El Niño Policy Issues
If El Niño forecast are good, but not perfect, what are the implications
for society? To answer the question, pretend you are a farmer in Texas.
You know El Niño tends to bring winter rains and that a wetter year
is good for crops. You can decide to plant crops that depend on good
rains and reap a bumper crop. But, what if the forecast is wrong? Then
planting crops that need rain might be a disaster. Do you bet the farm
on the forecast or do you hedge your bet?
and Societal Impacts Group at the University Center for Atmospheric
Research provides some guidance in their report on Prediction
In Policy which discusses how policy makers interpret and use
forecasts such as El Niño forecasts. They conclude that successful
forecasts are based on:
an institutional structure that allows policy makers, decision makers,
and scientists to interact closely throughout the entire prediction
process, so that each knows the needs and capabilities of the others.
It is crucial that this process be open, participatory, conducive to
mutual respect. Efforts to shield expert research and decision making
from public scrutiny and accountability invariably backfire and fuel
distrust and counterproductive policies and decisions.
and Societal Impacts Group.
Problems With Forecasts
- Are we forecasting El Niño or the results of El Niño
for some region? Usually it is the latter. Can we even know if some
event is due to El Niño? Newspapers often state one or another
local event was probably due to El Niño. In the example illustrated
below, a newspaper attributed some deaths that occurred during the
great ice storm of January 1998 (there were 30 fatalities) to El Niño.
Yet the chain of logic from the equatorial
Pacific to the deaths by carbon monoxide poisoning is very long, and
almost certainly incorrect.
From the La
Nina Summit Meeting.
- The forecast of a local event may be wrong. Forecasts
are not 100% accurate, and some are wrong, some are very wrong. Some
people bet the farm on the forecast, and are devastated when the forecast
turns out to be wrong.
- Or those
reading the forecast don't really understand what the forecast means even
if it is right.
An Example-The Red River Flood of April 1997
To see how even good forecasts can lead to trouble, consider the recommended
changes in forecast policy resulting
A. Pielke's analysis of the Red River Flood of 1997. During
the event, Grand Forks North Dakota was flooded.
Left: Red River flood at Grand Forks , North Dakota 1997. Right:
Downtown building destroyed by fire during the flood. From Grand
The North Central
River Forecast Center predicted weeks in advance that the Red
River would reach a very high stage of 47.5 feet based on one
assumption, and 49 feet based on another. Both numbers are uncertain
by about 10% based on previous flood predictions.
Yet the community
leaders misunderstood the numbers, and concluded 49 feet was
the maximum predicted height, not 49 + 4.9 feet.
In interviews conducted in May 1997 with
various decision makers in the Red River of the North basin, it is
clear that different people interpreted the flood stages outlooks
in different ways, some of which are demonstrably incorrect. These
different perspectives clearly influenced the choices made by local
officials. Some viewed the two numbers as a range, i.e., that the
maximum flood stage would be between 47.5-49 feet. Others viewed
the higher number as a maximum, i.e., a value that would not be exceeded.
For example, on April 8, 1997 The Grand Forks Herald wrote that "[NWS] experts are still forecasting a maximum
49-foot crest for the Red at East Grand Forks" (Foss 1997, emphasis
added). Others viewed the flood outlook as exact, i.e., "the crest
will be 49 feet." Still others viewed the 49 foot outlook as somewhat
uncertain; examples of the uncertainty ascribed to the outlook by various
decision makers ranged from 1 to 6 feet. Which decision maker might
have been correct is not known as the flood outlooks did not include
any quantitative information with respect to the uncertainty in the
outlook. Clearly throughout the community, people "anchored" their
thinking to the 49 foot outlook, which was reinforced through repetition
From Pielke's Analysis.
Roger A. Pielke's analysis
of the community's response to the forecasts led him to conclude:
- The NWS needs to better understand the uncertainty inherent in its
own outlooks and forecasts. Information about uncertainty and predictability
has potential value to decision makers.
- The NWS needs to explore how to better communicate uncertainty to
decision makers. Misuse of predictions can lead to greater costs than
if no prediction were provided.
- Local decision makers need to explore ways to become more forecast-independent.
Less reliance on forecasts will reduce the effects of uncertainty.
- Responsibility for flood flight decision making belongs at the local
level. The NWS should not place itself in the position of determining
how much risk a community should face.
- The policy research community needs to focus more attention on understanding
the actual use and misuse of predictions. As the forecast community
develops a greater range of more sophisticated products, more attention
will have to be paid to their appropriate use. Misuse of predictions
can result in large costs and loss of support for NWS activities.
Application to El Niño
Seasonal forecasts have impacts that can
rival the impacts of the phenomena being forecast. Thus, dealing
with the impacts of forecasts is one of the greatest challenges facing
the climate forecasting community... Advancement in the science of
seasonal forecasting seems to have outpaced advancements to the effective
use of those forecasts.
Roger Pielke (2000).
The recommendations for improving flood forecasts apply to all
environmental forecasts, including El Niño forecasts.
- Forecasts should contain clear statements of uncertainty.
- Users of the forecasts must understand what is being forecast, and
the uncertainty of the forecast.
- Users can not be expected to have a clear understanding of rare events.
We base our response to forecasts on previous experience, and we usually
have no prior experience with rare events such as the El Niño
of the century. We have no experience with forecasts of climate change
extending several decades into the future.
- Because all forecasts are uncertain, society should strive to
be forecast independent.
- Don't build cities in areas likely to fe flooded.
- Don't plant assuming an El Niño forecast is accurate.
- Don't build
home close to the beach on the Texas Coast.
Pielke, Roger A. 2000 Guest Editorial: A warning about seasonal forecasting.
The ENSO Signal,
16 July, 2009