Why would sea-level rise for global warming and polar ice-melt?

Two major causes of global sea level rise such as thermal expansion of the oceans and the loss of landbased ice for increased melting have been claimed by some researchers and recognized by the IPCC.However, other climate threat investigators revealed that atmosphere-ocean modeling is an imperfect representation, paleo-data consist of proxy climate information with ambiguities, and modern observations are limited in scope and accuracy. It is revealed that global warming and polar ice-melt although a reality would not contribute to any sea level rise. Floating-ice of the polar region on melting would reoccupy same displaced volume by floating ice-sheets. Land-ice cover in the polar region on melting can reduce load from the crust to activate elastic rebound that would raise land for its isostatic equilibrium.Such characteristics would not contribute to sea level rise. Equatorial bulge, polar flattening, elevation difference of the spheroidal surface between equator and pole with lower in the pole, strong gravity attraction of the polar region and week gravity attraction of the equatorial region, all these phenomena would play dominant role in preventing sea level rise. Palaeo-sea level rise and fall in macro-scale(10-100 m or so) were related to marine transgression and regression in addition to other geologic events like converging and diverging plate tectonics, orogenic uplift of the collision margin, basin subsidence of the extensional crust, volcanic activities in the oceanic region, prograding delta buildup, ocean floor height change and sub-marine mass avalanche. This study also reveals that geophysical shape, gravity attraction and the centrifugal force of spinning and rotation of the earth would continue acting against sea level rise.

Climate warming and sea level rise

Based on a large number of actual data, the author believe that the modem global warming and sea level rise resulted from climate warming after the cold front of the Little Ice Age about 200 years ago and the developmnet of the sea level rise phase. In the past 30 years, the rate of sea level rise was increasing, which is under the background of the average temperature uplift 0.2F°（0.11℃）every 10 years in succession from the 1980s to the past 10 years this century. On the basis of the absolute and relative sea-level rise rate that was calculated from the tidal data during the same period at home and abroad in the last 30 years, in accordance with the resolutions of the 2010 climate conference in Cancun, at the same time, considering the previous prediction and research, the world＇s sea levels and the relative sea level in Tianjin, Shanghai, Dongying, Xiamen, Haikou and other coastal cities that have severe land subsidence in 2050 and 2100 are calculated and evaluated.

Inferring the global mean sea level from a global tide gauge network

An attempt is made to infer the global mean sea level（GMSL） from a global tide gauge network and frame the problem in terms of the limitations of the network. The network,owing to its limited number of gauges and poor geographical distribution complicated further by unknown vertical land movements,is ill suited for measuring the GMSL. Yet it remains the only available source for deciphering the sea level rise over the last 100 a. The poor sampling characteristics of the tide gauge network have necessitated the usage of statistical inference. A linear optimal estimator based on the Gauss-Markov theorem seems well suited for the job. This still leaves a great deal of freedom in choosing the estimator. GMSL is poorly correlated with tide gauge measurements because the small uniform rise and fall of sea level are masked by the far larger regional signals. On the other hand,a regional mean sea level（RMSL） is much better correlated with the corresponding regional tide gauge measurements. Since the GMSL is simply the sum of RMSLs,the problem is transformed to one of estimating the RMSLs from regional tide gauge measurements. Specifically for the annual heating and cooling cycle,we separate the global ocean into 10-latitude bands and compute for each 10-latitude band the estimator that predicts its RMSL from tide gauges within. In the future,the statistical correlations are to be computed using satellite altimetry. However,as a first attempt,we have used numerical model outputs instead to isolate the problem so as not to get distracted by altimetry or tide gauge errors. That is,model outputs for sea level at tide gauge locations of the GLOSS network are taken as tide gauge measurements,and the RMSLs are computed from the model outputs. The results show an estimation error of approximately 2 mm versus an error of 2.7 cm if we simply average the tide gauge measurements to estimate the GMSL,caused by the much larger regional seasonal cycle and mesoscale variation plaguing the individual tide gauges. The numerical model,Los Alamos POP model Run 11 lasting 3 1/4 a,is one of the best eddy-resolving models and does a good job simulating the annual heating and cooling cycle,but it has no global or regional trend. Thus it has basically succeeded in estimating the seasonal cycle of the GMSL. This is still going to be the case even if we use the altimetry data because the RMSLs are dominated by the seasonal cycle in relatively short periods. For estimating the GMSL trend,longer records and low-pass filtering to isolate the statistical relations that are of interest. Here we have managed to avoid the much larger regional seasonal cycle plaguing individual tide gauges to get a fairly accurate estimate of the much smaller seasonal cycle in the GMSL so as to enhance the prospect of an accurate estimate of GMSL trend in short periods. One should reasonably expect to be able to do the same for longer periods during which tide gauges are plagued by much larger regional interannual（e. g.,ENSO events） and decadal sea level variations. In the future,with the availability of the satellite altimeter data,we could use the same approach adopted here to estimate the seasonal variations of GMSL and RMSL accurately and remove these seasonal variations accordingly so as to get a more accurate statistical inference between the tide gauge data and the RMSLs（therefore the GMSL） at periods longer than 1 a,i. e.,the long-term trend.

Sampling errors of the global mean sea level derived from TOPEX/Poseidon altimetry

Sampling errors of the global mean sea level derived from TOPEX/Poseidon （T/P） altimetry are explored using 31/ 4a of eddy-resolving numerical model outputs for sea level. By definition, the sampling errors would not exist if data were available everywhere at all times. Four problems with increasing and progressively added complexities are examined to understand the causes of the sampling errors. The first problem （P1） explores the error incurred because T/P with turning latitudes near 66° latitudes does not cover the entire globe. The second problem （P2） examines, in addition, the spatial sampling issue because samples are only available along T/P ground tracks. The third problem （P3） adds the additional complexity that sea level at any along track location is sampled only once every 10 d versus every 3 d for the model （i.e., the temporal sampling issue）. The fourth problem （P4） incorporates the full complexity with the addition of real T/P data outages. The numerical model （Los Alamos POP model Run 11） conserves the total water volume, thus generating no global mean sea level variation. Yet when the model sea level is sampled in the four problems （with P4 using the real T/P sampling）, variations occur as manifestations of the sampling errors. The results show root-mean-squares （rms） sampling errors for P1 of 0.67 （0.75） mm for 10 d （3 d） global mean sea level, 0.78 （0.86） mm for P2, 0.79 mm for P3, and 1.07 mm for P4, whereas the amplitudes of the sampling errors can be as large as 2.0 （2.7） mm for P1, 2.1 （2.7） mm for P2, 2.2 mm for P3, and 2.5 mm for P4. The results clearly show the largest source of the sampling errors to be the lack of global coverage （i.e., P1）, which the model has actually underestimated due to its own less-than-global coverage （between latitudes about 77° latitudes）. We have extrapolated that a truly global model would show the rms sampling error to be 1.14 （1.28） mm for P1, thus implying a substantially larger sampling error for P4.

Arctic Sea Level Variability from Oceanic Reanalysis and Observations

Quantifying the contributions to Arctic sea level(ASL)variability is critical to understand how the Arctic is responsing to ongoing climate change.Here,we use Ocean Reanalysis System 5(ORAS5)reanalysis data and tide gauge and satellite altimetry observations to quantify contributions from different physical processes on the ASL variability.The ORAS5 reanalysis shows that the ASL is rising with a trend of 2.5±0.3 mm yr−1(95%confidence level)over 1979-2018,which can be attributed to four components:(i)the dominant component from the global sea level increase of 1.9±0.5 mm yr−1,explaining 69.7%of the total variance of the ASL time series;(ii)the Arctic Oscillation-induced mass redistribution between the deep central basin and shallow shelves,with no significant trend and explaining 6.3%of the total variance;(iii)the steric sea level increase centering on the Beaufort Gyre region with a trend of 0.5±0.1 mm yr−1 and explaining 29.1%of the total variance of the ASL time series;and(iv)the intrusion of Pacific water into the Arctic Ocean,with no significant trend and contributing 14.2%of the total ASL variability.Furthermore,the dramatic sea ice melting and the larger area of open water changes the impact of the large-scale atmospheric forcing on the ASL variability after 1995,and the ocean dynamic circulation plays a more important role in the ASL variability.

Modeling the Risks of Climate Change and Global Warming to Humans Settled in Low Elevation Coastal Zones in Louisiana, USA

This paper seeks to identify high risk areas that are prone to flooding, caused by sea level rise because of high impacts of global climate change resulting from global warming and human settlements in low-lying coastal elevation areas in Louisiana, and model and understand the ramifications of predicted sea-level rise. To accomplish these objectives, the study made use of accessible public datasets to assess the potential risk faced by residents of coastal lowlands of Southern Louisiana in the United States. Elevation data was obtained from the Louisiana Statewide Light Detection and Ranging (LiDAR) with resolution of 16.4 feet (5 m) distributed by Atlas. The data was downloaded from Atlas website and imported into Environmental Systems Research Institute’s (ESRI’s) ArcMap software to create a single mosaic elevation image map of the study area. After mosaicking the elevation data in ArcMap, Spatial Analyst extension software was used to classify areas with low and high elevation. Also, data was derived from United States Geological Survey (USGS) Digital Elevation Model (DEM) and absolute sea level rise data covering the period 1880 to 2015 was acquired from United States Environmental Protection Agency (EPA) website. In addition, population data from U.S. Census Bureau was obtained and coupled with elevation data for assessing the risks of the population residing in low lying areas. Models of population trend and cumulative sea level rise were developed using statistical methods and software were applied to reveal the national trends and local deviations from the trends. The trends of population changes with respect to sea level rise and time in years were modeled for the low land coastal parishes of Louisiana. The expected years for the populations in the study area to be at risk due to rising sea level were estimated by models. The geographic information systems (GIS) results indicate that areas of low elevation were mostly located along the coastal Parishes in the study area. Further results of the study revealed that, if the sea level continued to rise at the present rate, a population of approximately 1.8 million people in Louisiana’s coastal lands would be at risk of suffering from flooding associated with the sea level having risen to about 740 inches by 2040. The population in high risk flood zone was modeled by the following equation: y = 6.6667x - 12,864, with R squared equal to 0.9964. The rate of sea level rise was found to increase as years progressed. The slopes of models for data for time periods, 1880-2015 (entire data) and 1970-2015 were found to be, 4.2653 and 6.6667, respectively. The increase reflects impacts of climate change and land management on rate of sea level rise, respectively. A model for the variation of years with respect to cumulative sea level was developed for use in predicting the year when the cumulative sea level would equal the elevation above sea level of study area parishes. The model is given by the following equation: y = 0.1219x + 1944.1 with R square equal to 0.9995.

Evaluation of sea level rise and associated responses in Hangzhou Bay from 1978 to 2017

The mean sea level, extreme sea level, and astronomical tide in Hangzhou Bay were analyzed using the tide gage data from 1978 to 2017 in Tanxu station, and the effects of rising sea levels on floods were estimated. The mean sea level in Hangzhou Bay showed a significant rising trend of 4.6mm per year in 1978-2017. This rate was much higher than the mean sea level of the China seas, and the extreme sea level in Hangzhou Bay increased at the rate of 0.011 m per year. During 1978-2017, the mean tide range increased at the rate of 1.30 cm per year. The amplitude of M2 significantly increased at the rate of 0.57cm per year, whereas the phase lag decreased at the rate of --0.21° per year. The amplitude and phase lag of the K1 tidal component slightly decreased at the rate of --0.03 cm per year and -0.07° per year, respectively. The changes in extreme sea level in Hangzhou Bay were mainly caused by variations in mean sea level. The coastal areas of Hangzhou Bay at risk from flooding due to the 100-year sea level return will increase by about 400 km^2 by 2050.

Impact of Global Warming on Indian Ocean

The sea level has increased abruptly in the Indian Ocean in the last four decades.Data shows that the increase in sea level is not homogeneous.The rise in bay of Bengal is more than that of the Arabian Sea. Global warming has caused an increase in sea surface temperature.The sea surface temperature has a direct impact on the sea level rise as well as on the intensity and frequency of storms and cyclones.This

Water Intrusion in the Chesapeake Bay Region: Is It Caused by Climate-Induced Sea Level Rise?

Sea level rise due to climate change is a contentious issue with profound geographic and economic implications. One region in the USA identified as being particularly susceptible to seal level rise is the Chesapeake Bay region, and it has been estimated that by the end of the century Norfolk, Virginia could experience sea level rise of 0.75 meters to more than 2.1 meters. Water intrusion is a serious problem in much of the Chesapeake Bay region. The question addressed here is whether this water intrusion is the result of climate-induced seal level rise or is being caused by other factors. Our findings indicate that the water intrusion problems in the region are due not to “sea level rise”, but primarily to land subsidence due to groundwater depletion and, to a lesser extent, subsidence from glacial isostatic adjustment. We conclude that water intrusion will thus continue even if sea levels decline. These findings are critical because the water intrusion problems in the Chesapeake Bay—and elsewhere—cannot be successfully solved unless their causes are correctly identified and appropriate remedies are devised. For the Chesapeake Bay region, the required remedy is the reversal of groundwater withdrawal rates, which has been used successfully elsewhere in the USA and other nations to solve water intrusion problems.

Evaluation and projection of marine heatwaves in the South China Sea: insights from CMIP6 multi-model ensemble

This study evaluates the performance of 16 models sourced from the coupled model intercomparison project phase 6(CMIP6)in simulating marine heatwaves(MHWs)in the South China Sea(SCS)during the historical period(1982−2014),and also investigates future changes in SCS MHWs based on simulations from three shared socioeconomic pathway(SSP)scenarios(SSP126,SSP245,and SSP585)using CMIP6 models.Results demonstrate that the CMIP6 models perform well in simulating the spatial-temporal distribution and intensity of SCS MHWs,with their multi-model ensemble(MME)results showing the best performance.The reasonable agreement between the observations and CMIP6 MME reveals that the increasing trends of SCS MHWs are attributed to the warming sea surface temperature trend.Under various SSP scenarios,the year 2040 emerges as pivotal juncture for future shifts in SCS MHWs,marked by distinct variations in changing rate and amplitudes.This is characterized by an accelerated decrease in MHWs frequency and a notably heightened increase in mean intensity,duration,and total days after 2040.Furthermore,the projection results for SCS MHWs suggest that the spatial pattern of MHWs remains consistent across future periods.However,the intensity shows higher consistency only during the near-term period(2021−2050),while notable inconsistencies are observed during the medium-term(2041−2070)and long-term(2071−2100)periods under the three SSP scenarios.During the nearterm period,the SCS MHWs are characterized by moderate and strong events with high frequencies and relatively shorter durations.In contrast,during the medium-term period,MHWs are also characterized by moderate and strong events,but with longer-lasting and more intense events under the SSP245 and SSP585 scenarios.However,in the long-term period,extreme MHWs become the dominant feature under the SSP585 scenario,indicating a substantial intensification of SCS MHWs,effectively establishing a near-permanent state.

Risk assessment of coastal flooding disaster by storm surge based on Elevation-Area method and hydrodynamic models:Taking Bohai Bay as an example

The future inundation by storm surge on coastal areas are currently ill-defined.With increasing global sealevel due to climate change,the coastal flooding by storm surge is more and more frequently,especially in coastal lowland with land subsidence.Therefore,the risk assessment of such inundation for these areas is of great significance for the sustainable socio-economic development.In this paper,the authors use Elevation-Area method and Regional Ocean Model System(ROMS)model to assess the risk of the inundation of Bohai Bay by storm surge.The simulation results of Elevation-Area method show that either a 50-year or 100-year storm surge can inundate coastal areas exceeding 8000 km^(2);the numerical simulation results based on hydrodynamics,considering ground friction and duration of the storm surge high water,show that a 50-year or 100-year storm surge can only inundate an area of over 2000 km^(2),which is far less than 8000 km^(2);while,when taking into account the land subsidence and sea level rise,the very inundation range will rapidly increase by 2050 and 2100.The storm surge will greatly impact the coastal area within about 10-30 km of the Bohai Bay,in where almost all major coastal projects are located.The prompt response to flood disaster due to storm surge is urgently needed,for which five suggestions have been proposed based on the geological background of Bohai Bay.This study may offer insight into the development of the response and adaptive plans for flooding disasters caused by storm surge.

Reconstruction of phytoplankton productivity and community structure in the South Yellow Sea

The sedimentary environment and ecological system in the South Yellow Sea (SYS) changed dramatically due to sea level change caused by glacial-interglacial cycles. The authors report the use of marine biomarkers (brassicasterol, dinosterol and C37 alkenones) and terrigenous biomarkers (C28+C30+C32 nalkanols) in core DLC70-3 from the SYS to reconstruct the variation in the phytoplankton productivity and community structure and possible mechanisms during the middle Pleistocene. The results show that the primary productivity and that of single algae presented a consistent trend for the whole core during the middle Pleistocene, which was high during interglacial periods and low during glacial periods, with the highest being in marine isotope stage (MIS) 5–9 and MIS 19–21. The main reason is that the Yellow Sea Warm Current (YSWC) carried much of high temperature, high salinity water into the SYS, causing upwelling and vertical mixing and stirring, which increased the nutrient supply in the photosynthetic layer. The phytoplankton community structure mainly showed an increase in the relative content of haptophytes in MIS 5–9 and MIS 19–21, while the relative content of diatoms and dinoflagellates decreased;there was no evidence for a haptophyte content in other stages. The results reveal a shift from a coccolitho-phoriddominated community during MIS 5 –9 and MIS 19 –21 to a diatom-dominated community during the other stages, mainly as a result of surface salinity variation, attributed to the invasion of the YSWC during high sea level periods.

青藏高原夏季极端降水研究进展与展望

在全球变暖背景下,极端降水正日益频繁、剧烈并影响更广泛的区域。特别是在气候变化的敏感区域——青藏高原(以下简称高原),这些现象的发生频率和强度均有显著增加,对当地乃至下游地区的生态环境和生产生活造成了重大影响。因此,对高原极端降水进行深入研究具有深远的科学意义和社会价值。本文从极端降水定义与指数、高原夏季极端降水特征、影响高原夏季极端降水的因素、高原夏季极端降水的灾害风险与未来预估四个方面,对近几十年来高原夏季极端降水的相关研究成果进行了回顾。通过梳理和分析相关领域的研究成果,以期为研究者提供清晰的研究脉络和前沿动态,促进学术交流与合作,共同推动高原极端降水领域的科学研究持续深入,为全球气候变化及极端天气气候事件研究和应对提供更加坚实和有力的科学支撑。

全球和南海海平面变化及其与厄尔尼诺的关系

利用卫星高度计资料,分析了1993年1月至2004年12月全球和南海的海平面变化特征。结果表明,在1993—2004年期间,全球和南海海平面的平均上升率分别为(2.5±0.2)mm/a和(4.8±1.2)mm/a。研究发现,全球和南海海平面的低频变化都与El Nio密切相关,但二者对El Ni?o的响应位相相反。1997—1998年El Nio初期,全球平均海平面升高,呈现正异常;El Nio后期,全球平均海平面下降并由正异常变为负异常。南海平均海平面在El Nio期间呈现负异常,在La Nia期间呈现正异常,其低频变化与南方涛动指数的低频分量位相变化几乎完全一致。ENSO可以通过南海季风和北太平洋环流(黑潮)的变化来影响南海海平面。El Nio发生前后的北风异常,以及同期黑潮流量的变化都对ENSO影响南海有一定的贡献。

海平面上升的灾害效应及其研究现状

海平面上升是全球变暖和沿海地区人类活动加剧的必然结果,海平面上升加剧了沿海灾害,威胁着沿海地区社会经济的持续发展。文章分析了海平面上升引起的风暴潮加剧、海岸侵蚀、潮滩湿地减少、涵闸废弃、洪涝灾害加剧、海堤破坏、海水入侵等灾害效应及研究现状,提出当前海平面上升灾害效应研究中存在的问题及今后的研究重点。

中国近海潮汐变化对外海海平面上升的响应

针对外海海平面上升对中国沿海潮波系统和潮汐水位可能带来的影响,通过西北太平洋潮波数学模型对边界海平面上升后潮波变化进行了数值模拟。研究发现边界海平面上升后,在无潮点附近东侧迟角增加,西侧迟角减小;无潮点北侧振幅增加,南侧振幅减小;辽东湾、渤海湾顶、辽东半岛东海域、海州湾至鲁南沿海、苏北沿海、台湾海峡至浙东沿海和南海平均潮差增加,海平面上升0.90 m后潮差最大增幅达0.40 m;长江口、杭州湾至对马海峡、朝鲜西海岸和莱州湾海域潮差减小。随着海平面上升量值的增加,渤海、台湾海峡潮差变化速率相对稳定,黄海、东海和南海站位变化速率有所变动;平均高水位的变化趋势与潮差一致;潮差增加的区域,高水位抬升幅度超过边界海平面上升幅度。海平面上升引起的高水位超幅变化,增加了沿海地区对风暴潮和其他灾害防护的风险。

中国陆架海MIS5(74~128 ka)以来地层及其沉积环境

基于中国海陆架28个晚第四纪钻孔实测数据和典型高分辨率地震剖面的综合地层分析,对比区内已有46个钻孔的研究成果,系统地开展晚第四纪地层特征识别和沉积环境重建。研究表明,MIS5以来中国海陆架沉积记录了3次明显的海侵过程,发育了3个相对完整的海相地层。其中,渤海和东海MIS3和MIS1相对较薄,而黄海海相地层较厚。渤海和黄海末次冰期的地层保存较少,东海外陆架末次冰期地层发育完整,较完整地记录10万年以来的沉积记录。南海晚更新世内陆架以滨岸和陆相沉积为主,外陆架为滨岸和浅海沉积;全新世发育浅海沉积。总体上,中国海陆架晚第四纪以来沉积环境演变依次是MIS5(74~128 ka)的浅海-滨海海相沉积,MIS4(60~74 ka)陆相潟湖-河流-滨海河口相沉积,MIS3(24~60 ka)浅海相-滨海潮坪沉积,MIS2(12~24 ka)/LGM时期陆相-河流相沉积,MIS1(现在~12 ka)/全新世海侵潮流砂和浅海泥质沉积。此外,新构造运动和区域沉降作用对地层的发育和保存具有一定的影响。上述认识为深入研究中国晚第四纪沉积与全球气候变化对比提供直接依据。

1993—2001年全球海面高度变化特征

应用TOPEX/POSEIDON(T/P)卫星高度计测高资料 ,对全球海洋的海面变化特征进行了分析 ,结果表明 ,1 993年 1月— 2 0 0 1年 6月期间 ,全球海平面呈现上升的态势 ;全球平均海平面高度的平均上升速率约为 1 .2mm/a ;海温的变化是引起海平面变化的重要原因 ,但其对海平面抬升的贡献不到 5 0 %。海平面的变化具有很强的地域特征。海平面变化的空间分布特征受风应力异常特别是纬向风应力异常的空间分布影响较大。

气候变化中海洋和冰冻圈的变化、影响及风险

本文系统梳理了IPCC《气候变化中的海洋和冰冻圈特别报告》(SROCC)的主要结论,并对主要观点进行了解读。报告主要关注全球变暖背景下高山、极地、海洋和沿海地区现在和未来的变化及其对人类和生态系统的影响,以及实现气候适应发展路径的方案。在全球变暖背景下,冰冻圈大面积萎缩,冰川冰盖质量损失,积雪减少,北极海冰范围和厚度减小,多年冻土升温,全球海洋持续增温,1993年以来,海洋变暖和吸热速度增加了一倍以上。同时,海洋表面酸化加剧,海洋含氧量减少。全球平均海平面呈加速上升趋势,2006—2015年全球海平面上升速率为3.6 mm/yr,是1901—1990年的2.5倍,但存在区域差异。高山、极地和海洋的生态系统的物种组成、分布和服务功能均发生变化,并对人类社会产生了显著负面影响。极端海洋气候事件发生频率增多,强度加大。1982年以来,全球范围内海洋热浪的发生频率增加了一倍,且范围更广,持续时间更长。海平面持续上升加剧了洪涝、海水入侵、海岸侵蚀等海岸带灾害,并影响沿海生态系统。海洋及冰冻圈的变化及其影响在未来一定时期仍将持续,应对这些影响而面临的挑战,应加强基于生态系统的适应和可再生能源管理,强化海岸带地区的海平面上升综合应对,打造积极有效、可持续和具有韧性的气候变化应对方案。

气候变暖背景下中国沿海降水变化特征

IPCC第六次评估报告指出,随着全球变暖,强降水事件通常会变得更加频繁和强烈。暴雨是我国主要气象灾害之一,是引发洪涝的最主要原因。目前洪涝灾害已成为影响滨海城市公共安全和经济社会发展的重要因素,了解降水的变化特征对于科学应对气候变化和防灾减灾具有重要意义。本文研究结果显示,1966-2020年中国沿海降水量和暴雨及以上级别降水日数(日降水量≥50 mm的日数)总体均呈增多趋势,但变化趋势不显著;降水日数(日降水量≥0.1 mm的日数)总体呈减少趋势,且变化趋势显著。降水量和暴雨及以上级别降水日数在长江口至福建北部沿海和海南沿海增加趋势明显,山东省及以北沿海、广东省东部沿海呈减少趋势;降水日数除在长江口附近呈增多趋势外,其他沿海地区均以减少为主。降水日数总体减少,降水量总体增多,表明降水过程中降水强度有增加趋势。中国沿海总体暴雨及以上级别强降水主要集中在5-9月,出现频率占全年81.7%。多地出现最大日降水量超过250 mm情况,局部最大日降水量超过500 mm。强降水过程期间往往伴随高海平面,影响滨海城市行洪排涝,增加淹没风险。