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– 1499 . Sengupta , D. , and M. Ravichandran , 2001 : Oscillations of Bay of Bengal sea surface temperature during the 1998 summer monsoon . Geophys. Res. Lett. , 28 , 2033 – 2036 . Seo , H. , S.-P. Xie , R. Murtugudde , M. Jochum , and A. J. Miller , 2009 : Seasonal effects of Indian Ocean freshwater forcing in a regional coupled model . J. Climate , 22 , 6577 – 6596 . Shankar , D. , S. R. Shetye , and P. V. Joseph , 2007 : Link between convection and meridional
– 1499 . Sengupta , D. , and M. Ravichandran , 2001 : Oscillations of Bay of Bengal sea surface temperature during the 1998 summer monsoon . Geophys. Res. Lett. , 28 , 2033 – 2036 . Seo , H. , S.-P. Xie , R. Murtugudde , M. Jochum , and A. J. Miller , 2009 : Seasonal effects of Indian Ocean freshwater forcing in a regional coupled model . J. Climate , 22 , 6577 – 6596 . Shankar , D. , S. R. Shetye , and P. V. Joseph , 2007 : Link between convection and meridional
1. Introduction Because of its proximity to land and the presence of coupled interaction processes, the seasonal climate of the tropical Atlantic Ocean is notoriously difficult to simulate accurately in coupled models ( Zeng et al. 1996 ; Davey et al. 2002 ; Deser et al. 2006 ; Chang et al. 2007 ; Richter and Xie 2008 ). Several recent studies, including those referenced above, have linked the ultimate causes of the persistent model biases to problems in simulating winds and clouds by the
1. Introduction Because of its proximity to land and the presence of coupled interaction processes, the seasonal climate of the tropical Atlantic Ocean is notoriously difficult to simulate accurately in coupled models ( Zeng et al. 1996 ; Davey et al. 2002 ; Deser et al. 2006 ; Chang et al. 2007 ; Richter and Xie 2008 ). Several recent studies, including those referenced above, have linked the ultimate causes of the persistent model biases to problems in simulating winds and clouds by the
and Rasch 2008 ; Neale et al. 2008 ; Deser et al. 2012 ). The land model is version 4 of the Community Land Model (CLM4; Lawrence et al. 2012 ) and adopts the same horizontal resolution as CAM4. Relative to previous versions, CLM4 includes an improved hydrology and a carbon–nitrogen biogeochemistry model that can impact the seasonal and interannual vegetation phenology. Importantly for the climate change simulations presented in this study, although the plant functional type distribution is
and Rasch 2008 ; Neale et al. 2008 ; Deser et al. 2012 ). The land model is version 4 of the Community Land Model (CLM4; Lawrence et al. 2012 ) and adopts the same horizontal resolution as CAM4. Relative to previous versions, CLM4 includes an improved hydrology and a carbon–nitrogen biogeochemistry model that can impact the seasonal and interannual vegetation phenology. Importantly for the climate change simulations presented in this study, although the plant functional type distribution is
variability (IAV), we used a 2–10-yr bandpass filter to remove seasonal variations at high frequency and the secular increase in CO 2 at low frequency. The first and last five years of the combined historical and RCP time series (1850–2100) were removed to avoid edge effects due to the implementation of the bandpass filter. Then we calculated the standard deviation within 30-yr blocks to estimate IAV. Although there were some quantitative differences in the IAV depending on filter cutoffs, the temporal
variability (IAV), we used a 2–10-yr bandpass filter to remove seasonal variations at high frequency and the secular increase in CO 2 at low frequency. The first and last five years of the combined historical and RCP time series (1850–2100) were removed to avoid edge effects due to the implementation of the bandpass filter. Then we calculated the standard deviation within 30-yr blocks to estimate IAV. Although there were some quantitative differences in the IAV depending on filter cutoffs, the temporal
incorporating both interactive nitrogen and interactive vegetation may improve simulations of the earth system and will reduce the need for input data to drive our models. Such data may change with changes in earth’s climate and may not be available for simulations of the past or future. This study evaluates the offline CNDV first by comparing the simulated vegetation cover against the satellite observations used in CLM4SP. Biophysical and biogeochemical effects of including dynamic vegetation or including
incorporating both interactive nitrogen and interactive vegetation may improve simulations of the earth system and will reduce the need for input data to drive our models. Such data may change with changes in earth’s climate and may not be available for simulations of the past or future. This study evaluates the offline CNDV first by comparing the simulated vegetation cover against the satellite observations used in CLM4SP. Biophysical and biogeochemical effects of including dynamic vegetation or including
cooling drives decreases it ( Takahashi et al. 2002 ). In contrast, in the subpolar ocean, winter is characterized by opposing effects of cold SSTs and entrainment of CO 2 -rich deep water by vertical mixing—in summer, warming drives up, while biological CO 2 drawdown by phytoplankton drives it down. CESM1 captures the resulting seasonality in quite well over most oceanic regions ( Fig. 2 ). Two notable exceptions are the polar Southern Ocean and the high-latitude North Atlantic (49°–80°N). Fig
cooling drives decreases it ( Takahashi et al. 2002 ). In contrast, in the subpolar ocean, winter is characterized by opposing effects of cold SSTs and entrainment of CO 2 -rich deep water by vertical mixing—in summer, warming drives up, while biological CO 2 drawdown by phytoplankton drives it down. CESM1 captures the resulting seasonality in quite well over most oceanic regions ( Fig. 2 ). Two notable exceptions are the polar Southern Ocean and the high-latitude North Atlantic (49°–80°N). Fig
1. Introduction Past studies indicate that managed and unmanaged terrestrial ecosystems interact with the atmosphere and other components of the earth system through a variety of biogeophysical and biogeochemical processes and characteristics. Levis (2010) reviews this topic. In the present study we consider such effects by simulating certain managed ecosystems. Managed ecosystems add to simulations of the earth system the uncertainty of human interference. Numerous climate-modeling studies
1. Introduction Past studies indicate that managed and unmanaged terrestrial ecosystems interact with the atmosphere and other components of the earth system through a variety of biogeophysical and biogeochemical processes and characteristics. Levis (2010) reviews this topic. In the present study we consider such effects by simulating certain managed ecosystems. Managed ecosystems add to simulations of the earth system the uncertainty of human interference. Numerous climate-modeling studies
been recognized as an important region in the earth’s coupled climate system, it has been challenging to model adequately by coupled climate models. A few recent reports summarize the advances in the understanding of the tropical Atlantic climate and its variability (e.g., Hurrell et al. 2006 ; Xie and Carton 2004 ; Garzoli and Servain 2003 ; Visbeck et al. 2001 ). Some of the main aspects of the tropical Atlantic Ocean discussed in the current study include the seasonal cycles and interannual
been recognized as an important region in the earth’s coupled climate system, it has been challenging to model adequately by coupled climate models. A few recent reports summarize the advances in the understanding of the tropical Atlantic climate and its variability (e.g., Hurrell et al. 2006 ; Xie and Carton 2004 ; Garzoli and Servain 2003 ; Visbeck et al. 2001 ). Some of the main aspects of the tropical Atlantic Ocean discussed in the current study include the seasonal cycles and interannual
. Stammerjohn , S. E. , D. G. Martinson , R. C. Smith , X. Yuan , and D. Rind , 2008 : Trends in Antarctic annual sea ice retreat and advance and their relation to El Niño–Southern Oscillation and southern annular mode variability . J. Geophys. Res. , 113 , C03S90 , doi:10.1029/2007JC004269 . Takahashi , T. , and Coauthors , 2002 : Global sea-air CO 2 flux based on climatological surface ocean p CO 2 , and seasonal biological and temperature effects . Deep-Sea Res. II , 49 ( 9
. Stammerjohn , S. E. , D. G. Martinson , R. C. Smith , X. Yuan , and D. Rind , 2008 : Trends in Antarctic annual sea ice retreat and advance and their relation to El Niño–Southern Oscillation and southern annular mode variability . J. Geophys. Res. , 113 , C03S90 , doi:10.1029/2007JC004269 . Takahashi , T. , and Coauthors , 2002 : Global sea-air CO 2 flux based on climatological surface ocean p CO 2 , and seasonal biological and temperature effects . Deep-Sea Res. II , 49 ( 9
1. Introduction Permafrost and seasonally frozen ground are key components of the Arctic and global climate system because of their influence on energy, water, and carbon cycles. The freeze–thaw status of the ground is a critical threshold in the terrestrial system that is closely linked to the timing and length of the vegetation growing season ( Black et al. 2000 ), boreal plant productivity ( Kimball et al. 2006 ), the seasonal evolution of land–atmosphere carbon dioxide ( Goulden et al. 1998
1. Introduction Permafrost and seasonally frozen ground are key components of the Arctic and global climate system because of their influence on energy, water, and carbon cycles. The freeze–thaw status of the ground is a critical threshold in the terrestrial system that is closely linked to the timing and length of the vegetation growing season ( Black et al. 2000 ), boreal plant productivity ( Kimball et al. 2006 ), the seasonal evolution of land–atmosphere carbon dioxide ( Goulden et al. 1998
