Search Results

You are looking at 1 - 9 of 9 items for :

  • Middle atmosphere x
  • Indian Ocean Climate x
  • All content x
Clear All
Roxana C. Wajsowicz

ocean–atmosphere–land system, ensemble bias, or model inadequacies. Forecasting ENSO more than a season or two in advance is apparently limited by a boreal “spring predictability barrier.” The rapid decline in observation–prediction correlations in boreal spring occurs irrespective of the initiation month of the forecast and in a wide range of coupled ocean–atmosphere models (see, e.g., Moore and Kleeman 1996 ; Chen et al. 1997 ). A similar springtime decline is found in the lagged correlations

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

variability in the tropical Indian Ocean. The content is organized as follows. A brief description of the CGCM along with its validity is given in the next section. In section 3 , two modes of decadal variability in the tropical Indian Ocean are presented. In particular, a detailed discussion on the real nature of the decadal IOD is given there. The final section summarizes the main results. 2. Model a. Model description The model data used in this study are obtained from an atmosphere–ocean–land CGCM

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

changes is large and highly predictable ( Kumar and Hoerling 1998a ; Peng and Kumar 2005 ). However, the response to SST changes upon the model atmosphere appears to depend on the details of the model numerics and physics, as well as the configuration of the climatological background flow ( Ting and Sardeshmukh 1993 ; Lau 1997 ). To accomplish robust results, we perform identical experiments with two different AGCMs that differ considerably in numerical formulation and physical parameterizations. To

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

of the tropical variability of the Pacific Ocean has increased since the Tropical Ocean Global Atmosphere (TOGA) decade, and the implementation of the Tropical Atmosphere Ocean (TAO) Array ( McPhaden et al. 1998 ), our knowledge of the variability in the IO is still limited because of the lack of sufficient observations. Under the auspices of the Climate Variability and Predictability (CLIVAR) Project, the IO Panel has proposed a 35-mooring array designed to observe the large-scale dynamical

Full access
Gabriel A. Vecchi and Matthew J. Harrison

). See Schott and McCreary (2001) , Annamalai and Murtugudde (2004) , and Yamagata et al. (2004) for reviews of Indian Ocean variability. Characterizing and understanding interannual variability in the Indian Ocean, its relationship to global ocean–atmosphere variability, and its relationship to weather and climate variability over land is a topic of significant interest (e.g., Ju and Slingo 1995 ; Nicholls 1995 ; Harrison and Larkin 1998 ; Webster et al. 1998 ; Saji et al. 1999 ; Webster

Full access
Jean Philippe Duvel and Jérôme Vialard

forced oceanic GCM that, due to the shallow ocean mixed layer in this region, the atmospheric fluxes could be sufficient to explain the observed SST anomalies with the subsurface cooling remaining negligible. This interpretation is closer to the results found by Shinoda and Hendon (1998 , 2001 ) over the western Pacific warm pool during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) (see also Weller and Anderson 1996 ; Anderson et al. 1996 ). DRV

Full access
Clémentde Boyer Montégut, Jérôme Vialard, S. S. C. Shenoi, D. Shankar, Fabien Durand, Christian Ethé, and Gurvan Madec

-thick layer. While there is no physical justification for this feedback term, as the atmosphere does not care about ocean surface salinity, it avoids SSS drift arising from the error in the prescribed freshwater budget. Simulating a proper SSS is, indeed, essential as it can have strong influences on the thermodynamic structure of the mixed layer (e.g., Vialard and Delecluse 1998 ; Durand et al. 2004 ). c. Mixed layer heat or salinity budget in the model One of the main goals of this work is to

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

diffusivity and viscosity coefficients are calculated from a 1.5-order turbulent closure scheme ( Blanke and Delecluse 1993 ). The ocean and atmosphere components exchange SST, surface momentum, heat, and water fluxes every 2 h. The coupling and the interpolation of the coupling fields is made through the Ocean Atmosphere Sea Ice Soil (OASIS2.4) coupler ( Valcke et al. 2000 ). No flux corrections are applied to the coupled model except for the sea ice cover that is relaxed to observed monthly climatology

Full access
R. J. Murray, Nathaniel L. Bindoff, and C. J. C. Reason

section in the Indian Ocean. Interior properties can be regarded as the integrated and smoothed result of rapidly changing and inadequately observed surface processes. Understanding how changes in these properties come about may be important in the context of climate change. From an analysis of the Third Hadley Centre Coupled Ocean–Atmosphere GCM simulations ( Gordon et al. 2000 ), Banks et al. (2000) concluded that the observed patterns of cooling, and hence freshening, on isopycnals were more

Full access