Uncertainties of ENSO-related Regional Hadley Circulation Anomalies within Eight Reanalysis Datasets

El Nino-Southern Oscillation(ENSO),the leading mode of global interannual variability,usually intensifies the Hadley Circulation(HC),and meanwhile constrains its meridional extension,leading to an equatorward movement of the jet system.Previous studies have investigated the response of HC to ENSO events using different reanalysis datasets and evaluated their capability in capturing the main features of ENSO-associated HC anomalies.However,these studies mainly focused on the global HC,represented by a zonal-mean mass stream function(MSF).Comparatively fewer studies have evaluated HC responses from a regional perspective,partly due to the prerequisite of the Stokes MSF,which prevents us from integrating a regional HC.In this study,we adopt a recently developed technique to construct the three-dimensional structure of HC and evaluate the capability of eight state-of-the-art reanalyses in reproducing the regional HC response to ENSO events.Results show that all eight reanalyses reproduce the spatial structure of HC responses well,with an intensified HC around the central-eastern Pacific but weakened circulations around the Indo-Pacific warm pool and tropical Atlantic.The spatial correlation coefficient of the three-dimensional HC anomalies among the different datasets is always larger than 0.93.However,these datasets may not capture the amplitudes of the HC responses well.This uncertainty is especially large for ENSO-associated equatorially asymmetric HC anomalies,with the maximum amplitude in Climate Forecast System Reanalysis(CFSR)being about 2.7 times the minimum value in the Twentieth Century Reanalysis(20CR).One should be careful when using reanalysis data to evaluate the intensity of ENSO-associated HC anomalies.

Long-Term Extreme Wave Characteristics in the Water Adjacent to China Based on ERA5 Reanalysis Data

Extreme waves have a profound impact on coastal infrastructure;thus,understanding the variation law of risky analysis and disaster prevention in coastal zones is necessary.This paper analyzed the spatiotemporal characteristics of extreme wave heights adjacent to China from 1979 to 2018 based on the ERA5 datasets.Nonstationary extreme value analysis is undertaken in eight repre-sentative points to investigate the trends in the values of 50-and 100-year wave heights.Results show that the mean value of extreme waves is the largest in the eastern part of Taiwan Island and the smallest in the Bohai Sea from 1979 to 2018.Only the extreme wave height in the northeastern part of Taiwan Island shows a significant increase trend in the study area.Nonstationary analysis shows remarkable variations in the values of 50-and 100-year significant wave heights in eight points.Considering the annual mean change,E1,E2,S1,and S2 present an increasing trend,while S3 shows a decreasing trend.Most points for the seasonal mean change demon-strate an increasing trend in spring and winter,while other points show a decreasing trend in summer and autumn.Notably,the E1 point growth rate is large in autumn,which is related to the change in typhoon intensity and the northward movement of the typhoon path.

The Performance of Downward Shortwave Radiation Products from Satellite and Reanalysis over the Transect of Zhongshan Station to Dome A, East Antarctica

The downward shortwave radiation(DSR) is an important part of the Earth's energy balance, driving Earth's system's energy, water, and carbon cycles. Due to the harsh Antarctic environment, the accuracy of DSR derived from satellite and reanalysis has not been systematically evaluated over the transect of Zhongshan station to Dome A, East Antarctica.Therefore, this study aims to evaluate DSR reanalysis products(ERA5-Land, ERA5, MERRA-2) and satellite products(CERES and ICDR) in this area. The results indicate that DSR exhibits obvious monthly and seasonal variations, with higher values in summer than in winter. The ERA5-Land(ICDR) DSR product demonstrated the highest(lowest) accuracy,as evidenced by a correlation coefficient of 0.988(0.918), a root-mean-square error of 23.919(69.383) W m^(–2), a mean bias of –1.667(–28.223) W m^(–2) and a mean absolute error of 13.37(58.99) W m^(–2). The RMSE values for the ERA5-Land reanalysis product at seven stations, namely Zhongshan, Panda 100, Panda 300, Panda 400, Taishan, Panda 1100, and Kunlun, were 30.938, 29.447, 34.507, 29.110, 20.339, 17.267, and 14.700 W m^(-2), respectively;with corresponding bias values of 9.887, –12.159, –19.181, –15.519, –8.118, 6.297, and 3.482 W m^(–2). Regarding seasonality, ERA5-Land, ERA5,and MERRA-2 reanalysis products demonstrate higher accuracies during spring and summer, while ICDR products are least accurate in autumn. Cloud cover, water vapor, total ozone, and severe weather are the main factors affecting DSR. The error of DSR products is greatest in coastal areas(particularly at the Zhongshan station) and decreases towards the inland areas of Antarctica.

Generalized polynomial chaos expansion by reanalysis using static condensation based on substructuring

This paper presents a new computational method for forward uncertainty quantification(UQ)analyses on large-scale structural systems in the presence of arbitrary and dependent random inputs.The method consists of a generalized polynomial chaos expansion(GPCE)for statistical moment and reliability analyses associated with the stochastic output and a static reanalysis method to generate the input-output data set.In the reanalysis,we employ substructuring for a structure to isolate its local regions that vary due to random inputs.This allows for avoiding repeated computations of invariant substructures while generating the input-output data set.Combining substructuring with static condensation further improves the computational efficiency of the reanalysis without losing accuracy.Consequently,the GPCE with the static reanalysis method can achieve significant computational saving,thus mitigating the curse of dimensionality to some degree for UQ under high-dimensional inputs.The numerical results obtained from a simple structure indicate that the proposed method for UQ produces accurate solutions more efficiently than the GPCE using full finite element analyses(FEAs).We also demonstrate the efficiency and scalability of the proposed method by executing UQ for a large-scale wing-box structure under ten-dimensional(all-dependent)random inputs.

Assessment of intraseasonal variabilities in China Ocean Reanalysis(CORA)

A regional reanalysis product-China Ocean Reanalysis（CORA）-has been developed for the China＇s seas and the adjacent areas. In this study, the intraseasonal variabilities（ISVs） in CORA are assessed by comparing with observations and two other reanalysis products（ECCO2 and SODA）. CORA shows a better performance in capturing the intraseasonal sea surface temperatures（SSTs） and the intraseasonal sea surface heights（SSHs） than ECCO2 and SODA do, probably due to its high resolution, stronger response to the intraseasonal forcing in the atmosphere（especially the Madden-Julian Oscillation）, and more available regional data for assimilation. But at the subsurface, the ISVs in CORA are likely to be weaker than reality, which is probably attributed to rare observational data for assimilation and weak diapycnal eddy diffusivity in the CORA model. According to the comparison results, CORA is a good choice for the study related to variabilities at the surface, but cares have to be taken for the study focusing on the subsurface processes.

A Global Ocean Reanalysis Product in the China Ocean Reanalysis(CORA) Project

The first version of a global ocean reanalysis over multiple decades （1979-2008） has been completed by the National Marine Data and Information Service within the China Ocean Reanalysis （CORA） project. The global ocean model employed is based upon the ocean general circulation model of the Massachusetts Institute of Technology. A sequential data assimilation scheme within the framework of 3D variational （3DVar） analysis, called multi-grid 3DVar, is implemented in 3D space for retrieving multiple-scale observational information. Assimilated oceanic observations include sea level anomalies （SLAs） from multi-altimeters, sea surface temperatures （SSTs） from remote sensing satellites, and in-situ temperature/salinity profiles. Evaluation showed that compared to the model simulation, the annual mean heat content of the global reanalysis is significantly approaching that of World Ocean Atlas 2009 （WOA09） data. The quality of the global temperature climatology was found to be comparable with the product of Simple Ocean Data Assimilation （SODA）, and the major ENSO events were reconstructed. The global and Atlantic meridional overturning circulations showed some similarity as SODA, although significant differences were found to exist. The analysis of temperature and salinity in the current version has relatively larger errors at high latitudes and improvements are ongoing in an updated version. CORA was found to provide a simulation of the subsurface current in the equatorial Pacific with a correlation coefficient beyond about 0.6 compared with the Tropical Atmosphere Ocean （TAO） mooring data. The mean difference of SLAs between altimetry data and CORA was less than 0.1 m in most years.

Decadal trends in precipitable water vapor over the Indus River Basin using ERA5 reanalysis data

Precipitable Water Vapor(PWV)constitutes a pivotal parameter within the domains of atmospheric science,and remote sensing due to its profound influence on Earth’s climate dynamics and weather patterns.It exerts a significant impact on atmospheric stability absorption and emission of radiation,thus engendering alterations in the Earth’s radiative equilibrium.As such,precise quantification of PWV holds the potential to enhance weather prognostication and fortify preparedness against severe meteorological phenomena.This study aimed to elucidate the spatial and temporal changes in seasonal and annual PWV across the Indus River Basin and its sub-basins using ERA5 reanalysis datasets.The present study used ERA5 PWV(entire atmospheric column),air temperature at 2 m(t2m)and 500 hPa(T_500hPa),evapotranspiration,and total cloud cover data from 1960 to 2021.Theil Sen slope estimator and Mann-Kendall test were used for trend analysis.Correlation and multiple regression methods were used to understand the association of PWV with other factors.The findings have unveiled the highest increase in mean PWV during the monsoon(0.40 mm/decade),followed by premonsoon(0.37 mm/decade),post-monsoon(0.27 mm/decade),and winter(0.19 mm/decade)throughout the study period.Additionally,the mean PWV exhibited the most pronounced positive trend in the sub-basin Lower Indus(LI),followed by Panjnad(P),Kabul(K),and Upper Indus(UI)across all seasons,except winter.Annual PWV has also risen in the Indus basin and its sub-basins over the last six decades.PWV exhibits a consistent upward trend up to an elevation of 3500 m within the basin which is most pronounced during the monsoon season,followed by the pre-monsoon.The escalating PWV within the basin is reasonably ascribed to increasing air temperatures,augmented evapotranspiration,and heightened cloud cover.These findings hold potential utility for pertinent authorities engaged in water resource management and planning.

Applicability evaluation of ERA5 wind and wave reanalysis data in the South China Sea

Wind and wave data are essential in climatological and engineering design applications.In this study,data from 15 buoys located throughout the South China Sea(SCS)were used to evaluate the ERA5 wind and wave data.Applicability assessment are beneficial for gaining insight into the reliability of the ERA5 data in the SCS.The bias range between the ERA5 and observed wind-speed data was-0.78-0.99 m/s.The result indicates that,while the ERA5 wind-speed data underestimation was dominate,the overestimation of such data existed as well.Additionally,the ERA5 data underestimated annual maximum wind-speed by up to 38%,with a correlation coefficient>0.87.The bias between the ERA5 and observed significant wave height(SWH)data varied from-0.24 to 0.28 m.And the ERA5 data showed positive SWH bias,which implied a general underestimation at all locations,except those in the Beibu Gulf and centralwestern SCS,where overestimation was observed.Under extreme conditions,annual maximum SWH in the ERA5 data was underestimated by up to 30%.The correlation coefficients between the ERA5 and observed SWH data at all locations were greater than 0.92,except in the central-western SCS(0.84).The bias between the ERA5 and observed mean wave period(MWP)data varied from-0.74 to 0.57 s.The ERA5 data showed negative MWP biases implying a general overestimation at all locations,except for B1(the Beibu Gulf)and B7(the northeastern SCS),where underestimation was observed.The correlation coefficient between the ERA5 and observed MWP data in the Beibu Gulf was the smallest(0.56),and those of other locations fluctuated within a narrow range from 0.82 to 0.90.The intercomparison indicates that during the analyzed time-span,the ERA5 data generally underestimated wind-speed and SWH,but overestimated MWP.Under non-extreme conditions,the ERA5 wind-speed and SWH data can be used with confidence in most regions of the SCS,except in the central-western SCS.

Evaluation of the China Ocean Reanalysis (CORA) in the South China Sea

The daily regional reanalysis product of the China Ocean Reanalysis(CORA)product was released in website in 2018.Using in situ observational data including Argo profiling floats,drifters,and cruise data,the performance of CORA in the South China Sea in terms of temperature,salinity,current and mixed layer depths is evaluated based on timescale(seasonal and interannual)and spatial distribution characteristics.The CORA temperature,salinity,and mixed layer depth show certain seasonal and interannual variations.In 50-400 m depth in the SCS,the CORA temperature is colder in winter and warmer in summer and autumn.In 0-150 m in the SCS,the CORA salinity is higher in most time of the year.However,in the second half of the year,the salinity is slightly weaker in 100-150 m depth.In most years,the CORA mixed layer depths tend to be shallower,and in season,shallower in winter and deeper in summer.In spatial distribution,the closer the area is to the coast,the greater the CORA errors would be.The CORA temperature is colder in the western side and warmer in the eastern side,resulting in a weaker SCS western boundary current(SCSwbc).In most areas,the CORA mixed layer depths are shallower.In the area close to the coast,the CORA mixed layer depths change rapidly,and the deviations in the mixed layer depths are larger.In the central SCS,the CORA mixed layer depths change slowly,and the deviations in the mixed layer depths are also small.

China Ocean ReAnalysis(CORA)version 1.0 products and validation for 2009-18

China Ocean ReAnalysis(CORA) version 1.0 products for the period 2009-18 have been developed and validated.The model configuration and assimilation algorithm have both been updated compared to those of the 51-year(1958-2008) products.The assimilated observations include temperature and salinity field data,satellite remote sensing sea surface temperature,and merged sea surface height(SSH) anomaly data.The validation includes the following three aspects:(1) Temperature,salinity,and SSH anomaly root-mean-square errors(RMSEs) are computed as a primary evaluation of the reanalysis quality.The 0-2000 m domain-averaged RMSEs of temperature and salinity are 0.61℃ and 0.08 psu,respectively.The SSH anomaly RMSE is less than 0.2 m in most regions.(2) The 35°N temperature section is used to evaluate the ability to reproduce the thermocline,mixing layer,and Yellow Sea cold water mass.In summer,the thermocline is reinforced,with the gradient changing from 3℃ in May to 10℃ in August.The mixing-layer depth reproduced by CORA is consistent with that computed from the observed climatology.The Yellow Sea cold water mass forms at a depth of 50 m.(3) The reanalysis current is examined against the tracks of some drifting buoys.The results show that the reanalysis current can capture the mesoscale eddies near the Kuroshio,which are similar to those described by the drifting buoys.Overall,the 2009-18 CORA reanalysis products are capable of reproducing major oceanic phenomena and processes in the coastal waters of China and adjacent seas.

Assessment of Atmospheric Reanalysis Data Based on Buoy Observations over the Tropical Western Indian Ocean in 2019

Atmospheric reanalysis data are an important data source for studying weather and climate systems.The sea surface wind and sea level pressure observations measured from a real-time buoy system deployed in Kenya’s offshore area in 2019 conducted jointly by Chinese and Kenyan scientists were used to evaluate the performance of the major high-frequency atmospheric reanalysis products in the western Indian Ocean region.Compared with observations,the sea level pressure field could be accurately simulated using the atmospheric reanalysis data.However,significant discrepancies existed between the surface wind reanalysis data,especially between meridional wind and the observational data.Most of the data provide a complete understanding of sea level pressure,except for the Japanese 55-year Reanalysis data,which hold a significant system bias.The Modern-Era Reanalysis for Research and Applications,Version-2,provides an improved description of all datasets.All the reanalysis datasets for zonal wind underestimate the strength during the study period.Among reanalysis data,NCEP-DOE Atmospheric Model Intercomparison Project reanalysis data presents an inaccurate description due to the worst correlation with the observations.For meridional wind,most reanalysis datasets underestimate the variance,while the European Centre for Medium-Range Weather Forecasts Atmospheric Composition Reanalysis 4 has a larger variance than the observations.In addition to the original data comparison,the diurnal variability of sea level pressure and surface wind are also assessed,and the result indicates that the diurnal variations have a significant gap between observation and reanalysis data.This study indicates that the current high-frequency reanalysis data still have disadvantages when describing the atmospheric parameters in the Western Indian Ocean region.

Applicability Evaluation of NCEP/NCAR Reanalysis Temperature,Geopotential Height and Wind Field Data in the Upper Troposphere and Lower Stratosphere

By using the data in 169 sounding stations over the world,NCEP/NCAR reanalysis data were tested,and the distribution characteristics of standard errors of geopotential height,temperature and wind speed field from the upper troposphere to the lower stratosphere over the world(most were the land zone) were analyzed.The results showed that the standard error distribution of reanalysis wind speed field data was mainly affected by the jet stream zone.There existed the obvious difference between the jet stream zone and the actual wind field.The distribution of standard error in the wind speed field had the obvious seasonal difference in winter,summer,and the average deviation was larger near the coastline.The high value zones of standard errors of reanalysis geopotential height and temperature field mainly concentrated in the low-latitude region in the Eastern Hemisphere(Indian Ocean coast).The distribution of standard error was basically consistent with average error.Therefore,the standard error could be explained well by the average error.The standard errors of reanalysis temperature and geopotential height data in the inland zone were lower.The high value zone mainly distributed along the coastline,and the average error of wind speed field was bigger near the coastline.It closely related to the quality of data in the sounding stations,the regional difference and the fact that the land observation stations were dense,and the ocean observation stations were fewer.

Validation of the Relationship between Precipitable Water and Surface Vapor Pressure by Means of Reanalysis Data

By means of ERA-40, JRA-25, NCEP/NCAR and NCEP/DOE reanalysis data, empirical relations between precipitable water and surface vapor pressure in spatial and temporal scale were calculated. The reliabilities of precipitable water from reanalysis data were validated based on comparing different W-e empirical relations of various reanalysis data, in order to provide basis and reference for reasonable application. The results showed that W-e empirical relation of ERA-40 was closest to that of sounding data in China, and precipitable water from ERA-40 was the most credible. The worldwide comparison among W-e empirical relations of four reanalysis data showed that there was little difference in annual mean W-e empirical relations in the middle latitudes and great differences in low and high latitudes. Seasonal mean W-e empirical relations in the middle latitudes of the northern Hemisphere had little difference in spring, autumn and winter, but great difference in summer. Therefore, the reliabilities of precipitable water from reanalysis data in spring, autumn and winter in the middle latitudes of the northern hemisphere were higher than other areas and seasons. W-e empirical relations of NCEP/NCAR and NCEP/DOE had good stability in different years, while there was poor stability in ERA-40 and JRA-25.

A New Version of Regional Ocean Reanalysis for Coastal Waters of China and Adjacent Seas

A new regional ocean reanalysis over multiple decades （1958 2008） for the coastal waters of China and adjacent seas has been completed by the National Marine Data and Information Service （NMD[S） under the CORA （China Ocean ReAnalysis） project. Evaluations were performed on three aspects： （1） the improvement of general reanalysis quality; （2） eddy structures; and （3） decadal variability of sea surface height anomalies （SSHAs）. Results showed that the quality of the new reanalysis has been enhanced beyond ~40% （39% for temperature, 44% for salinity） in terms of the reduction of root mean squared errors （RMSEs） for which the reanalysis values were compared to observed values in the observational space. Compared to the trial version released to public in 2009, the new reanalysis is able to reproduce more detailed eddy structures as seen in satellite and in situ observations. EOF analysis of the reanalysis SSHAs showed that the new reanalysis reconstructs the leading modes of SSHAs much better than the old version. These evaluations suggest that the new CORA regional reanalysis represents a much more useful dataset for the community of the coastal waters of China and adjacent seas.

A Regional Ocean Reanalysis System for Coastal Waters of China and Adjacent Seas

A regional ocean reanalysis system for the coastal waters of China and adjacent seas has been developed by the National Marine Data and Information Service（NMDIS）.It produces a dataset package called CORA （China ocean reanalysis）.The regional ocean model used is based on the Princeton Ocean Model with a generalized coordinate system（POMgcs）.The model is parallelized by NMDIS with the addition of the wave breaking and tidal mixing processes into model parameterizations.Data assimilation is a sequential three-dimensional variational（3D-Var） scheme implemented within a multigrid framework.Observations include satellite remote sensing sea surface temperature（SST）,altimetry sea level anomaly（SLA）,and temperature/salinity profiles.The reanalysis fields of sea surface height,temperature,salinity,and currents begin with January 1986 and are currently updated every year. Error statistics and error distributions of temperature,salinity and currents are presented as a primary evaluation of the reanalysis fields using sea level data from tidal gauges,temperature profiles,as well as the trajectories of Argo floats.Some case studies offer the opportunity to verify the evolution of certain local circulations.These evaluations show that the reanalysis data produced provide a good representation of the ocean processes and phenomena in the coastal waters of China and adjacent seas.

Evaluation of Mid-Depth Currents of NCEP Reanalysis Data in the Tropical Pacific Using ARGO Float Position Information

The global project of the Array for Real-time Geostrophic Oceanography （ARGO） provides a unique opportunity to observe the absolute velocity in mid-depths of the world oceans. A total of 1597 velocity vectors at 1000 （2000） db in the tropical Pacific derived from the ARGO float position information during the period November 2001 to October 2004 are used to evaluate the intermediate currents of the National Centers for Environmental Prediction reanalysis. To derive reliable velocity information from ARGO float trajectory points, a rigorous quality control scheme is applied, and by virtue of a correction method for reducing the drift error on the surface in obtaining the velocity vectors, their relative errors are less than 25%. Based on the comparisons from the quantitative velocity vectors and from the space-time average currents, some substantial discrepancies are revealed. The first is that the velocities of the reanalysis at mid-depths except near the equator are underestimated relative to the observed velocities by the floats. The average speed difference between NCEP and ARGO values ranges from about -2.3cm s^-1 to -1.8 cm s^-1. The second is that the velocity difference between the ocean model and the observations at 2000 dB seems smaller than that at 1000 dB. The third is that the zonal flow in the reanalysis is too dominant so that some eddies could not be simulated, such as the cyclonic eddy to the east of 160°E between 20°N and 30°N at 2000 dB. In addition, it is noticeable that many floats parking at 1000 dB cannot acquire credible mid-depth velocities due to the time information of their end of ascent （start of descent） on the surface in the trajectory files. Thus, relying on default times of parking, descent and ascent in the metadata files gravely confines their application to measuring mid-depth currents.

Accumulation over the Greenland Ice Sheet as Represented in Reanalysis Data

Annual precipitation,evaporation,and calculated accumulation from reanalysis model outputs have been investigated for the Greenland Ice Sheet （GrIS）,based on the common period of 1989-2001.The ERA-40 and ERA-interim reanalysis data showed better agreement with observations than do NCEP-1 and NCEP-2 reanalyses.Further,ERA-interim showed the closest spatial distribution of accumulation to the observation.Concerning temporal variations,ERA-interim showed the best correlation with precipitation observations at five synoptic stations,and the best correlation with in situ measurements of accumulation at nine ice core sites.The mean annual precipitation averaged over the whole GrIS from ERA-interim （363 mm yr 1） and mean annual accumulation （319 mm yr 1） are very close to the observations.The validation of accumulation calculated from reanalysis data against ice-core measurements suggests that further improvements to reanalysis models are needed.

Exploitable wave energy assessment based on ERA-Interim reanalysis data—A case study in the East China Sea and the South China Sea

Wave energy resources assessment is a very important process before the exploitation and utilization of the wave energy. At present, the existing wave energy assessment is focused on theoretical wave energy conditions for interesting areas. While the evaluation for exploitable wave energy conditions is scarcely ever performed. Generally speaking, the wave energy are non-exploitable under a high sea state and a lower sea state which must be ignored when assessing wave energy. Aiming at this situation, a case study of the East China Sea and the South China Sea is performed. First, a division basis between the theoretical wave energy and the exploitable wave energy is studied. Next, based on recent 20 a ERA-Interim wave field data, some indexes including the spatial and temporal distribution of wave power density, a wave energy exploitable ratio, a wave energy level, a wave energy stability, a total wave energy density, the seasonal variation of the total wave energy and a high sea condition frequency are calculated. And then the theoretical wave energy and the exploitable wave energy are compared each other; the distributions of the exploitable wave energy are assessed and a regional division for exploitable wave energy resources is carried out; the influence of the high sea state is evaluated. The results show that considering collapsing force of the high sea state and the utilization efficiency for wave energy, it is determined that the energy by wave with a significant wave height being not less 1 m or not greater than 4 m is the exploitable wave energy. Compared with the theoretical wave energy, the average wave power density, energy level, total wave energy density and total wave energy of the exploitable wave energy decrease obviously and the stability enhances somewhat. Pronounced differences between the theoretical wave energy and the exploitable wave energy are present. In the East China Sea and the South China Sea, the areas of an abundant and stable exploitable wave energy are primarily located in the north-central part of the South China Sea, the Luzon Strait, east of Taiwan, China and north of Ryukyu Islands; annual average exploitable wave power density values in these areas are approximately 10-15 kW/m; the exploitable coefficient of variation （COV） and seasonal variation （SV） values in these areas are less than 1.2 and 1, respectively. Some coastal areas of the Beibu Gulf, the Changjiang Estuary, the Hangzhou Bay and the Zhujiang Estuary are the poor areas of the wave energy. The areas of the high wave energy exploitable ratio is primarily in nearshore waters. The influence of the high sea state for the wave energy in nearshore waters is less than that in offshore waters. In the areas of the abundant wave energy, the influence of the high sea state for the wave energy is prominent and the utilization of wave energy is relatively difficult. The developed evaluation method may give some references for an exploitable wave energy assessment and is valuable for practical applications.

Evaluation on monthly sea surface wind speed of four reanalysis data sets over the China seas after 1988

This study investigates the long-term changes of monthly sea surface wind speeds over the China seas from 1988 to 2015. The 10-meter wind speeds products from four major global reanalysis datasets with high resolution are used: Cross-Calibrated Multi-Platform data set(CCMP), NCEP climate forecast system reanalysis data set(CFSR),ERA-interim reanalysis data set(ERA-int) and Japanese 55-year reanalysis data set(JRA55). The monthly sea surface wind speeds of four major reanalysis data sets have been investigated through comparisons with the longterm and homogeneous observation wind speeds data recorded at ten stations. The results reveal that(1) the wind speeds bias of CCMP, CFSR, ERA-int and JRA55 are 0.91 m/s, 1.22 m/s, 0.62 m/s and 0.22 m/s, respectively.The wind speeds RMSE of CCMP, CFSR, ERA-int and JRA55 are 1.38 m/s, 1.59 m/s, 1.01 m/s and 0.96 m/s,respectively;(2) JRA55 and ERA-int provides a realistic representation of monthly wind speeds, while CCMP and CFSR tend to overestimate observed wind speeds. And all the four data sets tend to underestimate observed wind speeds in Bohai Sea and Yellow Sea;(3) Comparing the annual wind speeds trends between observation and the four data sets at ten stations for 1988-1997, 1988–2007 and 1988–2015, the result show that ERA-int is superior to represent homogeneity monthly wind speeds over the China seaes.

Evaluating the Accuracy of ERA5 Wave Reanalysis in the Water Around China

Wave parameters, such as wave height and wave period, are important for human activities, such as navigation, ocean engineering and sediment transport, etc. In this study, wave data from six buoys around Chinese waters, are used to assess the quality of wave height and wave period in the ERA5 reanalysis of the European Centre for Medium-Range Weather Forecasts. Annual hourly data with temporal resolution are used. The difference between the significant wave height(SWH) of ERA 5 and that of the buoy varies from-0.35 m to 0.30 m for the three shallow locations;for the three deep locations, the variation ranges from-0.09 m to 0.09 m. The ERA5 SWH data show positive biases, indicating an overall overestimation for all locations, except for E2 and S1 where underestimation is observed. During the tropical cyclone period, a large(about 32%) underestimation of the maximum SWH in the ERA5 data is observed. Hence, the ERA5 SWH data cannot be used for design applications without site-specific validation. The difference between the annual wave period from ERA5 and the mean wave period from the buoys varies from-1.31 s to 0.4 s. Inter-comparisons suggest that the ERA5 dataset is consistent with the annual mean SWH. However, for the average period, the performance is not good, and half of the correlation coefficients in the four points are less 50%. Overall, the deep water area simulation effect is better than that in the shallow water.