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regression on the concurrent ( Qiu et al. 2007 ) or time lagged ( Liu et al. 2006 ) Niño-3.4 index to seasonally varying regression on two ENSO indices ( FS07 ). In this paper, the ENSO signal is removed by seasonally varying, asymmetric regression onto the first three principal components of the tropical Pacific SST anomalies, which seems quite effective (refer to the appendix ). Prior to the analysis, a cubic polynomial was removed from all variables by least squares fit, to reduce the influence of
regression on the concurrent ( Qiu et al. 2007 ) or time lagged ( Liu et al. 2006 ) Niño-3.4 index to seasonally varying regression on two ENSO indices ( FS07 ). In this paper, the ENSO signal is removed by seasonally varying, asymmetric regression onto the first three principal components of the tropical Pacific SST anomalies, which seems quite effective (refer to the appendix ). Prior to the analysis, a cubic polynomial was removed from all variables by least squares fit, to reduce the influence of
-derived fields and 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalyses that have been optimally combined using the Coupled Ocean–Atmosphere Response Experiment (COARE) 3.0 bulk flux algorithm. The OAFlux latent and sensible heat flux estimates are unbiased, and the root-mean-square (rms) difference is less than 8 W m −2 when compared with daily flux time series
-derived fields and 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalyses that have been optimally combined using the Coupled Ocean–Atmosphere Response Experiment (COARE) 3.0 bulk flux algorithm. The OAFlux latent and sensible heat flux estimates are unbiased, and the root-mean-square (rms) difference is less than 8 W m −2 when compared with daily flux time series
.1) [Geophysical Fluid Dynamics Laboratory (GFDL) Climate Model version 2.1], Knutson et al. (2006) find the standard deviation of interannual SST in the KOE to have maximum amplitude of about 0.6°C in an analysis of observations, while the model has a maximum amplitude of 1.5°C. Alexander et al. (2006) also show pronounced variability in a control simulation of the Community Climate System Model version 3 (CCSM3) of SST in the KOE that we explore in detail in this paper. While this overexpression of
.1) [Geophysical Fluid Dynamics Laboratory (GFDL) Climate Model version 2.1], Knutson et al. (2006) find the standard deviation of interannual SST in the KOE to have maximum amplitude of about 0.6°C in an analysis of observations, while the model has a maximum amplitude of 1.5°C. Alexander et al. (2006) also show pronounced variability in a control simulation of the Community Climate System Model version 3 (CCSM3) of SST in the KOE that we explore in detail in this paper. While this overexpression of
mechanisms that generate large-scale atmospheric anomalies in the extratropics from the data or numerical model output. Acknowledgments We thank two anonymous reviewers for their comments, which helped to improve the manuscript. REFERENCES Chang , E. K. M. , 1993 : Downstream development of baroclinic waves as inferred from regression analysis. J. Atmos. Sci. , 50 , 2038 – 2053 . Chang , E. K. M. , 2001 : GCM and observational diagnoses of the seasonal and interannual variations of the Pacific
mechanisms that generate large-scale atmospheric anomalies in the extratropics from the data or numerical model output. Acknowledgments We thank two anonymous reviewers for their comments, which helped to improve the manuscript. REFERENCES Chang , E. K. M. , 1993 : Downstream development of baroclinic waves as inferred from regression analysis. J. Atmos. Sci. , 50 , 2038 – 2053 . Chang , E. K. M. , 2001 : GCM and observational diagnoses of the seasonal and interannual variations of the Pacific
into the process of oceanic baroclinic adjustment, we have conducted a lag regression analysis for the wintertime south Indian Ocean ( Fig. 19 ), based on 6-hourly data of the NCEP–DOE reanalysis and SST data ( Reynolds et al. 2002 ). In this analysis, near-surface meteorological variables were linearly regressed on high-pass-filtered 850-hPa meridional wind velocity at a particular location in the APFZ, regarded as the reference time series. The timing of lag zero corresponds to the phase at which
into the process of oceanic baroclinic adjustment, we have conducted a lag regression analysis for the wintertime south Indian Ocean ( Fig. 19 ), based on 6-hourly data of the NCEP–DOE reanalysis and SST data ( Reynolds et al. 2002 ). In this analysis, near-surface meteorological variables were linearly regressed on high-pass-filtered 850-hPa meridional wind velocity at a particular location in the APFZ, regarded as the reference time series. The timing of lag zero corresponds to the phase at which
and in air–sea heat fluxes have been shown to be related to the dominant climate indices in each ocean, for example, the Pacific decadal oscillation (PDO) and the North Atlantic Oscillation (NAO; see Joyce et al. 2000 ; Qiu 2003 ; Kelly and Dong 2004 ; DiNezio et al. 2009 ). Further, a recent analysis of the Community Climate System Model, version 2 (CCSM2) has shown that interannual variations in air–sea fluxes in the KE region are correlated with changes in the strength of heat advection
and in air–sea heat fluxes have been shown to be related to the dominant climate indices in each ocean, for example, the Pacific decadal oscillation (PDO) and the North Atlantic Oscillation (NAO; see Joyce et al. 2000 ; Qiu 2003 ; Kelly and Dong 2004 ; DiNezio et al. 2009 ). Further, a recent analysis of the Community Climate System Model, version 2 (CCSM2) has shown that interannual variations in air–sea fluxes in the KE region are correlated with changes in the strength of heat advection
SSTAs of opposite sign in the mid and high latitudes, with the midlatitude maximum along the GS ( Fig. 10 ). The observational analysis of Frankignoul and Kestenare (2005) , however, does not show such teleconnections; rather, it supports an equatorial Atlantic SST influence on the east Atlantic pattern, as modeled by Haarsma and Hazeleger (2007) . Indian Ocean SSTAs can also influence the WBCs through atmospheric teleconnections. During boreal summer an east–west SST dipole in the equatorial
SSTAs of opposite sign in the mid and high latitudes, with the midlatitude maximum along the GS ( Fig. 10 ). The observational analysis of Frankignoul and Kestenare (2005) , however, does not show such teleconnections; rather, it supports an equatorial Atlantic SST influence on the east Atlantic pattern, as modeled by Haarsma and Hazeleger (2007) . Indian Ocean SSTAs can also influence the WBCs through atmospheric teleconnections. During boreal summer an east–west SST dipole in the equatorial
