• Alley, W. H., 1984: The Palmer Drought Severity Index: Limitations and assumptions. J. Climate Appl. Meteor., 23 , 11001109.

  • Cane, M. A., , A. C. Clement, , A. Kaplan, , Y. Kushnir, , D. Pozdnyakov, , R. Seager, , E. Zebiak, , and R. Murtugudde, 1997: Twentieth-century sea surface temperature trends. Science, 275 , 957960.

    • Search Google Scholar
    • Export Citation
  • Chen, M., , P. Xie, , J. E. Janowiak, , and P. A. Arkin, 2002: Global land precipitation: A 50-yr monthly analysis based on gauge observations. J. Hydrometeor., 3 , 249266.

    • Search Google Scholar
    • Export Citation
  • Dai, A., , and T. M. L. Wigley, 2000: Global patterns of ENSO-induced precipitation. Geophys. Res. Lett., 27 , 12831286.

  • Dai, A., , I. Y. Fung, , and A. D. Del Genio, 1997: Surface observed global land precipitation variations during 1980–88. J. Climate, 10 , 29432962.

    • Search Google Scholar
    • Export Citation
  • Dai, A., , K. E. Trenberth, , and T. Qian, 2004: A global data set of Palmer Drought Severity Index for 1870–2002: Relationship with soil moisture and effects of surface warming. J. Hydrometeor., 5 , 11171130.

    • Search Google Scholar
    • Export Citation
  • Delworth, T. L., , and M. E. Mann, 2000: Observed and simulated multidecadal variability in the Northern Hemisphere. Climate Dyn., 16 , 661676.

    • Search Google Scholar
    • Export Citation
  • Diaz, H. F., , and V. Markgraf, Eds.,. 2000: El Niño and the Southern Oscillation. Cambridge University Press, 496 pp.

  • Dima, M., , and G. Lohmann, 2007: A hemispheric mechanism for the Atlantic multidecadal oscillation. J. Climate, 20 , 27062719.

  • Dong, B., , R. T. Sutton, , and A. A. Scaife, 2006: Multidecadal modulation of El Niño-Southern Oscillation (ENSO) variance by Atlantic Ocean sea surface temperatures. Geophys. Res. Lett., 33 , L08705. doi:10.1029/2006GL025766.

    • Search Google Scholar
    • Export Citation
  • Enfield, D. B., , and E. J. Alfaro, 1999: The dependence of Caribbean rainfall on the interaction of the tropical Atlantic and Pacific Oceans. J. Climate, 12 , 20932103.

    • Search Google Scholar
    • Export Citation
  • Enfield, D. B., , and A. M. Mestas-Nuñez, 1999: Multiscale variabilities in global sea surface temperatures and their relationships with tropospheric climate patterns. J. Climate, 12 , 27192733.

    • Search Google Scholar
    • Export Citation
  • Enfield, D. B., , A. M. Mestas-Nuñez, , and P. J. Trimble, 2001: The Atlantic multidecadal oscillation and its relation to rainfall and river flows in the continental U.S. Geophys. Res. Lett., 28 , 277280.

    • Search Google Scholar
    • Export Citation
  • Fontaine, B., , and S. Janicot, 1996: Sea surface temperature fields associated with West African rainfall anomaly types. J. Climate, 9 , 29352940.

    • Search Google Scholar
    • Export Citation
  • Ghil, M., , and R. Vautard, 1991: Interdecadal oscillations and the warming trend in global temperature time series. Nature, 350 , 324327.

    • Search Google Scholar
    • Export Citation
  • Giannini, A., , R. Saravanan, , and P. Chang, 2003: Oceanic forcing of Sahel rainfall on interannual and interdecadal time scales. Science, 302 , 10271030.

    • Search Google Scholar
    • Export Citation
  • Gray, S. T., , J. L. Betancourt, , C. L. Fastie, , and S. T. Jackson, 2003: Patterns and sources of multidecadal oscillations in drought-sensitive tree-ring records from the central and southern Rocky Mountains. Geophys. Res. Lett., 30 , 1316. doi:10.1029/2002GL016154.

    • Search Google Scholar
    • Export Citation
  • Gray, S. T., , L. J. Graumlich, , J. L. Betancourt, , and G. T. Pederson, 2004: A tree-ring based reconstruction of the Atlantic Multidecadal Oscillation since 1567 A.D. Geophys. Res. Lett., 31 , L12205. doi:10.1029/2004GL019932.

    • Search Google Scholar
    • Export Citation
  • Hidalgo, H. G., 2004: Climate precursors of multidecadal drought variability in the western United States. Water Resour. Res., 40 , W12504. doi:10.1029/2004WR003350.

    • Search Google Scholar
    • Export Citation
  • Hoerling, M., , and A. Kumar, 2003: The perfect ocean for drought. Science, 299 , 691694.

  • Jones, P. D., , and A. Moberg, 2003: Hemispheric and large-scale surface air temperature variations: An extensive revision and an update to 2001. J. Climate, 16 , 206223.

    • Search Google Scholar
    • Export Citation
  • Kaplan, A., , M. Cane, , Y. Kushnir, , A. Clement, , M. Blumenthal, , and B. Rajagopalan, 1998: Analyses of global sea surface temperature 1856-1991. J. Geophys. Res., 103 , 1856718589.

    • Search Google Scholar
    • Export Citation
  • Kawamura, R., 1994: A rotated EOF analysis of global sea surface temperature variability with interannual and interdecadal scales. J. Phys. Oceanogr., 24 , 707715.

    • Search Google Scholar
    • Export Citation
  • Lees, J. M., , and J. Park, 1995: Multi-taper spectral analysis: A stand-alone C-subroutine. Comput. Geosci., 21 , 199236.

  • Mann, M. E., , and J. Park, 1994: Global scales modes of surface temperature variability on interannual to century timescales. J. Geophys. Res., 99 , 2581925833.

    • Search Google Scholar
    • Export Citation
  • Mann, M. E., , and J. Park, 1996: Joint spatiotemporal modes of surface temperature and sea level pressure variability in the Northern Hemisphere during the last century. J. Climate, 9 , 21372162.

    • Search Google Scholar
    • Export Citation
  • Mann, M. E., , and J. Park, 1999: Oscillatory spatiotemporal signal detection in climate studies: A multiple-taper spectral domain approach. Advances in Geophysics, Vol. 41, Academic Press, 1–131.

    • Search Google Scholar
    • Export Citation
  • Mantua, N. J., , and S. R. Hare, 2002: The Pacific Decadal Oscillation. J. Oceanogr., 58 , 3544.

  • McCabe, G. J., , and M. A. Palecki, 2006: Multidecadal climate variability of global lands and oceans. Int. J. Climatol., 26 , 849865.

  • McCabe, G. J., , M. A. Palecki, , and J. L. Betancourt, 2004: Pacific and Atlantic Ocean influences on multidecadal drought frequency in the United States. Proc. Natl. Acad. Sci. USA, 101 , 41364141.

    • Search Google Scholar
    • Export Citation
  • Mestas-Nuñez, A., , and D. Enfield, 1999: Rotated global modes of non-ENSO sea surface temperature variability. J. Climate, 12 , 27342746.

    • Search Google Scholar
    • Export Citation
  • Nicholson, S. E., , D. Leposo, , and J. Grist, 2001: The relationship between El Niño and drought over Botswana. J. Climate, 14 , 323335.

    • Search Google Scholar
    • Export Citation
  • Palmer, W. C., 1965: Meteorological drought. Research Paper 45, U.S. Weather Bureau, National Oceanic and Atmospheric Administration Library and Information Services Division, Washington, DC, 58 pp.

    • Search Google Scholar
    • Export Citation
  • Park, J., , C. R. Lindberg, , and F. L. Vernon III, 1987: Multitaper spectral analysis for high frequency seismograms. J. Geophys. Res., 92 , 1267512684.

    • Search Google Scholar
    • Export Citation
  • Pohlmann, H., , F. Sienz, , and M. Latif, 2006: Influence of the multidecadal Atlantic meridional overturning circulation variability on European climate. J. Climate, 19 , 60626067.

    • Search Google Scholar
    • Export Citation
  • Rajagopalan, B., , M. E. Mann, , and U. Lall, 1998: A multivariate frequency-domain approach to long-lead climatic forecasting. Wea. Forecasting, 13 , 5874.

    • Search Google Scholar
    • Export Citation
  • Rodwell, M. J., , D. P. Rowell, , and C. K. Folland, 1999: Oceanic forcing of the wintertime North Atlantic Oscillation and European climate. Nature, 398 , 320323.

    • Search Google Scholar
    • Export Citation
  • Ropelewski, C. F., , and M. S. Halpert, 1987: Global and regional scale precipitation patterns associated with the El Niño–Southern Oscillation. Mon. Wea. Rev., 115 , 16061626.

    • Search Google Scholar
    • Export Citation
  • Schubert, S. D., , M. J. Suarez, , P. J. Pegion, , R. D. Koster, , and J. T. Bacmeister, 2004: On the cause of the 1930s Dust Bowl. Science, 303 , 18551859.

    • Search Google Scholar
    • Export Citation
  • Seager, R., , Y. Kushnir, , C. Herweijer, , N. Naik, , and J. Velez, 2005: Modeling of tropical forcing of persistent droughts and pluvials over western North America: 1856–2000. J. Climate, 18 , 40684091.

    • Search Google Scholar
    • Export Citation
  • Shabbar, A., , and W. Skinner, 2004: Summer drought patterns in Canada and the relationship to global sea surface temperatures. J. Climate, 17 , 28662880.

    • Search Google Scholar
    • Export Citation
  • Solomon, S., , D. Qin, , M. Manning, , M. Marquis, , K. Averyt, , M. M. B. Tignor, , H. L. Miller Jr., , and Z. Chen, 2007: Climate Change 2007: The Physical Science Basis. Cambridge University Press, 996 pp.

    • Search Google Scholar
    • Export Citation
  • Sutton, R. T., , and D. L. R. Hodson, 2003: Influence of the ocean on North Atlantic climate variability 1871–1999. J. Climate, 16 , 32963313.

    • Search Google Scholar
    • Export Citation
  • Sutton, R. T., , and D. L. R. Hodson, 2005: Atlantic Ocean forcing of multidecadal variations in North American and European summer climate. Science, 309 , 115118.

    • Search Google Scholar
    • Export Citation
  • Sutton, R. T., , and D. L. R. Hodson, 2007: Climate response to basin-scale warming and cooling of the North Atlantic Ocean. J. Climate, 20 , 891907.

    • Search Google Scholar
    • Export Citation
  • Thomson, D. J., 1982: Spectrum estimation and harmonic analysis. Proc. IEEE, 70 , 10551096.

  • Tourre, Y., , B. Rajagopalan, , and Y. Kushnir, 1999: Dominant patterns of climate variability in the Atlantic Ocean region during the last 136 years. J. Climate, 12 , 22852299.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 1997: The definition of El Niño. Bull. Amer. Meteor. Soc., 78 , 27712777.

  • Trenberth, K. E., , and J. M. Caron, 2000: The Southern Oscillation revisited: Sea level pressures, surface temperatures, and precipitation. J. Climate, 13 , 43584365.

    • Search Google Scholar
    • Export Citation
  • von Storch, H., , and F. W. Zwiers, 1999: Statistical Analysis in Climate Research. Cambridge University Press, 484 pp.

  • View in gallery

    Grids (gray) with complete (a) PDSI and (b) SST data for the period 1925–2003.

  • View in gallery

    LFV spectrum from MTM–SVD analysis of (a) global SSTs and (b) Northern Hemisphere (20°N and above) SSTs. The horizontal lines are the various confidence levels.

  • View in gallery

    Spatial reconstruction of SSTs at (a) the secular trend and (b) the ENSO frequency (0.1956 cpy). The length of the arrows represents the relative amplitude of the signal and the angle from the horizontal represents the phase lag. For easier interpretation the arrows are colored according to angle from the horizontal [1°–90° (black), 91°–180° (red), 181°–270° (blue), and 271°–360° (green)].

  • View in gallery

    As in Fig. 3, but for the multidecadal signal (a) 0.0149 and (b) 0.0334 cpy.

  • View in gallery

    Temporal reconstruction of (a) frequencies significant in the ENSO band (summed for 0.1956, 0.2280, 0.2654, 0.2783, and 0.2942 cpy) at 2.5°S, 132.5°W (a grid point in the tropical Pacific) and a time series of the Niño-3.4 SST index; (b) frequencies in the decadal band (0.0334, 0.0549 and 0.0898 cpy) at 17.5°N, 127.5°W (a grid point in the North Pacific) compared with a time series of the PDO; and (c) the multidecadal frequency, 0.0149 cpy, compared with the AMO index.

  • View in gallery

    As in Fig. 2, but from a joint MTM–SVD analysis of global SSTs and global PDSI values.

  • View in gallery

    Spatial reconstruction of the PDSI at (a) the secular trend and (b) ENSO frequency (0.228 cpy). The length of the arrows represents the amplitude of the signal and the angle from the horizontal represents the phase lag. For easier interpretation the arrows are colored according to angle from the horizontal [1°–90° (black), 91°–180° (red), 181°–270° (blue), and 271°–360° (green)].

  • View in gallery

    Spatial reconstruction of the PDSI at 0.0149 cpy. The length of the arrows represents the amplitude of the signal and the angle from the horizontal represents the phase lag. For easier interpretation the arrows are colored according to angle from the horizontal [1°–90° (black), 91°–180° (red), 181°–270° (blue), and 271°–360° (green)].

  • View in gallery

    Temporal reconstructions of the PDSI at a suite of significant frequencies, which are the secular trend (0 cpy), the ENSO band (0.1956, 0.2122, 0.2280, 0.2654, 0.2783, and 0.2942 cpy), and the AMO (0.0149 cpy) at locations in the (a) northwestern United States at 43.75°N, 123.75°W; (b) southwestern United States at 36.25°N, 113.75°W; and (c) the East Coast at 38.75°N, 76.25°W. The reconstructions are performed separately at each frequency and are summed to result in a single combined temporal reconstruction. The actual PDSI time series is also shown.

  • View in gallery

    As in Fig. 8, but for (a) northeastern Brazil at 8.75°S, 36.25°W; (b) the Sahel at 11.25°N, 16.25°W; and (c) Eurasia at 68.75°N, 21.25°E.

  • View in gallery

    As in Fig. 8, but for (a) western Australia at 21.25°S, 113.75°E; (b) eastern Australia at 28.75°S, 153.75°E; and (c) South Africa at 31.25°S, 28.75°E.

  • View in gallery

    As in Fig. 8, but for (a) western India at 16.25°N, 73.75°E; (b) central India at 21.25°N, 78.75°E; and (c) eastern India at 21.25°N, 86.25°E.

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Joint Spatiotemporal Variability of Global Sea Surface Temperatures and Global Palmer Drought Severity Index Values

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  • 1 Office of Research and Development, Royal Irrigation Department, Nonthaburi, Thailand
  • 2 U.S. Geological Survey, Denver, Colorado
  • 3 Department of Civil, Environmental, and Architectural Engineering, and Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado
  • 4 AMEC Earth and Environmental, Boulder, Colorado
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Abstract

Dominant modes of individual and joint variability in global sea surface temperatures (SST) and global Palmer drought severity index (PDSI) values for the twentieth century are identified through a multivariate frequency domain singular value decomposition. This analysis indicates that a secular trend and variability related to the El Niño–Southern Oscillation (ENSO) are the dominant modes of variance shared among the global datasets. For the SST data the secular trend corresponds to a positive trend in Indian Ocean and South Atlantic SSTs, and a negative trend in North Pacific and North Atlantic SSTs. The ENSO reconstruction shows a strong signal in the tropical Pacific, North Pacific, and Indian Ocean regions. For the PDSI data, the secular trend reconstruction shows high amplitudes over central Africa including the Sahel, whereas the regions with strong ENSO amplitudes in PDSI are the southwestern and northwestern United States, South Africa, northeastern Brazil, central Africa, the Indian subcontinent, and Australia. An additional significant frequency, multidecadal variability, is identified for the Northern Hemisphere. This multidecadal frequency appears to be related to the Atlantic multidecadal oscillation (AMO). The multidecadal frequency is statistically significant in the Northern Hemisphere SST data, but is statistically nonsignificant in the PDSI data.

Corresponding author address: Gregory J. McCabe, U.S. Geological Survey, Denver Federal Center, MS 412, Denver, CO 80225. Email: gmccabe@usgs.gov

Abstract

Dominant modes of individual and joint variability in global sea surface temperatures (SST) and global Palmer drought severity index (PDSI) values for the twentieth century are identified through a multivariate frequency domain singular value decomposition. This analysis indicates that a secular trend and variability related to the El Niño–Southern Oscillation (ENSO) are the dominant modes of variance shared among the global datasets. For the SST data the secular trend corresponds to a positive trend in Indian Ocean and South Atlantic SSTs, and a negative trend in North Pacific and North Atlantic SSTs. The ENSO reconstruction shows a strong signal in the tropical Pacific, North Pacific, and Indian Ocean regions. For the PDSI data, the secular trend reconstruction shows high amplitudes over central Africa including the Sahel, whereas the regions with strong ENSO amplitudes in PDSI are the southwestern and northwestern United States, South Africa, northeastern Brazil, central Africa, the Indian subcontinent, and Australia. An additional significant frequency, multidecadal variability, is identified for the Northern Hemisphere. This multidecadal frequency appears to be related to the Atlantic multidecadal oscillation (AMO). The multidecadal frequency is statistically significant in the Northern Hemisphere SST data, but is statistically nonsignificant in the PDSI data.

Corresponding author address: Gregory J. McCabe, U.S. Geological Survey, Denver Federal Center, MS 412, Denver, CO 80225. Email: gmccabe@usgs.gov

1. Introduction

A number of changes in the global climate system have been attributed to increasing global temperatures resulting from anthropogenically supplied greenhouse gases (Solomon et al. 2007). To appropriately attribute climate changes to global warming it is necessary to understand the primary modes of global climate variability and to separate climate trends from natural climate variability (Ghil and Vautard 1991; Mann and Park 1996).

Dai et al. (1997) examined the variability of global precipitation using a dataset of gridded (2.5° × 2.5°) monthly precipitation for the period 1900–88. In their study, the first empirical orthogonal function (EOF) of the precipitation data indicated an (El Niño–Southern Oscillation) (ENSO–related pattern and the second EOF reflected a linear trend in global precipitation. The trends in precipitation were primarily increases in North America, mid- to high-latitude Eurasia, Argentina, and Australia. Dai et al. (1997) reported that this pattern of trends in precipitation was consistent with precipitation changes projected by general circulation model (GCM) experiments of future climate changes in response to increasing atmospheric concentrations of carbon dioxide.

In another study, Dai et al. (2004) examined the variability of global annual Palmer drought severity index (PDSI) values using principal components analysis. Similar to the study of global precipitation, Dai et al. (2004) reported that the first two principal components of global annual PDSI are related to long-term trends and the ENSO. However, for the analysis of the PDSI data, the first principal component reflected long-term trends in PDSI and the second component reflected ENSO variability. Long-term trends in PDSI represented more of the variability in the PDSI data than in precipitation data likely because PDSI values also include the effects of long-term trends in temperature.

McCabe and Palecki (2006) used principal components analysis and singular value decomposition (SVD) to examine primary modes of global PDSI and sea surface temperature (SST) variability on decadal to multidecadal (D2M) time scales. Results indicated two principal modes of D2M variability. The first mode of D2M variability is related to the Pacific decadal oscillation (PDO), Indian Ocean SSTs, and an index of ENSO, while the second mode is related to the Atlantic multidecadal oscillation (AMO).

Mann and Park (1996) performed a frequency analysis of the joint variability of twentieth-century Northern Hemisphere surface temperature and sea level pressure (SLP). Mann and Park (1996) identified significant modes of climate variability at quasi-biennial (2.1–2.2 yr), ENSO (3–7 yr), quasi-decadal (10–11 yr), and interdecadal (16–18 yr) time scales. Mann and Park also identified a secular trend as a significant mode of climate variability.

In a number of studies, variability in global SSTs have been shown to be a significant driving force of hydro-climate variability (Fontaine and Janicot 1996; Enfield and Alfaro 1999; Rodwell et al. 1999; Enfield et al. 2001; Nicholson et al. 2001; Giannini et al. 2003; Gray et al. 2003; Sutton and Hodson 2003; Hidalgo 2004; McCabe et al. 2004; Shabbar and Skinner 2004; Schubert et al. 2004; Seager et al. 2005; Sutton and Hodson 2005; McCabe and Palecki 2006). Some of these studies have shown the large influence of tropical Pacific Ocean SSTs (i.e., El Niño and La Niña events) on hydro-climate across the globe. More recently, several of these studies have shown substantial associations between North Atlantic SSTs and global hydro-climate, particularly on D2M time scales (McCabe and Palecki 2006; Dong et al. 2006; Dima and Lohmann 2007).

In this study we examine the joint spatiotemporal variability between global annual PDSI values and global annual SSTs. The PDSI data were chosen to represent land-based climate variability and SSTs were chosen to represent the large-scale climate variability forcing at interannual and interdecadal time scales. We use a nonparametric spectral domain technique called the multitaper method–singular value decomposition (MTM–SVD) on this joint dataset (Mann and Park 1996). This method aims to identify dominant modes (i.e., primary patterns of temporal and spatial climate variability that are identified through frequency analysis) of variability that are jointly shared by the two fields and subsequently, spatial and temporal patterns of these identified frequencies are reconstructed. This method is data driven and is unaffected by trends and other aliasing problems that commonly constrain the traditional time and frequency domain techniques. The dominant patterns will provide increased understanding of the low-frequency modes of variability, in particular, of the land surface conditions that are important for long-term drought monitoring and mitigation efforts. In addition, because this study uses a frequency domain approach it will provide a useful confirmation of previous research findings that were obtained using different methods. A brief description of the data is provided, followed by the description of the MTM–SVD methodology. The identified space–time modes of variability will be described and discussed in the results section.

2. Data

PDSI is a well known representation of meteorological drought and is computed from measured monthly precipitation and temperature data (Palmer 1965). Although there are known short comings inherent in the PDSI (Alley 1984), it has simple data requirements compared to more complex soil-moisture models. PDSI is also widely used by the water-resources engineering and science community as an important practical indicator of basin soil moisture state (e.g., dry, normal, wet, etc.).

Dai et al. (2004) have developed a dataset of gridded 2.5° × 2.5° (2.5° latitude × 2.5° longitude) monthly PDSI values for the global land surface for the period 1870–2003. The needed precipitation data follows from the work of Chen et al. (2002) for the 1948–2003 period and Dai et al. (1997) for the 1870–1947 period. An adjustment was made to place the two datasets on a compatible scale. Dai et al. (2004) applied step-change homogenization corrections to a small percentage of the original precipitation time series prior to gridding the data. The required temperature data were gridded by Jones and Moberg (2003) at a coarser 5° × 5° resolution, and the station temperature time series used were subjected to a variety of homogenization techniques by their sources. The monthly PDSI data were averaged to compute annual PDSI values. For grid cells with missing monthly data within a year, the annual value was not computed and designated as missing. Only 2.5° × 2.5° grid cells with complete annual data for the 1925–2003 period (1341 grid cells, Fig. 1a) were used for the analysis in this paper. The time period chosen provided a reasonable compromise between length of record and completeness of spatial coverage.

SST variability is strongly related to global climate variability (Diaz and Markgraf 2000; Mantua and Hare 2002; Hoerling and Kumar 2003; McCabe et al. 2004). Previous research indicates that SSTs over large areas vary simultaneously under preferred spatial modes and time scales (Kawamura 1994; Enfield and Mestas-Nuñez 1999; Mestas-Nuñez and Enfield 1999). In some of these studies, global-scale signals were removed prior to variability analysis, including trends and ENSO signals. For the analysis in this paper, 5° × 5° resolution grid cell SST data with complete annual records for 1925–2003 (1207 cells, Fig. 1b) were extracted from the Kaplan-extended SST dataset of monthly SSTs (Kaplan et al. 1998). The annual values were computed as 12-month averages of the monthly SSTs.

3. Frequency domain MTM–SVD approach

Robust diagnosis of the key low-frequency modes of large-scale climate entails capturing the coherent space–time variations across multiple climate state variables. Traditional time-domain decomposition approaches for univariate and multivariate data provide useful details on the broadscale patterns of variability. However, these approaches lack the ability to isolate narrowband frequency domain structure (Mann and Park 1994, 1996; more information available online at http://www.meteo.psu.edu/~mann/Mann/tools/tools.html).

Detailed methodology development and examples of the MTM–SVD methodology can be found in Thomson (1982), Mann and Park (1994, 1996, 1999), and Lees and Park (1995). Here, we describe the MTM–SVD methodology for decomposing the individual and joint global SST and PDSI datasets into few frequencies to identify the significant modes of variability. The method relies on the assumption that climate modes are narrowband and evolve in a noise background that varies smoothly across the frequencies. Subsequently, spectral domain equivalents of each grid point are computed based on the multitaper spectral analysis (Thomson 1982; Park et al. 1987). The output of the discrete Fourier transform of an N-point data series at grid location m is the complex-valued eigenspectrum at discrete frequency f, , as
i1520-0442-22-23-6251-e1
where Δt is the sampling interval (1 yr in this application), with being the kth member of the orthogonal sequence of -prolate Slepian tapers (Lees and Park 1995), k = 1, … , K, K is a small subset of orthogonal Slepian tapers; m = 1, … , M are the number of time series used for the analysis, and N is the length of each time series. Lees and Park (1995) provides an excellent description and necessary computer codes for estimating the -prolate Slepian tapers. In the -prolate Slepian tapers p is the “time-bandwidth” product and it scales the spectral information in a frequency band of half-bandwidth pfR, where fR = 1/(NΔt) is the Rayleigh frequency. Also, because the Slepian tapers are derived using eigendecomposition, the usual question of how many eigenvectors/tapers (i.e., the choice of K) to retain to explain a large fraction of the total variance remains. The level of compromise between the variance and frequency resolution of the Fourier transform depends on the choice of K. Mann and Park (1994) suggest p = 2 and K = 3 as a reasonable compromise between frequency resolution and also providing sufficient degrees of freedom for signal-to-noise decomposition. Based on the suggestion of Mann and Park (1994) we use p = 2 and K = 3 for the analyses included in this study.
For each frequency point to be resolved by this analysis, a M × K matrix 𝗔( f ) is formed for each of the M series:
i1520-0442-22-23-6251-e2
Note that each row is computed from a different series (grid point), and each column corresponds to using a different taper. Subsequently, a complex singular value decomposition is performed through
i1520-0442-22-23-6251-e3
where the kth dominant mode explains λk (relative fraction) variance. The left complex eigenvector uk represents the EOFs in the spatial domain, and υ*k, the complex right eigenvector represents the EOFs in the spectral domain. These eigenvectors can be inverted to obtain the smoothly varying envelope of the kth mode of variability at frequency f (Mann and Park 1996). The localized fractional variance (LFV) provides a measure of the distribution of variance by frequency, and above a select confidence level threshold (e.g., 90%, 95%), represents a dominant narrowband mode. The confidence levels are computed based on the locally white noise assumption, and are constant outside the secular band. Mann and Park (1996) describe a bootstrap method used to obtain the confidence bands for this study. In general, the computed principal eigenspectrum (described above) yields a number of narrowband peaks. The MTM–SVD technique has been effectively applied to the analysis of global SSTs and SLPs (Mann and Park 1994, 1996, 1999), identification of dominant modes of variability in the Atlantic basin (Tourre et al. 1999), and also for forecasting (Rajagopalan et al. 1998).

The LFV spectrum was used to identify significant frequencies, and temporal and spatial reconstructions were carried out to understand the global joint variability of SST and PDSI. The spatial reconstruction yields the spatial patterns associated with the given time scales, and their relative amplitude and phase relations.

Similar to standard EOF analysis where a space–time dataset is decomposed into two components (e.g., von Storch and Zwiers 1999) 1) a set of time coefficients–principal components or EOF coefficients, and 2) projected onto a set of fixed patterns that are orthogonal–eigenvectors. The uk spatial EOF or eigenvector corresponding to a specified frequency is a complex number. The spatial reconstruction (or vector plots) consists of the vector amplitudes (magnitude of the complex number) and direction (phase of the complex number). Spatial reconstruction plots (arrows representing the vectors) are plotted using these amplitude and phase values.

4. Results

As described earlier the premise here is that SST forcings influence the variability of PDSI. The SST forcings typically come from the tropics (e.g., ENSO) and to a lesser extent from the midlatitudes. First we identify the dominant frequencies of the SST forcings from these two sources and then investigate the spatial and temporal reconstructions of both SST and PDSI.

a. SST

The MTM–SVD analysis was first performed on the global SST and the LFV spectrum is shown in Fig. 2a. The significant peaks (the 90% confidence levels) are seen at approximately 0 cpy (a secular trend) and in the 0.2–0.3 cpy range; which is the ENSO band. These results are consistent with the findings of previous research (e.g., Mann and Park 1996).

The significance level of the peaks is computed through a bootstrap method. We generate bootstrap samples of the data in which the temporal dependence is destroyed. In other words, we randomly select an observation with replacement thus obtaining a bootstrap sample that has the temporal structure destroyed. The MTM–SVD method is applied to this sample and the LFV obtained. This is repeated for a large number (1000) of bootstrap samples thus, obtaining 1000 estimates of LFV at each frequency. The 50th, 90th, 95th, and 99th percentiles at each frequency are estimated and plotted; these are the horizontal lines in the figures. This method of significance is data driven and robust (Mann and Park 1996, 1999) and has been used in all the previous studies.

The decadal and multidecadal frequencies are not significant—this is due to the fact that the methodology isolates frequencies that are shared by much of the spatial domain and in this regard the ENSO forcing is dominant. Furthermore, the decadal forcings are mainly from the midlatitudes (Sutton and Hodson 2003, 2005). To demonstrate this we performed the analysis over the Northern Hemisphere (20°N and above) subdomain (Fig. 2b). Here the ENSO band and the secular trend are weaker but a multidecadal frequency 0.0549 cpy is above the 90% significance level. The frequency of the AMO (0.0149 cpy, Enfield et al. 2001; Gray et al. 2003, 2004) is evident albeit at less than 90% confidence level. A MTM–SVD analysis of only SSTs in the North Atlantic Ocean (not shown) indicates a significant peak (at a 90% confidence level) at 0.0149 cpy, and another significant peak at a multidecadal frequency is at 0.0334 cpy (significant at a 99% confidence level).

The MTM–SVD analysis of the global and Northern Hemisphere SST indicates that collectively the dominant frequencies are in the secular trend, ENSO, and multidecadal bands. Spatial reconstruction of the secular trend (zero frequency) and at one of the ENSO frequencies (0.1956 cpy; this frequency is common in both Figs. 2a,b) is shown in Fig. 3. The secular trend is strong in the Indian Ocean, South and North Atlantic, and North and South Pacific; all are regions known to have strong trends in the SST (Cane et al. 1997). Also note that the SST trends in the Indian Ocean and North Atlantic and North Pacific are predominantly opposite. That is, temperature increases in the Indian Ocean would correspond to cooling of the North Atlantic and North Pacific and vice versa. The ENSO reconstruction shows a strong signal in the tropical Pacific, North Pacific, and Indian Ocean regions. Notice that the arrows in the tropical Pacific are in antiphase with the North Pacific, yet in-phase with the Indian Ocean; this pattern of effects is consistent with the ENSO phenomenon. Spatial reconstruction of the multidecadal signal (0.0149 and 0.0334 cpy) shows (Fig. 4) a strong signature in the Atlantic and Pacific regions. In particular, the reconstruction at the AMO frequency (Fig. 4a) shows the signal to be dominated by the Atlantic (Sutton and Hodson 2003, 2005).

Another interesting feature of the spatial reconstruction at the AMO frequency is the difference in phase between the tropical and northern regions of the North Atlantic Ocean. These differences in phase suggest a lag of SSTs in the northern region behind those in the tropical region of the North Atlantic. This lag is possibly indicative of the movement of warm water from the tropics to the north in the North Atlantic Ocean related to the thermohaline circulation (Delworth and Mann 2000).

To demonstrate that the reconstructions also capture the temporal signal of the large-scale features we performed temporal reconstruction at selected locations and compare them to the standard indices. Temporal reconstruction of frequencies significant in the ENSO band (0.1956, 0.2280, 0.2654, 0.2783, and 0.2942 cpy, identified from Figs. 2a,b) at the location of 2.5°S and 132.5°W (a grid point in the tropical Pacific) were performed and summed up and compared to Niño-3.4 SST index (Fig. 5a). The Niño-3.4 SST index is the average of SSTs in the tropical Pacific Ocean between 5°S and 5°N and 170° and 120°W and represents the variability of ENSO (Trenberth 1997). Combined reconstructions at the significant frequencies in the multidecadal band (0.0334, 0.0549, and 0.0898 cpy, identified from Figs. 2a,b) at the location of 17.5°N and 127.5°W (a grid point in the North Pacific) compare very well with the PDO (Mantua and Hare 2002; Fig. 5b). The PDO is an index of the decadal variability of the North Pacific Ocean. Likewise, the reconstruction at the AMO frequency (0.0149 cpy) combined over the entire Northern Hemisphere Atlantic is compared to the AMO index (Fig. 5c). The AMO is an index of sea surface temperatures across the North Atlantic Ocean between the equator and 70°N latitude and exhibits a long-term, quasi-cyclic variation at time scales of 50–70 yr (Enfield et al. 2001). Recent modeling studies reveal that multidecadal variability in the North Atlantic Ocean is dominated by this single mode of sea surface temperature variability (Sutton and Hodson 2005, 2007). The temporal reconstructions capture the low-frequency variability of the indices of large-scale forcings well. In addition the correlations between the reconstructed and measured time series are all statistically significant at a 99% confidence level.

The MTM–SVD analysis of the SST and their spatial and temporal reconstructions isolate the important drivers of low-frequency climate variability. Consequently, we used these identified frequencies for the PDSI analysis.

b. PDSI

The LFV spectrum from a joint analysis of global SST and global PDSI is shown in Fig. 6. The significant frequencies are consistent (secular trend and ENSO) with those identified in the SST analysis (Fig. 2a). Notice that the low-frequency signal is subdued relative to what was seen in the Northern Hemisphere SST analysis (Fig. 2b). This is to be expected because, as mentioned earlier, the technique isolates significant frequencies that are shared by a majority of spatial locations in both the fields. Because the multidecadal frequencies are restricted to a smaller spatial region they are not statistically significant but it does not imply their absence. Based on the individual and joint analysis we can state that the frequencies of a secular trend, ENSO, and multidecadal variability are dominant and shared by both fields and also these can be viewed as drivers from the SST field of the PDSI. Spatial and temporal reconstructions in the rest of the paper will be based on these identified frequencies.

The secular trend in the PDSI is shown in Fig. 7a and the ENSO reconstruction (0.228 cpy, one of the significant frequencies, identified from Fig. 6) is shown in Fig. 7b. The trend reconstruction shows higher amplitudes over central Africa including the Sahel and are weaker elsewhere. The regions with strong ENSO amplitudes are the southwestern and northwestern United States, South Africa, northeastern Brazil, central Africa, the Indian subcontinent, and Australia. These are consistent with the typical ENSO teleconnections of global precipitation (Ropelewski and Halpert 1987). This pattern of PDSI variability is most likely forced by precipitation variability related with ENSO (Ropelewski and Halpert 1987; Dai and Wigley 2000; Trenberth and Caron 2000; Dai et al. 2004).

Figure 8 illustrates the PDSI reconstruction for 0.0149 cpy (this multidecadal frequency was significant from the MTM–SVD analysis of North Atlantic Ocean SSTs and appears to represent AMO variability). The pattern of reconstructed PDSI for 0.0149 cpy indicates a number of areas with coherent signals. Central North America is dominated by a common signal, as well as western Africa, South Africa, and Australia. This pattern is similar to the pattern of AMO effects on global PDSI (McCabe and Palecki 2006). There is a conspicuous dipole in Europe, with northern Europe indicating one signal and southern Europe indicating another. The dipole of signals is consistent with findings by Pohlmann et al. 2006 who found that a warm North Atlantic Ocean was associated with enhanced precipitation in northern Europe and decreased precipitation in southern Europe.

To demonstrate the ability of the technique to capture the low-frequency variability we performed temporal reconstructions at selected locations around the globe. The reconstructions are performed at several significant frequencies identified earlier, which are the secular trend (0 cpy), the ENSO band (0.1956, 0.2122, 0.2280, 0.2654, 0.2783, 0.2942 cpy), and the AMO (0.0149 cpy). The reconstructions are performed separately at each frequency and are summed up to result in a single combined temporal reconstruction. We selected 12 locations (the northwestern United States at 43.75°N, 123.75°W; the southwestern United States at 36.25°N, 13.75°W; the East Coast at 38.75°N, 76.25°W; northeastern Brazil at 8.75°S, 36.25°W; the Sahel at 11.25°N, 16.25°W; Eurasia at 68.75°N, 21.25°E; western Australia at 21.25°S, 113.75°E; eastern Australia at 28.75°S, 153.75°E; South Africa at 31.25°S, 28.75°E; western India at 16.25°N, 73.75°E; central India at 21.25°N, 78.75°E; and eastern India at 21.25°N, 86.25°E), and the reconstructions are shown in Figs. 9, 10, 11 and 12. All of the correlations between the reconstructed time series and the time series of measured data are statistically significant at a 99% confidence level. The reconstructions appear to be a smoothing of the PDSI time series at each specific location.

5. Summary

Using a robust spectral domain analysis technique, MTM–SVD, we identified joint modes of variability in global SST and PDSI. This technique isolates dominant frequencies that are shared spatially by both fields and it does not suffer from aliasing effects as the traditional time-domain techniques. We find the dominant signal to be in the secular trend and ENSO band and to a lesser extent in the interdecadal band. The ENSO and trend were robust in both fields independently and also jointly, while the interdecadal band was mostly in the Northern Hemisphere SST, which is consistent with other studies. The temporal reconstructions of SSTs at these significant frequencies reproduce very well the dominant forcings: ENSO, PDO, and AMO. Also, the combined temporal reconstructions of the PDSI at all the significant frequencies at a suite of locations around the globe capture the low-frequency variability very well. The difficulty of performing a global analysis is the issue of seasonality especially with respect to PDSI. While this is not likely to interfere with the low-frequency variability, it can on the high-frequency end (e.g., at the 2–3-yr periodicity). Regional analysis will help provide insights into variability over a specific area of interest. Regardless, the findings in this research provide potential for decadal prediction and simulation of PDSI that can be very useful for drought mitigation planning.

This study confirms results from previous analyses, which is a useful research contribution, and provides new insights because 1) this study provides a simultaneous analysis of land-based and ocean climate variability, 2) raw data are used in the analyses presented (without any preconditioning or smoothing) to identify important temporal and spatial modes of global climate variability, and 3) the phase–lag relationships identified provide increased understanding of temporal and spatial interrelationships between land-based and ocean climate for regions across the globe.

The modes of climate variability identified in this study refer to historical climate variability of the twentieth century. The long-term trend may or may not continue into the future, however, the variability related to ENSO and the AMO are likely to continue to be a part of future global climate variability. Through paleo-climate research both ENSO and AMO climate variability have been identified as important components of global climate variability for the past several centuries (Gray et al. 2003; Hidalgo 2004).

REFERENCES

  • Alley, W. H., 1984: The Palmer Drought Severity Index: Limitations and assumptions. J. Climate Appl. Meteor., 23 , 11001109.

  • Cane, M. A., , A. C. Clement, , A. Kaplan, , Y. Kushnir, , D. Pozdnyakov, , R. Seager, , E. Zebiak, , and R. Murtugudde, 1997: Twentieth-century sea surface temperature trends. Science, 275 , 957960.

    • Search Google Scholar
    • Export Citation
  • Chen, M., , P. Xie, , J. E. Janowiak, , and P. A. Arkin, 2002: Global land precipitation: A 50-yr monthly analysis based on gauge observations. J. Hydrometeor., 3 , 249266.

    • Search Google Scholar
    • Export Citation
  • Dai, A., , and T. M. L. Wigley, 2000: Global patterns of ENSO-induced precipitation. Geophys. Res. Lett., 27 , 12831286.

  • Dai, A., , I. Y. Fung, , and A. D. Del Genio, 1997: Surface observed global land precipitation variations during 1980–88. J. Climate, 10 , 29432962.

    • Search Google Scholar
    • Export Citation
  • Dai, A., , K. E. Trenberth, , and T. Qian, 2004: A global data set of Palmer Drought Severity Index for 1870–2002: Relationship with soil moisture and effects of surface warming. J. Hydrometeor., 5 , 11171130.

    • Search Google Scholar
    • Export Citation
  • Delworth, T. L., , and M. E. Mann, 2000: Observed and simulated multidecadal variability in the Northern Hemisphere. Climate Dyn., 16 , 661676.

    • Search Google Scholar
    • Export Citation
  • Diaz, H. F., , and V. Markgraf, Eds.,. 2000: El Niño and the Southern Oscillation. Cambridge University Press, 496 pp.

  • Dima, M., , and G. Lohmann, 2007: A hemispheric mechanism for the Atlantic multidecadal oscillation. J. Climate, 20 , 27062719.

  • Dong, B., , R. T. Sutton, , and A. A. Scaife, 2006: Multidecadal modulation of El Niño-Southern Oscillation (ENSO) variance by Atlantic Ocean sea surface temperatures. Geophys. Res. Lett., 33 , L08705. doi:10.1029/2006GL025766.

    • Search Google Scholar
    • Export Citation
  • Enfield, D. B., , and E. J. Alfaro, 1999: The dependence of Caribbean rainfall on the interaction of the tropical Atlantic and Pacific Oceans. J. Climate, 12 , 20932103.

    • Search Google Scholar
    • Export Citation
  • Enfield, D. B., , and A. M. Mestas-Nuñez, 1999: Multiscale variabilities in global sea surface temperatures and their relationships with tropospheric climate patterns. J. Climate, 12 , 27192733.

    • Search Google Scholar
    • Export Citation
  • Enfield, D. B., , A. M. Mestas-Nuñez, , and P. J. Trimble, 2001: The Atlantic multidecadal oscillation and its relation to rainfall and river flows in the continental U.S. Geophys. Res. Lett., 28 , 277280.

    • Search Google Scholar
    • Export Citation
  • Fontaine, B., , and S. Janicot, 1996: Sea surface temperature fields associated with West African rainfall anomaly types. J. Climate, 9 , 29352940.

    • Search Google Scholar
    • Export Citation
  • Ghil, M., , and R. Vautard, 1991: Interdecadal oscillations and the warming trend in global temperature time series. Nature, 350 , 324327.

    • Search Google Scholar
    • Export Citation
  • Giannini, A., , R. Saravanan, , and P. Chang, 2003: Oceanic forcing of Sahel rainfall on interannual and interdecadal time scales. Science, 302 , 10271030.

    • Search Google Scholar
    • Export Citation
  • Gray, S. T., , J. L. Betancourt, , C. L. Fastie, , and S. T. Jackson, 2003: Patterns and sources of multidecadal oscillations in drought-sensitive tree-ring records from the central and southern Rocky Mountains. Geophys. Res. Lett., 30 , 1316. doi:10.1029/2002GL016154.

    • Search Google Scholar
    • Export Citation
  • Gray, S. T., , L. J. Graumlich, , J. L. Betancourt, , and G. T. Pederson, 2004: A tree-ring based reconstruction of the Atlantic Multidecadal Oscillation since 1567 A.D. Geophys. Res. Lett., 31 , L12205. doi:10.1029/2004GL019932.

    • Search Google Scholar
    • Export Citation
  • Hidalgo, H. G., 2004: Climate precursors of multidecadal drought variability in the western United States. Water Resour. Res., 40 , W12504. doi:10.1029/2004WR003350.

    • Search Google Scholar
    • Export Citation
  • Hoerling, M., , and A. Kumar, 2003: The perfect ocean for drought. Science, 299 , 691694.

  • Jones, P. D., , and A. Moberg, 2003: Hemispheric and large-scale surface air temperature variations: An extensive revision and an update to 2001. J. Climate, 16 , 206223.

    • Search Google Scholar
    • Export Citation
  • Kaplan, A., , M. Cane, , Y. Kushnir, , A. Clement, , M. Blumenthal, , and B. Rajagopalan, 1998: Analyses of global sea surface temperature 1856-1991. J. Geophys. Res., 103 , 1856718589.

    • Search Google Scholar
    • Export Citation
  • Kawamura, R., 1994: A rotated EOF analysis of global sea surface temperature variability with interannual and interdecadal scales. J. Phys. Oceanogr., 24 , 707715.

    • Search Google Scholar
    • Export Citation
  • Lees, J. M., , and J. Park, 1995: Multi-taper spectral analysis: A stand-alone C-subroutine. Comput. Geosci., 21 , 199236.

  • Mann, M. E., , and J. Park, 1994: Global scales modes of surface temperature variability on interannual to century timescales. J. Geophys. Res., 99 , 2581925833.

    • Search Google Scholar
    • Export Citation
  • Mann, M. E., , and J. Park, 1996: Joint spatiotemporal modes of surface temperature and sea level pressure variability in the Northern Hemisphere during the last century. J. Climate, 9 , 21372162.

    • Search Google Scholar
    • Export Citation
  • Mann, M. E., , and J. Park, 1999: Oscillatory spatiotemporal signal detection in climate studies: A multiple-taper spectral domain approach. Advances in Geophysics, Vol. 41, Academic Press, 1–131.

    • Search Google Scholar
    • Export Citation
  • Mantua, N. J., , and S. R. Hare, 2002: The Pacific Decadal Oscillation. J. Oceanogr., 58 , 3544.

  • McCabe, G. J., , and M. A. Palecki, 2006: Multidecadal climate variability of global lands and oceans. Int. J. Climatol., 26 , 849865.

  • McCabe, G. J., , M. A. Palecki, , and J. L. Betancourt, 2004: Pacific and Atlantic Ocean influences on multidecadal drought frequency in the United States. Proc. Natl. Acad. Sci. USA, 101 , 41364141.

    • Search Google Scholar
    • Export Citation
  • Mestas-Nuñez, A., , and D. Enfield, 1999: Rotated global modes of non-ENSO sea surface temperature variability. J. Climate, 12 , 27342746.

    • Search Google Scholar
    • Export Citation
  • Nicholson, S. E., , D. Leposo, , and J. Grist, 2001: The relationship between El Niño and drought over Botswana. J. Climate, 14 , 323335.

    • Search Google Scholar
    • Export Citation
  • Palmer, W. C., 1965: Meteorological drought. Research Paper 45, U.S. Weather Bureau, National Oceanic and Atmospheric Administration Library and Information Services Division, Washington, DC, 58 pp.

    • Search Google Scholar
    • Export Citation
  • Park, J., , C. R. Lindberg, , and F. L. Vernon III, 1987: Multitaper spectral analysis for high frequency seismograms. J. Geophys. Res., 92 , 1267512684.

    • Search Google Scholar
    • Export Citation
  • Pohlmann, H., , F. Sienz, , and M. Latif, 2006: Influence of the multidecadal Atlantic meridional overturning circulation variability on European climate. J. Climate, 19 , 60626067.

    • Search Google Scholar
    • Export Citation
  • Rajagopalan, B., , M. E. Mann, , and U. Lall, 1998: A multivariate frequency-domain approach to long-lead climatic forecasting. Wea. Forecasting, 13 , 5874.

    • Search Google Scholar
    • Export Citation
  • Rodwell, M. J., , D. P. Rowell, , and C. K. Folland, 1999: Oceanic forcing of the wintertime North Atlantic Oscillation and European climate. Nature, 398 , 320323.

    • Search Google Scholar
    • Export Citation
  • Ropelewski, C. F., , and M. S. Halpert, 1987: Global and regional scale precipitation patterns associated with the El Niño–Southern Oscillation. Mon. Wea. Rev., 115 , 16061626.

    • Search Google Scholar
    • Export Citation
  • Schubert, S. D., , M. J. Suarez, , P. J. Pegion, , R. D. Koster, , and J. T. Bacmeister, 2004: On the cause of the 1930s Dust Bowl. Science, 303 , 18551859.

    • Search Google Scholar
    • Export Citation
  • Seager, R., , Y. Kushnir, , C. Herweijer, , N. Naik, , and J. Velez, 2005: Modeling of tropical forcing of persistent droughts and pluvials over western North America: 1856–2000. J. Climate, 18 , 40684091.

    • Search Google Scholar
    • Export Citation
  • Shabbar, A., , and W. Skinner, 2004: Summer drought patterns in Canada and the relationship to global sea surface temperatures. J. Climate, 17 , 28662880.

    • Search Google Scholar
    • Export Citation
  • Solomon, S., , D. Qin, , M. Manning, , M. Marquis, , K. Averyt, , M. M. B. Tignor, , H. L. Miller Jr., , and Z. Chen, 2007: Climate Change 2007: The Physical Science Basis. Cambridge University Press, 996 pp.

    • Search Google Scholar
    • Export Citation
  • Sutton, R. T., , and D. L. R. Hodson, 2003: Influence of the ocean on North Atlantic climate variability 1871–1999. J. Climate, 16 , 32963313.

    • Search Google Scholar
    • Export Citation
  • Sutton, R. T., , and D. L. R. Hodson, 2005: Atlantic Ocean forcing of multidecadal variations in North American and European summer climate. Science, 309 , 115118.

    • Search Google Scholar
    • Export Citation
  • Sutton, R. T., , and D. L. R. Hodson, 2007: Climate response to basin-scale warming and cooling of the North Atlantic Ocean. J. Climate, 20 , 891907.

    • Search Google Scholar
    • Export Citation
  • Thomson, D. J., 1982: Spectrum estimation and harmonic analysis. Proc. IEEE, 70 , 10551096.

  • Tourre, Y., , B. Rajagopalan, , and Y. Kushnir, 1999: Dominant patterns of climate variability in the Atlantic Ocean region during the last 136 years. J. Climate, 12 , 22852299.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 1997: The definition of El Niño. Bull. Amer. Meteor. Soc., 78 , 27712777.

  • Trenberth, K. E., , and J. M. Caron, 2000: The Southern Oscillation revisited: Sea level pressures, surface temperatures, and precipitation. J. Climate, 13 , 43584365.

    • Search Google Scholar
    • Export Citation
  • von Storch, H., , and F. W. Zwiers, 1999: Statistical Analysis in Climate Research. Cambridge University Press, 484 pp.

Fig. 1.
Fig. 1.

Grids (gray) with complete (a) PDSI and (b) SST data for the period 1925–2003.

Citation: Journal of Climate 22, 23; 10.1175/2009JCLI2791.1

Fig. 2.
Fig. 2.

LFV spectrum from MTM–SVD analysis of (a) global SSTs and (b) Northern Hemisphere (20°N and above) SSTs. The horizontal lines are the various confidence levels.

Citation: Journal of Climate 22, 23; 10.1175/2009JCLI2791.1

Fig. 3.
Fig. 3.

Spatial reconstruction of SSTs at (a) the secular trend and (b) the ENSO frequency (0.1956 cpy). The length of the arrows represents the relative amplitude of the signal and the angle from the horizontal represents the phase lag. For easier interpretation the arrows are colored according to angle from the horizontal [1°–90° (black), 91°–180° (red), 181°–270° (blue), and 271°–360° (green)].

Citation: Journal of Climate 22, 23; 10.1175/2009JCLI2791.1

Fig. 4.
Fig. 4.

As in Fig. 3, but for the multidecadal signal (a) 0.0149 and (b) 0.0334 cpy.

Citation: Journal of Climate 22, 23; 10.1175/2009JCLI2791.1

Fig. 5.
Fig. 5.

Temporal reconstruction of (a) frequencies significant in the ENSO band (summed for 0.1956, 0.2280, 0.2654, 0.2783, and 0.2942 cpy) at 2.5°S, 132.5°W (a grid point in the tropical Pacific) and a time series of the Niño-3.4 SST index; (b) frequencies in the decadal band (0.0334, 0.0549 and 0.0898 cpy) at 17.5°N, 127.5°W (a grid point in the North Pacific) compared with a time series of the PDO; and (c) the multidecadal frequency, 0.0149 cpy, compared with the AMO index.

Citation: Journal of Climate 22, 23; 10.1175/2009JCLI2791.1

Fig. 6.
Fig. 6.

As in Fig. 2, but from a joint MTM–SVD analysis of global SSTs and global PDSI values.

Citation: Journal of Climate 22, 23; 10.1175/2009JCLI2791.1

Fig. 7.
Fig. 7.

Spatial reconstruction of the PDSI at (a) the secular trend and (b) ENSO frequency (0.228 cpy). The length of the arrows represents the amplitude of the signal and the angle from the horizontal represents the phase lag. For easier interpretation the arrows are colored according to angle from the horizontal [1°–90° (black), 91°–180° (red), 181°–270° (blue), and 271°–360° (green)].

Citation: Journal of Climate 22, 23; 10.1175/2009JCLI2791.1

Fig. 8.
Fig. 8.

Spatial reconstruction of the PDSI at 0.0149 cpy. The length of the arrows represents the amplitude of the signal and the angle from the horizontal represents the phase lag. For easier interpretation the arrows are colored according to angle from the horizontal [1°–90° (black), 91°–180° (red), 181°–270° (blue), and 271°–360° (green)].

Citation: Journal of Climate 22, 23; 10.1175/2009JCLI2791.1

Fig. 9.
Fig. 9.

Temporal reconstructions of the PDSI at a suite of significant frequencies, which are the secular trend (0 cpy), the ENSO band (0.1956, 0.2122, 0.2280, 0.2654, 0.2783, and 0.2942 cpy), and the AMO (0.0149 cpy) at locations in the (a) northwestern United States at 43.75°N, 123.75°W; (b) southwestern United States at 36.25°N, 113.75°W; and (c) the East Coast at 38.75°N, 76.25°W. The reconstructions are performed separately at each frequency and are summed to result in a single combined temporal reconstruction. The actual PDSI time series is also shown.

Citation: Journal of Climate 22, 23; 10.1175/2009JCLI2791.1

Fig. 10.
Fig. 10.

As in Fig. 8, but for (a) northeastern Brazil at 8.75°S, 36.25°W; (b) the Sahel at 11.25°N, 16.25°W; and (c) Eurasia at 68.75°N, 21.25°E.

Citation: Journal of Climate 22, 23; 10.1175/2009JCLI2791.1

Fig. 11.
Fig. 11.

As in Fig. 8, but for (a) western Australia at 21.25°S, 113.75°E; (b) eastern Australia at 28.75°S, 153.75°E; and (c) South Africa at 31.25°S, 28.75°E.

Citation: Journal of Climate 22, 23; 10.1175/2009JCLI2791.1

Fig. 12.
Fig. 12.

As in Fig. 8, but for (a) western India at 16.25°N, 73.75°E; (b) central India at 21.25°N, 78.75°E; and (c) eastern India at 21.25°N, 86.25°E.

Citation: Journal of Climate 22, 23; 10.1175/2009JCLI2791.1

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