Elsevier

Ocean Modelling

Volume 70, October 2013, Pages 207-220
Ocean Modelling

Patterns and cycles in the Climate Forecast System Reanalysis wind and wave data

https://doi.org/10.1016/j.ocemod.2012.10.005Get rights and content

Abstract

The Climate Forecast System Reanalysis and the corresponding WAVEWATCH III hindcast datasets allow climatic interpretation of winds as well as their impacts on waves. In this paper, we analyze the continuous 31 years of global wind and wave data in terms of climate patterns and cycles. Quarterly averages and percentile plots of the wind speed and wave height illustrate the seasonal pattern and distributions of extreme events, while the annual and inter-annual variability demonstrates the wind and wave climate. The data is correlated with published indices of known atmospheric cycles. The datasets show good correspondence with the Arctic Oscillation, Antarctic Oscillation, El Nino Southern Oscillation, and the Madden-Julian Oscillation in both the wind and wave fields. The results compare well with published climate studies on regional scales and provide important linkage to the global wave climate characteristics.

Highlights

► We analyze CFSR winds and hindcast waves to describe the global patterns. ► Quarterly averages and percentiles reveal the seasonal variability. ► The inter-annual variability reveals the areas related to cycles longer than one year. ► The dataset shows strong correlation with the AO, AAO, ENSO, and MJO. ► The correlation reveals the regions strongly influenced by the oscillations.

Introduction

Wave climate has assumed greater importance in the daily utilization of ocean resources for commerce and recreation as well as long-term planning for coastal land-use and hazard mitigation. Winds transfer momentum to the ocean providing the energy source for seas and swells, and as the atmospheric climate evolves so does the wave climate. It is therefore necessary to analyze surface winds and waves simultaneously to understand the physical and climatic processes. Datasets of wide coverage and sufficient duration are essential for interpretation of the global phenomena at numerous temporal and spatial scales.

Wind and wave data is available from buoys, voluntary observing ships, satellite altimetry, and numerical models. Buoys provide ground truth for validation of observational and model data. Despite the lack of spatial coverage, buoy data represents a critical source of information for assessment of climate variability (e.g., Bromirski et al., 2005, Menendez et al., 2008, Genmrich et al., 2011). Ship visual observations were shown by Gulev and Grigorieva (2006) as an accurate source of information, but have limited coverage in the Southern Hemisphere and in extreme conditions (Gulev et al., 2003). Numerous studies have used satellite altimetry to describe the wind and wave climate as well as extreme events (e.g., Young, 1999, Woolf et al., 2002, Chen et al., 2002, Hemer et al., 2010, Young et al., 2011, Izaguirre et al., 2011, Vinoth and Young, 2011). Third generation phase-averaged wave models such as WAve Model (WAM) described by WAMDIG (1988) and WAVEWATCH III (WW3) of Tolman et al. (2002) have greatly complemented observational data in terms of resolution and coverage providing new insights into the global wave climate as well as smaller scale processes.

The quality of the wave data from numerical models depends on the wind forcing. Reanalysis datasets, which are based on the best available models and data assimilation techniques, are most suitable for production of wave datasets. The National Centers for Environmental Predictions (NCEP) produced the global Reanalysis I and II (R1 and R2) datasets covering respectively from 1948 and 1979 to the present time at 1.9° resolution every 6 h (Kalnay et al., 1996, Kistler et al., 2001). The European Centre for Medium-Range Weather Forecasts (ECMWF) created the ERA-15 and ERA-40 for 1979–1993 and 1957–2001 at 1.125° available every 6 h (Sterl et al., 1998, Uppala et al., 2005). ERA-40 is a coupled atmosphere-wave model utilizing WAM to produce wave heights, periods, and directions at 1.5° resolution. Sterl and Caires (2005) validated the wave heights and statistically corrected the dataset with measurements to produce a wave atlas. Studies of hindcast wave data have identified relationships between the wave field and atmospheric cycles in the north Atlantic (Wang and Swail, 2001, Dodet et al., 2010), the Pacific Basin (Graham and Diaz, 2001, Tsai et al., 2012), the Southern Ocean (Hemer et al., 2010, Bosserelle et al., 2011), and globally (Semedo et al., 2011, Reguero et al., 2012, Fan et al., 2012).

Newly available reanalysis datasets, such as NCEP’s Climate Forecast System Reanalysis (CFSR) for 1979–2009 at 0.34° available every hour and ECMWF’s ERA-Interim for 1979-present at ∼0.7° available every 3 h, have updated physics and improved assimilation (Saha et al., 2010, Dee et al., 2011). The CFSR and ERA-Interim datasets perform superior to earlier reanalysis datasets developed by NCEP and ECMWF. These datasets provide an opportunity to re-evaluate the global wind and wave climate at much higher fidelity and resolution than before. In particular, the main improvements in CFSR are coupling between ocean, atmosphere, and land surface processes; an interactive sea ice model; assimilation of satellite radiances to provide temperature and moisture profiles; and increased horizontal and vertical resolution in the atmosphere model. NCEP has utilized the CFSR surface winds to create a hindcast wave dataset at 0.5° resolution using WW3 v3.14 for the period 1979–2009 (Chawla et al., 2013). Using the two-way nesting algorithm of Tolman (2008), higher resolution grids as fine as 1/15° are nested into the global wave model to cover semi-enclosed seas and nearshore regions.

The present study provides a systematic analysis of the newly released CFSR wind and WW3 generated wave datasets from NCEP to complement the numerous studies at regional and global scales. The high-resolution data around the globe allows examination of large and small-scales features as well as their relationships with climate cycles. In this paper, we summarize atmospheric climate cycles and illustrate representative signals from the literature in Section 2. This provides the background for the subsequent analysis of the wind and wave climate. Section 3 provides a summary of the methodologies and validation of the CFSR data products from Saha et al. (2010) and Chawla et al. (2013). Data anomalies are pointed out and their effects on the climate analysis are discussed. Section 4 illustrates the seasonal pattern and statistical properties, while Section 5 discusses annual and inter-annual variability of the wind and wave datasets. Section 6 analyzes the variability of the datasets against published indices from atmospheric oscillations. The results from Sections 4 Seasonal patterns and statistics, 5 Annual and inter-annual variability, 6 Correlation to climate cycles are highlighted for discussion in terms of their implications to ocean modeling and the connections to climate cycles. Section 7 gives a summary of the major findings pertinent to climate research and wave modeling communities.

Section snippets

Climate cycles

The CFSR provides continuous and consistent coverage of the atmospheric conditions around the globe for 31 years. This allows examination of climate cycles on global and regional scales with periods up to several years. These include the Arctic Oscillation (AO), Antarctic Oscillation (AAO), El Nino Southern Oscillation (ENSO), and Madden-Julian Oscillation (MJO). Longer cycles such as the Pacific Decadal Oscillation (PDO) and the Atlantic Multi-decadal Oscillation (AMO), which have periods of 10

CFSR data products

CFSR comprises a suite of coupled atmosphere, ocean circulation, land surface, and sea ice models for environmental prediction (Saha et al., 2010). The Global Forecast System (GFS) of Yang et al. (2006) constitutes the atmosphere model, which has ∼38 km horizontal resolution and 64 vertical layers extending from the surface to 0.26 hPa. The Geophysical Fluid Dynamic Lab’s Modular Ocean Model (MOM) version 4 describes the ocean circulation at 0.25° resolution in the equatorial region and 0.5°

Seasonal patterns and statistics

Seasonal patterns are described by averaging the wind speed and significant wave height in the four quarters: December–January–February (DJF), March–April–May (MAM), June–July–August (JJA), and September–October–November (SON). Fig. 2, Fig. 3 show the respective seasonal averages from the 31 years of data. The directional arrows for the wind speed are computed from the two orthogonal components, while the directional arrows for the significant wave height present the average peak direction. DJF

Annual and inter-annual variability

The mean annual variability measures the spread of the data to provide an indication of the seasonal extremes. Let x denote the time series of wind speed or wave height over a period of m years with n records each. The mean annual variability (MAV), which is the average of the annual standard deviation normalized by the annual average, provides a measure of the variability within each year asMAV=1mj=1m1nk=1nxjk-1nk=1nxjk21nk=1nxjk-1=σjxj¯¯where the indices j and k refer to the year and

Correlation to climate cycles

The IAV and MAV hint that some of the variability in the winds and waves may be linked to atmospheric cycles. In this section, we examine the relationship between the wind and wave dataset as well as their correlation with the published indices of the AO, AAO, ENSO, and MJO in Section 2. Let xi and yi denote two time series with n records each. The correlation coefficient is defined asr=i=1n(xi-x¯)(yi-y¯)i=1n(xi-x¯)2i=1n(yi-y¯)2=cov(x,y)σxσywhere cov(x,y) is the covariance and σx and σy are

Conclusions

The CFSR wind and WW3 generated wave datasets provide a wealth of information for examination and interpretation of climate characteristics. The high resolution of 0.5° reveals large and small scale features over a continuous 31-year period for studies of the seasonal patterns as well as long-term climate cycles. Seasonal averages and percentiles reveal the overall features of the global wind and wave climate with strong zonal structures. The Northern Hemisphere has stronger seasonal patterns,

Acknowledgement

The Department of Energy supported this study through Grant No. DE-FG36-08G018180 via the National Marine Renewable Energy Center. We would like to thank the guest editor Dr. Mark Hemer, Dr. Fernando Mendez, and two anonymous reviewers for the comments and suggestions that have improved this paper. SOEST Contribution Number 8699.

References (50)

  • Chawla, A., Spindler, D.M., Tolman H.L., 2013. Validation of a thirty year wave hindcast using the climate forecast...
  • G. Chen et al.

    A global view of swell and wind sea climate in the ocean by satellite altimeter and scatterometer

    Journal of Atmospheric Oceanic Technology

    (2002)
  • Dee, D.P., Uppala, S.M., Simmons, A.J., coauthors, 2011. The ERA-Interim reanalysis: configuration and performance of...
  • Ek, M.B., Mitchell, K.E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G., Tarplay J.D., 2003. Implementation...
  • Y. Fan et al.

    Global ocean surface wave simulation using a coupled atmosphere-wave model

    Journal of Climate

    (2012)
  • N.E. Graham et al.

    Evidence for intensification of North Pacific winter cyclones since 1948

    Bulletin of American Meteorological Society

    (2001)
  • J. Genmrich et al.

    Observational changes and trends in the northeast Pacific wave records

    Geophysical Research Letters

    (2011)
  • S.K. Gulev et al.

    Variability of the winter wind waves and swell in the North Atlantic and North Pacific as revealed by voluntary observing ship data

    American Meteorological Society

    (2006)
  • Gulev, S.K., Grigorieva, V., Sterl, A., Woolf, D., 2003. Assessment of the reliability of wave observations from...
  • M.A. Hemer et al.

    Variability and trends in the directional wave climate of the Southern Hemisphere

    International Journal of Climatology

    (2010)
  • R.W. Higgins et al.

    Extreme precipitation events in the western United States related to tropical forcing

    Journal of Climate

    (2000)
  • C. Izaguirre et al.

    Global extreme wave height variability based on satellite data

    Geophysical Research Letters

    (2011)
  • Kalnay, E., Kanamitsu, M., Kistler, R., coauthors, 1996. The NCEP/NCAR 40-year reanalysis project. Bulletin of American...
  • Kistler, R., Kalnay, E., Collins, W., coauthors, 2001. The NCEP-NCAR 50-year reanalysis: monthly means CD-ROM and...
  • D.T. Kleist et al.

    Improving incremental balance in the GSI 3DVAR analysis system

    Monthly Weather Review

    (2009)
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