Introduction - Reducing Disaster: Early Warning Systems for Climate Change - page 12

them and their subsequent impact on the atmosphere is the goal of decadal predictions.
Many of the recent decadal predictability and prediction studies have focused on the
North Atlantic region. This is currently the region with the most long-term predic-
tion skill (on a 2-5 year timescale), including some hurricane prediction possibilities
(Metha et al. 2011 , Van Oldenborgh et al. 2012 ). Predictions over land are less well
developed, although there may be some skill in the predictability of extreme weather
event statistics (Metha et al. 2011).
In order to improve predication it is necessary to increase data collection in the
oceans, which infl uence decadal climate variability. Argo fl oats are now capable of
measuring ocean temperature down to depths of 800 m. While this is helpful, lack
of salinity data before the deployment of Argo fl oats hampers ability to describe
decadal climate variations. Further, even if prediction skill improves, the applica-
tion for disaster reduction and warning may be limited. As Smith writes, seasonal
forecasts have been available for over 15 years, but their use in disaster manage-
ment is still limited because:
inability to predict exactly where and when extreme events might occur; skill of predictions
varies from place to place, season to season, and year to year; uncertainties are inevitable
and sometimes very high, making it diffi cult for disaster managers to commit resources, and
diffi cult to communicate forecasts in a way that triggers appropriate and timely action;
relief operations are primarily funded by voluntary contributions but these are rarely
prompted by pre-emptive early warning systems, but rather by news of the impacts once a
disaster is well under way.
While signifi cant scientifi c advances are still needed, it is possible to imagine the
creation of “seamless integrated warning system.” Decadal modeling could help
identify regions at risk of increased number of hazards in future. These areas could
then be monitored further, more specifi c regional predictions created and vulnerable
groups or sectors of the economy identifi ed. The long-term forecasts could be con-
stantly updated, and more accurate warnings could be issued on a seasonal times-
cale. Warnings could be issued to different actors (businesses, government, local
communities) at different times according to needs. For example, local councils
may want to change planning processes years in advance of hazards. Farmers may
only need warnings on a seasonal scale, and households in the path of a hurricane
may only need warnings two weeks in advance. A seamless integrated early warn-
ing system could meet the needs of a broad diversity of users, allowing action to be
taken to both modify long-term policies and change short-term behaviors.
In developing such a system, it will be important to include the private sector.
Insurance is a popular form of disaster risk reduction. The insurance industry is
increasingly engaging in collaboration with governments and the scientifi c commu-
nity. In Chap. 17 , Patrick McSharry highlights innovative forms of modeling applied
by the insurance industry. Model-based risk assessments can be used to help policy
makers make appropriate investments, reduce risks, and possibly play a role in early
warning systems. McSharry argues for the construction of open-access models and
continued improvement of data. Funding should be provided for IT infrastructure,
data collection, and independent evaluation of model accuracy.