Seasonality of the Pacific decadal oscillation - Climate Prediction ...

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2. Data Our analysis is based on data from observations and a coupled model simulation. As will be shown, the spatial pattern and seasonality of the PDO are similar between observations and the coupled model simulation. Use of the model simulation allows us to investigate the seasonality of the PDO based on a longer and more consistent oceanatmosphere model data set. The SST used in this study includes observational data from the National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation SST (OISST) V2 (Reynolds et al. 2002), NOAA Extended Reconstructed SST (ERSST) V3b (Reynolds et al. 2007), and simulated data from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS; Saha et al. 2006) coupled model. The OISST is on a 1 o × 1 o (latitude × longitude) grid and over 29 years from 1982 to 2010. The 29-yr period of the satellite observations may be too short to represent PDO. The ERSST, with a longer record is also employed, which is on a 2 o × 2 o (latitude × longitude) grid and over 150 years from 1861 to 2010. The SST from the CFS is on a 1 o × 2 o (latitude × longitude) grid. The CFS was initialized with January 1, 1981 conditions obtained from the NCEP/Department of Energy (DOE) Reanalysis 2 (R2; Kanamitsu et al. 2002) for the atmosphere and from the NCEP Global Ocean Data Assimilation System (GODAS; Behringer and Xue 2004) for the oceans. The coupled model was integrated for 500 years. The last 480 years are used in the analysis. Data from the model simulation is divided into 16 segments of 30-yr periods. Each shorter-period segment is comparable with the 29 years in the OISST, and the analysis of PDO seasonality over 16 such 4
ealizations provides an estimate of variability in results due to sampling (as may be the case for the OISST). The CFS is a fully coupled ocean–atmosphere–land model, and was implemented for operational seasonal forecast at NCEP in 2004. The present version of the CFS has a horizontal resolution of T62 and 64 vertical levels in the atmospheric component of the model. This dynamical forecast system has demonstrated skillful seasonal forecasts for a number of important climate phenomena, including ENSO (Wang et al. 2005; Zhang et al. 2007), the Asian-Australian monsoon (Wang et al. 2008), and the North American monsoon (Yang et al. 2009). A detailed description of model physics and an overview of CFS performance can be found in Saha et al. (2006) and Wang et al. (2010). Oceanic and atmospheric fields from the CFS are utilized to analyze their roles in the PDO seasonal variation. Ocean temperatures (5–300 m) resolved with 26 vertical levels in the CFS are used to characterize the vertical structure of the PDO and to derive the MLD. The latter is critical to the effectiveness of SST response to atmospheric forcing (Alexander and Penland 1996). The MLD is estimated as the depth at which the temperature change from the ocean surface is 0.5 o C (Monterey and Levitus 1997). The atmospheric field from the CFS is inferred from the 1000-hPa wind on a 2.5 o × 2.5 o (latitude × longitude) grid. The results based on the CFS simulation are compared to ocean temperature and 1000-hPa wind at the same resolutions from the GODAS and R2, respectively, for the period from 1982 to 2010. Surface fluxes, including latent, sensible, long wave, short wave, and Ekman heat transport from the CFS simulation are also employed to illustrate their seasonal variations with the PDO. The contribution to change in surface heat flux due to Ekman transport is estimated using zonal and meridional 5
Page 1 and 2: AMERICAN METEOROLOGICAL SOCIETY Jou
Page 3 and 4: ABSTRACT The seasonality of the Pac
Page 5: The PDO can also modulate the impac
Page 9 and 10: more connection with SST variabilit
Page 11 and 12: and April the cold temperature anom
Page 13 and 14: wind over the North Pacific region
Page 15 and 16: first EOF of the 1000-hPa zonal win
Page 17 and 18: heat flux. This indicates that shor
Page 19 and 20: SST response to the surface wind le
Page 21 and 22: Barnett, T. P., D. W. Pierce, R. Sa
Page 23 and 24: Newman, M. E., and P. D. Sardeshmuk
Page 25 and 26: Figure Captions Fig. 1. Spatial pat
Page 27 and 28: Fig. 1. Spatial patterns of SST ass
Page 29 and 30: Fig. 3. Same as in Fig. 2, but base
Page 35 and 36: Fig. 9. Seasonal variations of area

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