Drought and Water Cruises: Science, Technology, and Management Issues - Chapter 2 pps

2 The Challenge of Climate Prediction in Mitigating Drought Impacts NEVILLE NICHOLLS, MICHAEL J.. Climate Prediction and Drought Early Warning Systems .... The second, and more recent, a

Part II

Drought and Water Management:

The Role of Science and Technology

2

The Challenge of Climate Prediction

in Mitigating Drought Impacts

NEVILLE NICHOLLS, MICHAEL J COUGHLAN,

AND KARL MONNIK

CONTENTS

I Forecasting Drought 34

A Introduction 34

B Seasonal to Interannual Prediction 34

1 Forecasts Based on Empirical Analysis of the Climate Record 33

2 Explicit Computer Model Predictions 36

C Can We Forecast Droughts on Even Longer Time Scales? 38

II Climate Prediction and Drought Early Warning Systems 39 III Impediments to Using Climate Predictions for

Drought Mitigation 44

IV Climate Change and Drought Mitigation 46 References 47

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I FORECASTING DROUGHT

A Introduction

Examination of the long-term climate records in some regions around the globe reveals persistent trends and periods of below-average rainfall extending over years to a decade or more, while other regions exhibit episodic, shorter droughts Hence it is useful to consider the prediction of droughts on seasonal to interannual timescales and, separately, on longer decadal timescales

B Seasonal to Interannual Prediction

Our theoretical ability to make an explicit, reliable prediction

of an individual weather event reduces to very low levels by about 10–15 days (this is called the “weather predictability barrier”), so forecasts with lead times longer than this should

be couched in probabilistic terms Consequently, a forecast with a lead time of a month or more requires a statistical basis for arriving at a set of probability estimates for the ensuing seasonal to interannual conditions Two approaches allow us to derive these estimates The first is based on sta-tistical analyses of the climatic record and assumptions about the degree to which the statistics of the future record will differ from the past record The second, and more recent, approach is based on the generation of statistics from multi-ple, explicit predictions of weather conditions using computer models of the climate system

1 Forecasts Based on Empirical Analysis of the Climate Record

The fact that the earth’s climate system is driven primarily

by the regular rotation of the earth around the sun led to many efforts during the last two centuries to link the recur-rence of droughts with cycles observed in the movements and features of heavenly bodies Notable among these efforts were schemes based on the phases of the moon and the occurrence of sunspots These purported linkages have been

The Challenge of Climate Prediction in Mitigating Drought Impacts 35

proven to be statistically insignificant, evanescent, or of little practical value Nonetheless, there are recurring climate patterns, caused by the interacting dynamics of the earth’s atmosphere and oceans, that provide some scope for predic-tion The development of comprehensive climate records and the growth of computing power over the past 20 years or so have enabled a wide range of powerful statistical tools to be brought to bear to tease out these patterns and incorporate them into empirical algorithms for predicting future sea-sonal patterns

One of the earliest identified and most powerful of these rhythms, apart from the annual cycle itself, is the El Niño/Southern Oscillation phenomenon, often referred to as ENSO The robustness of ENSO-related patterns over time

in the distribution of rainfall, air and sea temperatures, and other climatic variables, and the fact that the phenomenon is caused by slowly varying components of the ocean–atmo-sphere system, renders it useful as a predictor ENSO-based indices (e.g., Troup, 1965; Wolter and Timlin, 1993) are the dominant predictors for statistically based seasonal predic-tion schemes over many parts of the globe, although other indices are now being combined with ENSO for different regions—for example, North Australia/Indonesia (Nicholls, 1984), the Indian Ocean (Drosdowsky, 1993), and the North Atlantic (McHugh and Rogers, 2001)

One of the simplest of the statistical prediction methods

is based on the underlying premise that the behavior of a dominant pattern in the future climate will continue to rep-licate the behavior observed in the past record A systematic scan of the record of the Southern Oscillation Index (SOI), for example, can reveal occurrences, or “analogs,” when the track

of the index over recent months was “similar” to the track in corresponding months in several past years (Stone and Aul-iciems, 1992)

More complex approaches for deriving empirically based forecasting schemes have been implemented in several oper-ational forecasting centers throughout the world A typical example is the methodology developed for the scheme used

by the Australian National Climate Centre for forecasting DK2949_book.fm Page 35 Friday, February 11, 2005 11:25 AM

probability ranges of seasonal (3-month) rainfall and temper-atures (maximum and minimum) This methodology (Dros-dowsky and Chambers, 1998) involves:

1 Identification of predictands (e.g., rainfall and perature) and possible predictors (sea surface tem-peratures representative of one or more areas)

2 Construction of the statistical model, including pro-cedures for the optimum selection and weighting of predictors

3 Verification or estimation of forecast skill

Improvements in the forecast skill of such statistical schemes likely will plateau, because they are generally con-strained by a limited number of useful predictors and rela-tively short periods of data Most statistical methods also exhibit large variations in their skill level throughout the year—because of seasonal variations in statistical relation-ships between climate variables—and for particular regions Further, if there are slow or even rapid changes of climate underway that are not adequately captured in the past record (as has indeed occurred in recent decades), it is pos-sible that the skill of the forecasts may be lower than would

be the case in a more stable climate Despite these problems, statistically based schemes will likely remain useful and sometimes potent weapons for forecasting meteorological droughts

2 Explicit Computer Model Predictions Between about 1970 and 1980, the basis for generating daily weather forecasts moved from sets of empirical, observation-ally based rules and procedures to explicit predictions made

by computer models of the three-dimensional structure of the atmosphere However, in order to make similar progress in computer-based forecasting on longer time scales, it was essential to incorporate the slower contributions to variability from ocean circulations and variations of the land surface In the last two decades, there have been significant improve-ments in the understanding of processes in the atmosphere

The Challenge of Climate Prediction in Mitigating Drought Impacts 37

and the ocean and in the way in which the atmosphere inter-acts with, or is coupled to, the various underlying surfaces These advances in knowledge, combined with an expanded range of data and a massive increase in computer power, have made it possible to develop prediction schemes based on com-puter models that represent the entire earth/ocean/atmo-sphere system (e.g., Stockdale et al., 1998)

Although such schemes are still in their infancy, rapid developments are underway For example, it is now evident that the details of a season’s outcome are modulated by pro-cesses occurring on shorter, intraseasonal timescales, which may affect, for example, the timing and intensity of patterns

of decreased or increased rainfall (Slingo et al., 1999; Schiller and Godfrey, 2003) Hence, efforts are being made to ensure that computer models of the coupled system can simulate and predict such short-term modes of variability It is likely, too, that improvements in predictive skill on seasonal to interan-nual timescales, and hence improvements in prediction of droughts, will be realized from further expansions in the observational base, especially from the oceans (e.g., Smith, 2000); from the ability to generate larger prediction ensem-bles from individual computer models (Kumar and Hoerling, 2000); and from combined ensembles from several different computer models (Palmer et al., 2004)

Work is also underway to improve the spatial resolution

at which seasonal forecasts can be made, through statistical

“downscaling” techniques, through the nesting of high-reso-lution regional-scale climate models within coarser resohigh-reso-lution global-scale models, and by increasing the resolution of the global models

Despite these developments, it will never be possible to consistently generate forecasts of individual events beyond the 10–15-day weather predictability barrier What these developments promise, however, is the generation of reliable short-term model-based “forecast climatologies” from which one can then generate probabilistic assessments of likely cli-mate anomalies over a month, a season, or longer—for exam-ple, of conditions conducive to the onset, continuation, or retreat of drought

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C Can We Forecast Droughts on Even Longer

Time Scales?

Improvements in seasonal forecasting have arisen from advances in knowledge made as a result of the careful analysis

of data collected over time The growth in knowledge about the circulation of the oceans and its modes of variability, which was stimulated in large measure during the 1980s with the implementation of the Tropical Ocean Global Atmosphere (TOGA) and World Ocean Circulation Experiment (WOCE) projects of the World Climate Research Program, is beginning

to reap rewards in the identification and understanding of even slower modes of variability than are at work on seasonal timescales In particular, in the two ocean basins that extend

to both polar regions, evidence exists in both oceanic and atmospheric records of quasi-rhythmic variations with times-cales of a decade or so known as the North Atlantic Oscillation (Hurrell, 1995) and the Pacific Decadal Oscillation (Nigam et al., 1999) There is also evidence of decadal variations in ENSO Its signal, for example, has been more evident in rainfall patterns of the western regions of the United States since the late 1970s compared to the previous quarter century, when its influence was stronger over southern and central regions (Rajagopalan et al., 2000) Slow variations of this nature complicate the challenge of forecasting drought using the statistics of the historical record alone

Much has yet to be learned about what drives these slow variations (Miller and Schneider, 2000; Alexander et al., 2001) and thence how to predict them We must continue to advance our knowledge in this area if we are to improve our skill in forecasting drought, especially in those areas that have seen downward trends in rainfall—for example, the Sahel region

of West Africa (Zeng et al., 1999) and the far southwest of Western Australia (IOCI, 2002)

The path to better prediction of droughts on the decadal scale involves identifying correlated patterns of variability in atmospheric and oceanic records, investigating the physical and dynamic processes at work, representing those processes within a hierarchy of computer models, and developing sets of

The Challenge of Climate Prediction in Mitigating Drought Impacts 39

statistics from a range of predictive models Although research tends to focus on one scale or the other, implementation of the results at the practical level must integrate the outcomes of many complex processes across all timescales This will be best done by those models of the coupled system that have the capacity to represent all the key processes involved, whatever the timescale This is clearly not a trivial task

II CLIMATE PREDICTION AND DROUGHT

EARLY WARNING SYSTEMS

Early warning systems (EWSs) have become increasingly suc-cessful at recognizing the development of potential famines and droughts Saidy (1997) pointed out that in 1992 EWSs were successful in sounding the alarms about the drought emergency Although some warnings, such as those given in southern Africa during 1997–1998, were not followed by full-blown droughts and famines, such events are not necessarily forecast failures because most, if not all, seasonal forecasts are issued as probabilities for dry, near-normal, or wet condi-tions Although there has been increasing focus on economic and social indices to complement physical information, a sea-sonal forecast for drought potentially provides an early indi-cation of impending conditions Economic and social indices tend to follow the development of drought and are valuable

to confirm the existence of drought conditions

Food security will exist when all people, at all times, have access to sufficient, safe, and nutritious food for a healthy and active life (World Food Summit, 1996) However, certain parts

of the globe have shown themselves to be more vulnerable to droughts and famines because of variable climate, marginal agriculture, high dependence on agriculture, and social and military conflict The populations of many countries in sub-Saharan Africa suffer from chronic malnutrition, with fre-quent famine episodes Achieving food and water security will remain a development priority for Africa for years to come Even in a nation that is food secure at the national level, household food security is not guaranteed

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A “famine EWS” has been defined as a system of data collection to monitor people’s access to food (Buchanan-Smith, 1997) However, this definition suggests the collection of mon-itoring data is sufficient to provide an early warning The provision of prediction information (a forecast) increases the time available to elicit a response, but it does not guarantee that the appropriate response will result A famine EWS should consider the demand side (what is required), the sup-ply side (what is available), and food entitlement (the ability

to access what is available) Drought early warning plays an important role in forecasting the supply side

Before too much investment of time and effort is placed

in drought or rainfall early warning (as a physical event), one needs to ask what the “drought early warning system” is intended to achieve A drought early warning forecast must identify components of a drought that strongly affect food supply and the development of famine conditions, along with factors affecting water supply Drought EWSs should incor-porate a broad range of information in order to provide a balanced perspective of conditions Although no particular kind of information is a unique indicator, a famine EWS cannot do without physical information such as rainfall (including forecasts) or drought early warning In fact, these types of information are practically the only types that can provide a longer lead-time forecast to the development of a drought

Glantz (1997) defined famine as “a process during which

a sharp decline in nutritional status of at-risk population leads to sharp increases in mortality and morbidity, as well

as to an increase in the total number of people at risk.” Quoting Murton (1991), he goes on to say that the purpose of

an early warning system is “to inform as many people as possible in an area-at-risk that a dangerous and/or damaging event is imminent and to alert them to actions that can be taken to avoid losses.”

The first purpose of a drought EWS is to determine the probability of a drought event and to monitor its spatial extent, duration, severity, and those who may be potentially

The Challenge of Climate Prediction in Mitigating Drought Impacts 41

affected This requires an appreciation of the climatology of the area and the crop calendars As described by Walker (1989), a famine EWS should detect, evaluate, and predict the hazard It uses monitoring tools such as remote sensing, mar-ket conditions, and climate forecasts, as well as geographical information systems to isolate the extent of the hazard area Huss-Ashmore (1997) examined the question of what predic-tions are needed for a famine EWS In order to pursue an increase in food imports at a national level, governments require a significantly earlier indicator of potential problems However, information such as drought early warning indi-cates only the potential for problems, whereas output-related indicators show the emergence of actual problems Delaying

a response until this information is available would generally result in some level of food shortage

A significant challenge in developing a drought EWS is the range of spatial and temporal scales of the information available On one hand, market prices of staple crops on a week-to-week basis may be monitored But this information needs to be integrated with global three-monthly (and even possibly longer) regional climate forecasts Related to this problem is information that only partly reflects the real infor-mation requirement For example, global climate forecasts generally forecast seasonal rainfall totals, but this informa-tion may not relate to the necessary agricultural rainfall dis-tribution during the season or the required crop growth season

It is important to ensure that the information is used

to the best advantage in order to determine a timely and appropriate response Walker (1989) noted that this involves interpreting the available information and preparing a mes-sage that is clear and easily understood To realize the ben-efits of early warning, response is the issue, not developing ever-more sophisticated indicators (IFRC, 1995) This requires careful interpretation and presentation of the data Bulletins such as those prepared by the FEWS NET, South-ern African Development Community Food, Agriculture and Natural Resources Vulnerability Assessment Committee, DK2949_book.fm Page 41 Friday, February 11, 2005 11:25 AM