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2
Climate Forecasting and Its Uses
This chapter examines recent and expected developments in the
scientific capability to make seasonal-to-interannual climate
forecasts and discusses the types of forecasts that are likely to
be socially useful. As background for readers unfamiliar with
climate forecasting, we begin by discussing the distinction between
weather and climate and how climate forecasts are made.
Weather and Climate
We are all familiar with the progression of the weather. Every
few days, the temperature changes, rain comes and goes, or a severe
storm hits. The characteristic time scale for changes in weather in
the mid-latitudes is a few days or less. In the tropics, especially
over the ocean, the weather tends to be much steadier, with sunny
weather and steady trade winds punctuated by an hour of daily
downpour (usually in the late afternoon) or by a squall every few
days.
We are also intuitively familiar with the concept of climate: we
recall an especially warm summer or an especially snowy winter. The
definition of climate is in accord with our intuitive concept:
climate is the statistics of weather averaged over a time
period that contains many weather events, usually at least a month.
The mean summer temperature (the temperature taken every day for 90
days during the summer and then averaged) is a climatic quantity,
as is the mean February rainfall. The characteristic time scale of
climate is therefore a month or longer.
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Climatic means can be changed in two distinct ways: by a small
change acting over the entire averaging period or by a changed
number of extreme events within the averaging period. Thus a summer
can be especially hot if the daily temperature is hotter every day
during the summer or if there are, say, three heat waves instead of
the usual two. Extreme events therefore contribute in an important
way to climatic means but the events themselves are weather, not
climate.
Other climatic statistics include the variances of quantities
averaged over the climatic period. For example, two winters with
the same mean temperature may differ in that one has a wider range
of maximum and minimum temperatures. Orange growers in Florida
would certainly be more concerned by a winter in which the lowest
daily temperatures often went below freezing than a winter in which
they did not, even if the mean winter temperature were the same for
both.
The climatic statistics for a given month (say December) are not
the same each year. When there is significant variability of
December averaged temperature from year to year (compare December
1982 with December 1983, say), the climate is said to vary
interannually. Although a certain amount of interannual variability
is intrinsic to any monthly averaged process (the time average over
any varying short-term weather process will vary depending on the
statistics of the weather process), there are global patterns of
interannual variation that have characteristic properties in space
and time.
The strongest known pattern of interannual variability in the
earth's climate system is El Niño/Southern Oscillation
(ENSO): it consists of both a warming and a cooling of the waters
of the equatorial Pacific Ocean occurring irregularly every few
years and a concomitant set of worldwide climatic changes that
statistically depend on these changes of sea surface temperature in
the equatorial Pacific. A detailed description of the ENSO
phenomenon appears in National Research Council (1996a) and a
simple description may be found on the web at
<http://www.pmel.noaa.gov/toga-tao/el-nino/home.html>. The
regions affected by ENSO are shown in Figure 2-1. Although ENSO is
the strongest known interannual signal, it is not the only one. A
region may have interannual variability for reasons that may or may
not include ENSO.
How Seasonal-to-Interannual Climate
Forecasts Are Made
The Weather Forecasting Paradigm
Society has come to take for granted the benefits of weather of
forecasting and is accepting of the considerable costs incurred to
make the fore-
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Figure 2-1.
Typical rainfall and temperature patterns associated with the
warm phases of ENSO conditions for the Northern Hemisphere winter
season.
Source:
http://nic.fb4.noaa.gov/products/analysis_monitoring/impacts/warm.gif
,
based on Ropelewski and Halpert (1987) and Halpert and Ropelewski
(1992).
casts. Weather forecasts are useful, in perception and in fact,
and their usefulness makes them valuable.
Weather forecasts, a prediction of the state of the atmosphere a
few days in advance, are made in various countries using standard
procedures. Upper air and surface data are collected in standard
formats from balloons, airplanes, satellites, and surface stations
and transmitted to the Global Telecommunications System (GTS),
where they become accessible to all national weather services.
Crucial to the data enterprise are global coverage and the free
distribution of the data. These depend on the ability of the poorer
countries of the world to maintain upper air stations and on the
willingness of all countries to make weather data available
freely.
The quality of the collected data is controlled by a variety of
means: simple consistency checks, location checks, gross agreement
with previous data, gross agreement with previous forecasts, and
comparison with other data. Because the data are taken at different
times within the weather forecast window (usually three to six
hours on either side of 0 and 12 o'clock Greenwich Mean Time), the
data are time interpolated to standard times.
The data are assimilated into the atmospheric forecast model and
the optimal estimate of the current state of the atmosphere is
made. This optimal estimate, or ''nowcast,'' is an analysis of the
current state of the atmosphere, not simply a collection of
observations: observational data alone are too sparse to define the
atmospheric state adequately for initial-
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izing forecasts. The nowcast may be thought of as interpolating
in space and time dynamically consistent model data to augment the
limited amount of observational data available.
The forecasting model is initialized. The initial atmospheric
state is estimated in creating the nowcast, but additional
constraints may be needed so that the model may be run efficiently.
In particular, certain boundary conditions need to be known (these
are more slowly varying conditions at the boundaries of the
atmosphere, in particular the lower boundary, that determine the
statistics of the atmosphere). These boundary conditions are the
sea surface temperature, sea ice coverage, land ice, snow cover,
the amount of vegetation cover on land, and soil moisture. Starting
from the nowcast and the initial state of the forecast model, the
atmospheric model is run for a time (usually 10 days) and the
forecasts for all times up to 10 days from the initial time are
made. The skill of the forecast is evaluated after the fact by
comparing the nowcast of the atmosphere at a given time with all
the forecasts made of its state at that time.
Because the atmosphere is a chaotic system in the mathematical
sense (i.e., it is very sensitive to changes in its initial
conditions), there is an ultimate limit of predictability,
determined by the rate at which the inevitable errors in estimating
the initial state of the atmosphere grow. This ultimate limit has
been determined to be on the order of two weeks (Lorenz, 1982; a
popular account of the chaotic nature of the atmosphere and the two
week predictability limit may be found in Lorenz, 1993). No matter
how precisely the initial state is estimated, the precise state of
the atmosphere cannot be predicted more than two weeks in
advance.
Weather forecasting by numerical means began in 1948 and rapidly
expanded. Global forecasts are currently made by public agencies
(at least in the United States) from publicly gathered data and
disseminated publicly. A multibillion-dollar private weather
forecasting industry has grown up in the United States that
provides specialized weather services to a variety of private
sources. These services usually involve providing specific
information to specific sectors of industry to guide resource
growth, distribution, and allocation.
What Is Seasonal-to-Interannual
Climate Forecasting?
Seasonal-to-interannual climate prediction grew out of the
international Tropical Ocean Global Atmosphere program. A history
of seasonal-to-interannual climate prediction and an recent
assessment of the status of the field is given in National Research
Council (1996a).
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Why Is Climate Predictable?
If weather is predictable for only two weeks in advance, how can
climate be predictable at lead times of months to a year or two
(i.e., on seasonal-to-interannual time scales)? The definition of
climate provides an answer to this question and also shows the path
toward prediction: "Climate" refers to the statistics of the
atmosphere. The atmosphere interacts strongly with the surface
through the interchange of fluxes of heat, momentum, and water. The
climatic state of the atmosphere therefore depends strongly on the
state of the surface, which can be characterized by its
temperature, reflectivity, and surface moisture. Because of the
interaction of the atmosphere with the surface, the surface
conditions will generally change, in turn causing the atmospheric
statistics to change in response. The evolution of the climate is
therefore dependent on the boundary conditions at the surface with
which the atmosphere interacts.
Among the more important statistics of the atmosphere are the
averaged temperature and the averaged precipitation (the average
must be taken over several time scales for weather
systems—usually a month or more). In general, we want to
predict monthly averaged temperature and precipitation. We also
want to predict the variance of these quantities in order to have
some indication of changes in the number of extreme events during
the averaging periods and of how much reliance should be placed on
predictions of the averages. Since the statistics of the atmosphere
depend on the boundary conditions, the key to predicting these
statistics is predicting the boundary conditions.
How Have Climate Forecasts Been Made
Previously?
There is a long history of trying to predict the climate, just
as there was a long history of weather prediction before the advent
of numerical weather prediction. There are traditional methods of
forecasting: by divination, by perceived patterns (e.g., a
perception borne of experience that, in a given region, a warm
summer follows a cold winter), by precursors (the appearance of
wooly bear caterpillars are followed by cold winters), and by other
traditional means (e.g., The Farmer's Almanac).
This century has seen the development of statistical forecasting
techniques, both univariate (e.g., predicting the rain in terms of
the past history of rainfall) and multivariate (predicting the rain
in terms of other quantities that seem to correlate with the rain,
such as temperature and pressure). These methods are still in
widespread use, but, when directly compared with numerical model
predictions (described below), they generally have lower skill at
shorter prediction lead times. For reasons described below, the
numerical methods are limited by the availability of
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Representative terms from entire chapter:
climate prediction
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ocean data (as well as by other considerations) so that
prediction by statistical methods is sometimes the best available
(sometimes the only available) prediction for a given region.
How Are the Forecasts Made by
Numerical Models?
The essence of climate prediction is predicting the evolution of
the surface boundary conditions and the atmospheric properties with
which they interact. In general, some aspects of the surface change
slowly (e.g., sea surface temperature, because of the immense heat
capacity of the ocean) and some change rapidly (e.g., surface
moisture). Sea surface temperature provides a convenient example of
how climate forecasts are made, and the basic idea applies for the
other boundary conditions as well (sea ice, land ice, snow cover,
soil moisture, vegetative cover, etc.). The key difference between
the mechanics of weather prediction and the mechanics of climate
prediction by numerical methods is that climate prediction involves
the interaction of the atmosphere with a more slowly varying
component—in the case of ENSO, the ocean. Climate prediction
(of sea surface temperature) is distinguished by the need for
initial data from the interior of the ocean, and it is this slow
ocean component of climate that carries the information forward in
time and allows a prediction over time scales longer than weather
time scales. Climate scientists say that most of the "memory" of
the climate system is in the ocean.
Sea surface temperature is determined by fluxes (exchanges of
heat and momentum) from the atmosphere and by heat transported by
motions in the ocean. In turn, sea surface temperature helps
determine the fluxes in the atmosphere. The only way to keep track
of these mutually dependent interactions and to predict their
course is with a model that consistently couples the atmosphere to
the ocean: a coupled atmosphere-ocean model.
Sea surface temperature is predicted by following a series of
steps: First, data are collected in the ocean and combined with the
atmospheric data routinely gathered for weather prediction. As of
1998, there are a variety of instruments sparsely deployed in the
ocean, most of which provide data that are reserved for research
before they are released for public use. The so-called ENSO
Observing System is different. It collects data specifically to
predict the sea surface temperature in the tropical Pacific where
ENSO holds sway; the data are transmitted in real time (i.e., as
soon as possible after the observations are taken) to the Global
Telecommunications System in a manner similar to weather data.
The centerpiece of the ENSO observing system is the Tropical
Atmosphere/Ocean (TAO) Array, which transmits data over the
Internet (http://www.pmel.noaa.gov/toga-tao/realtime.html).
The TAO Array
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consists of 70 stationary platforms moored to the ocean bottom
by 5 kilometers of nylon and kevlar line. Each surface platform
measures winds, humidity, atmospheric pressure, and the sea surface
temperature. Attached to the line are a series of instruments that
measure the temperature and pressure at intervals down to 500m
below the surface. The entire observing system for the tropical
Pacific can be viewed at
http://www.pmel.noaa.gov/toga-tao/noaa/elnino.html and is
described in National Research Council (1994). The ocean data are
quality controlled by a variety of checks.
Second, the ocean data are combined with the atmospheric data to
provide an estimate of the initial state of the coupled system. In
practice, the time scales of the atmosphere are short compared with
those of the ocean so that the surface winds and subsurface
temperatures (at various depths) are assimilated into an ocean
model to gain an estimate of the initial state of the ocean alone.
The atmospheric state is estimated from the analysis performed for
weather prediction. This gives an initial state for the coupled
atmosphere-ocean system.
Third, starting from the initial state, a forecast is made. The
coupled system is allowed to evolve freely for a given lead time
and the forecast is the state of the coupled model after that lead
time. Sometimes the initial atmospheric state is not known even
though the ocean initial state is known, so that an ensemble of
forecasts is made starting from various possible atmospheric
initial states. This approach provides an envelope of possible
forecasts, and, from the distribution of the final ensemble
members, an estimate of the uncertainty of the forecasts. Finally,
the forecast is evaluated by statistically comparing the forecast
state with the analysis of the current state at the time for which
the forecast was made, due regard being paid to the uncertainty of
the current analysis.
The forecasts are made at ranges of months to years, so that,
for each forecast made, it would normally take months to years to
find out to what extent it proved accurate. It is therefore very
cumbersome (basically impractical) to develop forecast systems by
waiting for the many forecast-analysis cycles needed to evaluate a
system. For example, since the first successful ENSO forecast by a
coupled atmosphere-ocean model (Cane et al., 1986), a forecast
every month would yield a total of only about 120 forecasts. By
contrast, since numerical weather prediction was developed in 1948,
over 20,000 forecasts have been made. To develop prediction systems
more efficiently, past data are used to initialize the state of a
model and "forecasts" are made of events that have already occurred
and scored by data already in hand. These retrospective forecasts
are called "hindcasts."
We do not want to leave the impression that the only way to
forecast is with numerical coupled models. Statistical methods are
routinely used
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to predict quantities of interest when other methods are not
available. Statistical methods depend on correlations between
predictors (the quantities used to make the prediction) and the
quantities of interest (predictands). For example, the rainfall in
the Nordeste region of Brazil (the predictand) correlates with sea
surface temperature in both the tropical Pacific and subtropical
Atlantic (the predictors), and statistical forecast schemes using
both of these these predictors have proven useful in predicting
rainfall in the Brazilian northeast (e.g., Hastenrath, 1990; Uvo et
al., 1998). When the predictors are correctly chosen (including,
perhaps, internal ocean data) and the relationship between the
predictors and predictands is simple and direct, there is no reason
that statistical methods would not have as high a skill as
numerical methods. In general, numerical models contain most of the
processes in the atmosphere and the ocean and keep track of them in
a consistent way. Thus, they have the potential to provide more
accurate and complete information. However, there is no reason that
statistical methods that keep track of all the predictors should
not have a comparable skill to numerical methods. Which method is
preferred when both are available is judged by the skill of
prediction.
Which Quantities Are Forecast?
Scientists forecast sea surface temperature (SST) by numerical
methods, but, in general, it is temperature and precipitation over
land that people most want to predict. At the moment, only SST in
the tropical Pacific Ocean characteristic of ENSO is forecast;
however, because ENSO has such a global influence, forecasting
tropical Pacific SST has predictive value for temperature and
precipitation in many specific regions around the world (Figure
2-1). We emphasize that forecasts of ENSO predict a physical
quantity, the SST. When the SST in the tropical Pacific is
predicted to be anomalously high, it may be said that forecasters
have predicted El Niño, but since this term has no agreed-on
definition in terms of value of SST, this is an interpretation. The
key is that the value of SST is predicted and the value of the
forecast resides in the consequences of the predicted value of SST.
The statement that El Niño has been forecast is a
journalistic rather than a scientific statement.
In the tropics, atmospheric circulations are driven directly by
the latent heat released in regions of persistent precipitation.
Thus in the far western Pacific, the normal persistent rainfall is
accompanied by rising motion and lowered surface pressure. In the
eastern Pacific, the circuit is completed with downward motion,
lack of precipitation, and higher surface pressure. These regions
of persistent precipitation can emit planetary waves, which
propagate to higher latitudes and affect local circulations
Page 26
and rainfall. The role of tropical SST is to determine the
locations of these regions of persistent precipitation, which, in
general, lie over warmest waters. When SST in the eastern Pacific
increases (during warm phases of ENSO), the regions of persistent
precipitation expand eastward into the central and eastern Pacific
and may affect the west coast of South America while moving away
from the western Pacific, causing droughts in the normally wet
regions around the Indonesian archipelago. This motion of the
regions of persistent precipitation affects higher latitudes
similarly, but less robustly.
In the vicinity of the tropical Pacific, where the variability
of temperature and precipitation is low, knowing tropical Pacific
SST translates directly into knowing average temperature and
precipitation over land. Peru, Ecuador, Chile, Australia, and the
Pacific Islands use these forecasts directly to plan their
agriculture and water management. In the midlatitudes (for
instance, the Pacific Northwest of the United States), where
weather variability is high, knowing tropical Pacific SST allows
prediction of shifts in the probable averages of temperature and
precipitation, but the information must be used with care since
there is so much variation around the averages. In such regions, it
requires a certain sophistication to use the information
effectively. For example, it can be useful to have estimates of the
likelihoods of particular outcomes at some variance from the
predicted average.
How Are the Forecasts Evaluated?
An objective measure of the skill of a series of forecasts is
defined by comparison of a quantity forecast with the quantity
observed at the forecast time. For example, if the quantity
forecast is the NINO3 index (the SST spatially averaged over the
eastern tropical region 90°W to 150°W, 5°S to 5°N),
then records of forecast and observed NINO3 would be correlated and
a single number, the correlation coefficient of the two time
series, would represent the measure of how accurate, on the
average, the phasing of the forecasts has been. Similarly, the root
mean square (rms) difference of the values in the observed time
series and the values in the forecast time series would indicate
how accurate, on the average, the amplitude of the forecasts has
been. These two numbers, the correlation coefficient and the rms
error, then give objective measures of how good the long series of
forecasts has been.
We emphasize that these measures of skill apply only to long
series of forecasts, not to an individual forecast. In order to
think about the accuracy of an individual forecast, we must think
of the individual forecast as a probability of occurrence. To
oversimplify, if the forecast system exhibits an averaged
correlation coefficient of .8 over a long series of forecasts
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at a given lead time, all we can say about the next forecast at
that lead time is that the probability of the system having the
predicted phase (positive or negative) is 64 percent. The meaning
of this probabilistic forecast must be understood as the averaged
number of correct forecasts over a long series of forecasts.
How Good Are the Forecasts?
The perfect forecast for a series of forecasts of NINO3 would
have a correlation coefficient of 1.00 and a rms error of 0.00.
Needless to say, perfect forecasts do not exist. Figure 2-2 shows
the correlation coefficients and rms errors from a long series of
forecasts using the Cane-Zebiak model (Chen et al., 1997). The
heavy black lines correspond to persistence—that is, the
forecast that any initial SST anomaly would remain constant.
Persistence provides a good forecast for a few months—in fact,
it is hard for any existing forecast scheme to outperform the
forecast of persistence over this time scale. The coupled
forecasting model has a rather large nowcast error that arises when
the models are coupled: the ocean data generate surface winds in
the model that are slightly inconsistent with the observed surface
winds. The nowcast error can be decreased by better initialization.
The difference between the dashed and solid thin lines in Figure
2-2 are due entirely to different initialization procedures. The
figure shows that the better initialized model offers real
predictive skill above persistence in predicting NINO3 for at least
12 months. Similarly, the rms error of the better initialized model
is below that of the persistence forecast for more than 12 months.
Similar considerations apply to other coupled models. The general
issue of initialization and the correction of nowcast error is a
difficult problem in climate prediction and is not unique to the
Cane-Zebiak model.
Whether or not this degree of skill is enough depends entirely
on the use to which the forecast is being put: the usefulness of
skill is subjective. Scientists usually consider that correlations
above .5 or .6 (indicating that 25 to 36 percent of the variability
is predicted) offer a useful degree of skill, but different uses,
and similar uses in different regions, probably require different
degrees of skill to be useful.
Because the skill increases with decreasing forecast lead time
(Figure 2-2), and because a forecast is made every month, the
forecast is updated as the forecast time approaches. The closer we
are to the forecast time, the shorter is the lead time and the
better the forecast. This updating and improvement of the forecast
with time as the forecast time is approached allows actors to
adjust and correct their initial expectations. For some uses of the
forecast, this allows a staged or continually adjusted response
Page 28
Figure 2-2.
Measures of skill of prediction of the Cane-Zebiak model.
The upper diagrams represent correlations of the forecast and
observed
NINO3 temperature index (the averaged monthly temperature in a
region
bounded by 90°W to 150°W, 5°S to 5°N) for various
time periods.
The thick line gives the persistence correlation (the correlation
of the
initial value with values later in time) which can be considered
the skill of
a forecast that always predicts the value of the NINO3 index to be
the value
at the initial time. The dashed line gives the correlation skill
of the
model under the original initialization procedures and the thin
black line gives
the correlation skill under improved initialization procedures. The
lower
diagrams represent the root mean square error between the forecast
and
observed NINO3 index. The thick line is the rms error of the
persistence
forecast, with the dashed and thin black line as above. Source:
Chen et al. (1997).
Reprinted by permission of the American Meteorological
Society.
Page 29
rather than a one-time response, analogous to the adjustments
people make as weather forecasts become shorter-term.
Problems and Prospects for
Seasonal-to-Interannual Climate Prediction
The forecasts whose skill is summarized in Figure 2-2 were made
with a relatively simple coupled atmosphere-ocean model that was
constructed many years ago. More complex models are being developed
(see the review by Delecluse et al., 1998), and it is likely that
the models will improve significantly. Techniques for
initialization are also improving as more sophisticated ocean
models can better accept the observed data and can better represent
the mix of processes that change temperature in the ocean. The
current status of prediction of tropical SST has recently been
reviewed in Latif et al. (1998). A bibliography of papers on
seasonal-to-interannual prediction is maintained at
http://www.atmos.washington.edu/tpop/pop.htm.
The dominant limitation of these forecasts for use around the
world is the paucity of data available to initialize the coupled
models, especially ocean data. Only in the tropical Pacific do we
have a system built specifically for climate prediction. There are
regions of the world in which an inadequacy of ocean data implies
that relevant SST cannot be reliably predicted by numerical means.
Pilot arrays of instruments have recently been deployed in both the
tropical Atlantic and Indian Oceans with a view toward eventually
removing this limitation.
The unusual warm phase of ENSO that occurred during 1997-1998
led to a new reexamination of models and predictions. This was not
only one of the largest amplitude warm phases of the century, but
also it was predicted well enough and with enough lead time for the
forecasts to be used. A directory of how the forecasts were done
and information on the impacts of this warm phase and the use of
the forecasts is given in http://www.ogp.noaa.gov/enso.
Toward Usable Knowledge
A key to making climate prediction more socially useful is to
develop links between those making the predictions and those who
can benefit from them. The users need to know what kinds of
predictions are made and what kinds may be possible in the future.
The forecasters need to know which predictions are most useful and
how they should be presented. In general, the forecaster needs to
understand the system or sector to which the prediction is applied.
The acquisition of the needed knowledge by both the forecasting and
user communities must be consid-
Page 30
ered sequential and arises from the social and physical learning
engendered by a series of forecasts (some correct and some
incorrect) and the responses they provoke in the user community.
This section discusses current and potential uses of ENSO forecasts
and some possible new directions in making climate prediction more
useful.
From Tropical Pacific SST to Other
Quantities
In theory, because the coupled atmosphere and ocean have global
extent, a model that predicts SST in the tropical Pacific should
also predict SST and the concomitant atmospheric response
(temperature, pressure, precipitation) everywhere on the globe. As
data in the ocean become more abundant, as global coupled models
become better, and as computers get faster, predictions will
approach the theoretical limit of predictability. At present,
however, data for the world ocean outside the tropical Pacific are
inadequate to initialize seasonal-to-interannual predictions.
Therefore, different practical strategies are used. Either a
limited region of the atmosphere-ocean system that includes the
tropical Pacific is used to predict SST or a global coarse
resolution atmosphere-ocean model is used to initialize only the
tropical Pacific part of the ocean and therefore to predict Pacific
SST only.
In order to go from tropical Pacific SST to quantities of wider
usefulness, especially air temperature and precipitation, an
additional step is required. Given a prediction of tropical Pacific
SST, a high-resolution atmospheric model is run using
climatological SST (i.e., normal SSTs for that time of year)
everywhere but in the tropical Pacific, where the predicted values
are used instead. Such models make predictions from tropical
Pacific SST to climate in many other parts of the world, as shown
in Figure 2-1. The models directly predict tropical Pacific
precipitation, atmospheric temperature, and surface winds. To
forecast these quantities at higher latitudes, the model must be
run for a month or so to predict averages of land temperature,
precipitation and winds, and it must be run many times with
differing initial atmospheric and oceanic conditions, all
consistent with the imperfect specification of these initial
conditions.
The resulting ensemble of forecasts provides a distribution of
all the conditions that could occur given these imperfectly known
initial conditions. The resulting forecast can then be converted
into a probability distribution. For example, Figure 2-3 shows a
seasonal forecast of precipitation over North America for the
unusually warm phase of ENSO that occurred during the winter of
1997-1998, in which the southern tier of states was predicted to
have 60 percent chance of above average rainfall (these forecasts
are available at http://iri.ucsd.edu/forecast/net_asmt). This
method of presenting the forecast is valuable in that it makes
explicit
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Figure 2-3.
Assessment forecast for North America produced routinely
by the International Research Institute for Climate Prediction.
The three
numbers in each box represent forecast probabilities that the
predicted
precipitation is above normal by more than one standard deviation,
within
one standard deviation of normal, and more than one standard
below
normal, respectively. Source:
http://iri.ucsd.edu/forecast/net_asmt/
that, even though the ENSO phase is warm, and the southern
states were expected to have above-normal precipitation (see Figure
2-1), the probability of below-average rainfall is 20 percent and
of normal rainfall also 20 percent.
Uses of ENSO Nowcasts
Certain regions of the world experience characteristic climatic
patterns during warm and cold phases of ENSO (i.e., when the water
in the tropical Pacific is anomalously warm or cold). Since ENSO
evolves slowly, it may be useful to simply know, say in the North
temperate fall, that ENSO is entering a warm phase. The physics of
ENSO is known well enough to know that the warm phase usually peaks
in the North temperate winter. A nowcast that a warm phase of ENSO
is evident in the fall therefore conveys information that can be
acted on.
Page 32
For example, warm phases of ENSO in the Pacific Northwest of the
United States are generally (but not always) characterized by
anomalously warm and dry conditions. A nowcast of evolving warm
conditions in the tropical Pacific implies a number of conditions
to be expected regionally, such as less snowpack in the mountains
and earlier peaking and overall decreased streamflow in the major
river systems fed by mountain snowpack. Since large parts of the
Pacific Northwest depend on streamflow for irrigation,
hydroelectric power, river transport, and city reservoirs, actions
can be taken in advance to mitigate the effects of reduced
streamflow.
Specific ENSO Forecast Needs—Time
and Space Resolution
Forecast needs depend on the sector that may use the forecast
and on the particular use within the sector to which the forecast
is applied. Many users desire precipitation forecasts, averaged
over the weather time scales—this usually means monthly
averaged precipitation predicted a season to a year in advance.
Such forecasts are useful for agriculture, sanitation and sewer
management, hydroelectric power generation, river transportation,
flood control, forest fire control, and mosquito control. Some
users desire monthly averaged temperature forecasts for a season to
a year in advance. Such forecasts are useful for coastal fishery
management, fuel distribution and storage planning, construction
involving concrete pouring, and the tourism, recreation, and retail
sales industries. Climate scientists believe there are fewer
practical applications of forecasts of other physical quantities
(we regard the winds that go with hurricanes as part of hurricane
prediction rather than wind prediction). The match between forecast
information and its users' needs is discussed further in Chapter
4.
Applications that require averaged precipitation or temperature
can benefit from ENSO forecasts, but applications that require
information on when forecast events will occur cannot benefit,
because of limitations in forecasting capability. Agriculture in
India, for example, depends on planting relatively soon before the
onset of the summer monsoon rains. Planting too soon means the
seeds will die in the ground, whereas waiting too long to plant
means that the ground may be too soft or muddy for planting. ENSO
climate models may forecast the intensity of the monsoon rainfall
in advance, but they cannot (and probably will never be able to)
forecast the specific date of onset of the monsoon rains, because
such onsets depend strongly on the details of weather patterns that
are essentially unpredictable more than a week or so in
advance.
In general, forecasts of averaged precipitation and temperature
are made on the same spatial scale as the atmospheric model that is
directly
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coupled to the ocean model that forecasts tropical Pacific SST
or on the scale of the model used afterward to forecast from the
SST forecast to the global effects of ENSO. This level of spatial
resolution (on the order of 400km) is adequate for some practical
purposes, but in regions with significant variations in elevation
or terrain, a more finely grained regional forecast is often
needed. For example, in the U.S. Pacific Northwest, weather systems
come from the Pacific over the Olympic mountains leaving large rain
shadows on the eastern slopes—locations no more than 50km
apart can have annually averaged precipitation differing by a
factor of five. Since precipitation is generally specific to
spatial patterns of elevation and since many applications require
specific locations for rainfall (e.g., rain falling on opposite
sides of a mountain divide will fall in different catchment basins
and therefore raise different reservoirs), these applications
require finer spatial resolution. To make the forecasts useful for
these purposes will require the use of finer-grained atmospheric
models. This approach is under considerable development (Giorgi and
Mearns, 1991) and these so-called mesoscale atmospheric models
promise to be the tool of choice in downscaling
seasonal-to-interannual forecasts.
Mesoscale models are also useful for examining the evolution of
predicted extreme events. In some regions of the United States, the
most important type of forecast is that of severe storms (e.g.,
tornadoes and hailstorms in the Great Plains in summer, hurricanes
on the Atlantic and Gulf coasts in fall). Although no climate
forecast scheme can predict a specific storm even a season in
advance, mesoscale models embedded in larger-scale climate
prediction models can indicate that the conditions under which
storms form may be present and give some indications of where they
might form and of their likely frequency.
Using ENSO Forecasts
ENSO forecasts have been used most where their skill is highest
and weather variations are relatively small. In Peru, Ecuador,
Australia, and the Pacific Islands, precipitation and temperature
are tightly tied to the variations of tropical Pacific SST
connected with ENSO, and the skill of predicting SST variations is
relatively high (Figure 2-2). It is not surprising that these
forecasts are used extensively. In the United States, however, the
skill of the forecasts is lower and the variability in the
phenomena to be predicted is higher. At least before the 1997-1998
ENSO events, the forecasts were not uniformly used in the sectors
affected by seasonal-to-interannual climate variability.
An industry that has used the forecasts is California squid
fishing. A forecast of warm water in the tropical Pacific implies
warm water off the coast of California and therefore also implies
declines in the squid catch.
Page 34
Fishing companies sign contracts to deliver squid at a given
price at some time in the future. When the catch declines, the
contracts must be honored with squid bought at prices that may be
much higher. With a forecast of warm water, options are bought at
current (more reasonable) prices to hedge the possibility of a bad
catch (Glantz, 1996). In contrast, managers of U.S. water resources
did not use ENSO forecasts much through 1996. In their judgment,
the forecasts were neither skillful enough (although few managers
know their skill) nor obviously useful in the absence of
demonstrations of their effectiveness (Pulwarty and Redmond, 1997).
The reasons that some decision makers act on the forecasts and
others do not are a potential topic for research. We not that an
important issue in the use of forecasts is that users have an
appropriate understanding of the level of predictability they offer
for local conditions.
Possible New Directions in Climate
Forecasting
Non-ENSO Bases for
Seasonal-to-Interannual Forecasts
ENSO is not the only signal of interannual climate variation.
For example, although rainy season (February to April)
precipitation in northeast Brazil is negatively correlated with SST
in the tropical Pacific, it is more strongly positively correlated
with SST in the subtropical South Atlantic and negatively
correlated with SST in the subtropical North Atlantic (Uvo et al.,
1998). On longer time scales, SST in the North and South Atlantic
varies out of phase and affects rainfall in the Sahel. Forecasts of
precipitation in northeast Brazil have been made with statistical
models (Hastenrath and Greischar, 1993) and with models that assume
persistence of Atlantic SST (Graham, 1994). Recently, there have
been indications that the tropical Atlantic SST may be predictable
(Chang et al., 1998). Skill at predicting SST in this ocean region
will have applications in northeast Brazil and elsewhere.
ENSO has also been recognized to interact with a decadal signal
in the tropical Pacific that couples strongly to decadal variations
of SST in the North Pacific (Zhang et al., 1997). The North Pacific
manifestation of this decadal signal has definite effects on the
climate of the northwestern part of North America, in particular
for the salmon fisheries (Mantua et al., 1997). Similar
seasonal-to-interannual and decadal variability exists in a
phenomenon called the North Atlantic Oscillation (Hurrell and van
Loon, 1997), which correlates strongly with climatic conditions
over Europe and the Siberian subcontinents. Neither the Pacific
Decadal Oscillation nor the North Atlantic Oscillation has yet been
shown to be predictable.
Page 35
The Potential to Develop Leading
Climate Indicators
Interest has grown recently in the construction and use of
climate indices that foreshadow or lead certain classes of human
impacts. Such indices borrow from the tradition of indices of
leading economic indicators that have been in use for several
decades by the U.S. National Bureau of Economic Research
(Easterling and Kates, 1995). They are not predictions, but rather
historical integrations of climate data with embedded trends that
may lead to increased vulnerability to impacts. Karl et al. (1995)
developed and disseminated two climate change-related indices known
as the ''climate extremes index'' and the "index of greenhouse
climate response." Easterling and Kates (1995) proposed, but did
not test, a number of potential indices that may lead impacts of
seasonal-to-interannual climate variability. Among them are a
hazard warning index, which would integrate such hazard precursors
as depth of snowpack in advance of flood events, and an index of
ecosystem health, which would integrate long-term climate
precursors of species distribution, such as the climatic
determinants of the Holdridge Life Ozone Classification.
Considerable testing of such indices is needed before it can be
determined if they provide useful knowledge. Leading climate
indicators, if validated, could become essential components of
early warning systems for famine, disease outbreaks, drought,
increased flood potential, and other events of practical interest
to individuals, firms, and disaster preparedness agencies.
Processes for Identifying Usable
Knowledge
Until now, research decisions on how to improve
seasonal-to-interannual climate prediction have been made entirely
by the community of climate scientists. Great advances in
understanding and predictive skill have been achieved that may have
substantial social benefit. Nevertheless, the usefulness of the
predictions has been largely a by-product of scientific progress
rather than of an interaction between scientists and those who may
use scientific findings, aimed at matching scientific capabilities
and social needs.
As climate prediction moves from a purely scientific exercise to
an enterprise justified to a greater degree by its social utility,
both potentials for conflict and opportunities for collaboration
arise. Conflict can arise when science that is promoted as
decision-relevant is not seen as such by the participants in the
affected decisions. Past experience with major scientific efforts
at risk assessment indicates that scientific activities that are
intended to be relevant to practical decision making are more
effective and useful when they are designed in a process that
integrates the needs
Page 36
and perspectives of those who would use the scientific results
with the judgment of scientific specialists (National Research
Council, 1996b).
It may be possible to bring considerations of usefulness
alongside those of science in making future research decisions, so
that future development of predictive skill will move in useful
directions to the extent scientifically possible. For example,
although there are many climate statistics that are potentially
predictable, scientists have concentrated on only a few, such as
average monthly temperature and precipitation. For some purposes,
other climate statistics, such as average daily minimum temperature
or the frequency of 24-hour periods with precipitation greater than
60mm may be particularly useful, so that efforts to develop and
improve predictions of those quantities may have great social
value.
Until now, there has been no process to try to identify such
needs and consider whether they can be accommodated by scientific
analysis. An important new direction might be in developing a
process that tries more systematically than in the past to find
matches between potential new scientific developments in climate
prediction and the informational needs of users.
Two strategies might be used to bring scientific output and
users' needs closer together. One relies on developing quantitative
models of the sensitivity of the outcomes of weather-sensitive
human activities to climate variation and using these models to
identify the climatic parameters to which particular sectors or
groups are highly sensitive or vulnerable. Chapter 5 reviews the
current state of this sort of modeling. Information on climate
sensitivity could be relayed to climate scientists as input to
their decisions about which climate parameters to estimate. Another
strategy relies on direct communication between the producers and
consumers of climate forecast information, in which consumers
discuss and identify the information they would find useful and the
producers discuss the information they could provide. Chapter 4
discusses evidence pointing to the likely value of this
participatory approach.
Findings
Recent scientific advances have resulted in unprecedented levels
of skill in predicting climate—averages of such variables as
temperature and precipitation—months to a year or more in the
future. Forecast skill is continuing to improve. With respect to
the usefulness of such climate forecasts to human decision makers,
the following conclusions are justified:
1.
Uncertainty is embedded in climate forecasts because of the
chaotic processes inherent in the atmospheric system. Although
forecast skill can be
Page 37
expected to improve along with improved measurement of sea
temperatures and other boundary conditions and better
initialization of the forecasting models, forecast information will
always include uncertainty.
2.
The skill of climate predictions varies by geographic region,
by climate parameter, and by time scale. This situation can be
expected to continue.
3.
Research addressed to questions framed by climate science is
not necessarily useful to those whom climate affects. A climate
forecast is useful to a particular recipient only if it is
sufficiently skillful, timely, and relevant to actions the
recipient can take to make it possible to undertake behavioral
changes that improve outcomes.
4.
Progress in measuring and modeling ocean-atmosphere
interactions is likely to improve predictive skill in regions and
for climatic parameters for which very limited skill now exists,
thus increasing the potential for forecasts to be useful in new
regions and for new purposes.
5.
The utility of forecasts can be increased by systematic
efforts to bring scientific output and users' needs closer
together. These efforts may include both analytic efforts to
identify the climatic parameters to which particular sectors or
groups are highly sensitive or vulnerable and social processes that
foster continual interaction between the producers and the
consumers of forecasts.
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