Ciguatera and Climate

Ciguatera incidence in the Caribbean: Climate connection

The Caribbean region, one of the major areas in which ciguatera is endemic, also contains the warmest waters in the western hemisphere during the summer and early fall months of July-August-September. This is important to the study of disease incidence, as Gambierdiscus is influenced by sea surface temperature (SST; Hales et al. 1999; Ghateau-Degat et al. 2005). This region of warm SST (> 28.50C), also known  as the Atlantic Warm Pool (AWP), extends over the Gulf of Mexico, the Caribbean Sea, and the western part of the tropical north Atlantic Ocean, as depicted in the Fig. 1. The AWP has a very robust seasonal cycle, reaching maximum area in the July-August-September (JAS) season and nearly disappearing in winter (November through March).


Caribbean Coral


It has been shown from observational studies that the AWP undergoes frequency modulation on several time scales including intraseasonal (30-50 days; Maloney et al. 2007; Higgins and Shi 2001), seasonal (Wang and Enfield 2003; Fig. 1), interannual (year to year; Wang et al. 2006; Fig. 2) and decadal (Wang et al. 2008). The variability of the AWP in each of these time scales is also related to tropical cyclone activity in the Atlantic Ocean (Fig. 3). Wang et al. (2007) suggest that the AWP serves as a conduit for the observed relationship between the Atlantic Multidecadal Oscillation (AMO; a decadal mode manifested primarily in SST north of the equator in the Atlantic Ocean) and Atlantic hurricane activity. It is seen that the warm (cool) phases of the AMO are characterized by large (small) AWPs. Since the AWP resides in the genesis region of most Atlantic tropical cyclones, the influence of the AMO on Atlantic tropical cyclone activity may operate through the mechanism of the AWP-induced atmospheric changes. Similarly, such teleconnections of cyclone activity in the Atlantic with the AWP are established at other time scales (Wang et al. 2006). We believe that this relationship of the AWP with Atlantic tropical cyclone activity could be related to the bloom of Gambierdiscus toxicus, a parasitic microalgal species that are associated with dislocation of algae in the coral reef areas from with disturbances like tropical cyclones and dislocation of algae in the coral reef areas from tropical cyclone activity and or coral bleaching.

On interannual time scales, the large AWPs that appear in certain years can be almost 3 times larger than small AWP occurrences (Wang et al. 2006; Fig. 2). This variation is almost comparable to what is observed in the seasonal cycle (Fig. 1). These anomalous AWP events affect rainfall over the Caribbean, Central America, and eastern South America. Furthermore, such large (small) AWP events are associated with a decrease (increase) in sea level pressure and an increase (decrease) in atmospheric convection and cloudiness, which corresponds to a weak (strong) tropospheric vertical wind shear and a deep (shallow) warm upper ocean, thus increasing (decreasing) Atlantic hurricane activity (Fig. 3). Despite the large interannual variations of the AWP events, they seem to be largely disconnected from the interannual variations associated with El Niño Southern Oscillation (ENSO) in the equatorial Pacific Ocean (Wang and Enfield 2003). Nearly two thirds of the overall AWP occurrences do not depend on ENSO. This becomes a challenge when predicting anomalous AWP events, as ENSO is a well-studied phenomenon and is considered to be the largest interannual phenomenon (Philander 1983). There is some newfound success in predicting ENSO at two and sometimes three seasons ahead (Saha et al. 2006). Any connection between human health risks such as Ciguatera and the AWP would give an impetus to increase efforts to understand the AWP and improve its prediction.

From a climate change perspective, our current knowledge of climate variability, based on observations and analyses of the 20th century, may become increasingly compromised as the effects of anthropogenic warming begin to interact with natural variability. For example, the International Panel for Climate Change Assessment Report 4 (IPCC-AR4) suggests a weakening of the Atlantic Meridional Overturning Circulation (AMOC) during the 21st century, accompanied by a slower warming of the North Atlantic. However, in regards to the AWP, the projections based on the IPCC-AR4 are highly unreliable. This is best illustrated in Figs. 4c-j, which show the AWP climatology of the 20th century climate from 8 “best” IPCC models. A majority of these models, with the exception of the Max Planck Institute (MPI) ECHAM5 and the UK MET HADCM3 model, are not even able to simulate the mean AWP in the JAS season.

In contrast to the IPCC-AR4 models, the U.S. National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS), which is used for routine seasonal prediction, has a far superior simulation of the AWP (shown in Fig. 4b). Although this result seems encouraging, a detailed analysis of the retrospective forecasts from the same model showed dismal prediction skills of the AWP (Misra and Chan 2009). This was related to initialization issues of the climate model, wherein the initial state of the ocean sub-surface was found to be at odds with observed decadal variations. As a result the predictability skill of the retrospective forecasts was shown to change from one decade to the other.


Higgins, R. W., and W. Shi, 2001: Intercomparison of the principal modes of interannual and intraseasonal variability of the North American Monsoon System. J. Climate, 14, 403-417.

Maloney, E. D. and S. K. Esbensen, 2007: Satellite and buoy observations of boreal summer intraseasonal variability in the tropical northeast Pacific. Monthly Weather Review, 135, 3-19.

Misra, V., S. Chan, 2009: Seasonal predictability of the Atlantic Warm Pool in the NCEP CFS. Geophys. Res. Lett., 36, L16708, doi:10.1029/2009GL039762.

Philander, S. G. H., 1983. El Niño-Southern Oscillation phenomena. Nature, 302, 295-301.

Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Powell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophy. Res., 108, 4407, doi:10.1029/2002JD002670.

Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W. Wang, 2002: An improved in situ and satellite SST analysis for climate. J. Climate., 15, 1609-1625.

Saha , S. and co-authors, 2006: The NCEP climate forecast system. J. Climate, 19, 3483-3517.

Wang, C. and D. B. Enfield, 2003: A further study of the tropical Western Hemisphere warm pool. J. Climate, 16, 1476-1493.

Wang, C., D. B. Enfield, S.-K. Lee, and C. Landsea, 2006: Influences of the Atlantic warm pool on Western Hemisphere summer rainfall and Atlantic hurricanes. J. Climate, 19, 3011-3028.

Wang, C., S.-K. Lee, and D. B. Enfield, 2007: Impact of the Atlantic Warm Pool on the Summer Climate of the Western Hemisphere. J. Climate, 20, 5021. Wang, C., S. –K. Lee, and D. B. Enfield, 2008: Climate response to anamlously large and small Atlantic Warm pools during the summer. J. Climate, 21, 2437-2450.


Figure 1: The observed monthly climatology of the Sea Surface Temperature (SST), depicting the seasonal cycle of the Atlantic Warm Pool (AWP). Values of SST >28.50C , which essentially define the warm pool, are shaded.


Figure 2: The composite of the Sea Surface Temperature (SST) from the five largest (left) and smallest (right) AWP years between 1950-2003. Values of SST >28.50C, which essentially define the warm pool, are contoured with thick black contour. Courtesy:


Figure 3: Atlantic Hurricane track composites for 18 years with small Atlantic warm pool (top) and 18 years with large warm pools (bottom). Courtesy: David


Figure 4: The July-August-September (JAS) climatological mean SST from a) observations (Reynolds et al. 2001) and b) NCEP CFS and c, d, e, f, g, h, i, j) eight International Panel for Climate Change –Assessment Report 4 (IPCC –AR4) models overlaid with the corresponding JAS climatology of rainfall (mm/day). For brevity only 8 IPCC-AR4 models are shown. From Misra and Chan (2009).