GETTING CLIMATE CHANGE RIGHT --By providing practical insights and applications to enhance resilience to seasonal climate change, Columbia’s IRI is finding solutions to increase adaptability to long term climate change. by William Dowell |
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In May 1997, a mass of warm water began expanding across the Pacific Ocean in the fiercest El Niño ever recorded. The impact dramatically altered weather patterns around the world. Before it was over, storms sent unprecedented 6-foot waves crashing across San Francisco’s downtown Embarcadero. Million-dollar homes were destroyed in unprecedented floods and landslides. Farmers across the traditional monsoon regions faced crippling droughts, while in the Horn of Africa, outbreaks of malaria, dengue fever, meningitis and Rift Valley Fever ravaged local populations and closed international borders to badly needed trade. By the time the El Niño and the subsequent, more moderate, La Niña (an expansion of cooler water across the Pacific) had ended, the damage from natural catastrophes had exceeded the total for the entire previous decade. Swiss Re, the Swiss underwriting giant, estimated the total cost at nearly $100 billion.
The catastrophic 1997-98 El Niño, struck a little more than a year after the International Research Institute for Climate Prediction (IRI), part of Columbia University’s Earth Institute, had begun looking at practical applications of new knowledge concerning the relationship between El Niño and seasonal weather around the globe. IRI had been created largely at the urging of the U.S. National Oceanic and Atmospheric Agency’s Office of Global Plans and other international climate organizations. In contrast to other climate institutions, its mandate from the beginning was focused on practical applications of the climate knowledge being produced by accelerated research programs around the world. Its overall mission is to collect vital climate information, develop advanced computer models and disseminate useful data in an intelligible form to governments and end-user organizations. Steve Zebiak, IRI’s current Director General, explains that the original idea grew out of a growing realization by the world’s climate science community that insights derived from the study of El Niño, make it possible to produce the kind of short-term climate predictions that were considered impossible in the past. “There was a feeling, “says Zebiac,”that something should be done with the information.”
The 1997-98 El Niño not only served as a wakeup call to the potential damage that climate change can cause, it also turned out to be a critical turning point in climate science. An earlier El Niño in 1982-83, was already underway for several months before scientists realized that it was there. In contrast, everyone knew that the 1997-98 El Niño was coming. NOAA had predicted it as early as April 1997, and a month later, other meteorological agencies in Australia and Japan had reinforced the warnings. The weeks of advanced notice enabled governments to take emergency precautions that saved thousands of lives. Even more significant for climate scientists, the 1997-98 El Niño marked the first time in history that computer modeling proved more effective than the analysis of previous weather patterns in accurately predicting what to expect.
For a long time, conventional wisdom had held that it was unlikely that anyone would be able to predict year-to-year weather variations accurately enough to be of any practical use. But by the late 1970s, it had become apparent that ocean temperatures and rapidly changing atmospheric temperatures actually form a coupled system. By analyzing part of the system, it is possible to predict certain behaviors in other parts of the system. The key to revealing the entire system, scientists realized, was a phenomenon they named ENSO for El Niño Southern Oscillation, the change in surface air currents that accompany the El Niño.
By 1985, the scientific community had launched TOGA (Tropical Oceans/Global Atmosphere), a broad project aimed at developing physical/mathematical models of how temperatures were behaving in the tropical portion of the Pacific Ocean. This was quickly followed by TAO (Tropical Atmosphere Ocean), a project that positioned 70 fixed buoys across the Pacific to monitor ocean temperature to a depth of 1600 feet as well as atmospheric and wind conditions above the ocean’s surface. The information, beamed up to a satellite, would make it possible to get far more precise data on how the whole system operates. For these projects to come together in a meaningful way, an international institution would have to be created to disseminate the insights being produced from the mountains of data and to develop practical applications. At the United Nations Conference on Climate and Development in Rio de Janeiro in 1992, a Brazilian climate scientist, Dr. A. D. Moura presented a plan for creating IRI. The next year, Dr. J. Michael Hall, Director of Global Plans for the U.S. National Oceanic and Atmospheric Administration, put up the funding for a pilot project. More than 100 scientists from 46 countries were recruited for climate training, and at a forum on IRI convened in Washington D.C. in 1995, the project received an enthusiastic green light from more than 300 scientists. After an intense open competition, a cooperative agreement was signed between Columbia’s Lamont/Doherty Earth Observatory, Scripps Institute of Oceanography and NOAA on June 1, 1996 (The Scripps participation was eventually relocated to Lamont/Doherty, a sprawling research complex on the Palisades cliffs overlooking the Hudson River).
Today, IRI has come a long way from its original inception roughly a decade ago. “IRI derived from the climate community, “ Steve Zebiak, IRI’s Director General, explains. “We all wanted better climate information, but we felt also that this knowledge had to connect to society, and we really didn’t have much of a clue about how that was going to get done. “ The process of transforming a fledgling IRI, which was heavy on climate science, to a more mature one that is more broadly based in the social sciences and the other dimensions of climate impact meant engaging and training a cadre of international scientists empowered with a wide range of scientific expertise, much of which seems only vaguely connected to climate prediction.
The main areas of concentration today are prediction, monitoring, public policy, and the impact of climate on society. Operations are concentrated in Latin America and the Caribbean, Asia and the Pacific and Africa.
Possibly the most revolutionary idea that IRI is trying to get across is the notion that a probabilistic paradigm is better suited to climate prediction than one that is deterministic. Government policy planners and farmers want definite answers: either it will rain or it won’t. IRI’s approach is closer to the odds in a Las Vegas poker game. “Certainty to a climate scientist is wooly uncertainty to anyone else,” says Madeleine Thomson, who, with Max Dilley, co-directs the Impact program mapping climate effects on life in Africa. Instead of a “yes” or “no” approach to critical weather questions, IRI proposes a calculated “maybe.” In other words, there may be a 70% chance that drought conditions will prevail in the first few weeks of an upcoming planting season, but there is still a 30% chance that there will be rain. Policy makers need to hedge their bets by being aware of both eventualities. In a typical example, a farmer, not knowing what to expect, might plant seeds after the first seasonal rainfall, only to have the plants dry up and die when no subsequent rains follow. With an accurate climate prediction, he would have the option of planting only part of his seeds after the first rainfall, or he might spend the extra money to invest in seeds that take longer to mature and need less water. He would in a sense be gambling, and he would still need to prepare for the unexpected. Hedging one’s bets is key in the climate game.
Once the notion of probability has been inculcated, IRI shifts its emphasis to a series of revolutionary tools designed to enable government policy planners to make educated guesses about the future. These software tools are works in progress, constantly being modified to obtain greater degrees of accuracy. Much like the computer game Sym City, they allow policy planners and agricultural organizations to visualize scenarios that are likely to follow specific actions in different climate situations.
The Philippine’s Angat reservoir is one of the most promising projects currently being developed. The single reservoir provides 97% of Manila’s drinking water and 65% of the hydroelectric power for Luzon, as well as irrigation for 30,000 hectares of land. During the 1997-98 El Niño, rainfall was reduced so drastically that irrigation had to be stopped, and finally the hydroelectric turbines had to be shut down. At the last minute, the Philippine government desperately scrambled to buy coal at elevated prices from Australia and Indonesia in order to keep generating electric power. To make matters worse, in late September 1998, the El Niño gave way to a La Niña and the reservoir level raised so rapidly that water had to be released to save the dam. Lowland farmers who had been starved for irrigation throughout 1998, watched as their crops washed away in an uncontrolled flood. “It was a shock to the system, and it could have been avoided if they had been working on seasonal climate forecasting, ” says Shiv Somseshwar, who heads IRI’s operations in the Pacific basin, and also directs the program on Institutions and Public Policy.
In fact, there is a close correlation between the El Niño and the flow of water into the Angat basin. 50% of the reservoir’s water is supplied by rains during October, November and December. Angat, which Someshwar describes as a very well-run reservoir, had tried to base its water strategy by averaging out historical records, which were unable to predict the unexpected variations likely to result from an unexpectedly strong El Niño. In contrast, if the reservoir management knows in advance that rainfall is going to be heavy, it can release added water for irrigation early on.
Someshwar stresses that IRI’s contribution is always made through partnerships with already established regional institutions, notably the Philippine National Water Resource Bureau. He notes that it was necessary early on to caution against illusions about infallibility in climate prediction. “You can’t put all your eggs in one basket,” he says, “What we do not want is for them to use the season forecast in a deterministic way. Even in 1998, there was a 30% chance that the weather could have gone the other way. The last thing you want to do is to oversell the power of science, without a full understanding of the limitations.” A large part of Someshwar’s job in Asia has been to find converts to climate prediction who are ready to become champions of the idea within local administrations. Getting officials used to making decisions based on probability was less than evident. “Most government institutions are not structured to take risks,” says Someshwar. But, in the Philippines, where detailed records have been kept for decades, a convincing argument is provided by “timecasting,” Historical data going back 30 years or more can be run through computer models and officials can see what would have happened if earlier planners had access to modern climate tools. In some cases, it is simply a question of not failing to take advantage of an excellent harvest year. “It’s not only managing negatives,” Someshwar explains,” It is also capitalizing on and managing the positive.” Besides the Angat project, IRI is also developing reservoir management software for Kenya’s Tana River basin, which provides 70% of Kenya’s electricity, and in northeastern Brazil’s generally arid state of Ceará.
Now that the powers of computer climate modeling are beginning to make an impact, an increased emphasis is being placed on developing a sensitive to the needs of users in the field who can use the information to actually make a difference. “When the IRI started, the approach was that we had some of the best climate scientists in our institute, We can provide you with all this information,” says Walter Baethgen, who currently manages operations in Latin America and the Caribbean. “With time, the institute matured into a demand-driven approach. Instead of saying we know what you need. We started at the other end asking what really was needed. Then we went back to the climate scientists and asked them to translate the information into those terms. The main challenge is how do you start from robust scientific information and translate that into something that is useful? We still have some of the best climate prediction scientists, but we also have people translating that information so that it has a social impact.”
In Latin America, IRI learned that the climate data needed by large ranches in Argentina and Uruguay might be quite different from that required by potato farmers in Peru. More important, it is not enough to provide generalities such as a broad announcement that rainfall will be reduced across all of South America. To be useful, climate predictions have to take into account local exceptions. Farmers want to know what will happen on their specific plot of ground. At IRI, this process of tailoring computer models to include local variations is called “down scaling” and it has become a major focus at IRI. Regional variations derived from studying past histories are worked into the overall computer model until more and more accurate regional predictions become possible. In 1999, Baethgen says, a strong La Niña led to predictions that there was a strong risk of reduced rainfall. That October, IRI predicted reduced seasonal rainfall, but more important, IRI also produced satellite maps showing which vegetation was being hit the hardest. The maps made it possible for government policy planners to track the process of the drought on a weekly basis and eventually to distribute aid to the farmers who needed it the most. “At the end of the season we received a letter from Uruguay’s Minister of Agriculture, telling us that this was the first time that relief could be distributed on a needs basis,” says Baethgen. In previous emergencies, aid money had simply gone to whoever had lobbied the most for it.
In Latin America, IRI is developing a broad array of computer modeling tools that enable decision makers to see the impact of policy choices. The tools are not only useful to government policy planners, but to farmers’ associations as well.
Baethgen also considers the notion of probablility to be one of the most difficult ideas to get across. At this point climate models can generally favor one of three possibilities: that there will be more rain, less rain, or that the situation is likely to continue the same. The added climate information means that a blind guess that has a 33% chance of being right is replaced by an educated guess that has a 66% chance. But there is still a lingering possibility that the weather can turn in an unpredictable way. In one instance, Baethgen says, IRI made a three month prediction that was astonishingly on-target. Policy makers thought they had found the magic bullet. “Everyone thought that we had the problem solved,” says Baethgen, “and of course at the very next meeting, the exact opposite happened. The prediction wasn’t wrong. It just wasn’t the prediction that had been considered the most likely scenario.” Over a difficult period, the users went from being extremely confident, to thinking that climate prediction was worthless to being somewhere in the middle. The process was rendered more difficult, Baethgen says, because of the role of the media. Climate scientists need the media to get the information out, but editors don’t like the fact that vague forecasts tend to confuse readers. The natural instinct is to jump to a deterministic conclusion in order to make dramatic headlines at the risk of being wrong and discrediting the whole system. “Knowing something is much better than not knowing anything,” Baethgen says, “but we do not know everything. Your decision-making process will be much stronger, but there will always be uncertainties.”
Another difficulty in South America is establishing credibility with an indigenous population. In northern Brazil, farmers tended to listen to “rain prophets,” who predict rain based on the flight of birds and other obscure criteria. The gap, IRI decided, was largely due to the way scientific information was presented. The solution was to hire an anthropologist, who quickly pointed out that in Brazil information is often communicated through music. One option under consideration is the hiring of wandering minstrels to sing ballads that explain the impact of El Niño in terms that the villagers can relate to. “That can have more of an impact than any number of power point presentations.” Baethgen explains.
Africa may be the continent in which IRI’s work makes the greatest impact. Maxx Dilley and Madeleine Thomson, who co-direct the Africa program, are developing programs that cover everything from the role that the climate plays in the outbreak of diseases, to food security, disaster relief and even conflict resolution.
Thomson, who originally trained as an entomologist, has been working on the effect that sudden rainfalls and stagnant water have on local mosquito populations. The chief climate-influenced risks in the northern semi-arid sub-Saharan regions of Africa are malaria, dengue Fever, meningitis and Rift Valley Fever. The most effective drugs and vaccines are too expensive, and have shelf lives that are too limited to be kept in stock on a permanent basis, so an early warning system is essential to heading off and controlling epidemics. At times, the decision may be as simple as investing in mosquito nets and a reliable bed prior to a sudden mosquito population surge. More significant to policy planners, it allows decision-makers to purchase medicine and other emergency equipment just in time.
Maxx Dilley’s background was in disaster risk management before joining IRI. One of the functions of his operation is to map the lifestyles and work habits of an entire region with respect to climate and then to model how changes are likely to impact on various segments of society. For the most part, IRI operates through regional networks such as IGAD (International group on African Development) and the Southern Africa Drought Monitoring Center. “We are injecting capacity and expertise into a thriving, ongoing process,” Dilley explains.” We have to find out how they understand their own problems, and how climate will effect the outcome and then work with them.” By mapping climate impact at a local level, policy makers can determine months in advance which segments of a population are most likely to be affected by a crisis. “Once you know where the agricultural and livestock areas are,” says Dilley, “you can tailor the information so that it is relevant to the needs of the specific areas.” One interested client is the World Food Program, which distributes roughly half its emergency supplies in the Horn of Africa, and with advanced climate predictions can now stockpile supplies and develop a distribution strategy months in advance of an actual disaster.
An even more vital project is a scheme being developed with the Red Sea Livestock Trade Commission to develop a risk assessment model for Rift Valley Fever, which affects both livestock and human beings. The 1997-98 El Niño produced torrential rains in the Horn of Africa which increased the mosquito population and triggered an outbreak. The risk to humans was exacerbated because sheep are used in Muslim religious sacrifices—especially in Mecca and after Ramadan-- and the blood tends to be aerosolized. After infected animals began turning up in Saudi Arabia, the government slapped a ban on livestock imports from the Horn. The prohibition lasted until 2003. Since livestock exports to the Arab countries had provided from 70 to 80% of Somalia’s Gross National Product, the macroeconomic implications were enormous. As the ban continued, livestock importers in several Arab countries began looking as far as Australia for sheep imports that could be used in sacrifices, even though regional sheep were preferred. The risk was simply considered too great. In fact, as Maxx Dilley explains, Rift Valley Fever tends to die out after about 12 weeks, if the infected animals are properly quarantined. What is needed to resume the traditional trade is a reliable monitoring system that can briefly shut down exports when a problem arises. Since mosquitos are a critical element in the chain, climate prediction suddenly takes on a highly significant role in determining the risk. Ultimately, climate information is likely to be a factor in peace and war as well. Dilley points out that there is a general perception that dry conditions in pastoral areas trigger conflict. In the Horn, where livestock markets exist, farmers can sell off excess herds if they know in advance that they are about to face a drought. In southern Africa, Dilley notes, the tendency is to double the size in the hopes that some animals will survive and in that case the climate information may be less useful. But no one denies that everyone wants to know what the weather will be. “This information is life and death security for a lot of sectors of society,” says Dilley. “There have always been forecast methods, whether they were based on reality or not.”
The broad social analysis which is an integral part of the short-term climate prediction that IRI is carrying out now may have long range implications for longer range climate changes that many scientists believe are being caused by global warming. The incidence of climate-related natural disasters appears to be increasing on a yearly basis. While the kind of climate science being developed at IRI doesn’t mitigate long term climate change, it does explore the best possible solutions for adapting to it.
“Most of the climate related events that affect societies play out over a short period of time, a given year, one rainy season, a cyclone or a flood,” says Steve Zebiak. “What we are really trying to do is to manage the impact. Building resilience to these kinds of problems now may be the best way to develop resilience to global climate change later on. The kinds of things that happen will be the same, there will just be more of them, and they will be happening more often. On the adaptation side, this is a way of worrying about now and the future.”
--Lamont Earth Observatory, Palisades, New Jersey, 2005