Chaotic influences and the problem of deterministic seasonal predictions

Abstract

An analysis has been performed of three simulations made by forcing a global climatic model with observed sea-surface temperature and sea-ice distributions for the period 1979-1988. These simulations were started from different years of an extended control run, otherwise all conditions were identical. The analysis concentrates on the regional and local differences in the simulations caused by chaos attributable to the differing initial conditions interacting with the non-linearities of the model climatic system. Of particular interest is whether the presence of sea-surface temperature variations in individual years is able to overcome chaotic influences. This interest arises because of the increasing international appreciation that such temperature variations provide the basis for the development of multi-seasonal prediction schemes.The analysis shows that between about 20"N and 20"s a coherent climatic reponse is obtainable owing to the boundary value forcing associated with the sea-surface temperature field. At high latitudes the signal-to-noise ratio rapidly deteriorates. Even at low latitudes, in a sensitive variable such as rainfall, chaos creates substantial differences between the three simulations, except over the Pacific Ocean where a very coherent response is found. In general, over land areas, even those within recognized regions of ENSO (El Niiio-Southern Oscillation) influence, the impact of chaos is much higher. Examination of Pacific North American teleconnection patterns for 1988 reveals substantial differences between the three simulations, and the associated impact on rainfall and surface temperatures over the North American continent. Agreement with observations ranges from rather good to poor, indicating the need for multiple runs for any given situation. Overall the results establish that sea-surface temperature variations can overwhelm chaos in appropriate circumstances and specific regions. Importantly, this indicates that given accurate predictions of sea-surface temperature anomalies a few seasons in advance, then model-based predictions will be possible over many regions of the world despite the omnipresence of chaos.

Current and future latitudinal gradients in stream macroinvertebrate richness across North America

“…These include seasonal predictability (Dix and Hunt 1995) and equilibrium climate change experiments; for studies of climate change and variability see Suppiah (1994Suppiah ( , 1995, Kidson and Watterson (1995), Watterson and Dix (1996), and Watterson (1997).…”

Long‐range predictability of Zimbabwe summer rainfall

“…India (Shea et al, 1995) and New Zealand . In theory, the presence of noise and intra-seasonal oscillations will always limit long-range climate predictability (Dix and Hunt, 1995). Long-range predictability might arise from the atmosphere's slow response to slowly varying external forcing, such as SSTs.…”

“…Even in regions where there is a strong ENSO influence, the impact of atmospheric chaos is quite high (Dix and Hunt, 1995). Similarly, SST variations in areas outside of the equatorial Pacific can provide some predictability in some regions, although the influence of SSTs may be highly complex.…”

“…Similarly, SST variations in areas outside of the equatorial Pacific can provide some predictability in some regions, although the influence of SSTs may be highly complex. A unique atmospheric response to a given set of boundary conditions, such as SST forcing, does not occur, because slight differences in initial conditions can cause widely different responses in a non-linear dynamical system such as the atmosphere (Allan, 1993;Dix and Hunt, 1995).…”

Ensemble Hindcast Experiments for the Flood Period over China in 1998 by Use of the CCSR/NIES Atmospheric General Circulation Model

“…The global and regional precipitation patterns associated with ENSO cycle were detected by Ropelewski and Halpert (1987), in which the signal over East Asia was rather weak. This generally weak relation between the East Asian precipitation, and the global sea surface temperature (SST) anomaly was supported by some potential atmospheric predictability studies using climate models (Palmer 1994;Kumar and Hoerling 1995;Dix and Hunt 1995;Brankovic and Palmer 1997;Rowell 1998). The study of Rowell (1998) also found that most locations in mid-latitudes (40N-60N) have their highest model predictability during winter and spring, rather than summer.…”