海表温度日变化特征对海陆风模拟的影响研究

doi:10.13745/j.esf.sf.2025.3.17

摘要/Abstract

摘要：

海陆风是由于陆地和海洋热力差异导致的滨海地区一天内风向明显变化的天气现象,是滨海地区最为显著的区域大气中尺度环流过程之一。海陆风环流的强度、结构对滨海地区的大气边界层高度、大气化学过程、空气质量和辐射平衡等均有影响。作为影响海陆风环流的直接热力条件,海洋表面温度的日变化受太阳辐射、海洋热容量、风速、云量等多种因素综合影响,其对滨海地区海陆风发生发展的影响仍不明确。本研究利用高分辨率海表温度模拟数据结合中尺度天气研究与预报模型(WRF),分析了中国近海典型海域海表温度日变化的特征及其对海陆风的影响机制。研究结果表明:我国近海地区平均海表温度呈现出从南向北递减的趋势,渤海海域年均海温最低,为10.78 ℃,东海海域年均海表温度较渤海海域高94.6%,南海海域的年均海温最高,为25.19 ℃。渤海海域海温日变化的年内波动最大,可达0.55 ℃,最低仅为0.03 ℃,均值为0.25 ℃。东海海域海温日变化波动幅度适中,年均日温差为0.20 ℃,研究期间涉及的最高海温日变化幅度为0.45 ℃,约为渤海海域极值的82.0%,高出南海海域33%以上。通过考虑海温日变化的情景与以往模式海温恒定不变的假设情景对比发现,海温日变化可导致我国近海典型海域海陆风日数量增加,南海滨海地区年海陆风日数量增加了14天,涨幅为56.0%;渤海北部滨海地区年海陆风日数量增长了7天,涨幅为20.0%。从季节来看,海温日变化增加可导致冬季海陆风日数量增多,夏季海陆风日数量减少,对春秋两季的影响不大,从而导致我国沿海地区海陆风日季节差异减小。

关键词: 全球变化, 海表温度日变化, 海陆风环流, 数值模拟

Abstract:

Sea-land breezes (SLB) are a mesoscale atmospheric circulation phenomenon driven by thermal differences between land and ocean during the day and night, resulting in obvious diurnal variations in wind direction in coastal areas. It is one of the most prominent mesoscale atmospheric circulations in coastal regions. The intensity of the SLB influences the atmospheric boundary layer height, atmospheric chemical processes, air quality, and radiation balance in coastal areas. As a direct thermal condition affecting the SLB, the diurnal variation of sea surface temperature (SST) is influenced by multiple factors, including solar radiation, ocean heat capacity, wind speed, and cloud cover. However, the impact of SST diurnal variation on the occurrence and development of SLB in coastal areas remains unclear. This study utilizes high-resolution SST simulation data combined with the Weather Research and Forecasting (WRF) model to analyze the characteristics of SST diurnal variation and its impact on the SLB in typical coastal areas of China. The results show that the average SST in China’ s coastal regions decreases from south to north, with the Bohai Sea having the lowest annual mean SST of 10.78 ℃, the East China Sea exhibiting a 94.6% higher annual mean SST than the Bohai Sea, and the South China Sea having the highest annual mean SST of 25.19 ℃. The diurnal variation of SST in the Bohai Sea exhibits the greatest annual fluctuation, reaching up to 0.55 ℃, with a minimum of only 0.03 ℃ and an average of 0.25 ℃. The diurnal SST variation in the East China Sea fluctuates moderately, with an annual average diurnal temperature difference of 0.20 ℃. The maximum SST diurnal variation during the study period was 0.45 ℃, about 82.0% of the Bohai Sea’ s extreme value and more than 33% higher than the South China Sea. Comparing scenarios that consider diurnal SST variation with the traditional assumption of constant SST in models, the results indicate that diurnal SST variation can lead to an increase in the number of SLB days in typical coastal areas of China. The number of SLB days in the South China Sea coastal region increased by 14 days annually, with a growth rate of approximately 56.0%, while the number of SLB days in the northern Bohai Sea coastal region increased by 7 days annually, with a growth rate of 20.0%. Seasonally, the diurnal SST variation increased the number of SLB days in winter and decreased them in summer, with little effect on the spring and fall. This results in a reduction in the seasonal difference of SLB days along China’ s coastline.

Key words: global change, sea surface temperature diurnal, sea land breeze, numeric simulations

中图分类号:

P425.41

肖昀廷, 蔡晨康, 黄亦心, 朱佳雷. 海表温度日变化特征对海陆风模拟的影响研究[J]. 地学前缘, 2025, 32(3): 218-230.

XIAO Yunting, CAI Chenkang, HUANG Yixin, ZHU Jialei. Study on the impact of daily sea surface temperature variation characteristics on the simulation of sea land breeze[J]. Earth Science Frontiers, 2025, 32(3): 218-230.

图/表 10

表1 模拟使用的参数化方案

Table 1 Parameterizations schemes used for simulation

物理过程	参数化方案
微物理过程	WSM方案^[41]
短波辐射	Goddard短波方案^[42]
近地面层	MYJ Monin-Obukhov方案^[43]
陆面过程	Noah陆面过程方案^[43]
边界层	Eta Mellor-Yamada-Janjic TKE方案^[44]
积云参数化	Kain-Fritsch方案^[45]

图1 研究区域示意(引自国家地理信息公共服务平台标准地图服务中国地图1∶3 200万 32开线划一)

Fig.1 Schematic of the simulation area. Quoted from the National Platform for Common Geospatial Information Services China map 1∶32 million 32 cuttings version with line delineation 1

图2 海洋表面日温差年际变化趋势圆点表示年均海温日变化,误差线上端为年内海表温度日变化极大值,下端为年内海表温度日变化极小值。

Fig.2 Inter-annual trends in diurnal sea surface temperature (DSST) differences. with dots indicating daily changes in mean DSST, and the upper end of the error line showing the maximum daily change in DSST during the year, and the lower end showing the minimum daily change in DSST during that year.

图3 海洋表面季节平均日温差年际变化趋势其中(a)为春季,(b)为夏季,(c)为秋季,(d)为冬季。

Fig.3 Interannual variation trends of seasonal average DSST. Panels (a) to (d) represent the spring, summer, fall, and winter seasons, respectively.

图4 区域年均海温年际变化特征点线图为研究区域相应年份的平均海温,阴影部分为该年份海温最高值和最低值的分布区间。

Fig.4 Interannual variation characteristics of regional annual mean SST. The line graph represents the average SST for each corresponding year in the study region, while the shaded area indicates the range between the maximum and minimum SST values for that year.

图5 海陆风日变化逐月统计

Fig.5 Monthly statistics of changes in sea land breeze days

图6 南海区域4小时平均风玫瑰图其中红色扇形表示陆风风向区间,蓝色扇形表示海风风向区间。(a)为当地时间0:00-3:00;(b)为当地时间4:00-7:00;(c)为当地时间8:00-11:00;(d)为当地时间12:00-15:00;(e)为当地时间16:00-19:00;(f)为当地时间20:00-23:00。

Fig.6 Four-hourly average wind rose for part of the South China Sea. The red sectors represent the land breeze wind direction range, while the blue sectors represent the sea breeze wind direction range. Panels (a) to (f) correspond to the local times of 00:00-03:00, 04:00-07:00, 08:00-11:00, 12:00-15:00, 16:00-19:00, and 20:00-23:00, respectively.

图7 渤海区域4小时平均风玫瑰图其中红色扇形表示陆风风向区间,蓝色扇形表示海风风向区间。其中(a)为当地时间0:00-3:00;(b)为当地时间4:00-7:00;(c)为当地时间8:00-11:00;(d)为当地时间12:00-15:00;(e)为当地时间16:00-19:00;(f)为当地时间20:00-23:00。

Fig.7 Four-hourly average wind rose for the Bohai Sea. The red sectors represent the land breeze wind direction range, while the blue sectors represent the sea breeze wind direction range. Panels (a) to (f) correspond to the local times of 00:00-03:00, 04:00-07:00, 08:00-11:00, 12:00-15:00, 16:00-19:00, and 20:00-23:00, respectively.

图8 海陆风日风向频率分布图其中(a)为南海海域;(b)为渤海海域。

Fig.8 Frequency distribution of wind direction on sea and land wind days, where (a) is the South China Sea area and (b) is the Bohai Sea area.

图9 海陆风日风速频率分布其中(a)为南海海域;(b)为渤海海域。

Fig.9 Frequency distribution of daily wind speeds for land and sea winds, where (a) is the South China Sea area and (b) is the Bohai Sea area.

参考文献 54

[1]	MILLER S T K, KEIM B D, TALBOT R W, et al. Sea breeze: structure, forecasting, and impacts[J]. Reviews of Geophysics, 2003, 41(3): 1011.
[2]	沈傲, 田春艳, 刘一鸣, 等. 海陆风环流中海盐气溶胶对大气影响的模拟[J]. 中国环境科学, 2019, 39: 1427-1435.
[3]	CROSMAN E T, HOREL J D. Sea and lake breezes: a review of numerical studies[J]. Boundary-Layer Meteorology, 2010, 137(1): 1-29.
[4]	MA Y, XIN J, ZHANG X, et al. Land-sea breeze circulation structure on the west coast of the Yellow Sea, China[J]. Atmospheric and Oceanic Science Letters, 2021, 14(1): 100003.
[5]	XIAO Y T, YANG J B, CUI L H, et al. Weakened sea-land breeze in a coastal megacity driven by urbanization and ocean Warming[J]. Earths Future, 2023, 11(8): e2022EF003341.
[6]	AZORIN-MOLINA C, CHEN D. A climatological study of the influence of synoptic-scale flows on sea breeze evolution in the Bay of Alicante (Spain)[J]. Theoretical and Applied Climatology, 2009, 96(3/4): 249-260.
[7]	DAVIS S R, FARRAR J T, WELLER R A, et al. The land-sea breeze of the Red Sea: observations, simulations, and relationships to regional moisture transport[J]. Journal of Geophysical Research: Atmospheres, 2019, 124(24): 13803-13825.
[8]	YAO Y, ZOU X, ZHAO Y, et al. Rapid changes in land-sea thermal contrast across China’s coastal zone in a warming climate[J]. Journal of Geophysical Research: Atmospheres, 2019, 124(4): 2049-2467.
[9]	DING A, WANG T, ZHAO M, et al. Simulation of sea-land breezes and a discussion of their implications on the transport of air pollution during a multi-day ozone episode in the pearl river delta of China[J]. Atmospheric Environment, 2004, 38(39): 6737-6750.
[10]	ZHANG Q, HU W, REN H, et al. Diurnal variations in primary and secondary organic aerosols in an eastern China coastal city: the impact of land-sea breezes[J]. Environmental Pollution, 2023, 319: 121016.
[11]	FAN J, ZHANG Y, LI Z, et al. Urbanization-induced land and aerosol impacts on sea-breeze circulation and convective precipitation[J]. Atmospheric Chemistry and Physics, 2020, 20(22): 14163-14182.
[12]	CHEN Y P, YANG C, XU L L, et al. Variations of chemical composition of NR-PM1 under the influence of sea land breeze in a coastal city of Southeast China[J]. Atmospheric Research, 2023, 285: 106626
[13]	SHEN L, ZHAO C, YANG X, et al. Observed slump of sea land breeze in Brisbane under the effect of aerosols from remote transport during 2019 Australia mega fire events[J]. Atmospheric Chemistry and Physics, 2022, (20): 419-439
[14]	SHI J R, SANTER B D, KWON Y O, et al. The emerging human influence on the seasonal cycle of sea surface temperature[J]. Nature Climate Change, 2024, 14(4): 364-372
[15]	GARCIA-SOTO C, CHENG L J, CAESAR L, et al. An overview of ocean climate change indicators: sea surface temperature, ocean heat content, ocean pH, dissolved oxygen concentration, Arctic Sea ice extent, thickness and volume, Sea level and strength of the AMOC (Atlantic meridional overturning circulation)[J]. Frontiers in Marine Science, 2021, (8): 642372
[16]	JIN S S, WEI Z X, WANG D Q, et al. Simulated and projected SST of Asian marginal seas based on CMIP6 models[J]. Frontiers in Marine Science, 2023, 10: 1178974
[17]	CHENG L J, ABRAHAM J, TRENBERTH K E, et al. Another record: ocean warming continues through 2021 despite La Nina conditions[J]. Advances in Atmospheric Sciences, 2022, 39(3): 373-385.
[18]	ZHANG Q B, LIU B, LI S L, et al. Understanding models’ global sea surface temperature bias in mean state: from CMIP5 to CMIP6[J]. Geophysical Research Letters, 2023, 50(4): 2022GL100888
[19]	彭京备. 东印度洋海温对中国南方冬季降水的影响[J]. 气候与环境研究, 2012, 17(3): 327-338.
[20]	徐海明, 何金海, 董敏. 江淮入梅的年际变化及其与北大西洋涛动和海温异常的联系[J]. 气象学报, 2001, 59(6): 694-706.
[21]	李刚, 李崇银, 江晓华, 等. 1900—2009年全球海表温度异常的时空变化特征分析[J]. 热带海洋学报, 2015, 34(4): 12-22. DOI
[22]	易香妤, 董文杰, 李劭怡, 等. 中国东海黑潮海温变化特征及成因分析[J]. 海洋预报, 2021, 38(3): 38-51.
[23]	周明颉, 简茂球. 热带印度洋及周边海温对ENSO响应的年代际变化[J]. 中山大学学报(自然科学版)(中英文), 2023, 62(1): 64-74.
[24]	蔡榕硕, 谭红建, 黄荣辉. 中国东部夏季降水年际变化与东中国海及邻近海域海温异常的关系[J]. 大气科学, 2012, 36(1): 35-45.
[25]	王洁, 王杰, 许佳峰, 等. 长江口邻近海域海表温度变化特征分析[J]. 海洋科学进展, 2020, 38(4): 624-634.
[26]	BIGUINO B, ANTUNES C, LAMAS L, et al. 40 years of changes in sea surface temperature along the Western Iberian Coast[J]. Science of the Total Environment, 2023(888): 164193
[27]	GUAN W N, JIANG X A, REN X J, et al. Pacific Sea surface temperature anomalies as important boundary forcing in driving the interannual warm arctic-cold continent pattern over the North American Sector[J]. Journal of Climate, 2021, 34(14): 5923-5937.
[28]	MAZON J, PINO D. The influence of an increase of the mediterranean sea surface temperature on two nocturnal offshore rainbands: a numerical experiment[J]. Atmosphere, 2017, 8(3): 58
[29]	肖杭芳, 邓文峰, 韦刚健. 近2000年南海北部海表温度变化特征及其对全球变暖的启示[J]. 厦门大学学报(自然科学版), 2018, 57(6): 748-759.
[30]	TIAN F, STORCH J-S V, HERTWIG E. Impact of SST diurnal cycle on ENSO asymmetry[J]. Climate Dynamics, 2018, 52(3/4): 2399-2411.
[31]	SUN C, LIU Y S, WEI T, et al. Cross-hemispheric SST propagation enhances the predictability of tropical western Pacific climate[J]. NPJ climate and atmospheric science, 2022, 5(1): 38
[32]	ARMOUR K C, PROISTOSESCU C, DONG Y, et al. Sea-surface temperature pattern effects have slowed global warming and biased warming-based constraints on climate sensitivity[J]. Proceedings of the National Academy of Sciences, 2024, 121(12): e2312093121.
[33]	L’HEUREUX M L, TIPPETT M K, WHEELER M C, et al. A relative sea surface temperature index for classifying ENSO events in a changing climate[J]. Journal of Climate, 2024, 37(4): 1197-1211.
[34]	马强, 颜京辉, 魏敏, 等. 北京气候中心CMIP6数据共享平台及应用[J]. 应用气象学报, 2022, 33(5): 617-627.
[35]	NCAR. NCEP FNL operational model global tropospheric analyses, continuing from July 1999[M]. Boulder, CO: the National Center for Atmospheric Research, 2000.
[36]	HAN S Q, HAO T Y, YANG X, et al. Land-sea difference of the planetary boundary layer structure and its influence on PM_2.5: observation and numerical simulation[J]. Science of the Total Environment, 2023(858): 159881
[37]	CHEN Y P, HU R, BEDRA K B. Investigating the cooling impacts of Green Hearts in summer: a case study in the Changsha-Zhuzhou-Xiangtan urban agglomeration based on Weather Research Forecasting (WRF) simulations[J]. Urban Climate, 2024(58): 102235
[38]	HASSAN E M, KARIMKHANI M, SEPEHRI J. Evaluating and comparison of WRF-chem model configurations for wind field impact on the April 2022 dust episode in western Iran[J]. Atmospheric Environment, 2025(340): 120892
[39]	CHOU S C, CHANG H L, TOTH Z, et al. Evaluation, calibration, and application of probabilistic 100 m wind speed forecasts produced by the WRF ensemble prediction system in Taiwan[J]. Journal of Applied Meteorology and Climatology, 2025, 64(1): 3-19.
[40]	POWERS J G, COAUTHORS. The weather research and forecasting model: overview, system efforts, and future directions[J]. Bulletin of the American Meteorological Society, 2017, 98: 1717-1737.
[41]	HONG S-Y, LIM J-O J. The WRF single-moment 6-class microphysics scheme (WSM6)[J]. Asia-Pacific Journal of Atmospheric Sciences, 2006, 42(2): 129-151.
[42]	MIELIKAINEN J, HUANG B, HUANG H L A, et al. GPU acceleration of the updated Goddard shortwave radiation scheme in the weather research and forecasting (WRF) Model[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(2): 555-562.
[43]	MELLOR G L, YAMADA. Development of a turbulence closure model for geophysical fluid problems[J]. Review of Geophysics, 1982, 20(4): 851-875
[44]	CHEN F. The Noah land surface model in WRF: a short tutorial,[J]. Journal of Advances in Modeling Earth Systems, 2019, 11: 231-256.
[45]	KAIN J S. The Kain-Fritsch convective parameterization: an update[J]. Journal of applied meteorology, 2004, 43(1): 170-181.
[46]	SHEN L X, ZHAO C F, YANG X C. Climate-driven characteristics of sea-land breezes over the globe[J]. Geophysical Research Letters, 2021, 48(7): 2020GL092308
[47]	SHEN L X, ZHAO C F, YANG X C, et al. Observed slump of sea land breeze in Brisbane under the effect of aerosols from remote transport during 2019 Australian mega fire events[J]. Atmospheric Chemistry and Physics, 2022, 22(1): 419-439.
[48]	SHEN L X, ZHAO C F, MA Z S, et al. Observed decrease of summer sea-land breeze in Shanghai from 1994 to 2014 and its association with urbanization[J]. Atmospheric Research, 2019(227): 198-209.
[49]	KARAGALI I, HOYER J L. Characterizations and quantification of regional diurnal SST cycles from SEVIRI[J]. Ocean Science, 2014, 10(5): 745-758.
[50]	KAWAI Y, WADA A. Diurnal Sea surface temperature variation and its impact on the atmosphere and ocean: A review[J]. Journal of Oceanography, 2007, 63(5): 721-744.
[51]	EMBURY O, MERCHANT C J, GOOD S A, et al. Satellite-based time-series of sea-surface temperature since 1980 for climate applications[J]. Scientific Data, 2024, 11(1): s41597-024-03147-w
[52]	罗嘉琪, 李响, 张蕴斐, 等. 南海海表温度日变化特征及其影响因素研究[J]. 海洋预报, 2024, 41(1): 31-41.
[53]	YANG C Y, NUTAKKI T U K, ALGHASSAB M A, et al. Optimized integration of solar energy and liquefied natural gas regasification for sustainable urban development: dynamic modeling, data-driven optimization, and case study[J]. Journal of Cleaner Production, 2024(447): 141405
[54]	ALEXANDER M A, SCOTT J D, DESER C. Processes that influence sea surface temperature and ocean mixed layer depth variability in a coupled model[J]. Journal of Geophysical Research: Oceans, 2000, 105(C7): 16823-16842.