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Roxana C. Wajsowicz

Centers for Environmental Prediction (NCEP) Coupled Forecast System (CFS) and the National Aeronautics and Space Administration (NASA) Seasonal-to-Interannual Prediction Project (NSIPP) system. There are two types of climate prediction problem ( Lorenz 1975 ). In the boundary value problem, the task is to assess the change in climate due to some change in external forcing, for example, anthropogenic changes. In the initial value problem considered here, compare with ENSO forecasting, the task is to

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Lisan Yu, Xiangze Jin, and Robert A. Weller

the European Centre for Medium-Range Weather Forecasts (ECMWF; Gibson et al. 1997 ) and NCEP models are both underestimated. They also found that the University of Wisconsin—Madison (UWM)/COADS climatology ( da Silva et al. 1994 ) is physically less representative if the fluxes are constrained in a way that the time mean globally integrated air–sea heat flux is zero. The accuracy of the heat flux estimates impacts the extent and scope of the use of the flux products in climate research studies

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Tommy G. Jensen

show that a system using a 10-day sampling interval for floats combined with moored arrays is capable of capturing interannual and subseasonal variability in this region. The final paper addresses the important issue of predictability and forecasting. Wajsowicz investigates the predictability and potential predictability of SSTA in the regions of the two IOD index regions using coupled ocean–atmosphere ensemble forecasts. A late spring predictability barrier is identified for SSTA in both regions

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Suryachandra A. Rao, Sebastien Masson, Jing-Jia Luo, Swadhin K. Behera, and Toshio Yamagata

African short rains. Some attempts have also been made recently to pursue the prospects of forecasting climate variability over the tropical Indian Ocean sector, specifically extreme positive events of the IOD ( Luo et al. 2005a ; Wajsowicz 2005 ). Though many CGCMs are used to understand and forecast IOD phenomena, none of these CGCMs addressed the role of intraseasonal disturbances (ISDs) on the IOD with the lone exception of Gualdi et al. (2003) . Using the Scale Interactions Experiment (SINTEX

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H. Annamalai, H. Okajima, and M. Watanabe

; 1985 ; Lau and Nath 1994 ; Hoerling et al. 1997 ; Hoerling and Kumar 1997 ; Kumar and Hoerling 1997 , among others). The observed relationships, theoretical frameworks, successful simulations by AGCMs, and, more importantly, our ability to predict tropical SST 6–12 months ahead ( Latif et al. 1994 ) led to a great interest in the possibility of producing skillful seasonal forecasts over North America (e.g., Kumar et al. 1996 ; Hoerling et al. 1997 ; Hoerling and Kumar 1997 ; Shukla 1998

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Peter R. Oke and Andreas Schiller

the design of specific observing systems and the configuration of multisensor observing networks, provide an assessment of the potential for future observing systems and innovative uses of existing systems to achieve major improvements in forecast skill, test advanced data assimilation methods, and to assess the relative role of observations and forecasting methods in improving the utility of forecasts. For some time now atmospheric models have been used to aid the design of observing systems (e

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Tomoki Tozuka, Jing-Jia Luo, Sebastien Masson, and Toshio Yamagata

Centre for Medium-Range Weather Forecasts forecasting system. J. Geophys. Res. , 96 , 9121 – 9132 . Nitta , T. , and S. Yamada , 1989 : Recent warming of tropical sea surface temperature and its relationship to the Northern Hemisphere circulation. J. Meteor. Soc. Japan , 67 , 375 – 383 . Philander , S. G. H. , and R. C. Pacanowski , 1986 : The mass and heat budget in a model of the tropical Atlantic Ocean. J. Geophys. Res. , 91 , 14212 – 14220 . Philander , S. G. H. , and W

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Annalisa Cherchi, Silvio Gualdi, Swadhin Behera, Jing Jia Luo, Sebastien Masson, Toshio Yamagata, and Antonio Navarra

40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40), realized from 1958 to 2002 (for more details see the Web site ). Global distribution of ocean temperature is taken from an ocean analysis for the period 1948–99 ( Masina et al. 2004 ). All observations and reanalysis datasets refer to the 1958–2002 period for consistency with the ERA-40 time record length. The Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP

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Joaquim Ballabrera-Poy, Eric Hackert, Raghu Murtugudde, and Antonio J. Busalacchi

along with these five equatorial points from a pool of the available 173 mooring candidates. The ensemble of six points is then used to estimate the error field of the remaining 172 (i.e., 178 − 5 − 1) points. Again, the location with the highest average explained variance is chosen as an optimal location. In this way, the 35 optimal points are chosen in an objective fashion by using the forecast error field of the Kalman filter results. The 35 optimal points are shown in Fig. 4 (dots) along with

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Karumuri Ashok, Hisashi Nakamura, and Toshio Yamagata

-Range Weather Forecasts, although the variability over the south Indian Ocean is slightly weaker in our result than in theirs. Likewise, the interannual variability in the upper-level storm-track activity, measured as the local standard deviation of 300-hPa Z e , is also strongest over the midlatitude South Pacific and Atlantic ( Fig. 2b ). In the regions of large interannual variability in U 300 and Z e , the local correlation is generally positive between the two variables ( Fig. 2c ), exceeding the

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