3.1. The datasets used in this study
The 12 monthly mean marine wind speed datasets used in this study are all freely available and their characteristics are summarized in Table 1. There are however several notable datasets that have not been included. Two recent reanalysis products, the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA; Rienecker et al., 2011) and the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR; Saha et al., 2010), were not included because they do not provide monthly mean wind speed data. Previous versions of some of the datasets are compared by Wallcraft et al. (2009) and Smith et al. (2011).
Table 1. Summary of characteristics of monthly mean wind products compared in this study. All products are referenced to 10 m height. Note that not all input data sources listed are available for the full period of any particular dataset
|ERS1 scatterometer||August 1991 to May 1996||1° × 1°, 80°S–80°N||Neutral|
|ERS2 scatterometer||April 1996 to December 2000||1° × 1°, 80°S–80°N||Neutral|
|CERSAT QuikSCAT (QS_CERSAT) |
|QuikSCAT scatterometer||August 1999 to October 2009||0.5° × 0.5°, 80°S–80°N||Neutral|
|RSS v4 QuikSCAT (QS_RSS4), |
Ricciardulli and Wentz (2011)
|QuikSCAT scatterometer||July 1999 to November 2009||0.25° × 0.25° 80°S–80°N||Neutral|
Andersson et al. (2007)
|SSM/I||July 1989 to December 2005||0.5° × 0.5°, global||Neutral|
Berry and Kent (2009, 2011)
|VOS||January 1973 to December 2009||1° × 1°, global ice-free||Stability-dependent|
Kalnay et al. (1996)
|VOS, buoys||January 1948 to present||2.5° × 2.5°, global, including land||Stability-dependent|
|Twentieth Century Reanalysis (V2) http://www.esrl.noaa.gov/psd/data/20thC_Rean/|
Compo et al. (2011)
|none||January 1871 to December 2010||2° × 2°, global, including land||Stability-dependent|
ECMWF (2007); Uppala et al. (2008a, 2008b); Dee et al. (2011)
|VOS, buoys, SSM/I and ERS1&2 scatterometers, QuikSCAT (post March 2000)||January 1989 to present||1.5° × 1.5°, global, including land||Stability-dependent|
Zhang et al. (2006a, 2006b)
|SSM/I, AMSR-E, TMI, QuikSCAT, plus directions from NCEP/DOE2||July 1987 to present||0.25° × 0.25°, global||Neutral|
|CCMP v1.1, Level 3.5a, fast look |
Atlas et al. (2011)
|SSM/I, AMSR-E, TRMM TMI, QuikSCAT, VOS, buoys, ERA40 and ECMWF Operational Analysis||July 1987 to December 2009||0.25° × 0.25°, global||Neutral|
Yu and Weller (2007); Yu et al. (2008)
|NCEP, NCEP2, ERA40, SSMI, QuikSCAT, AMSR-E||January 1958 to December 2009||1° × 1°, global||Neutral|
3.1.1. European Remote Sensing Satellites 1 and 2 (ERS1 and ERS2; IFREMER/CERSAT, 2002a)
These datasets comprise C-band microwave scatterometer data from ERS1 and ERS2 as processed by the Center for Satellite Exploitation and Research (CERSAT) of the French Institute of Research for the Exploration of the Sea (IFREMER, IFREMER/CERSAT, 2002a). Over ice the retrieval is not related to wind speed and the anomalous backscatter over ice is used to provide an ice mask. Details of the land mask used are not given. Scatterometer wind values are neutral winds at a 10-m reference level. The nominal cell size for ERS scatterometer data is 50 km. The sensors have a single swath of 500 km requiring 3–4 days to provide global coverage.
The documentation (IFREMER/CERSAT, 2002a) notes that winds below 3.5 ms−1 are likely to be inaccurate. ERS scatterometers are expected to overestimate low wind speeds and underestimate high wind speeds. Ten-metre neutral winds between 3.5 and 15 ms−1 were thought to be without significant bias (IFREMER/CERSAT, 2002a). Documentation of the European Space Agency's (ESA) current Advanced Scatterometer (ASCAT; Verhoef and Stoffelen, 2011) indicates a low bias in ERS1 and ERS2 of 0.5 ms−1 relative to buoy winds at all wind speeds.
In order to reconstruct gap-filled fields from discrete observations a statistical interpolation is performed using an objective method; the standard errors of the parameters estimated by this method are also computed and provided (IFREMER/CERSAT, 2002a). It is not clear from the documentation which combination of data quality flag values has been used to generate the monthly mean product.
3.1.2. NASA SeaWinds scatterometer (QuikSCAT: QS_CERSAT, QS_RSS4)
The SeaWinds scatterometer aboard QuikSCAT provided twice-daily near-global coverage from 1999 to 2009 with a swath width of 1800 km. The Jet Propulsion Laboratory (JPL) and Remote Sensing Systems (RSS) have each produced their own level 2b (swath) and level 3 (gridded) wind data since the launch of the instrument. Whilst generally consistent with each other, the datasets are based on different geophysical model functions that relate sea-surface roughness to wind and subtle differences exist (Fangohr and Kent, 2012). In April 2011, RSS reprocessed their entire data from version 3.0 to version 4.0 reducing some of the more substantial discrepancies at high wind speeds. Differences remain but are expected to be small on large spatio-temporal scales. Two different monthly mean datasets of QuikSCAT observations are used in this study. The first uses wind vectors produced by the JPL processed to give monthly mean wind speeds by CERSAT. The second is from RSS who apply their own processing.
22.214.171.124. JPL/CERSAT (QS_CERSAT)
This dataset comprises Ku-band microwave scatterometer data from the NASA SeaWinds scatterometer, onboard QuikSCAT (IFREMER/CERSAT, 2002b). Data have been processed up to level 2b (calibrated, geo-referenced swath winds) by the JPL (Dunbar et al., 2006) and gridded fields constructed by CERSAT. The recommended vector wind ambiguities are used and wind vectors outside the range 0.5–30 ms−1 are excluded. The land mask is at 0.5° resolution. There are no wind values over polar sea-ice as determined from the scatterometer backscatter, the mask edge is at approximately the 40% sea-ice concentration limit.
Comparisons with in situ collocated winds (1 h and 25 km) from the National Data Buoy Center (NDBC), the Tropical Atmosphere Ocean (TAO) buoy array and European buoys converted to 10-m neutral winds using the method of Liu et al. (1979), showed an root mean square (RMS) error of less than 1.9 ms−1 and an overestimate by QuikSCAT relative to the buoys of about 0.35 ms−1 (IFREMER/CERSAT, 2002b). It is stated that QuikSCAT underestimates at low winds and overestimates at high winds (the opposite of the ERS scatterometers) but also that daily average values were overestimated at low wind speeds (<4 ms−1; IFREMER/CERSAT, 2002b). RMS differences were smallest in the range 4–8 ms−1 and low wind speeds show larger RMS differences. As for ERS it is not clear from the documentation what combination of data quality flags have been used in the calculation of the monthly mean product. Winds are referenced to 10 m height at neutral stability.
126.96.36.199. RSS v4.0 (QS_RSS4)
RSS published their version 4.0 data in April 2011. Data have been processed up to level 2a (backscatter, gridded swath data) by JPL (D. Smith, personal communication, 2011), version 4.0 level 2b (swath) and level 3 (gridded) 0.25° × 0.25° fields are then produced by RSS at temporal resolutions of 12 h, 3 d, weekly and monthly, using their new model function Ku-2011 (Ricciardulli and Wentz, 2011). These wind speeds have been calibrated to rain-free WindSat data. Ricciardulli and Wentz (2011) present a comparison of QuikSCAT winds from Ku-2011 with buoy measurements that indicate agreement of 0.1 ± 0.9 ms−1, although there is no information about which buoys were used or any adjustments applied to the buoy measurements.
RSS provide a rain flag along with their monthly mean wind files, however, the flag is only set when more than 20 (out of possible 60) observations were rain contaminated according to the rain flags provided with the 12-hourly wind product (D. Smith, personal communication, 2004). RSS recommend that for research purposes, daily files should be used to create monthly means according to the user's requirements concerning rain flags. For consistency with the CERSAT/JPL dataset we use RSS v4.0 as provided without the application of rain flags. Using the monthly rain flag as provided by RSS masks less than 1% of the data and has no effect on our findings. Winds are referenced to 10 m height at neutral stability.
3.1.3. Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data Set version 3 (HOAPSv3; Andersson et al., 2007)
HOAPSv3 comprises data from the Special Sensor Microwave Imager (SSM/I). Intercalibration of brightness temperatures from different satellites is performed. Wind speeds are derived from brightness temperatures with a neural network (Andersson et al., 2010). No information on direction is available. The neural network derives the 10-m wind speed directly from the brightness temperatures allowing for the nonlinear relationships involved and also for different atmospheric conditions such as clear sky or cloud. The network is trained using two datasets. The first is derived from radiosondes and contains simulated SSM/I brightness temperatures from the radiosonde profiles and near surface wind speeds. The second is collocated (within 30 min and 50 km) SSM/I brightness temperatures and buoy wind speeds from NDBC (20 buoys) and TAO (59 buoys). The buoy wind speed measurements were individually converted to a height of 10 m wind using a logarithmic wind profile assuming neutral stratification. The network is then trained using these two datasets combined, which is thought to ensure the representativeness of the input and output data from the neural network (Andersson et al., 2010).
The mean fields are computed from the pixel-level data by averaging all SSM/I pixels that have their centre within the respective 0.5° grid box each month. The resulting data fields are multi-satellite averages and are supplemented by basic statistical information about standard deviation and number of observations per grid box. Winds are referenced to 10 m height at neutral stability (K. Fennig, personal communication, 2012). The HOAPS dataset has recently been updated to version 3.2 and contains data up to the end of 2008 (Fennig et al., 2012).
3.1.4. National Oceanography Centre Surface Flux Dataset v2.0 (NOCv2.0; Berry and Kent, 2009, 2011)
NOCv2.0 is constructed from VOS in situ wind speed observations from version 2.4 of the International Comprehensive Ocean–atmosphere Data Set (ICOADS; Woodruff et al., 1998; Worley et al., 2005). From 2007 version 2.5 (Woodruff et al., 2011) is used including real-time updates from 2008 onwards. Although ICOADS also contains observations from moored and drifting buoys these are excluded. Only observations within 4.5 standard deviations of the climatological monthly mean value, as determined from the ICOADS trimming flags, are used. Additionally, observations shown to be mislocated were excluded (Kent and Challenor, 2006).
ICOADS VOS wind speeds are either measured using an anemometer or visually estimated from the sea state and converted to a speed using a Beaufort equivalent scale (Kent and Taylor, 1997). The methods of measurement preferred by the VOS have changed over time, with the use of anemometers becoming more common (Thomas et al., 2008), and the average measurement height has increased (Kent et al., 2007). Visual wind estimates have been adjusted to account for biases in the Beaufort scale used to report the data (Kent and Taylor, 1997) following Lindau (1995). Anemometer wind speeds are adjusted to a standard level of 10 m above sea level using the wind profile relation of Smith (1980) and known measurement heights, where available (Kent et al., 2007). Where heights were unknown, the defaults were based on a 2° area monthly gridded dataset of anemometer heights.
Comparisons of adjusted visual and anemometer winds confirmed the conclusion of Thomas et al. (2008) that additional adjustments are required to visual winds to improve agreement with adjusted anemometer winds (Berry and Kent, 2011). The additional adjustment was applied to individual visual wind speed estimates using a simple scaling factor. Prior to the end of 1985 the factor is 1, from the start of 2000 the factor is 0.95, and values in the intervening period are found by linear interpolation. Visual wind speeds dominated until the end of the 1970s and by 2004 had dropped to less than a third of observations (Thomas et al., 2008). The residual bias uncertainty in the mean wind speed from each method was estimated to be 0.2 ms−1 (Berry and Kent, 2011). Wind speeds are referenced to 10 m height and are stability-dependent.
The optimal interpolation (OI) scheme used follows Reynolds and Smith (1994). OI is performed on the individual observations, relative to a first guess field, and normalized by the uncertainty in the first guess (Berry and Kent, 2011). A weekly ice mask, based on Reynolds et al. (2002), was used to exclude those regions covered by ice from the analysis. Daily wind speed fields were produced which were averaged to give a monthly mean. Daily uncertainties are produced from the OI scheme and were weighted by the expected correlations in the data to produce monthly estimates of the uncertainty (Berry and Kent, 2011).
3.1.5. National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis version 1 (NCEP; Kalnay et al., 1996)
Wind speeds from the first-generation NCEP reanalysis are available from 1948 until the present. The analysis system was identical to the NCEP global operational model implemented in January 1995, except that it had a horizontal resolution of about 210 km. Fields are not expected to be of as high quality as a modern forecast model. However NCEP/NCAR1 has been widely studied, is available for the full satellite period and does not incorporate satellite winds from scatterometers or from SSM/I and is therefore independent of those datasets (Table 1). Wind speeds from NCEP are referenced to 10 m height and are stability-dependent.
Output parameters are classified according to their dependence on observations or on the model. Level A parameters are those which are strongly constrained by observations, and include the wind components at the model grid levels. Winds at 10 m are an output of the boundary layer scheme and are therefore more model dependent and hence classified as a level B parameter (Kalnay et al., 19961996).
3.1.6. Twentieth Century Reanalysis version 2 (C20Rv2; Compo et al., 2011)
C20Rv2 provides 6-hourly, daily and monthly fields on a 2° grid from 1871 to 2010. C20Rv2 is a global atmospheric circulation dataset, assimilating only surface pressure reports over both the land and ocean and using observed monthly SST and sea-ice as boundary conditions. It uses an Ensemble Kalman Filter data assimilation method with background first guess fields supplied by an ensemble of forecasts from a global numerical weather prediction model. This directly yields a global analysis every 6 h as the most likely state of the atmosphere, and also an uncertainty estimate of that analysis through the spread of the ensemble. The SST and sea-ice come from the HadISST dataset (Rayner et al., 2003) and marine pressure observations come from ICOADS Release 2.4 (from 1952; Worley et al., 2005) and ICOADS Release 2.5 (from 1871; Woodruff et al., 2011). No wind observations are assimilated into C20Rv2. The fields are referenced to 10 m height and are stability-dependent.
3.1.7. European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERAI; Dee et al., 2011)
ERAI covers the time period 1979 until the present, using a 12-h 4D-Var data assimilation system (Uppala et al., 2008a, 2008b; Dee et al., 2011). Presently data for the period 1979–2012 are publicly available from the ECMWF data server at 0.75° × 0.75° (Berrisford et al., 2009). However this study uses data from 1989 to 2009 at 1.5° × 1.5° as was available prior to May 2012. Uppala et al. (2008a, 2008b) describe the main advances of the ERAI data assimilation over the previous reanalysis (ERA40; Uppala et al., 2005). ERAI relies mostly on observations prepared for ERA40 supplemented by data for later years from the ECMWF operational archive. Several different SST and sea-ice concentration datasets are used with changes in July 2001, January 2002 and February 2009 (Dee et al., 2011). Some new or reprocessed datasets have been utilized including winds obtained from feature tracking of Meteosat images. Table 1 summarizes the input surface wind speed data. ERAI is available as monthly means of daily means of 10-m, stability-dependent, wind speed data.
Initial indications are that the interannual variability of ERAI winds is better than that in ERA40, which was already superior to other reanalysis wind products (Trenberth et al., 2010). Surface wind speeds are also stronger than in ERA40 due to the improved model resolution.
3.1.8. Blended sea winds (BSW; Zhang et al., 2006a, 2006b)
The National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data and Information Service (NESDIS) BSW product (Zhang et al., 2006a, 2006b) contains globally gridded, high resolution ocean surface vector winds and wind stresses on a 0.25° grid, and multiple time resolutions of 6-hourly, daily, monthly and 11-year (1995–2005) climatological months. The period of record is 9 July 1987 – present. The wind speeds were generated by blending observations from multiple satellites: the Defense Meteorological Satellites Program (DMSP) SSM/I; the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI); QuikSCAT and the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E). The wind directions are from NCEP/Department of Energy (DOE) Reanalysis 2 (Kanamitsu et al., 2002), and are interpolated onto the blended speed grids. All satellite data are sourced from RSS and were the latest versions as of October 2005 as described by Zhang et al. (2006b). BSW does not make use of any C-band scatterometer data (e.g. ERS1, ERS2).
Interpolation is by a simple objective analysis method, namely a spatio-temporally weighted interpolation, which is used to generate a 12-hourly blended product from multiple satellites. Space-time aliases are addressed following Zeng and Levy (1995). Weighting is Gaussian within a window of 62.5 km and 6 h either side from the grid box centre. Daily and monthly data fields are obtained by averaging the 12-hourly values, 6-hourly data fields are interpolated from 12-hourly values. Before 1999 (i.e. prior to the launch of QuikSCAT), BSW data have systematic gaps at low latitudes caused by sparse sampling in these regions. These gaps are not filled by the interpolation technique. No account is taken of the expected quality of individual data sources or of potential biases between data sources. Ten-metre neutral equivalent wind speeds are provided.
3.1.9. Cross-calibrated multi-platform (CCMP; Atlas et al., 2011)
CCMP Ocean Surface Wind Components blended product from NASA combines data from several satellite and in situ sources using ERA40 (Uppala et al., 2005) as a background field from 1987 to 1999 and ERA operational model output thereafter. In situ observations are obtained from NCAR and include ICOADS, further observations provided and quality controlled by NCEP (D. Moroni, personal communication, 2012) and additional observations from the TAO and PIRATA moored buoy arrays. Observations are adjusted to 10 m assuming neutral stability, where instrument heights are not available a default of 19.5 m is used for ship observations and 5 m for buoys. A variational analysis method (VAM) is used to combine wind measurements derived from SeaWinds on QuikSCAT, SeaWinds on ADEOS-II, AMSR-E, TRMM TMI and SSM/I for the period July 1987 to the present. All satellite data are obtained from RSS.
The VAM follows Atlas et al. (1996) and satisfies multiple constraints including minimizing the misfit of the analysis to the background field and the assimilated input data. The additional constraints include the magnitude of differences between analysis and background, that differences in speed, vorticity and divergence should be smooth and also that the rate of change of vorticity should be small. The VAM has been shown to be able to represent cyclones and storms that are missing or under-represented in the background field (Atlas et al., 2011). Improvements were required over the Atlas et al. (1996) scheme to allow for asynopticity. No information on the relative weighting of the different input data sources is given. Details of the cross-calibration of data sources are not currently available.
The VAM requires a prior estimate of the wind field. For the period July 1987 to December 1998, 10-m winds from ERA40 were used and from 1999 the ECMWF operational analysis (Atlas et al., 2011). The winds provided are 10-m neutral wind speeds, although the background field is stability-dependent.
3.1.10. OAFlux (Yu and Weller, 2007)
The Woods Hole Oceanographic Institution (WHOI) OAFlux-blended product combines data from models and satellites according to weightings derived from comparisons with moored buoys deployed by WHOI (Yu and Weller, 2007). Wind data sources are the two NCEP Reanalyses, ERA40, SSM/I (version 6), AMSR-E (version 5) and QuikSCAT (version 3). All the satellite data were obtained from RSS. Daily 1° resolution fields are estimated using objective analysis from all data sources available at a particular time. Each of the reanalyses has a weight of 1, each satellite source a weight of 4 (Yu et al., 2008). The objective analysis minimizes differences between the solution and high quality buoy deployments. Solution is by a conjugate-gradient method used iteratively (Yu and O'Brien, 1991, 1995).
The variable estimates are sensitive to weights in regions where input data sources have large uncertainties and are less dependent on weights in regions where input datasets have good accuracy (Yu et al., 2004). Error estimates are computed based on the assumptions that the errors from every input data source are uncorrelated with the errors from another input data source, and that, at a given location and a given day, the accuracy of the field estimate depends on the scatter among the input data (Yu et al., 2008). The input datasets are a mixture of stability-dependent and neutral wind speeds. The output is a 10-m neutral equivalent wind speed (Yu et al., 2008).
3.2. Data processing
For global datasets (ERAI, NCEP and C20Rv2), the appropriate land mask was used to generate an ocean-only dataset. The remainder of the datasets are ocean-only. Each dataset was then averaged to a 1° latitude–longitude grid referenced to the centre of each 1° range. Averages are calculated using cos(latitude) area weighting and any grid boxes on the original grid that fall partially within a grid box on the target grid are weighted appropriately.
Neutral values for ERAI and NOCv2.0 were calculated using stability-dependent bulk formulae at full resolution (1.5° × 6 h for ERAI and 1° × daily for NOCv2.0). There were small differences between the monthly mean wind speeds provided by ECMWF and the monthly average of the 6-hourly analysis values. We therefore used the average of the 6-hourly values for both the stability-dependent and neutral values for consistency. Any 6-hourly value where the corresponding fractional ice concentration was greater than zero was discarded, monthly grid box means were formed from any remaining 6-hourly values. Then any grid boxes with sampling of less than 75% over the ERAI period were also discarded.