PI: Vikram Mehta
Center for Research on the Changing Earth System (CRCES)
Email: vikram@crces.org
There is an increasing awareness in the scientific community about decadal climate variability phenomena in the tropical-subtropical Atlantic (Moura and Shukla, 1981; Hastenrath, 1990, 1991; Mehta and Delworth, 1995; Hansen and Bezdek, 1996; Delworth and Mehta, 1998; Rajagopalan et al., 1998; Mehta, 1998; Enfield et al., 1999; Tourre et al., 1999; Yang, 1999; Dommenget and Latif, 2000) and Pacific (Mann and Park, 1996; Mantua et al., 1997; White et al., 1997; Zhang Y. et al., 1997; Zhang R. and Levitus, 1997; Latif et al., 1997; Nakamura et al., 1997; Nakamura and Yamagata, 1999; Zhang R., 1998; White and Cayan, 1998; Pierce et al., 2000; Mehta, 2001); the Indo-Pacific Warm Pool (Mehta and Mehta, 2001); modulations of ENSO (Balmaseda et al., 1995) and its climate impacts (Gershunov and Barnett, 1998; Power et al., 1999, Mehta and Suarez, 2001); modulations of interannual ENSO-monsoon relationship (Mehta and Lau, 1997; Kumar et al., 1999; Krishnamurthy and Goswami, 2000; Gershunov et al., 2001; ); and variations/trends in the North Atlantic Oscillation (Hurrell, 1995; Thompson and Wallace, 1998; Rodwell et al., 1999; Mehta et al., 2000; Latif et al., 2000). These and other such phenomena appear to influence, to varying degrees, regional and/or global climate. These phenomena can also potentially interfere in the detection and quantification of climate change, especially at sub-continental scales, due to increasing CO2 in the Earth-atmosphere system. Therefore, it is very important to understand mechanisms of such phenomena, quantify their predictability, and ascertain the likely influence of increasing CO2 on these phenomena and their predictability. Global coupled models can be very useful tools in these studies. The century-long (or longer) runs of the CMIP2+ global coupled models can be especially useful because these models are also used to estimate future climate change in various scenarios of increasing CO2.
There are three major objectives of the proposed work:
It is proposed to analyse variability of model sea surface temperatures (SSTs) from the control runs first to compare with the observed SST variability in the above-mentioned decadal phenomena. Spatial patterns, time scales, and evolution of decadal SST anomalies will be analyzed, using various spectrum analysis and space-time structure analysis techniques such as Fourier, Singular, Wavelet, Multi-Taper, SVD and other types of EOF analysis, space-time lag-correlation, and composites. These results will then be compared with corresponding results from the observed SST analyses. If there are significant, decadal spectral peaks, there may be specific processes responsible for generating the spectral peaks. To study these processes, SST, sub-surface ocean temperature and velocity fields; ocean-atmosphere momentum, heat and freshwater fluxes; and atmospheric fields will be bandpass-filtered and the evolution of the anomalies will be studied. Horizontal and vertical propagations of ocean heat content signals, if any, will also be studied in conjunction with the ocean-atmosphere fluxes to see whether the decadal signals in SSTs precede or follow decadal signals in surface fluxes and upper-ocean heat content. This may shed some light on whether the atmosphere forces the SST signal or whether the ocean also plays an important role in it.
Results from the models will be compared with SST observations from the Global Ocean surface Temperature Atlas (GOSTA; Bottomley et al., 1990), the Global sea-Ice and Sea Surface Temperature (GISST; Rayner et al., 1996), the HadISST, reconstructed SST data from Kaplan et al. (1998), and the model-assimilated ocean data from the Simple Ocean Data Assimilation (SODA) system. The atmospheric componet of the results will be compared with corresponding results from the NCEP/NCAR Reanalyses data sets. Simulated rainfall variability will be compared with rainfall data from Dai et al. (1997) and Hulme et al. (1999).
These analyses will also be performed on the model data from the increasing CO2 runs and also on data from varying solar radiation runs if such runs will be available.
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