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of this system is ocean-initialized climate prediction, we focus our regional evaluations on the Northern Hemisphere Atlantic and the tropical Pacific, areas that have been identified as potentially relevant for that application. These are presented in sections 4d and 4e . In section 5 , we discuss the challenges that emerged in this work and outline areas of potential improvement for the system. 2. Subsurface temperature and salinity observations Subsurface temperature and salinity
of this system is ocean-initialized climate prediction, we focus our regional evaluations on the Northern Hemisphere Atlantic and the tropical Pacific, areas that have been identified as potentially relevant for that application. These are presented in sections 4d and 4e . In section 5 , we discuss the challenges that emerged in this work and outline areas of potential improvement for the system. 2. Subsurface temperature and salinity observations Subsurface temperature and salinity
linearization assumption, Δ z 1 cannot be very thin and has to be larger than order a few meters. The global integral of the ocean volume remains constant because the freshwater fluxes are treated as virtual salt fluxes, using a constant reference salinity. Below, we present a summary of the ocean model setup and major developments since CCSM3. The standard ocean model uses the same horizontal grid with its displaced grid North Pole and nominal 1° resolution described in Danabasoglu et al. (2006
linearization assumption, Δ z 1 cannot be very thin and has to be larger than order a few meters. The global integral of the ocean volume remains constant because the freshwater fluxes are treated as virtual salt fluxes, using a constant reference salinity. Below, we present a summary of the ocean model setup and major developments since CCSM3. The standard ocean model uses the same horizontal grid with its displaced grid North Pole and nominal 1° resolution described in Danabasoglu et al. (2006
. We note however that the ocean model in CCSM3 had only 40 vertical levels as opposed to the 60 levels used in the CCSM4 POP2. Consequently, CCSM3 and CCSM4 ocean configurations differ in their representations of the bottom topography as well as some details of the ocean–land mask. The CCSM3 present-day control was integrated for 700 yr, starting with the January-mean climatological potential temperature and salinity [a blending of Levitus et al. (1998) and Steele et al. (2001) datasets] and
. We note however that the ocean model in CCSM3 had only 40 vertical levels as opposed to the 60 levels used in the CCSM4 POP2. Consequently, CCSM3 and CCSM4 ocean configurations differ in their representations of the bottom topography as well as some details of the ocean–land mask. The CCSM3 present-day control was integrated for 700 yr, starting with the January-mean climatological potential temperature and salinity [a blending of Levitus et al. (1998) and Steele et al. (2001) datasets] and
accounts for roughly 15% of the ensemble mean SST trend, whereas cooling between 0.2° and 0.6°C in the Pacific sector counteracts the warming trends. This suggests that the trends in SST cannot be attributed to secular changes in this mode of variability. Enhanced atmospheric temperatures due to changes in external forcing must be held responsible for warming of the surface ocean (Bates et al. 2011, manuscript submitted to J. Climate ). 4. Southern Ocean water masses a. Temperature and salinity
accounts for roughly 15% of the ensemble mean SST trend, whereas cooling between 0.2° and 0.6°C in the Pacific sector counteracts the warming trends. This suggests that the trends in SST cannot be attributed to secular changes in this mode of variability. Enhanced atmospheric temperatures due to changes in external forcing must be held responsible for warming of the surface ocean (Bates et al. 2011, manuscript submitted to J. Climate ). 4. Southern Ocean water masses a. Temperature and salinity
and carbonate ion concentrations) diagnostically, as a function of prognostic dissolved inorganic carbon (DIC), alkalinity, and temperature- and salinity-dependent equilibrium coefficients ( Dickson and Goyet 1994 ). Alkalinity is modified by the consumption and remineralization of nitrate (and ammonium), as well as biogenic calcification and dissolution of CaCO 3 ( Najjar and Orr 1998 ). Biogenic calcification is modeled as proportional to a temperature-dependent fraction of small phytoplankton
and carbonate ion concentrations) diagnostically, as a function of prognostic dissolved inorganic carbon (DIC), alkalinity, and temperature- and salinity-dependent equilibrium coefficients ( Dickson and Goyet 1994 ). Alkalinity is modified by the consumption and remineralization of nitrate (and ammonium), as well as biogenic calcification and dissolution of CaCO 3 ( Najjar and Orr 1998 ). Biogenic calcification is modeled as proportional to a temperature-dependent fraction of small phytoplankton
( Grodsky et al. 2003 ; Hagos and Cook 2009 ). Rainfall affects sea surface salinity (SSS), which in turn affects SST through its impact on the upper-ocean stratification and barrier layers. These impacts have been found in uncoupled and coupled models ( Carton 1991 ; Breugem et al. 2008 ). Observational analyses of Pailler et al. (1999) , Foltz and McPhaden (2009) , and Liu et al. (2009) have also suggested that salinity and barrier layers are important for the climate of the tropical Atlantic
( Grodsky et al. 2003 ; Hagos and Cook 2009 ). Rainfall affects sea surface salinity (SSS), which in turn affects SST through its impact on the upper-ocean stratification and barrier layers. These impacts have been found in uncoupled and coupled models ( Carton 1991 ; Breugem et al. 2008 ). Observational analyses of Pailler et al. (1999) , Foltz and McPhaden (2009) , and Liu et al. (2009) have also suggested that salinity and barrier layers are important for the climate of the tropical Atlantic
branched off from CONT after year 500 and continued for 300 years. Unless otherwise noted, the comparisons will be done between the means of years 651–700 of CONT and the years 751–800 of OP115. Although CONT is considered fully spun up the comparison is not quite clean, because there is a small drift in ocean temperature and salinity. The two different time intervals were chosen because at year 700 the AMOC in OP115 did not reach equilibrium yet, and we did not have the computational resources to
branched off from CONT after year 500 and continued for 300 years. Unless otherwise noted, the comparisons will be done between the means of years 651–700 of CONT and the years 751–800 of OP115. Although CONT is considered fully spun up the comparison is not quite clean, because there is a small drift in ocean temperature and salinity. The two different time intervals were chosen because at year 700 the AMOC in OP115 did not reach equilibrium yet, and we did not have the computational resources to
2x1 control and twentieth-century simulations can be found in Gent et al. (2011) . The T31x3_1850 control simulation was initialized using the Polar Science Center Hydrographic Climatology dataset (PHC2) of potential temperature and salinity data [representing a blending of the Levitus et al. (1998) and Steele et al. (2001) data for the Arctic Ocean], and state of rest in the ocean model. The twentieth-century simulation was integrated for 150 years using aerosol, greenhouse gas, volcanic
2x1 control and twentieth-century simulations can be found in Gent et al. (2011) . The T31x3_1850 control simulation was initialized using the Polar Science Center Hydrographic Climatology dataset (PHC2) of potential temperature and salinity data [representing a blending of the Levitus et al. (1998) and Steele et al. (2001) data for the Arctic Ocean], and state of rest in the ocean model. The twentieth-century simulation was integrated for 150 years using aerosol, greenhouse gas, volcanic
, thickness, and multiyear ice coverage. In addition, the observed decreasing trend in ice extent at the end of summer since the early 1980s is within the spread of ensemble members. The largest bias in the sea ice simulation is the ice motion field, due to a Beaufort Gyre that is too weak. CCSM4 captures the observed stratification of the upper Arctic Ocean, producing a warm and saline Atlantic layer overlain by a cold and fresh surface layer. The main oceanic bias is that the Atlantic layer is too
, thickness, and multiyear ice coverage. In addition, the observed decreasing trend in ice extent at the end of summer since the early 1980s is within the spread of ensemble members. The largest bias in the sea ice simulation is the ice motion field, due to a Beaufort Gyre that is too weak. CCSM4 captures the observed stratification of the upper Arctic Ocean, producing a warm and saline Atlantic layer overlain by a cold and fresh surface layer. The main oceanic bias is that the Atlantic layer is too
freshwater fluxes both in and out of the Arctic. This is done in the following subsections ( sections 4a – b ). a. Water masses and vertical structure The simulated surface salinity in the central Arctic (80°–90°N) is 0.35 psu too large compared to the Polar Science Center Hydrographic Climatology version 2 (PHC2) data ( Steele et al. 2001 ), whereas the simulated salinities between 50 m and 350 m depth in the central Arctic are between 0.1 and 0.2 psu too fresh, with good agreement below 350 m (see Fig
freshwater fluxes both in and out of the Arctic. This is done in the following subsections ( sections 4a – b ). a. Water masses and vertical structure The simulated surface salinity in the central Arctic (80°–90°N) is 0.35 psu too large compared to the Polar Science Center Hydrographic Climatology version 2 (PHC2) data ( Steele et al. 2001 ), whereas the simulated salinities between 50 m and 350 m depth in the central Arctic are between 0.1 and 0.2 psu too fresh, with good agreement below 350 m (see Fig
