Understanding Simulated Causes of Damaging Surface Winds in a Derecho-Producing Mesoscale Convective System near the East China Coast Based on Convection-Permitting Simulations

A mesoscale convective system(MCS) occurred over the East China coastal provinces and the East China Sea on 30April 2021, producing damaging surface winds near the coastal city Nantong with observed speeds reaching 45 m s^(–1). A simulation using the Weather Research and Forecasting model with a 1.5-km grid spacing generally reproduces the development and subsequent organization of this convective system into an MCS, with an eastward protruding bow segment over the sea. In the simulation, an east-west-oriented high wind swath is generated behind the gust front of the MCS. Descending dry rear-to-front inflows behind the bow and trailing gust front are found to feed the downdrafts in the main precipitation regions. The inflows help to establish spreading cold outflows and enhance the downdrafts through evaporative cooling. Meanwhile, front-to-rear inflows from the south are present, associated with severely rearward-tilted updrafts initially forming over the gust front. Such inflows descend behind(north of) the gust front, significantly enhancing downdrafts and near-surface winds within the cold pool. Consistently, calculated trajectories show that these parcels that contribute to the derecho originate primarily from the region ahead(south) of the east-west-oriented gust front, and dry southwesterly flows in the low-to-middle levels contribute to strong downdrafts within the MCS. Moreover, momentum budget analyses reveal that a large westward-directed horizontal pressure gradient force within the simulated cold pool produced rapid flow acceleration towards Nantong. The analyses enrich the understanding of damaging wind characteristics over coastal East China and will prove helpful to operational forecasters.

Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model

Numerical weather prediction(NWP)models have always presented large forecasting errors of surface wind speeds over regions with complex terrain.In this study,surface wind forecasts from an operational NWP model,the SMS-WARR(Shanghai Meteorological Service-WRF ADAS Rapid Refresh System),are analyzed to quantitatively reveal the relationships between the forecasted surface wind speed errors and terrain features,with the intent of providing clues to better apply the NWP model to complex terrain regions.The terrain features are described by three parameters:the standard deviation of the model grid-scale orography,terrain height error of the model,and slope angle.The results show that the forecast bias has a unimodal distribution with a change in the standard deviation of orography.The minimum ME(the mean value of bias)is 1.2 m s^(-1) when the standard deviation is between 60 and 70 m.A positive correlation exists between bias and terrain height error,with the ME increasing by 10%−30%for every 200 m increase in terrain height error.The ME decreases by 65.6%when slope angle increases from(0.5°−1.5°)to larger than 3.5°for uphill winds but increases by 35.4%when the absolute value of slope angle increases from(0.5°−1.5°)to(2.5°−3.5°)for downhill winds.Several sensitivity experiments are carried out with a model output statistical(MOS)calibration model for surface wind speeds and ME(RMSE)has been reduced by 90%(30%)by introducing terrain parameters,demonstrating the value of this study.

Effect of sea surface wind on the seasonal variation of sea level in the east of China seas

Sea level seasonal variations in the east of China seas from 2004 to 2006 are simulated by the advanced ROMS model. The results show similar sea level spatial features with TOPEX/Poseidon observations, with annual ranges decreasing gradually from the sea coast to the Kuroshio region. By getting rid of wind stress in ROMS model, the simulated sea level results still show obvious seasonal variations. However, the phenomenon of sea level anomaly disappears in Min Zhe Current Coastwise （MZCF） and Su Bei current coastwise （SBCF）, and the change of it from coastal area to ocean recedes. The seal level difference between Bohai, Yellow Sea （BYS） and East China Sea （ECS） becomes weaker in spring and autumn. The annual differences decrease obviously, and the gradual change of annual ranges from seacoast to the Kuroshio almost disappears. The annual ranges in BYS are nearly identical. The annual range ratio without the wind stress to with the wind stress increases gradually from the sea coast to Kuroshio region.

Sea Surface Roughness Derivation from Wind Speed Estimated by Satellite Altimeter

For open sea conditions the sea surface roughness is described as a function of surface stress and wind speed over sea surface by Charnock relation. The sea surface roughnessn in the North-west Pacific Ocean is derived successfully using wind speed data estimated by the TOPEX satellite altimeter. From the results we find that： （1） the mean sea surface roughness in winter is greater than in summer; （2） compared with other sea areas, the sea surface roughness in the sea area east of Japan （ N30°- 40°, E135°- 150°） is larger than in other sea areas; （3） sea surface roughness in the South China Sea changes more greatly than that in the Bohai Sea, Yellow Sea and East China Sea.

Evaluation of ENVISAT ASAR data for sea surface wind retrieval in Hong Kong coastal waters of China

The C-band wind speed retrieval models, CMOD4, CMOD - IFR2, and CMOD5 were applied to retrieval of sea surface wind speeds from ENVISAT （European environmental satellite） ASAR （advanced synthetic aperture radar） data in the coastal waters near Hong Kong during a period from October 2005 to July 2007. The retrieved wind speeds are evaluated by comparing with buoy measurements and the QuikSCAT （quick scatterometer） wind products. The results show that the CMOD4 model gives the best performance at wind speeds lower than 15 m/s. The correlation coefficients with buoy and QuikSCAT winds are 0.781 and 0.896, respectively. The root mean square errors are the same 1.74 m/s. Namely, the CMOD4 model is the best one for sea surface wind speed retrieval from ASAR data in the coastal waters near Hong Kong.

Applications of AMSR-E Measurements for Tropical Cyclone Predictions PartⅠ: Retrieval of Sea Surface Temperature and Wind Speed

Existing satellite microwave algorithms for retrieving Sea Surface Temperature （SST） and Wind （SSW） are applicable primarily for non-raining cloudy conditions. With the launch of the Earth Observing System （EOS） Aqua satellite in 2002, the Advanced Microwave Scanning Radiometer （AMSRoE） onboard provides some unique measurements at lower frequencies which are sensitive to ocean surface parameters under adverse weather conditions. In this study, a new algorithm is developed to derive SST and SSW for hurricane predictions such as hurricane vortex analysis from the AMSRoE measurements at 6.925 and 10.65 GHz. In the algorithm, the effects of precipitation emission and scattering on the measurements are properly taken into account. The algorithm performances are evaluated with buoy measurements and aircraft dropsonde data. It is found that the root mean square （RMS） errors for SST and SSW are about 1.8 K and 1.9 m s^- 1, respectively, when the results are compared with the buoy data over open oceans under precipitating clouds （e.g., its liquid water path is larger than 0.5 mm）, while they are 1.1 K for SST and 2.0 m s^-1 for SSW, respectively, when the retrievals are validated against the dropsonde measurements over warm oceans. These results indicate that our newly developed algorithm can provide some critical surface information for tropical cycle predictions. Currently, this newly developed algorithm has been implemented into the hybrid variational scheme for the hurricane vortex analysis to provide predictions of SST and SSW fields.

Chlorophyll a increase induced by surface winds in the northern South China Sea

The response of chlorophyll a （Chl a） concentration to wind stress is analyzed in the South China Sea （SCS）, using in-situ data of Chl a and remote sensing data （QuikScat-sea surface wind （SSW）, AVHRR-sea surface temperature （SST）, AVISO merged-sea level anomalies （SLA）, SeaWiFS- derived Chl a and MODIS Terra-derived Chl a） in August/September/October 2004, 2006 and 2009. The variability of SSW, SST and SLA 7 d before in-situ Chl a sampling （including the work day of in^situ Chl a sampling） with the same latitude and longitude of the study area are investigated, and the correlation coefficients are calculated between these hydrographic factors and in-situ Chl a concentration. The results show that the Chl a-SSW correlation coefficients at upper layers （such as 0 m and 25 m） are more significant than those at deeper layers （such as 50, 75 and 100 m） 1 3 d before, which indicates that there is a time lag of strong surface winds stimulating phytoplankton bloom. By analyzing the relationship among the daily remote sensing derived （RS- derived） SSW, SST, SLA and 3 d averaged SeaWiFS/MODIS-derived Chl a concentration in the northern SCS in September 2004 and 2009 respectively, it shows that the intensity and speed of surface winds could have great influence on extend of Chl a increase. If surface winds reach 4-5 m/s over, Chl a concentration would increase 1-3 d after the process of strong surface winds in open sea area of the northern SCS mainly during September.

The seasonal variations in the significant wave height and sea surface wind speed of the China's seas

Long-term variations in a sea surface wind speed （WS） and a significant wave height （SWH） are associated with the global climate change, the prevention and mitigation of natural disasters, and an ocean resource exploitation, and other activities. The seasonal characteristics of the long-term trends in China＇s seas WS and SWH are determined based on 24 a （1988-2011） cross-calibrated, multi-platform （CCMP） wind data and 24 a hindcast wave data obtained with the WAVEWATCH-III （WW3） wave model forced by CCMP wind data. The results show the following. （1） For the past 24 a, the China＇s WS and SWH exhibit a significant increasing trend as a whole, of 3.38 cm/（s.a） in the WS, 1.3 cm/a in the SWH. （2） As a whole, the increasing trend of the China＇s seas WS and SWH is strongest in March-April-May （MAM） and December-January-February （DJF）, followed by June-July-August （JJA）, and smallest in September-October-November （SON）. （3） The areal extent of significant increases in the WS was largest in MAM, while the area decreased in JJA and DJF; the smallest area was apparent in SON. In contrast to the WS, almost all of China＇s seas exhibited a significant increase in SWH in MAM and DJF; the range was slightly smaller in JJA and SON. The WS and SWH in the Bohai Sea, the Yellow Sea, East China Sea, the Tsushima Strait, the Taiwan Strait, the northern South China Sea, the Beibu Gull and the Gulf of Thailand exhibited a significant increase in all seasons. （4） The variations in China＇s seas SWH and WS depended on the season. The areas with a strong increase usually appeared in DJF.

Distinct Modes of Winter Arctic Sea Ice Motion and Their Associations with Surface Wind Variability

Using monthly mean sea ice velocity data obtained from the International Arctic Buoy Programme （IABP） for the period of 1979–1998 and the monthly mean NCEP/NCAR re-analysis dataset （1960–2002）, we investigated the spatiotemporal evolution of the leading sea ice motion mode （based on a complex correlation matrix constructed of normalized sea ice motion velocity） and their association with sea level pressure （SLP） and the predominant modes of surface wind field variability. The results indicate that the leading winter sea ice motion mode’s spatial evolution is characterized by two alternating and distinct sea ice modes, or their linear combination. One mode （M1） shows a nearly closed cyclonic or anti-cyclonic circulation anomaly in the Arctic Basin and its marginal seas, resembling to a large extent the response of sea ice motion to the Arctic Oscillation （AO）, as many previous studies have revealed. The other mode （M2） displays a coherent cyclonic or anti-cyclonic circulation anomaly with its center close to the Laptev Sea, which has not been identified in previous observational studies. In fact, M1 and M2 respectively reflect the responses of sea ice motion to two predominant modes of winter surface wind variability north of 70 ？ N, which well correspond, with slight differences, to the first two modes of EOF analysis of winter monthly mean SLP north of 70 ？ N. These slight differences in SLP anomalies lead to a difference of M2 from the response of sea ice motion to the dipole anomaly. Although the AO significantly influences sea ice motion, it is not crucial for the existence of M1. The new sea ice motion mode （M2） has the largest variance and clearly differs from the response of winter monthly mean sea ice motion to the dipole anomaly in SLP fields, and corresponding SLP anomalies also show differences compared to the dipole anomaly. This study indicates that in the Arctic Basin and its marginal seas, slight differences in SLP anomaly patterns can force distinctly different sea ice motion anomalies.

A Method for Sea Surface Wind Field Retrieval from SAR Image Mode Data

To retrieve wind field from SAR images, the development for surface wind field retrieval from SAR images based on the improvement of new inversion model is present. Geophysical Model Functions （GMFs） have been widely applied for wind field retrieval from SAR images. Among them CMOD4 has a good performance under low and moderate wind conditions. Although CMOD5 is developed recently with a more fundamental basis, it has ambiguity of wind speed and a shape gradient of normalized radar cross section under low wind speed condition. This study proposes a method of wind field retrieval from SAR image by com-bining CMOD5 and CMOD4 Five VV-polarisation RADARSAT2 SAR images are implemented for validation and the retrieval re-suits by a combination method （CMOD5 and CMOD4） together with CMOD4 GMF are compared with QuikSCAT wind data. The root-mean-square error （RMSE） of wind speed is 0.75 m s-1 with correlation coefficient 0.84 using the combination method and the RMSE of wind speed is 1.01 m s-1 with correlation coefficient 0.72 using CMOD4 GMF alone for those cases. The proposed method can be applied to SAR image for avoiding the internal defect in CMOD5 under low wind speed condition.

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.

A preliminary assessment of the sea surface wind speed production of HY-2 scanning microwave radiometer

A scanning microwave radiometer（RM） was launched on August 16,2011,on board HY-2 satellite.The six-month long global sea surface wind speeds observed by the HY-2 scanning microwave radiometer are preliminarily validated using in-situ measurements and WindSat observations,respectively,from January to June 2012.The wind speed root-mean-square（RMS） difference of the comparisons with in-situ data is 1.89 m/s for the measurements of NDBC and 1.72 m/s for the recent four-month data measured by PY30-1 oil platform,respectively.On a global scale,the wind speeds of HY-2 RM are compared with the sea surface wind speeds derived from WindSat,the RMS difference of 1.85 m/s for HY-2 RM collocated observations data set is calculated in the same period as above.With analyzing the global map of a mean difference between HY-2 RM and WindSat,it appears that the bias of the sea surface wind speed is obviously higher in the inshore regions.In the open sea,there is a relatively higher positive bias in the mid-latitude regions due to the overestimation of wind speed observations,while the wind speeds are underestimated in the Southern Ocean by HY-2 RM relative to WindSat observations.

Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model

WindSat/Coriolis is the first satellite-borne polarimetric microwave radiometer, which aims to improve the potential of polarimetric microwave radiometry for measuring sea surface wind vectors from space. In this paper, a wind vector retrieval algorithm based on a novel and simple forward model was developed for WindSat. The retrieval algorithm of sea surface wind speed was developed using multiple linear regression based on the simulation dataset of the novel forward model. Sea surface wind directions that minimize the difference between simulated and measured values of the third and fourth Stokes parameters were found using maximum likelihood estimation, by which a group of ambiguous wind directions was obtained. A median filter was then used to remove ambiguity of wind direction. Evaluated with sea surface wind speed and direction data from the U.S. National Data Buoy Center （NDBC）, root mean square errors are 1.2 rn/s and 30~ for retrieved wind speed and wind direction, respectively. The evaluation results suggest that the simple forward model and the retrieval algorithm are practicable for near-real time applications, without reducing accuracy.

Retrieval of sea surface winds under hurricane conditions from GNSS-R observations

Reflected signals from global navigation satellite systems（GNSSs） have been widely acknowledged as an important remote sensing tool for retrieving sea surface wind speeds.The power of GNSS reflectometry（GNSS-R）signals can be mapped in delay chips and Doppler frequency space to generate delay Doppler power maps（DDMs）,whose characteristics are related to sea surface roughness and can be used to retrieve wind speeds.However,the bistatic radar cross section（BRCS）,which is strongly related to the sea surface roughness,is extensively used in radar.Therefore,a bistatic radar cross section（BRCS） map with a modified BRCS equation in a GNSS-R application is introduced.On the BRCS map,three observables are proposed to represent the sea surface roughness to establish a relationship with the sea surface wind speed.Airborne Hurricane Dennis（2005） GNSS-R data are then used.More than 16 000 BRCS maps are generated to establish GMFs of the three observables.Finally,the proposed model and classic one-dimensional delay waveform（DW） matching methods are compared,and the proposed model demonstrates a better performance for the high wind speed retrievals.

Satellite SAR observation of the sea surface wind field caused by rain cells

Rain cells or convective rain,the dominant form of rain in the tropics and subtropics,can be easy detected by satellite Synthetic Aperture Radar（SAR） images with high horizontal resolution.The footprints of rain cells on SAR images are caused by the scattering and attenuation of the rain drops,as well as the downward airflow.In this study,we extract sea surface wind field and its structure caused by rain cells by using a RADARSAT-2 SAR image with a spatial resolution of 100 m for case study.We extract the sea surface wind speeds from SAR image by using CMOD4 geophysical model function with outside wind directions of NCEP final operational global analysis data,Advance Scatterometer（ASCAT） onboard European Met Op-A satellite and microwave scatterometer onboard Chinese HY-2 satellite,respectively.The root-mean-square errors（RMSE） of these SAR wind speeds,validated against NCEP,ASCAT and HY-2,are 1.48 m/s,1.64 m/s and 2.14 m/s,respectively.Circular signature patterns with brighter on one side and darker on the opposite side on SAR image are interpreted as the sea surface wind speed（or sea surface roughness） variety caused by downdraft associated with rain cells.The wind speeds taken from the transect profile which superposes to the wind ambient vectors and goes through the center of the circular footprint of rain cell can be fitted as a cosine or sine curve in high linear correlation with the values of no less than 0.80.The background wind speed,the wind speed caused by rain cell and the diameter of footprint of the rain cell with kilometers or tens of kilometers can be acquired by fitting curve.Eight cases interpreted and analyzed in this study all show the same conclusion.

Sea surface wind speed retrieval from Sentinel-1 HH polarization data using conventional and neural network methods

Conventional retrieval and neural network methods are used simultaneously to retrieve sea surface wind speed(SSWS)from HH-polarized Sentinel-1(S1)SAR images.The Polarization Ratio(PR)models combined with the CMOD5.N Geophysical Model Function(GMF)is used for SSWS retrieval from the HH-polarized SAR data.We compared different PR models developed based on previous C-band SAR data in HH-polarization for their applications to the S1 SAR data.The recently proposed CMODH,i.e.,retrieving SSWS directly from the HHpolarized S1 data is also validated.The results indicate that the CMODH model performs better than results achieved using the PR models.We proposed a neural network method based on the backward propagation(BP)neural network to retrieve SSWS from the S1 HH-polarized data.The SSWS retrieved using the BP neural network model agrees better with the buoy measurements and ASCAT dataset than the results achieved using the conventional methods.Compared to the buoy measurements,the bias,root mean square error(RMSE)and scatter index(SI)of wind speed retrieved by the BP neural network model are 0.10 m/s,1.38 m/s and 19.85%,respectively,while compared to the ASCAT dataset the three parameters of training set are–0.01 m/s,1.33 m/s and 15.10%,respectively.It is suggested that the BP neural network model has a potential application in retrieving SSWS from Sentinel-1 images acquired at HH-polarization.

MODELING AND QUANTITATIVE RETRIEVAL OF FINITE FIELD FOR THE TROPICAL SEA SURFACE WIND SPEED OF THE FY-3B MICROWAVE IMAGER

The purpose of this study is to select a suitable sea wind retrieval method for FY-3B(MWRI). Based on the traditional empirical model of retrieving sea surface wind speed, and in the case of small sample size of FY-3B satellite load regression analysis, this paper analyzes the channel differences between the FY-3B satellite microwave radiation imager(MWRI) and TMI onboard the TRMM. The paper also analyzes the influence of these differences on the channel in terms of receiving temperature, including channel frequency, sensitivity and scaling precision. Then, the limited range of new model coefficient regression analysis is determined(in which the channel range settings include the information and features of channel differences), the regression methods of the finite field are proposed, and the empirical model of wind speed retrieval applicable to MWRI is obtained, which achieves robust results. Compared to the TAO buoy data, the mean deviation of the new model is 0.4 m/s, and the standard deviation is 1.2 m/s. In addition,the schematic diagram of the tropical sea surface wind speed retrieval is provided.

Role of Downward Momentum Transport in the Formation of Severe Surface Winds

The Weather Research and Forecasting (WRF) model was used to investigate the role of downward momentum transport in the formation of severe surface winds for a squall line on 3-4 June 2009 across regions of the Henan and Shandong Provinces of China. The results show that there was a strong westerly jet belt with a wind speed greater than 30 m s 1 and a thickness of 5 km at an altitude of 11-16 km. The jet belt was accelerated, and it descended while the squall line convective system occurred. It was found that the appearance of strong negative perturbation pressure accompanied by the squall line caused the acceleration of the upper-level westerly jet and increased the horizontal wind speed by a maximum of 18%. Meanwhile, the negative buoyancy due to the loading, melting, and evaporation of cloud hydrometeors induced the downward momentum transport from the upper levels. The downward momentum transport contributed approximately 70% and the surface cold pool 30% to the formation of severe surface winds.

Evaluation of reanalysis and satellite-based sea surface winds using in situ measurements from Chinese Antarctic Expeditions

Sea surface winds from reanalysis （NCEP-2 and ERA-40 datasets） and satellite-based products （QuikSCAT and NCDC blended sea winds） are evaluated using in situ ship measurements from the Chinese National Antarctic Research Expeditions （CH1NAREs） from 1989 through 2006, with emphasis on the Southern Ocean （south of 45°S）. Compared with ship observations, the reanalysis winds have a positive mean bias （0.32 m·s-1 for NCEP-2 and 0.13 m·s-1 for ERA-40）, and this bias is more pronounced in the Southern Ocean （0.57 m·s-1 and 0.45 m·s-1, respectively）. However, mean biases are negative in the tropics and subtropics. The satellite-based winds also show positive mean biases, larger than those of the reanalysis data. All four wind products overestimate ship wind speed for weak winds （〈4 m·s-1） but underestimate for strong winds （〉10 m·s-1）. Differences between the reanalysis and satellite winds are examined to identify regions with large discrepancies.

Dominant patterns of winter Arctic surface wind variability

Dominant statistical patterns of winter Arctic surface wind （WASW） variability and their impacts on Arctic sea ice motion are investigated using the complex vector empirical orthogonal function （CVEOF） method. The results indicate that the leading CVEOF of Arctic surface wind variability, which accounts for 33% of the covariance, is characterized by two different and alternating spatial patterns （WASWP1 and WASWP2）. Both WASWP1 and WASWP2 show strong interannual and decadal variations, superposed on their declining trends over past decades. Atmospheric circulation anomalies associated with WASWPI and WASWP2 exhibit, respectively, equivalent barotropic and some baroclinic characteristics, differing from the Arctic dipole anomaly and the seesaw structure anomaly between the Barents Sea and the Beaufort Sea. On decadal time scales, the decline trend of WASWP2 can be attributed to persistent warming of sea surface temperature in the Greenland--Barents--Kara seas from autunm to winter, reflecting the effect of the Arctic warming. The second CVEOF, which accounts for 18% of the covariance, also contains two different spatial patterns （WASWP3 and WASWP4）. Their time evolutions are significantly correlated with the North Atlantic Oscillation （NAO） index and the central Arctic Pattern, respectively, measured by the leading EOF of winter sea level pressure （SLP） north of 70~N. Thus, winter anomalous surface wind pattern associated with the NAO is not the most important surface wind pattern. WASWP3 and WASWP4 primarily reflect natural variability of winter surface wind and neither exhibits an apparent trend that differs from WASWP1 or WASWP2. These dominant surface wind patterns strongly influence Arctic sea ice motion and sea ice exchange between the western and eastern Arctic. Furthermore, the Fram Strait sea ice volume flux is only significantly correlated with WASWP3. The results demonstrate that surface and geostrophic winds are not interchangeable in terms of describing wind field variability over the Arctic Ocean. The results have important implications for understanding and investigating Arctic sea ice variations： Dominant patterns of Arctic surface wind variability, rather than simply whether there are the Arctic dipole anomaly and the Arctic Oscillation （or NAO）, effectively affect the spatial distribution of Arctic sea ice anomalies.