By: Raghu Murtugudde
Published: December 3, 2014
Lewis Fry Richardson — an English mathematician, physicist, meteorologist, psychologist and pacifist — made the first attempt at modern weather prediction during World War I and failed. It was only in the 1950s that weather prediction became routine thanks to the invention of the computer and the innovation of Massachusetts Institute of Technology meteorologist Jules Charney.
Now weather prediction has advanced to a remarkably high level of accuracy, progressing as a result of routine data gathering from Earth- and space-based instruments and mathematical techniques that merge observations with numerical models. Individuals, societies, corporations and governments use weather prediction on a daily basis, and as a result, it has been monetized as a tool for the private weather-forecasting industry, agriculture, and other industries where such information can hedge against financial losses.
What is the value in climate prediction?
Among the most pressing challenges facing U.S. National Oceanic and Atmospheric Administration (NOAA) administrator Kathryn Sullivan is the issue of modernizing the National Weather Service (NWS). According to NOAA, there were nine U.S. weather- and climate-disaster events in 2013 that exceeded $1 billion, each, in losses with events ranging from droughts to floods and from heat waves to tornadoes. Those cost estimates typically do not include the cost of lives lost or the psychological impacts. [Hurricane Sandy’s Toll on Health]
Given the value of the NWS, why does NOAA continue to struggle to modernize the nation’s weather service? It is likely that such success in weather prediction has made NOAA highly vulnerable to the vagaries of budget negotiations. It is also obvious that federal efforts to monetize weather predictions are not as successful as they should be, because the public-private partnerships required for keeping the NWS fully funded have yet to materialize.
Building a weather-ready, and climate-ready, nation clearly remains a monumental challenge if the United States fails to bridge that gap. An extensive survey by Chapman University indicated that most Americans fear natural disasters, but remain unprepared for them. That does not bode well for climate change projections and actions needed to adapt to, or mitigate, the worst impacts from those changes. For those projections, the greatest concern is not simply one of showing financial impact. The trust generated by skillfully prepared weather predictions have not been extended to climate predictions. The detailed assessments issued by the UN Intergovernmental Panel on Climate Change (IPCC) every few years have focused on multi-decadal to century-long projections. But it is apparent that public trust must extend from weather to climate predictions in order to close the credibility gap for climate-change projections. NWS products should play a role in ordinary citizens’ everyday decisions, and extend to multiple weeks and seasons.
El Niño: A climate prediction success
The poster-child for skillful, seasonal, climate predictions is El Niño, which has significant global impacts and can be predicted with lead times of up to nine months. The most powerful El Niño recorded by modern instrumentation occurred during the 1997-1998 season, and it was indeed successfully forecasted nine months in advance.
The weather spun-off over the United States by that monstrous El Niño resulted in economic losses of more than $4 billion, ranging from agricultural losses to lost tourist dollars, property losses, state and federal government assistance costs, and more. But the successful predictions also produced massive benefits of more than $19 billion, achieved as operational-cost savings for the transportation industry, home sales, lower bills for heating and ice/snow removal. While 189 lives were lost due to extreme weather events associated with that El Niño, the forecasts saved 850 lives.
The observational network that has proven indispensable for successful El Niño predictions is in the tropical Pacific, and funded and maintained by an international partnership of countries that includes the United States. A major part of the network that lies in international waters has relied on U.S. leadership for its continued maintenance.
However, just as the financial woes of NOAA have handicapped NWS, the same problems have hit that network, the Tropical Atmosphere Ocean (TAO) buoy array, which was designed to send the tropical Pacific Ocean temperatures and winds to NOAA every minute via satellites. Such data would provide subsurface temperature information during the spring and summer of an El Niño year, information critical for initializing prediction models — and those data cannot be captured by satellites. The failed El Niño forecasts of 2012 and 2014 are partially because of the lack of this subsurface information from a part of the TAO network which has failed due to a lack of maintenance.
Is there a financial fix?
The public has come to expect more and more accuracy in local weather forecasts, which also rely on a well-maintained network of radar systems and weather stations to provide dense coverage. Nonetheless, weather forecasting has improved over the years, especially four to seven days out.
Despite undeniable evidence for the accuracy and value of weather forecasts, they have only been partially monetized and NOAA continues to struggle to secure the resources to maintain its basic observational and modeling infrastructure for the NWS. The most successful example of the private weather forecasting based on NWS global forecasts is the Weather Channel, with over 100-million subscribers, but this success has not translated into sustained support for NWS.
Climate predictions are not as consistent as weather forecasts, in part because climate prediction is a more recent activity compared to the long history of weather forecasting. Moreover, climate predictability relies on data provided by ocean heat content, but the data from remote regions of the ocean are often lacking.However, the research community has the scientific knowledge and necessary models to build the bridge from weather to climate. The observational data necessary for climate predictions have to be sustained, long-term observations since the ocean stores and releases heat on multi-year timescales.
Monetizing climate predictions is thus a bigger challenge considering the difficulty in monetizing shorter-term weather forecasts. But unless the voting public is convinced of the value of climate predictions, it is unrealistic to expect they will support investments in climate change mitigation and adaptation efforts.
Climate change is here and is caused by humans, but the actions needed to address it cross national and continental boundaries. Uncertainties in the impacts of climate change at local scales tend to be high, just as weather forecasts at the local scale are less accurate without local data, and this is a major handicap in generating action to navigate the complex terrain of climate change.
But for ordinary people to extend the range of their decisions from daily activities based on weather to longer-t
erm plans based on climate information, it is critical to establish the use and usability of climate predictions. Nations can then use tools such as climate insurance, where losses have to be proven to claim compensation, or weather and climate derivatives, which do not need proof-of-loss but limit one’s exposure to the risks of extreme weather and climate events. Those tools can be valuable not only for individuals and groups, but also for communities and townships.
Once confidence in climate prediction rises to a level where it is considered marketable by insurance companies or derivatives markets, then private partners will step in to translate NOAA’s climate predictions into products for routine, long-term decisions by members of the public. That should bring support from the market-solution aficionados who are still sitting on the fence when it comes to climate change action.
Reprinted from livescience with permission. www.livescience.com/49000-money-lacking-for-climate-data.html.