Written with Paulo Nobre
Both authors are with the Center for Earth System Science, INPE, Brazil
At present, there are a number of early warning systems based on seasonal-to-interannual climate forecasts in several countries (for example, Ogalo et al., 2008). These systems are based on the use of available monitoring data and state-of-the art climate models. Both observations and model-based predictions are analyzed by climatologists to predict climate anomalies one or two seasons ahead.
|Photo © iStockphoto.com |
Much of the success of such short-term climate predictions is based on the ability of current climate models to predict the evolution of the coupled tropical upper ocean-atmosphere state over seasons. The best example of this is the prediction of El Niño-Southern Oscillation (ENSO) episodes.
Such climate predictions have been used in an array of applications, ranging from seasonal rainfall predictions guiding agriculture, fisheries, and water resources to natural hazards and health applications (Meza and Osgood, 2008; Abawi et al., 2008; Connor et al., 2008).
A number of developing countries have acquired this capacity. For instance, in Brazil, a pioneering effort to elaborate seasonal climate predictions with the participation of the community of users of this information has been underway for more than a decade. Along with other colleagues, we found that this has had encouraging results in terms of seasonal climate predictability (Nobre et al, 2006) and social impacts.
Another example of the use of climate information for adaptation to global climate change is the establishment of a crop insurance program for areas of Africa, such as in Ethiopia villages as part of the Horn of Africa Risk Transfer for Adaptation (HARITA) project , which is based solely on an accumulated rain-gauge index to determine payouts (Hellmuth et al, 2009).
However, as data density for both land and oceans increases, and climate models improve, early warning systems should evolve to provide very detailed information about weather and climate fluctuations, including the capacity to predict climate extremes at least in the short-term and at the regional level.
Climate centers in developed nations have recently started experimenting with decade-long climate predictions (Haines et al., 2008). They envision that such seasonal-to-interannual climate forecasts will become effective tools for climate change adaptation within 10 to 20 years.
In principle, even today’s specialized weather forecasting systems can provide accurate short range forecast of extreme events such as hurricanes/typhoons (Leslie and Abey, 2000).
Along longer time scales, even if we can’t predict exact future extremes, short-term forecasting systems must have the capability to forecast accurately (much better than today) the extremes that are likely to gradually dominate.
Societies will then be able to rely at least on that information to cope with those extremes in the short run. Of course, this is different from being able to project the probabilistic distribution of climate extremes for the next 10, 20, 30, and 50 years. This information would allow for the planning of drastically enhanced adaptation measures and is an area that requires great scientific development and financial support.
The recommendations that emerged from the 3rd World Climate Conference (WWC-3) held last August in Geneva acknowledge the large gap in both the scientific knowledge, global environmental observations, and computer power necessary to make current climate models sufficiently reliable to predict the regional details of climate extremes, relevant to adaptation measures and policies to future global climate change.
Societies that are already facing varied challenges of climate variability and change will require access to the best possible climate science and information in the current century and beyond, and effective application of such information through climate services.
The challenges for the future go beyond establishing ‘climate-only’ early warning systems. Such systems have to be enlarged to acquire a broader Earth system perspective, that is, include observational and potential predictive capabilities to manage the balance between humans and nature, and address vulnerability and resilience to global environmental change.
Abawi, Y., P. Llanso, M. Harrison, S. J. Mason, 2008: Water, Health and Early Warnings. In: Seasonal Climate: Forecasting and Managing Risk. Springer, pp 351-395. DOI: 10.1007/978-1-4020-6992-5_13
Connor, S.J. ;M.C. Thomson ;B. Menne, 2008: A Multimodel Framework in Support of Malaria Surveillance and Control. In: Seasonal Forecasts, Climatic Change and Human Health. Health and Climate Series: Advances in Global Change Research, Thomson, M.C. ;R. Garcia-Herrera ;M. Beniston (Eds.), 30, DOI: 10.1007/978-1-4020-6877-5
Haines, D., L. Hermanson, C. Liu, D. Putt, R. Sutton, A. Iwi, and D. Smith, 2008: Decadal climate prediction (project GCEP). doi: 10.1098/rsta.2008.0178 Phil. Trans. R. Soc. A, 367, 925-937.
Hellmuth M.E., Osgood D.E., Hess U., Moorhead A. and Bhojwani H. (eds) 2009. Index insurance and climate risk: Prospects for development and disaster management. Climate and Society No. 2. International Research Institute for Climate and Society (IRI), Columbia University, New York, USA.
Leslie, L. M., and R. F. Abbey Jr, 2000: Hurricane predictability: are there simple linear invariants within these complex nonlinear dynamical systems? Meteorology and Atmospheric Physics, 74, pp. 57-62.
Meza, F.J., J.W. Hansen, and D. Osgood, 2008: Economic Value of Seasonal Climate Forecasts for Agriculture: Review of Ex-Ante Assessments and Recommendations for Future Research. J. Appl. Meteor. Climatol., 47, 1269–1286.
Nobre, P., J.A. Marengo, I.F.A. Cavalcanti, G. Obregon, V. Barros, I. Camilloni, N. Campos, and A.G. Ferreira, 2006: Seasonal-to-Decadal Predictability and Prediction of South American Climate. J. Climate, 19, 5988–6004.
Ogallo, L., P. Bessemoulin, J.-P. Ceron, S. Mason and S. J. Connor, 2008: Adapting to climate variability and change: the Climate Outlook Forum process. BAMS, 57, 93-102