A Study on Reconstruction of Surface Wind Speed in China Due to Various Climate Variabilities

Using European Centre for Medium-Range Weather Forecasts Reanalysis V5(ERA5)reanalysis data,this study investigated the reconstruction effects of various climate variabilities on surface wind speed in China from 1979 to 2022.The results indicated that the reconstructed annual mean wind speed and the standard deviation of the annual mean wind speed,utilizing various climate variability indices,exhibited similar spatial modes to the reanalysis data,with spatial correlation coefficients of 0.99 and 0.94,respectively.In the reconstruction of six major wind power installed capacity provinces/autonomous regions in China,the effects were notably good for Hebei and Shanxi provinces,with the correlation coefficients for the interannual regional average wind speed time series being 0.65 and 0.64,respectively.The reconstruction effects of surface wind speed differed across seasons,with spring and summer reconstructions showing the highest correlation with reanalysis data.The correlation coefficients for all seasons across most regions in China ranged between 0.4 and 0.8.Among the reconstructed seasonal wind speeds for the six provinces/autonomous regions,Shanxi Province in spring exhibited the highest correlation with the reanalysis,with a coefficient of 0.61.The large-scale climate variability indices showed good reconstruction effects on the annual mean wind speed in China,and could explain the interannual variability trends of surface wind speed in most regions of China,particularly in the main wind energy provinces/autonomous regions.

Changes of spring wind erosion based on wind erosion climate factor in the black soil region of Northeast China

The dry and windy climate and low ground cover in spring in the black soil region of Northeast China make the soil strongly affected by wind erosion,which seriously threatens the food security and ecological security of this region.In this paper,based on the daily observation data of 124 meteorological stations in study area from 1961 to 2020,seasonal and monthly wind erosion climate factor(C)in spring(March to May)were calculated by using the method proposed by the Food and Agriculture Organization of the United Nations(FAO),the wind erosion characterization in spring were systematically analyzed based on C by various statistical analysis methods.The results showed that in the past 60 years,spring wind erosion climate factor(CSp)and monthly C of the whole region and each province(region)all showed highly significant decreasing trend,but they began to show rebounded trend in the middle or late 2000s.CSp of the study area showed a significant upward trend since 2008 with an increase of 4.59(10a)^(-1).The main contributors to this upward trend are the changes of C in March and in April.For the four provinces(regions),CSp in Heilongjiang,Jilin,Liaoning and eastern Inner Mongolia all showed rebounded since 2008,2011,2008 and 2009,respectively.The rebounded trend of CSp in eastern Inner Mongolia was the most obvious with a tendency rate of 11.27(10a)^(-1),and its mutation occurred after 1984.The rebound trend of CSp in Heilongjiang Province takes the second place,with a trend rate of 4.72(10a)^(-1),but there’s no obvious time mutation characteristics.The spatial characteristics of CSpand monthly C are similar,showing decreasing characteristics centered on the typical black soil belt of Northeast China.Compared with 1961-1990,in the period from 1991 to 2020,the proportion of high value areas(CSp>35,monthly C>10)has decreased to varying degrees,while the proportion of low value areas(CSp≤10,monthly C≤4)has increased.The trends of seasonal and monthly C in 82.2%~87.7%of the stations show significant decreases at 95%confidence level.CSp is closely related to wind speed at 2m height,temperature difference,minimum temperature and precipitation in the same period,of which the correlation between CSp and wind speed is the strongest,indicating that the main control factor for CSp in the study area is wind speed,but the impact of the change of temperature and precipitation on CSp cannot be ignored.

An Observational Study on the Local Climate Effect of the Shangyi Wind Farm in Hebei Province

Zhangjiakou is an important wind power base in Hebei Province,China.The impact of its wind farms on the local climate is controversial.Based on long-term meteorological data from 1981 to 2018,we investigated the effects of the Shangyi Wind Farm(SWF)in Zhangjiakou on air temperature,wind speed,relative humidity,and precipitation using the anomaly or ratio method between the impacted weather station and the non-impacted background weather station.The influence of the SWF on land surface temperature(LST)and evapotranspiration(ET)using MODIS satellite data from 2003 to 2018 was also explored.The results showed that the SWF had an atmospheric warming effect at night especially in summer and autumn(up to 0.95℃).The daytime air temperature changes were marginal,and their signs were varying depending on the season.The annual mean wind speed decreased by 6%,mainly noted in spring and winter(up to 14%).The precipitation and relative humidity were not affected by the SWF.There was no increase in LST in the SWF perhaps due to the increased vegetation coverage unrelated to the wind farms,which canceled out the wind farm-induced land surface warming and also resulted in an increase in ET.The results showed that the impact of wind farms on the local climate was significant,while their impact on the regional climate was slight.

Projections of Wind Changes for 21st Century in China by Three Regional Climate Models

This paper examines the capability of three regional climate models (RCMs), i.e., RegCM3 (the International Centre for Theoretical Physics Regional Climate Model), PRECIS (Providing Regional Climates for Impacts Studies) and CMM5 (the fifth-generation Pennsylvania State University-the National Center for Atmospheric Research of USA, NCAR Mesoscale Model) to simulate the near-surface-layer winds (10 m above surface) all over China in the late 20th century. Results suggest that like global climate models (GCMs), these RCMs have the certain capability of imitating the distribution of mean wind speed and fail to simulate the greatly weakening wind trends for the past 50 years in the country. However, RCMs especially RegCM3 have the better capability than that of GCMs to simulate the distribution and change feature of mean wind speed. In view of their merits, these RCMs were used to project the variability of near-surface-layer winds over China for the 21st century. The results show that 1) summer mean wind speed for 2020-2029 will be lower compared to those in 1990-1999 in most area of China; 2) annual and winter mean wind speed for 2081-2100 will be lower than those of 1971-1990 in the whole China; and 3) the changes of summer mean wind speed for 2081-2100 are uncertain. As a result, although climate models are absolutely necessary for projecting climate change to come, there are great uncertainties in projections, especially for wind speed, and these issues need to be further explored.

Forecasting method of monthly wind power generation based on climate model and long short-term memory neural network

Predicting wind power gen eration over the medium and long term is helpful for dispatchi ng departme nts,as it aids in constructing generation plans and electricity market transactions.This study presents a monthly wind power gen eration forecast!ng method based on a climate model and long short-term memory(LSTM)n eural n etwork.A non linear mappi ng model is established between the meteorological elements and wind power monthly utilization hours.After considering the meteorological data(as predicted for the future)and new installed capacity planning,the monthly wind power gen eration forecast results are output.A case study shows the effectiveness of the prediction method.

Linking moisture and near-surface wind with winter temperature to reveal the Holocene climate evolution in arid Xinjiang region of China

An increasing number of palaeo-climatic records have been reported to identify the Holocene climate history in the arid Xinjiang region of northwest China. However, few studies have fully considered the internal linkages within the regional climate system, which may limit our understanding of the forcing mechanisms of Holocene climate change in this region. Here, we systematically consider three major issues of the moisture/precipitation, temperature and near-surface wind relevant to the Holocene climate history of Xinjiang. First, despite there still has debated for the Holocene moisture evolution in this region, more climatic reconstructions from lake sediments, loess, sand-dunes and peats support a long-term regional wetting trend. Second, temperature records from ice cores, peats and stalagmites demonstrate a long-term winter warming trend during the Holocene in middle-to high-latitudes of Asia. Third, recent studies of aeolian sedimentary sequences reveal that the near-surface winds in winter gradually weakened during the Holocene, whereas the winter mid-latitude Westerlies strengthened in the Tienshan Mountains. Based on this evidence, in the arid Xinjiang region we propose an early to middle Holocene relatively cold and dry interval, with strong near-surface winds;and a warmer, wetter interval with weaker near-surface winds in the middle to late Holocene during winter. Additionally,we develop a conceptual model to explain the pattern of Holocene climate changes in this region.From the early to the late Holocene, the increasing atmospheric COcontent and winter insolation,and the shrinking of high-latitude continental ice-sheets, resulted in increasing winter temperatures in middle to high latitudes in the Northern Hemisphere. Subsequently, the increased winter temperature strengthened the winter mid-latitude Westerlies and weakened the Siberian high-pressure system,which caused an increase in winter precipitation and a decrease in near-surface wind strength. This scenario is strongly supported by evidence from geological records, climate simulation results, and modern reanalysis data. Our hypothesis highlights the important contribution of winter temperature in driving the Holocene climatic evolution of the arid Xinjiang region, and it implies that the socio-economic development and water resources security of this region will face serious challenges presented by the increasing winter temperature in the future.

Spatio-Temporal Analysis of Climate Change Impact on Future Wind Power Potential in Burundi (East Africa)

This paper assessed climate change impact on future wind power potential across highlands and western lowlands of Burundi. Hourly observed and MERRA-2 data were considered for the historical period 1980-2016, and a Multi-model ensemble for future projections data of eight selected Regional Climate Models under RCP 4.5 and RCP 8.5 over the periods 2019-2040 and 2071-2100 was used. Variability and trend analysis were adopted using standardized index and Mann-Kendall’s test, respectively while wind power density (WPD) in quartiles was adopted for changes distribution. As results, diurnal wind speeds (WS) were higher from 9:00 AM to 2:00 PM, while monthly wind speeds reached the maximum during summer time. An increasing trend in WPD was detected all over the studied area. Over the period 2019-2040, the lowest WPD change is projected at Northern Highlands (NHL) under RCP 4.5 with 28.04 W·m−2 while the highest WPD change of 47.35 W·m−2 is projected under RCP 8.5 at Southern Imbo plain (SIP). As for the period 2071-2100, the highest change is expected at SIP under RCP 8.5 with 152.39 W·m−2 while the minimum change of 83.96 W·m−2 is projected under RCP 4.5 at NHL. The findings showed that areas nearby the Lake Tanganyika are expected to have high positive WDP changes.

Coping with climate change and China's wind energy sustainable development

Greenhouse gas emissions are the main cause of today's climate change. To address this problem, the world is in an era of new round energy transformation, and the existing energy structure is being reformed. In this paper, according to the Chinese government's action plan for coping with climate change, the China's wind energy sustainable development goals and development route are discussed, and the countermeasures and suggestions are put forward. Wind energy is currently a kind of important renewable energy with matured technology which can be scale-up developed and put into commercial application, and in this transformation, wind energy will play a key role with other non-fossil energy sources. The development and utilization of wind energy is a systematic project, which needs to be solved from the aspects of policy, technology and management. At present, China is in the stage of transferring from "large wind power country" to "strong wind power country", opportunities and challenges coexist, and the advantages of China's socialist system could be fully used, which can concentrate power to do big things and make contribution in the process of realizing global energy transformation.

A positive trend in the stability of global offshore wind energy

The recognition on the trend of wind energy stability is still extremely rare,although it is closely related to acquisition efficiency,grid connection,equipment lifetime,and costs of wind energy utilization.Using the 40-year(1979–2018)ERA-Interim data from the European Center for Medium-Range Weather Forecasts,this study presented the spatial-temporal distribution and climatic trend of the stability of global offshore wind energy as well as the abrupt phenomenon of wind energy stability in key regions over the past 40 years with the climatic analysis method and Mann-Kendall(M-K)test.The results show the following 5 points.(1)According to the coefficient of variation(C_(v))of the wind power density,there are six permanent stable zones of global offshore wind energy:the southeast and northeast trade wind zones in the Indian,Pacific and Atlantic oceans,the Southern Hemisphere westerly,and a semi-permanent stable zone(North Indian Ocean).(2)There are six lowvalue zones for both seasonal variability index(S_(v))and monthly variability index(M_(v))globally,with a similar spatial distribution as that of the six permanent stable zones.M_(v) and S_(v) in the Arabian Sea are the highest in the world.(3)After C_(v),M_(v) and S_(v) are comprehensively considered,the six permanent stable zones have an obvious advantage in the stability of wind energy over other sea areas,with C_(v) below 0.8,M_(v) within 1.0,and S_(v) within 0.7 all the year round.(4)The global stability of offshore wind energy shows a positive climatic trend for the past four decades.C_(v),M_(v) and S_(v) have not changed significantly or decreased in most of the global ocean during 1979 to2018.That is,wind energy is flat or more stable,while the monthly and seasonal variabilities tend to shrink/smooth,which is beneficial for wind energy utilization.(5)C_(v) in the low-latitude Pacific and M_(v) and S_(v) in both the North Indian Ocean and the low-latitude Pacific have an obvious abrupt phenomenon at the end of the20th century.

Modeling Analysis of Factors Influencing Wind-Borne Seed Dispersal: A Case Study on Dandelion

A weed is a plant that thrives in areas of human disturbance, such as gardens, fields, pastures, waysides, and waste places where it is not intentionally cultivated. Dispersal affects community dynamics and vegetation response to global change. The process of seed disposal is influenced by wind, which plays a crucial role in determining the distance and probability of seed dispersal. Existing models of seed dispersal consider wind direction but fail to incorporate wind intensity. In this paper, a novel seed disposal model was proposed in this paper, incorporating wind intensity based on relevant references. According to various climatic conditions, including temperate, arid, and tropical regions, three specific regions were selected to establish a wind dispersal model that accurately reflects the density function distribution of dispersal distance. Additionally, dandelions growth is influenced by a multitude of factors, encompassing temperature, humidity, climate, and various environmental variables that necessitate meticulous consideration. Based on Factor Analysis model, which completely considers temperature, precipitation, solar radiation, wind, and land carrying capacity, a conclusion is presented, indicating that the growth of seeds is primarily influenced by plant attributes and climate conditions, with the former exerting a relatively stronger impact. Subsequently, the remaining two plants were chosen based on seed weight, yielding consistent conclusion.

Changes in Climate Factors and Their Impacts on Wind Erosion Climatic Erosivity in Farming-pastoral Zone of Northern China

Climate change can affect wind erosion power and hence induce changes in wind erosion rates.In this study,the wind erosion climate factor(C-factor),proposed by the Food and Agriculture Organization of the United Nations,was used to assess the impact of changes in climate on wind erosion climatic erosivity.The Mann-Kendall test was employed to detect trends in the C-factor during the period of 1961–2017 in the farming-pastoral zone of northern China.Sensitivity analysis was used to determine the sensitivity of the C-factor to changes in key climate factors.Furthermore,a comparison of the contributions of different climate factors was carried out to understand their impact on changes in the C-factor.The results indicated that most of the surveyed region exhibited decreasing trends in wind speed at a confidence level of 90%,while maximum and minimum temperatures showed increasing trends throughout the study area.As a consequence of decreasing wind speed,the annual C-factor exhibited significant decreasing trends,with a mean slope of–0.58/yr.Seasonal analysis revealed that in most regions,the changes in the C-factor had significant decreasing trends in spring,winter,and autumn,while in more than two-thirds of the study area,no significant change trends in the C-factor were detected in summer at a confidence level of 90%.Sensitivity analysis showed that the C-factor was most sensitive to wind speed,and that the sensitivity coefficients from July to September were much higher than those in other months.Contribution analysis revealed that,for most stations,wind speed(with greater values of sensitivity coefficients)was the dominant factor in the change of C-factor,while for some stations,the minimum temperature made the most contribution to the C-factor’s change due to its dramatic changes during the study period.Although the minimum temperature sensitivity coefficient was the lowest of all the sensitivity coefficients,it is urgent to evaluate the expected impact of minimum temperature due to its possible changes in the future.

The Climate Change Impact on Russia’s Wind Energy Resource: Current Areas of Research

Exploration of the climate change impact on wind energy is a focus of scientific analysis and research in many countries around the world. Previous studies have demonstrated that over the last three decades measured wind in the boundary and surface layer of the atmosphere has changed all over the globe. However, effects of climate change on the wind energy sector of Russia are not well explored. Therefore, the Russian climate change research needs to focus on improving the analysis and prediction of wind characteristics that are most relevant to Russia’s wind energy development. This paper analyzes the effects of global climate change on the patterns of the general circulation of the atmosphere, large-scale atmospheric temperature field and dynamics, as well as wind speed in the planetary boundary layer and, in particular, in the atmospheric surface layer, with regards to Russia’s geographical location and its climatic characteristics. This paper also explores and discusses current areas of climate change research relevant for estimating the wind energy potential in Russia. Two areas of research are emphasized: study of the impact of global warming on poleward shifts of the large-scale synoptic eddies which strongly affect the weather patterns and wind field over large territories;and the study of the effects of ice melting in Arctic seas which significantly alter the properties of the underlying surface and, thus, speed and direction of wind in the surface layer.

Hudson Bay Climate Change and Local Winter Wind Circulation

The meteorological mechanisms causing the recent increase in winter wind speed on Hudson Bay are investigated by examining the NARR dataset (The North American Regional Reanalysis Model) for the past several decades. Winter seasonal changes for atmospheric variables are examined and their interconnections are studied. Yearly mean near-surface temperatures are analyzed from 1948 to denote a rapid warming over Hudson Bay from late 1998 onwards. The surface albedo, air temperatures, mean sea level pressure and wind vector anomalies from 1998 to 2015 have also been studied. The comparison of the 1000 hPa wind vector mean and departures from 1981-2010 (normal period) averages have shown an intensification of anti-cyclonic anomaly pattern over most parts of Hudson Bay. The structure of the wind vector anomalies has revealed a contrast between cyclonic and anti-cyclonic local wind circulations mostly in the east, north and north-west regions along with wind speed increasing at 10 m, increases in near-surface air temperature and decreasing of the surface albedo. The anomalies of the wind vector analysing at different pressure levels show the change in wind direction mostly from northwesterly (zonal wind weakening) to south and easterlies. The polar jet wind vectors at 200 mb during anomaly time (1998-2015) have revealed the changes in magnitude and position. During winter anomaly time, the polar jet at 200 mb has been shifted mostly from rather mean colder north westerly currents to the rather warmer south and easterly anomaly currents over Hudson Bay areas. The yearly historical total accumulated Hudson Bay ice coverage during 1980-2015, using Canadian Ice Service data has shown a slight reduction in the north, north-west and eastern Hudson Bay. The linear regressions of the winter temperature anomaly at 2 m against albedo anomaly, alongside the wind speed anomaly at 10 m against air temperature at 2 m, have shown a relationship between these variables. Also, there is a statistically meaningful relation between decreased albedo and increased evaporation.

Simulated Influence of Mountainous Wind Farms Operations on Local Climate

Renewable energy sources,especially wind power,were believed to be able to slow down global warming;however,evidence in recent years shows that wind farms may also induce climate change.With the rapid development of wind power industry,the number of wind farms installed in mountains has gradually increased.Therefore,it is necessary to study the impact of wind farms in mountainous areas on local climate.The Suizhou and Dawu wind farms in northern Hubei Province were chosen for the present study on the impact of wind farm operations on the local climate in mountainous areas.The mesoscale meteorological numerical model Weather Research and Forecasting Model(WRF)and the Fitch model,together with turbulence correction factor,were used to simulate wind farm operations and study their effects on local climate.The results showed the characteristics of wind speed attenuation in mountainous wind farms:the amplitude and range of wind speed attenuation were stronger in the nighttime than in the daytime,and stronger in summer than in winter.The surface temperature increased and became more significant in summer.However,a cooling variation was observed above the surface warming center.The height of this center was higher in the daytime than it was in the nighttime.The latent heat flux in the wind farms decreased at night,accompanied by an increase in sensible heat flux.However,these changes were not significant.Some differences were observed between the impact of wind farms on the climate in the plains and the mountains.Such differences are more likely to be related to complex terrain conditions,climate conditions,and the density of wind turbines.The present study may provide support for the development and construction of wind farms in mountainous areas.

The El Ni?o-Southern Oscillation cycle simulated by the climate system model of Chinese Academy of Sciences

On the basis of more than 200-year control run, the performance of the climate system model of Chinese Academy of Sciences （CAS-ESM-C） in simulating the E1 Nifio-Southern Oscillation （ENSO） cycle is evalu- ated, including the onset, development and decay of the ENSO. It is shown that, the model can reasonably simulate the annual cycle and interannual variability of sea surface temperature （SST） in the tropical Pacif- ic, as well as the seasonal phase-locking of the ENSO. The model also captures two prerequisites for the E1 Nino onset, i.e., a westerly anomaly and a warm SST anomaly in the equatorial western Pacific. Owing to too strong forcing from an extratropical meridional wind, however, the westerly anomaly in this region is largely overestimated. Moreover, the simulated thermocline is much shallower with a weaker slope. As a result, the warm SST anomaly from the western Pacific propagates eastward more quickly, leading to a faster develop- ment of an E1 Nino. During the decay stage, owing to a stronger E1Nino in the model, the secondary Gill-type response of the tropical atmosphere to the eastern Pacific warming is much stronger, thereby resulting in a persistent easterly anomaly in the western Pacific. Meanwhile, a cold anomaly in the warm pool appears as a result of a lifted thermocline via Ekman pumping. Finally, an E1 Nino decays into a La Nina through their interactions. In addition, the shorter period and larger amplitude of the ENSO in the model can be attribut- ed to a shallower thermocline in the equatorial Pacific, which speeds up the zonal redistribution of a heat content in the upper ocean.

Climate change indirectly enhances sandstorm prevention services by altering ecosystem patterns on the Qinghai-Tibet Plateau

Climate change influences both ecosystems and ecosystem services.The impacts of climate change on ecosystems and ecosystem services have been separately documented.However,it is less well known how ecosystem changes driven by climate change will influence ecosystem services,especially in climate-sensitive regions.Here,we analyzed future climate trends between 2040 and 2100 under four Shared Socioeconomic Pathway(SSP) scenarios(SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5) from the Coupled Model Intercomparison Project 6(CMIP6).We quantified their impacts on ecosystems patterns and on the ecosystem service of sandstorm prevention on the Qinghai-Tibet Plateau(QTP),one of the most climate-sensitive regions in the world,using Random Forest model(RF) and Revised Wind Erosion Equation(RWEQ).Strong warming(0.04℃/yr) and wetting(0.65 mm/yr) trends were projected from 2015 to 2100.Under these trends,there will be increased interspersion in the pattern of grassland and sparse vegetation with meadow and swamp vegetation,although their overall area will remain similar,while the areas of shrub and needleleaved forest classes will increase and move toward higher altitudes.Driven by the changes in ecosystem patterns caused by climate change indirectly,grassland will play an irreplaceable role in providing sandstorm prevention services,and sandstorm prevention services will increase gradually from 2040 to 2100(1.059-1.070 billion tons) on the QTP.However,some areas show a risk of deterioration in the future and these should be the focus of ecological rehabilitation.Our research helps to understand the cascading relationship among climate change,ecosystem patterns and ecosystem services,which provides important spatio-temporal information for future ecosystem service management.

Wind Climatology for Alaska: Historical and Future

Wind is a climate variable with major impacts on humans, ecosystems and infrastructure, especially in coastal regions with cold climates. Climate-related changes in high-wind events therefore have major implications for high-latitude residents, yet there has heretofore been no systematic evaluation of such changes in a framework spanning historical and future timeframes. In this study, hourly winds from surface station reports and from dynamical downscaling of winds simulated by two different global climate models have been synthesized into historical and future wind climatologies for Alaska. Quantile mapping procedures are used to calibrate wind simulations driven by an atmospheric reanalysis, and the calibrated winds are then used to bias-adjust the full distributions of historical and future winds downscaled from the global climate models. In the resulting climatologies, winds are generally stronger at coastal and offshore (island) locations than at interior sites, where calm conditions are frequent in winter. The season of peak wind speed varies from winter in the coastal and offshore locations to summer in interior areas. High-wind events determined from the hourly data are most frequent during winter at coastal locations. Projected changes for the late 21st century are statistically significant at many locations, and they show a qualitatively similar seasonality in the output from the two models: an increase of mean wind speeds in the cold season and a decrease of mean wind speeds in the warm season. High-wind events are projected by both models to become more frequent in the northern and western Alaska coastal regions, which are precisely the regions in which the protective sea ice cover has decreased (and is projected to decrease further), pointing to increased risks of coastal flooding and erosion.

Numerical experimental study on the potential climatic impacts oflarge-scale wind farms in China

Wind energy has been rapidly developed in China during the past decades and the installed capacity has been the largest in the world. In the future, utilization of wind power is still expected to carry out in China mainly with a large-scale centralized layout. Here, we examine the potential climatic impacts of large-scale windfarms associated with deployment scale in China using numerical experiments, in which four deployment scenarios were designed. These four scenarios represented relatively small- (484 GW), medium- (2165 GW) and large-scale (3490 GW and 5412 GW) installed wind power capacities, respectively. Results showed that turbulent kinetic energy, wind velocity, and air temperature varied consistently within those windfarms with the largest changes in turbine hub heights. Moreover, the above relatively large- scale windfarms could induce regional wanning with a maximum of above 0.8 °C in North China. This regional warming may be linked to an anomalous circulation pattern with a negative pressure anomaly center in Northeast China and a positive pressure anomaly center in the middle and lower reaches of the Yangtze-Huaihe River Basin.

Climatology of Wind-Seas and Swells in the China Seas from Wave Hindcast

The wind-sea and swell climates in the China Seas are investigated by using the 27-yr Integrated Ocean Waves for Geophysical and other Applications(IOWAGA)hindcast data.A comparison is made between the significant wave height from the IOWAGA hindcasts and that from a jointly calibrated altimetry dataset,showing the good performance of the IOWAGA hindcasts in the China Seas.A simple but practical method of diagnosing whether the sea state is wind-sea-dominant or swell-dominant is proposed based on spectral partitioning.Different from the characteristics of wind-seas and swells in the open ocean,the wave fields in the enclosed seas such as the China Seas are predominated by wind-sea events in respect of both frequencies of occurrences and energy weights,due to the island sheltering and limited fetches.The energy weights of wind-seas in a given location is usually more significant than the occurrence probability of wind-sea-dominated events,as the wave energy is higher in the wind-sea events than in the swell events on average and extreme wave heights are mostly related to wind-seas.The most energetic swells in the China Seas(and other enclosed seas)are‘local swells’,having just propagated out of their generation areas.However,the swells coming from the West Pacific also play an important role in the wave climate of the China Seas,which can only be revealed by partitioning different swell systems in the wave spectra as the energy of them is significantly less than the‘local swells’.

Climate Projections of Spatial Variations in Coastal Storm Surges Along the Gulf of Mexico and U.S.East Coast

Using statistically downscaled atmospheric forcing, we performed a numerical investigation to evaluate future climate's impact on storm surges along the Gulf of Mexico and U.S. east coast. The focus is on the impact of climatic changes in wind pattern and surface pressure while neglecting sea level rise and other factors. We adapted the regional ocean model system(ROMS) to the study region with a mesh grid size of 7–10 km in horizontal and 18 vertical layers. The model was validated by a hindcast of the coastal sea levels in the winter of 2008. Model's robustness was confirmed by the good agreement between model-simulated and observed sea levels at 37 tidal gages. Two 10-year forecasts, one for the IPCC Pre-Industry(PI) and the other for the A1 FI scenario, were conducted. The differences in model-simulated surge heights under the two climate scenarios were analyzed. We identified three types of responses in extreme surge heights to future climate: a clear decrease in Middle Atlantic Bight, an increase in the western Gulf of Mexico, and non-significant response for the remaining area. Such spatial pattern is also consistent with previous projections of sea surface winds and ocean wave heights.