**1. Introduction**

556 Risk Management – Current Issues and Challenges

PRAA-PACC. 52 pages. 2011.

pages. 2008.

Forestal 2007.

pages. 2001.

malaria transmission in two Colombian endemic-regions: contributions to a National Malaria Early Warning System. *Malaria Journal* 5:66, doi:10.1186/1475-2875-5-66. 2006. [22] Poveda, G., M.L. Quiñones, I.D. Vélez, W. Rojas, G.L. Rúa, D. Ruiz, J.S. Zuluaga, L.E. Velásquez, M.D. Zuluaga, and O. Hernández. Desarrollo de un Sistema de Alerta Temprana para la malaria en Colombia. Universidad Internacional de Andalucía. 182

[23] Ruiz, D., A.M. Molina, M.L. Quiñónes, M.M. Jiménez, M. Thomson, S. Connor, M.E. Gutiérrez, P.A. Zapata, C. López, and A. Londoño. Simulating malaria transmission dynamics in the pilot areas of the Colombian Integrated National Adaptation Pilot

[24] Muñoz, Á.G., and C. Recalde. Reporte metodológico sobre el experimento de predicibilidad de malaria en el Litoral Ecuatoriano. Proyecto INAMHI-MAE-SCN-

[25] Muñoz, Á.G., D. Ruiz, and C. Recalde. Malaria biological models and dynamical downscaling for northwestern South America in the Observatorio Andino framework. ICID+18 / 2nd International Conference: Climate, Sustainability and Development in

[26] INE-Instituto Nacional de Estadísticas de Chile (2007) VII Censo Agropecuario y

[27] Dirección Meteorológica de Chile (DMC). Climatología Regional. Informe Técnico. 47

[28] Urrutia de Hazbun, Rosa y Carlos Lanza Lazcano. Catástrofes en Chile 1541-1992.

[29] Norero, Aldo y Carlos Bonilla (ed). Las sequías en Chile: causas, consecuencias y mitigación. Colección en agricultura, Facultad de Agronomía e Ingeniería Forestal,

[30] Quintana, J. and P. Aceituno. Changes in the rainfall regime along the extratropical

[31] Baethgen, W. Climate Risk Management for Adaptation to Climate Variability and

west coast of South America (Chile): 30 - 43ºS. *Atmósfera* 25 (1), 1-22. 2012.

project. Escuela de Ingeniería de Antioquia, 374 pages. 2011.

Semi-arid Regions, Fortaleza, Ceara (Brazil). 2010.

Santiago, Editorial La Noria. 440 pp. 1993.

Universidad Católica de Chile. 128 pp. 1999.

Change. *Crop Sci.* 50:S-70–S-76. 2010.

Seasonal climate refers to average conditions in the atmosphere and ocean over time scales of the order of three months. When considering risks associated with seasonal climate we are concerned with deviations from normal conditions, or 'climate anomalies'. Summers that are hotter than usual, extended drought conditions and exceptionally active tropical cyclone seasons are examples of seasonal climate anomalies.

The countries of the Pacific Ocean are exposed to climate risk across a range of sectors, most notably in water resources, agriculture and disaster preparedness. In Fiji, the forestry industry is affected by an increased likelihood of fires in dry conditions and by access roads becoming too muddy to work on in wet conditions. In Samoa and Fiji the supply of hydroelectric power is vulnerable to rainfall deficiencies, as dams tend to be relatively small in comparison to average inflows. Extreme weather conditions threaten tourism revenue for islands such as Rarotonga in the Cook Islands. Seasonal variations of ocean temperatures, which can drive the migration of species such as Tuna and cause the bleaching of coral reefs in which fish spawn affect the productivity of fisheries which are an important economic resource for countries such as Kiribati. Seasonal variations in surface water and temperature can create more favourable conditions for host vectors of diseases such as malaria, increasing their prevalence. [1]

While many climate anomalies are essentially chaotic and not predictable, there exists large-scale coupling (feedback) between the atmosphere and the ocean, which imparts a degree of predictability to variations of seasonal climate in the atmosphere-ocean-land surface system. The most significant manifestation of this coupling, and the physical source of much of this predictability is the El Niño Southern Oscillation (ENSO), a quasiperiodic mode of variability of the equatorial Pacific Ocean [2]. The primary manifestation

© 2012 Charles et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 Charles et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

of ENSO is in the patterns of sea surface and sub-surface temperature in the Pacific Ocean, with cooler than normal central equatorial Pacific sea surface temperatures termed 'La Niña' and warmer than normal temperatures termed 'El Niño'. During La Niña and El Niño events, feedbacks between the ocean and atmosphere lead to changes in the dominant atmospheric patterns, which influence climatic conditions throughout the world. The ocean processes are slower and more predictable than the atmospheric processes responsible for weather, and their influence on the likelihood of atmospheric states can be used to make predictions, either through characterising this relationship empirically using historical data, or by using a physically motivated model of the coupled ocean-atmosphere system.

Managing Climate Risk with Seasonal Forecasts 559

The Tuvalu drought of 2011 provides an example of vulnerability to seasonal climate risk.

**Figure 2.** Funafuti (Tuvalu) rainfall in millimetres, composite, during all years, El Niño / La Niña events. Analysis: A.Cottril, Data: Pacific Climate Change Science Project, Tuvalu Meteorological Service

delivering fresh water supplies and portable desalination units.

Institute for Climate and Society (portal.iri.columbia.edu).

http://www.bom.gov.au/climate/current/statements/scs38.pdf

Populations on low coral atolls such as Funafuti (located at 8 South, 179 East) rely heavily on rainwater harvesting for water resources as there are no natural streams or lakes. Rainfall from December 2010 to January 2011 was up to 600mm below normal levels for the western central Pacific region in which Funafuti is located (Figure 1)[3]. Long range rainfall outlooks for the March to May season forecast a continuation of the pattern of suppressed rainfall1. These outlooks turned out to be substantially correct, with analysed rainfall deficits of up to 400mm in the region for the period March to May2. On the 28th of September 2011, critically low water supplies caused the government of Tuvalu to declare a state of emergency. In early October the governments of Australia, New Zealand, Korea and Japan began

The physical cause of the lack of rainfall in Funafuti in 2011 was cooler than normal waters in the equatorial Pacific, associated with the strongest La Nina3 episode in recent recorded history, which peaked in the Southern Hemisphere summer of 2010-2011. La Niña events typically decay in Southern Hemisphere Autumn, but in this case the event weakened and then re-established itself in the second half of 2011. The cooler than normal waters in the region of Tuvalu suppressed the rainfall generating convection of moist air, which led to

1 Based on Island Climate Update, a monthly summary of seasonal climate monitoring and prediction in the tropical South Pacific Outlooks issued by the National Institute of Water and Atmospheric Research (http://www.niwa.co.nz/climate/icu) and on seasonal outlooks issued for the region by the International Research

2 CAMPS\_OPI blended rainfall analysis data from the National Center for Environmental Prediction, Climate

3 The La Niña phase of ENSO is associated with cooler than normal equatorial Pacific waters and suppressed rainfall in this region. The 2012 La Niña saw record values of indices used to measure the strength of such events

Prediction Center USA, http://www.cpc.ncep.noaa.gov/products/global\_precip/html/wpage.cams\_opi.html.

(http://informet.net/tuvmet/).

**Figure 1.** Left: CCGM-based predictions (left) and analysis (right) of seasonal rainfall anomalies in millimetres in the tropical South Pacific region during 2011 for the four calendar seasons starting December-January-February (DJF).

The Tuvalu drought of 2011 provides an example of vulnerability to seasonal climate risk.

558 Risk Management – Current Issues and Challenges

ocean-atmosphere system.

December-January-February (DJF).

of ENSO is in the patterns of sea surface and sub-surface temperature in the Pacific Ocean, with cooler than normal central equatorial Pacific sea surface temperatures termed 'La Niña' and warmer than normal temperatures termed 'El Niño'. During La Niña and El Niño events, feedbacks between the ocean and atmosphere lead to changes in the dominant atmospheric patterns, which influence climatic conditions throughout the world. The ocean processes are slower and more predictable than the atmospheric processes responsible for weather, and their influence on the likelihood of atmospheric states can be used to make predictions, either through characterising this relationship empirically using historical data, or by using a physically motivated model of the coupled

**Figure 1.** Left: CCGM-based predictions (left) and analysis (right) of seasonal rainfall anomalies in millimetres in the tropical South Pacific region during 2011 for the four calendar seasons starting

**Figure 2.** Funafuti (Tuvalu) rainfall in millimetres, composite, during all years, El Niño / La Niña events. Analysis: A.Cottril, Data: Pacific Climate Change Science Project, Tuvalu Meteorological Service (http://informet.net/tuvmet/).

Populations on low coral atolls such as Funafuti (located at 8 South, 179 East) rely heavily on rainwater harvesting for water resources as there are no natural streams or lakes. Rainfall from December 2010 to January 2011 was up to 600mm below normal levels for the western central Pacific region in which Funafuti is located (Figure 1)[3]. Long range rainfall outlooks for the March to May season forecast a continuation of the pattern of suppressed rainfall1. These outlooks turned out to be substantially correct, with analysed rainfall deficits of up to 400mm in the region for the period March to May2. On the 28th of September 2011, critically low water supplies caused the government of Tuvalu to declare a state of emergency. In early October the governments of Australia, New Zealand, Korea and Japan began delivering fresh water supplies and portable desalination units.

The physical cause of the lack of rainfall in Funafuti in 2011 was cooler than normal waters in the equatorial Pacific, associated with the strongest La Nina3 episode in recent recorded history, which peaked in the Southern Hemisphere summer of 2010-2011. La Niña events typically decay in Southern Hemisphere Autumn, but in this case the event weakened and then re-established itself in the second half of 2011. The cooler than normal waters in the region of Tuvalu suppressed the rainfall generating convection of moist air, which led to

<sup>1</sup> Based on Island Climate Update, a monthly summary of seasonal climate monitoring and prediction in the tropical South Pacific Outlooks issued by the National Institute of Water and Atmospheric Research (http://www.niwa.co.nz/climate/icu) and on seasonal outlooks issued for the region by the International Research Institute for Climate and Society (portal.iri.columbia.edu).

<sup>2</sup> CAMPS\_OPI blended rainfall analysis data from the National Center for Environmental Prediction, Climate Prediction Center USA, http://www.cpc.ncep.noaa.gov/products/global\_precip/html/wpage.cams\_opi.html.

<sup>3</sup> The La Niña phase of ENSO is associated with cooler than normal equatorial Pacific waters and suppressed rainfall in this region. The 2012 La Niña saw record values of indices used to measure the strength of such events http://www.bom.gov.au/climate/current/statements/scs38.pdf

rainfall deficiencies over a sustained period. Figure 1 illustrates that seasonal outlooks based on dynamical models provided guidance anticipating the persistence of these rainfall deficiencies throughout 2011. The tendency towards suppressed rainfall at Funafuti during La Niña events is evident from the composite time series shown in Figure 2. This event illustrates the real nature of climate risk and that, for some phenomena, we now have the capability to predict the features of the earth system that are responsible well in advance.

Managing Climate Risk with Seasonal Forecasts 561

large scale, long time-scale coupled ocean-atmosphere processes, probabilistic forecasts can be made of the likely tendency of conditions in the coming season. Seasonal predictions are not deterministic, in other words they do not make a prediction that a single outcome will or will not happen. Rather they give a statement of risk, typically about the likelihood of wetter

A range of potential applications for seasonal outlooks has been identified. As noted, in countries dependant on rainwater harvesting for water supplies, advance knowledge of drought conditions can allow pre-emptive water saving or water supply bolstering. Knowledge of the relative likelihood of fires or inaccessibility due to rainfall could be used to plan forestry activities. Rainfall outlooks can be used to estimate the availability of water for hydroelectric power generation, and to pre-emptively purchase fuel for backup generators, avoiding the payment of expensive spot rates for fuel. Tourism operators can develop forward plans that take into account changes in the likelihood of climatic disturbances. Reefs likely to suffer from elevated temperatures can be declared off-limits for fishing and tourism to reduce other sources of stress on corals [11]. Seasonal variations in surface water and temperature can increase the prevalence of certain diseases such as malaria by causing more or less favourable conditions for host vectors [1]. The beef industry in Vanuatu can benefit from forward estimates of how many head of cattle a pasture will be able to support. Seasonal forecasts have been shown to be of economic utility in the management of wheat farming in Australia by guiding changes in practice such as crop row

**2. The limitations of empirical models and the imperative for a** 

Empirical models (or 'statistical models') are currently used by many meteorological services for seasonal climate outlooks. These models are based on empirical relationships, usually between ENSO based indices ('the predictors') and variables such as local rainfall and temperature ('the predictands'). Using current observed values of ENSO indices these

A warming of the climate system due to greenhouse gas forcing is predicted by theory, demonstrated by numerical predictions and has been observed over the course of the past century [14]. While the empirical relationships between climate predictors and predictands such as rainfall may be robust, in a warming climate, environmental indicators used as predictors are now frequently outside of the range of historical records, meaning that relationships are being assumed for events which do not have an historical analogue. In general, empirical models cannot reliably account for aspects of climate variability and change that are not represented in the historical record. Empirical forecasting usually depends on the assumption of stationary relationships between predictors and predictands. This also renders such schemes susceptible to periodic changes in these relationships due to

**dynamical model basis for seasonal forecasting** 

past relationships can be used to create forecasts [13].

than normal, or warmer than normal conditions.

spacing and fertilizer application [12].

decadal timescale variability.

Many of the examples in this chapter will revolve around the island countries of the Pacific that are directly affected by ENSO and are able to benefit directly from advances in the ability to predict it. Routine seasonal outlooks are issued regularly by national meteorological agencies including the Australian Bureau of Meteorology and The United States National Oceanic and Atmospheric Administration (NOAA), as well as by organisations such as the International Research Institute for Climate and Society (IRI). The availability of seasonal outlooks for the coming seasons gives important information for governments and aid agencies to plan their assistance.

Seasonal outlooks of the likelihood of extreme, synoptic timescale events such as tropical cyclones are also of use for planning disaster preparedness. Tropical cyclones are the most destructive weather systems that impact on coastal areas in the Pacific. While individual tropical cyclones are not predictable beyond timescales of the order of one day, the distribution of tropical cyclone activity is influenced by large-scale climatic features such as ENSO [4].

Climate risk may be assessed in a historically averaged sense, by using the past distribution of extreme events such as droughts or tropical cyclones to give predictive probabilities of the events in the future. Climate change complicates this approach, because while observed changes in the mean state of the climate systems so far have been small, this small change in the mean state can lead to large changes in the frequency and magnitude of extreme events[5]. We refer to this as the influence of climate change on climate variability. The effect of climate change on weather patterns is likely to be considerably more complex than a simple shift of the existing probability distribution. As an example, a recently completed global analyses has found a near 50-fold increase in the frequency of extremely hot temperatures during the northern summer, meaning that the historical occurrence now greatly underestimates the risks of extremes[6]. It has been proposed that a change in climate forcing projects onto the existing modes of variability of the climate system, altering the frequencies and intensities of existing weather regimes[7] [8]. An example of such a mechanism is the prospect that global warming has intensified the hydrological cycle, causing more extreme flooding and droughts [9]. The current set of coarse resolution GCMs used to evaluate anthropogenic climate change may not be sufficiently detailed to capture such nuanced responses, and as such considerable uncertainties remain about the impact of climate change on weather events. In the face of these uncertainties, an effective and low cost option to reduced vulnerability to climate change is to improve the accuracy, availability and use of forecasts[10].

The aim of seasonal forecasting is to predict the average weather or aggregate weather over a long period, usually three months. By exploiting the relationship of weather systems with large scale, long time-scale coupled ocean-atmosphere processes, probabilistic forecasts can be made of the likely tendency of conditions in the coming season. Seasonal predictions are not deterministic, in other words they do not make a prediction that a single outcome will or will not happen. Rather they give a statement of risk, typically about the likelihood of wetter than normal, or warmer than normal conditions.

560 Risk Management – Current Issues and Challenges

governments and aid agencies to plan their assistance.

availability and use of forecasts[10].

rainfall deficiencies over a sustained period. Figure 1 illustrates that seasonal outlooks based on dynamical models provided guidance anticipating the persistence of these rainfall deficiencies throughout 2011. The tendency towards suppressed rainfall at Funafuti during La Niña events is evident from the composite time series shown in Figure 2. This event illustrates the real nature of climate risk and that, for some phenomena, we now have the capability to predict the features of the earth system that are responsible well in advance.

Many of the examples in this chapter will revolve around the island countries of the Pacific that are directly affected by ENSO and are able to benefit directly from advances in the ability to predict it. Routine seasonal outlooks are issued regularly by national meteorological agencies including the Australian Bureau of Meteorology and The United States National Oceanic and Atmospheric Administration (NOAA), as well as by organisations such as the International Research Institute for Climate and Society (IRI). The availability of seasonal outlooks for the coming seasons gives important information for

Seasonal outlooks of the likelihood of extreme, synoptic timescale events such as tropical cyclones are also of use for planning disaster preparedness. Tropical cyclones are the most destructive weather systems that impact on coastal areas in the Pacific. While individual tropical cyclones are not predictable beyond timescales of the order of one day, the distribution of tropical cyclone activity is influenced by large-scale climatic features such as ENSO [4].

Climate risk may be assessed in a historically averaged sense, by using the past distribution of extreme events such as droughts or tropical cyclones to give predictive probabilities of the events in the future. Climate change complicates this approach, because while observed changes in the mean state of the climate systems so far have been small, this small change in the mean state can lead to large changes in the frequency and magnitude of extreme events[5]. We refer to this as the influence of climate change on climate variability. The effect of climate change on weather patterns is likely to be considerably more complex than a simple shift of the existing probability distribution. As an example, a recently completed global analyses has found a near 50-fold increase in the frequency of extremely hot temperatures during the northern summer, meaning that the historical occurrence now greatly underestimates the risks of extremes[6]. It has been proposed that a change in climate forcing projects onto the existing modes of variability of the climate system, altering the frequencies and intensities of existing weather regimes[7] [8]. An example of such a mechanism is the prospect that global warming has intensified the hydrological cycle, causing more extreme flooding and droughts [9]. The current set of coarse resolution GCMs used to evaluate anthropogenic climate change may not be sufficiently detailed to capture such nuanced responses, and as such considerable uncertainties remain about the impact of climate change on weather events. In the face of these uncertainties, an effective and low cost option to reduced vulnerability to climate change is to improve the accuracy,

The aim of seasonal forecasting is to predict the average weather or aggregate weather over a long period, usually three months. By exploiting the relationship of weather systems with A range of potential applications for seasonal outlooks has been identified. As noted, in countries dependant on rainwater harvesting for water supplies, advance knowledge of drought conditions can allow pre-emptive water saving or water supply bolstering. Knowledge of the relative likelihood of fires or inaccessibility due to rainfall could be used to plan forestry activities. Rainfall outlooks can be used to estimate the availability of water for hydroelectric power generation, and to pre-emptively purchase fuel for backup generators, avoiding the payment of expensive spot rates for fuel. Tourism operators can develop forward plans that take into account changes in the likelihood of climatic disturbances. Reefs likely to suffer from elevated temperatures can be declared off-limits for fishing and tourism to reduce other sources of stress on corals [11]. Seasonal variations in surface water and temperature can increase the prevalence of certain diseases such as malaria by causing more or less favourable conditions for host vectors [1]. The beef industry in Vanuatu can benefit from forward estimates of how many head of cattle a pasture will be able to support. Seasonal forecasts have been shown to be of economic utility in the management of wheat farming in Australia by guiding changes in practice such as crop row spacing and fertilizer application [12].
