szoeke, S. P. D., Chelton, D. B., Samelson, R. M., Skyllingstad, E. D., & O'Neill, L. W. (2014). Modeling the atmospheric boundary
layer wind response to mesoscale sea surface temperature perturbations. Monthly Weather Review, 142(11), 4284–4307. https://doi.org/
10.1175/MWR‐D‐13‐00332.1

SHI ET AL.

3723

Journal of Geophysical Research: Atmospheres

10.1029/2018JD029528

Sahany, S., Venugopal, V., & Nanjundiah, R. S. (2010). Diurnal‐scale signatures of monsoon rainfall over the Indian region from TRMM
satellite observations. Journal of Geophysical Research, 115, D02103. https://doi.org/10.1029/2009JD012644
Sato, T., Miura, H., Satoh, M., Takayabu, Y. N., & Wang, Y. Q. (2009). Diurnal cycle of precipitation in the tropics simulated in a global
cloud‐resolving model. Journal of Climate, 22(18), 4809–4826. https://doi.org/10.1175/2009JCLI2890.1
Seo, H. (2017). Distinct inﬂuence of air‐sea interactions mediated by mesoscale sea surface temperature and surface current in the ArabianSea. Journal of Climate, 30(20), 8061–8080. https://doi.org/10.1175/JCLI‐D‐16‐0834.1
Seo, H., Subramanian, A. C., Miller, A. J., & Cavanaugh, N. R. (2014). Coupled impacts of the diurnal cycle of sea surface temperature on
the Madden‐Julian Oscillation. Journal of Climate, 27(22), 8422–8443. https://doi.org/10.1175/JCLI‐D‐14‐00141.1
Shi, R., Chen, J., Guo, X., Zeng, L. L., Li, J., Xie, Q., et al. (2017). Ship observations and numerical simulation of the marine atmosphericboundary layer over the spring oceanic front in the northwestern South China Sea. Journal of Geophysical Research: Atmospheres, 122,
3733–3753. https://doi.org/10.1002/2016JD026071
Shi, R., Guo, X., Wang, D., Zeng, L. L., & Chen, J. (2015). Seasonal variability in coastal fronts and its inﬂuence on sea surface wind in thenorthern South China Sea. Deep‐Sea Research Part II, 119, 30–39. https://doi.org/10.1016/j.dsr2.2013.12.018
Shu, Y., Wang, D., Zhu, J., & Peng, S. (2011). The 4‐D structure of upwelling and Pearl River plume in the northern South China Sea during
summer 2008 revealed by a data assimilation model. Ocean Modelling, 36(3–4), 228–241. https://doi.org/10.1016/j.ocemod.2011.01.002
Skamarock, W. C. (2008). A description of the advanced research WRF version 3. NCAR Technical, 113, 7‐25.
Slingo, J., Inness, P., Neale, R., & Woolnough, S. (2003). Scale interactions on diurnal to seasonal timescales and their relevance to model
systematic errors. Annales Geophysicae, 46(1), 139–155.
Small, R. J., Deszoeke, S. P., Xie, S. P., O'Neill, L., Seo, H., Song, Q., et al. (2008). Air–sea interaction over ocean fronts and eddies. Dynamics
of Atmospheres and Oceans, 45(3‐4), 274–319. https://doi.org/10.1016/j.dynatmoce.2008.01.001
Small, R. J., Tomas, R. A., & Bryan, F. O. (2014). Storm track response to ocean fronts in a global high‐resolution climate model. Climate
Dynamics, 43(3–4), 805–828. https://doi.org/10.1007/s00382‐013‐1980‐9Song, Q., Chelton, D. B., Esbensen, S. K., Thum, N., & O'Neill, L. W. (2009). Coupling between sea surface temperature and low‐level winds
in mesoscale numerical models. Journal of Climate, 22(1), 146–164. https://doi.org/10.1175/2008JCLI2488.1
Su, J., & Pohlmann, T. (2009). Wind and topography inﬂuence on an upwelling system at the eastern Hainan coast. Journal of GeophysicalResearch, 114, C06017. https://doi.org/10.1029/2008JC005018
Su, J., Xu, M., Pohlmann, T., Xu, D., & Wang, D.‐R. (2013). A western boundary upwelling system response to recent climate variation(1960–2006). Continental Shelf Research, 57, 3–9. https://doi.org/10.1016/j.csr.2012.05.010
Tanimoto, Y., Xie, S.‐P., Kai, K. H., Okajima, H., Tokinaga, H., Murayama, T., et al. (2009). Observations of marine atmospheric boundary
layer transitions across the summer Kuroshio Extension. Journal of Climate, 22(6), 1360–1374. https://doi.org/10.1175/2008JCLI2420.1
Tao, W., Huang, G., Wu, R., Hu, K., Wang, P., & Chen, D. (2016). Asymmetry in summertime atmospheric circulation anomalies over the
Northwest Paciﬁc during decaying phase of El Niño and La Niña. Climate Dynamics, 107(D19), 2007–2023. https://doi.org/10.1029/
2001JD000393
Tiedtke, M. (1989). A comprehensive mass ﬂux scheme for cumulus parameterization in large‐scale models. Monthly Weather Review,
117(8), 1779–1800. https://doi.org/10.1175/1520‐0493(1989)117<1779:ACMFSF>2.0.CO;2Toy, M. D., & Johnson, R. H. (2014). The inﬂuence of an SST front on a heavy rainfall event over coastal Taiwan during TiMREX. Journal of
the Atmospheric Sciences, 71(9), 3223–3249. https://doi.org/10.1175/JAS‐D‐13‐0338.1
Vincent, C. L., & Lane, T. P. (2016). Evolution of the diurnal rainfall cycle with the passage of a Madden–Julian Oscillation event throughthe Maritime Continent. Monthly Weather Review, 144(5), 1983–2005. https://doi.org/10.1175/MWR‐D‐15‐0326.1
Wang, B., Wu, R., & Fu, X. (2000). Paciﬁc–east Asian teleconnection: How does Enso affect east Asian climate? Journal of Climate, 13(9),1517–1536. https://doi.org/10.1175/1520‐0442(2000)013<1517:PEATHD>2.0.CO;2
Wang, C., & Wang, X. (2013). Classifying El Niño Modoki I and II by different impacts on rainfall in southern China and typhoon tracks.
Journal of Climate, 26(4), 1322–1338. https://doi.org/10.1175/JCLI‐D‐12‐00107.1
Wang, D., Liu, Y., Qi, Y., & Shi, P. (2001). Seasonal variability of thermal fronts in the northern South China Sea from satellite data.
Geophysical Research Letters, 28(20), 3963–3966. https://doi.org/10.1029/2001GL013306
Wang, D., Shu, Y., Xue, H., Hu, J., Chen, J., Zhuang, W., et al. (2014). Relative contributions of local wind and topography to the coastal
upwelling intensity in the northern South China Sea. Journal of Geophysical Research: Oceans, 119, 2550–2567. https://doi.org/10.1002/
2013JC009172
Wang, G., Li, J., Wang, C., & Yan, Y. (2012). Interactions among the winter monsoon, ocean eddy and ocean thermal front in the South
China Sea. Journal of Geophysical Research, 117, C08002. https://doi.org/10.1029/2012JC008007
Wapler, K., & Lane, T. P. (2012). A case of offshore convective initiation by interacting land breezes near Darwin, Australia. Meteorology
and Atmospheric Physics, 115(3–4), 123–137. https://doi.org/10.1007/s00703‐011‐0180‐6
Williams, M., & Houze, A. R. (1987). Satellite‐observed characteristics of winter monsoon cloud clusters. Monthly Weather Review, 115(2),505–519. https://doi.org/10.1175/1520‐0493(1987)115<0505:SOCOWM>2.0.CO;2
Wu, R., Chen, W., Wang, G., & Hu, K. (2014). Relative contribution of ENSO and East Asian winter monsoon to the South China Sea SST
anomalies during ENSO decaying years. Journal of Geophysical Research: Atmospheres, 119, 5046–5064. https://doi.org/10.1002/
2013JD021095
Xie, S.‐P., Du, Y., Huang, G., Zheng, X.‐T., Tokinaga, H., Hu, K., & Liu, Q. (2010). Decadal shift in El Niño inﬂuences on indo–western
Paciﬁc and East Asian climate in the 1970s. Journal of Climate, 23(12), 3352–3368. https://doi.org/10.1175/2010JCLI3429.1
Xie, S.‐P., Hu, K., Hafner, J., Tokinaga, H., Du, Y., Huang, G., & Sampe, T. (2009). Indian Ocean capacitor effect on Indo‐Western Paciﬁcclimate during the summer following El Niño. Journal of Climate, 22(3), 730–747. https://doi.org/10.1175/2008JCLI2544.1
Xie, S. ‐P., Xie, Q., Wang, D., & Liu, W. T. (2003). Summer upwelling in the South China Sea and its role in regional climate variations.
Journal of Geophysical Research, 108(C8). https://doi.org/10.1029/2003JC001867
Xu, H., Xu, M., Xie, S., & Wang, Y. (2011). Deep atmospheric response to the spring Kuroshio over the East China Sea. Journal of Climate,24(18), 4959–4972. https://doi.org/10.1175/JCLI‐D‐10‐05034.1
Yamamoto, M. K., Furuzawa, F. A., Higuchi, A., & Nakamura, K. (2008). Comparison of diurnal variations in rainfall systems observed by
TRMM PR, TMI, and VIRS. Journal of Climate, 21(16), 4011–4028. https://doi.org/10.1175/2007JCLI2079.1
Yang, G., & Slingo, J. M. (2001). The diurnal cycle in the tropics. Monthly Weather Review, 129(4), 784–801. https://doi.org/10.1175/1520‐
0493(2001)129<0784:TDCITT>2.0.CO;2
Yokoi, S., Mori, S., Katsumata, M., Geng, B., Yasunaga, K., Syamsudin, F., et al. (2017). Diurnal cycle of precipitation observed in thewestern coastal area of Sumatra island: Offshore preconditioning by gravity waves. Monthly Weather Review, 145(9), 3745–3761. https://doi.org/10.1175/MWR‐D‐16‐0468.1

SHI ET AL.

3724

Journal of Geophysical Research: Atmospheres

10.1029/2018JD029528

Zhang, C., Wang, Y., & Hamilton, K. (2011). Improved representation of boundary layer clouds over the Southeast Paciﬁc in ARW‐WRF
using a modiﬁed Tiedtke cumulus parameterization scheme. Monthly Weather Review, 139(11), 3489–3513. https://doi.org/10.1175/
MWR‐D‐10‐05091.1
Zhang, R. H., Min, Q. Y., & Su, J. Z. (2017). Impact of El Niño on atmospheric circulations over East Asia and rainfall in China: Role of theanomalous western North Paciﬁc anticyclone. Science China Earth Sciences, 60(6), 1124–1132. https://doi.org/10.1007/s11430‐016‐
9026‐xZipser, E. J. (1977). Mesoscale and convective‐scale downdrafts as distinct components of squall‐line structure. Monthly Weather Review,105(12), 1568–1589. https://doi.org/10.1175/1520‐0493(1977)105<1568:MACDAD>2.0.CO;2

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