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Xiangzhou Song and Lisan Yu

process, wind advects the heat away from the source and helps to reestablish the sea–air thermal gradient and facilitates a faster rate of turbulent conduction. Hence, two questions arise as to the role wind has played in decadal variability of SHF, and the relative importance of wind and thermal effects in giving rise to the changing characteristics of the SHF time series. These questions will be addressed in this study. Fig . 1. Monthly time series of global averaged LHF (solid line, W m −2 ; left

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ChuanLi Jiang, Sarah T. Gille, Janet Sprintall, Kei Yoshimura, and Masao Kanamitsu

Reanalysis Downscaling at 10 km (CARD10) produced for the California current region with some improvement in the boundary conditions and model physics ( Yoshimura and Kanamitsu 2009 ; Kanamitsu et al. 2010 ). Small-scale features are generated by forcing a high-resolution regional atmospheric model with large-scale NCEP–NCAR reanalysis fields on the domain boundaries. For the California downscaling CARD10, daily SSTs from ECMWF reanalysis (1° × 1°) were used ( Fiorino 2004 ; Kanamitsu and Kanamaru 2007

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Matthew R. Mazloff

Hemisphere zonal winds just north of the Drake Passage would have the greatest effect on ACC strength. Projecting a wind stress perturbation onto the zonally integrated sensitivity to zonal wind stress reveals a predicted mean transport perturbation ( Table 1 ). The perturbations chosen are a shift of the mean winds by 0.5° north or south, an increase in the mean winds over the region, and a series of local increases. The regional increases chosen are 1) an addition of magnitude 0.001 N m −2 and 2) an

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Ivana Cerovečki, Lynne D. Talley, and Matthew R. Mazloff

water vapor, aerosol, and clouds. The exclusion of strongly absorbing aerosols in GCMs can regionally cause excessive insolation at the ground (e.g., Cusack et al. 1998 ). The crude aerosol climatologies typically used in current GCMs and reanalyses do not properly account for these aerosol effects ( Wild 1999 ), thus introducing a bias not only in NSW radiation estimates, but also in, for example, the oceanic heat distribution ( Cai et al. 2006 ). c. SOSE and LY09 We next turn to the SOSE and LY

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