Comparison of surface wind stress anomalies over the tropical pacific simulated by an AGCM and by a simple atmospheric model

In this paper, surface wind stress anomalies over the tropical Pacific simulated by an AGCM and by a simple atmospheric model are compared with observed. The AGCM is the higher resolution global spectral model-COLA R40 model and the simple atmospheric model is the atmospheric component of the Cane-Zebiak coupled ocean-atmosphere model

Tropical air-sea interaction in general circulation models

An intercomparison is undertaken of the tropical behavior of 17 coupled ocean-atmosphere models in which at least one component may be termed a general circulation model (GCM). The aim is to provide a taxonomy—a description and rough classification—of behavior across the ensemble of models, focusing on interannual variability. The temporal behavior of the sea surface temperature (SST) field along the equator is presented for each model, SST being chosen as the primary variable for intercomparison due to its crucial role in mediating the coupling and because it is a sensitive indicator of climate drift. A wide variety of possible types of behavior are noted among the models. Models with substantial interannual tropical variability may be roughly classified into cases with propagating SST anomalies and cases in which the SST anomalies develop in place. A number of the models also exhibit significant drift with respect to SST climatology. However, there is not a clear relationship between climate drift and the presence or absence of interannual oscillations. In several cases, the mode of climate drift within the tropical Pacific appears to involve coupled feedback mechanisms similar to those responsible for El Niño variability. Implications for coupled-model development and for climate prediction on seasonal to interannual time scales are discussed. Overall, the results indicate considerable sensitivity of the tropical coupled ocean-atmosphere system and suggest that the simulation of the warm-pool/cold-tongue configuration in the equatorial Pacific represents a challenging test for climate model parameterizations

Pacific island regional preparedness for El Niño

The El Niño Southern Oscillation (ENSO) cycle is often blamed for disasters in Pacific island communities. From a disaster risk reduction (DRR) perspective, the challenges with the El Niño part of the ENSO cycle, in particular, are more related to inadequate vulnerability reduction within development than to ENSO-induced hazard influences. This paper analyses this situation, filling in a conceptual and geographic gap in El Niño-related research, by reviewing El Niño-related preparedness (the conceptual gap) for Pacific islands (the geographic gap). Through exploring El Niño impacts on Pacific island communities alongside their vulnerabilities, resiliences, and preparedness with respect to El Niño, El Niño is seen as a constructed discourse rather than as a damaging phenomenon, leading to suggestions for El Niño preparedness as DRR as part of development. Yet the attention which El Niño garners might bring resources to the Pacific region and its development needs, albeit in the short term while El Niño lasts. Conversely, the attention given to El Niño could shift blame from underlying causes of vulnerability to a hazard-centric viewpoint. Instead of focusing on one hazard-influencing phenomenon, opportunities should be created for the Pacific region to tackle wider DRR and development concerns

A review of ENSO prediction studies

A hierarchy of ENSO (El Niño/Southern Oscillation) prediction schemes has been developed which includes statistical schemes and physical models. The statistical models are, in general, based on advanced statistical techniques and can be classified into models which use either low-frequency variations in the atmosphere (sea level pressure or surface wind) or upper ocean heat content as predictors. The physical models consist of coupled ocean-atmosphere models of varying degrees of complexity, ranging from simplified coupled models of the lsquoshallow waterrsquo-type to coupled general circulation models. All models, statistical and physical, perform considerably better than the persistence forecast on predicting typical indices of ENSO on lead times of 6 to 12 months. The most successful prediction schemes, the fully physical coupled ocean-atmosphere models, show significant prediction abilities at lead times exceeding one year period. We therefore conclude that ENSO is predictable at least one year in advance. However, all of this applies to gross indices of ENSO such as the Southern Oscillation Index. Despite the demonstrated predictability, little is known about the predictability of specific features known to be associated with ENSO (e.g. Indian Monsoon rainfall, Southern African drought, or even off-equatorial sea surface temperature). Nor has the relative importance for prediction of different regional anomalies or different physical processes yet been established. A seasonal dependence in predictability is well established, but the processes responsible for it are not fully understood