**Socio-Economic Benefits of Climatological Services in the United
States:**

**A Report to the WMO**

## Laurie Houston

**Oregon State University**

**Richard. M. Adams**

**Oregon State University**

**Rodney Weiher**

**National Oceanic and Atmospheric Administration**

In 1996, the WMO produced an extensive review of Economic and Social
Benefits of Climatological Information and Services conducted by J.M.
Nicholls (WMO/TD-No. 780). In 1997, Katz and Murphy edited a book of
case studies estimating the economic value of weather and climate
forecasts for various sectors titled *Economic Value of Weather and
Climate Forecasts.* In light of these past reviews of economic
assessments of the value of climatological services, this report focuses
on literature published after 1996, and studies not included in the
previous two review articles. Many of the following examples of economic
benefits of climatology services are also contained in two previous
reports, Adams et al. in 2004, and Houston et al. in 2004.

1.  **Agriculture**

Of all economic sectors, agriculture is the most dependent on weather
conditions and is expected to benefit greatly from improved weather and
climate forecasts. Agricultural output of food and fiber is also of
critical importance to human welfare. Thus it is not surprising that
there have been more studies regarding the value of weather information
to agriculture than any other economic sector. Several of the studies
include estimates for both perfect and imperfect forecasts. From a
practical perspective, perfect forecasts are an unrealistic expectation,
but they are useful in determining an upper bound on the value of
additional information. Similarly, reporting a range of imperfect
forecasts (skill levels) is helpful in determining the degree of
accuracy that is needed to make a project economically feasible.

This report will focus on Long-term forecasts (one month or longer).
Such forecasts are useful for decisions regarding crop choice, crop
variety, planting dates, and investments in certain types of
infrastructure. Most of the economic evaluations of long-term weather
forecasts presented here are based on expected systematic disturbances
in climatic conditions, such as the El Niño-Southern Oscillation (Adams
et al., 2003, 1999, Chen et al., 2001, and Solow et al., 1998) and the
Pacific decadal oscillation (Hamlet et al., 2002).

More than half of the long-term weather forecast values presented here
are reported at the individual farm level, on a per hectare basis, with
most ranging from a few dollars to more than \$100 per hectare (Table
1). Each of the values has been converted to 2004 US dollars using the
CPI from the Economic Research Service and historical exchange rates
from [http://www.x-rates.com](http://www.x-rates.com/). The majority of
these studies base the value of weather forecasts on long-term
precipitation and temperature forecasts which can aid in numerous farm
level decision making strategies. For example, long-term forecasts can
aid farmers in wheat harvest strategies such as early harvesting, drying
and contract harvesting (Abawi et al., 1995), fertilizer application
decisions (Hill et al., 2000, Mjelde et al 1997, Mjelde and Penson
2000), land allocation or crop choice decisions (Messina et al., 1999,
Mjelde et al., 1996), and decisions regarding participation in federal
farm programs and crop insurance programs (Mjelde et al., 1996).

The remaining value estimates for long-term weather forecasts are
provided at the national or global level. Most of these studies focus on
the value of ENSO forecasts, as manifested in crop yield changes
associated with ENSO events and decisions farmers make regarding crop
and livestock enterprises based on these expected events and associated
yields (Adams et al., 1995 and 2003; Chen et al., 2001 and 2002; Solow
et al., 1998). For example, Adams et al. (2003) estimated the value of
an ENSO early warning system for the agricultural sector in five Mexican
states (not including livestock) to be approximately \$20-\$30 million
for imperfect information and up to \$79 million for perfect information
(Table 1). Values for imperfect ENSO forecasts for the agricultural
sector in the United States (including livestock and secondary products)
have been estimated to range from \$300 to over \$900 million, depending
on the ENSO strengths and frequencies in the future (Chen et al., 2001
and 2002; Solow et al., 1998), with a perfect forecast having a value of
more than \$1.7 billion, which represents nearly 2 percent of GDP from
US agriculture (Table 1).

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2.  **Food Security**

In general, anything that improves food production should add to food
security. Many of the references cited above in the agricultural sector
can be linked to food security. For example, long-term forecasts play a
role in food security by allowing resource managers to make informed
decisions regarding crop choices, crop variety, planting dates, and
water and fire management decisions. All of these decisions play a role
in maintaining a sustainable level of food production.

3.  **Urban/Town Planning and Construction**

Accurate and timely weather forecasts can also reduce costs that severe
weather imposes on urban/town planning and construction. The efficient
scheduling of construction and outdoor work such as roofing, paving,
landscaping, building construction, and snow removal, relies heavily on
weather forecasts. Ignoring weather conditions or not integrating
weather forecasts into scheduling and construction decisions decreases
revenues and profits due to safety deterioration issues, such as the
increased risks of hypothermia and hyperthermia, or the risk of damage
to construction materials. Ignoring weather and climate forecasts also
increases efficiency losses due to delays or damages from weather events
such as heavy rains, high winds, or extreme high or low temperatures,
and increases losses from inefficiently designed construction projects.
. For example, architects have been able to build shallower
frost-protected foundations using air freezing index and soil freeze
depth maps constructed from data provided by NOAA Satellites and
Information's Air-Freezing Index (AFI). This data enabled architects to
reduce construction costs by an estimated \$330 million per year, and
save and equivalent of 8.6 million gallons of heating fuel (NOAA 2001).

4.  **Energy**

One of the key components in forecasting energy demand is temperature
because of the need for cooling in hot weather and heating in cold
weather. Improved knowledge of future temperatures allows managers to
more accurately forecast peak loads and optimally schedule electric
generating plants to meet the demands at lower cost. An important
component in energy supply is precipitation, because a substantial
portion of the nation's energy is supplied by hydro power. Improved long
range precipitation forecasts allow reservoir managers to better manage
energy production while meeting multiple use demands for stream flows.
Examples of forecast values in 2004 US dollars are summarized below and
presented in Table 2. Some of these studies are closely related to the
water management sector as well because they involve streamflow
estimates which affect water supplies for agriculture and other
beneficial uses, as well as electricity production.

The following three studies by Hamlet et al. (2202), Maurer and
Lettenmaier (2004), and Yeh et al. (1982) provide estimates of the value
of improvements in long-range weather and climate forecasts, which allow
for improved streamflow estimates.

Hamlet et al. (2002) estimated the value of improved stream flow
forecasts for Columbia River hydropower derived from improved
forecasting of El Niño Southern Oscillation (ENSO) and the Pacific
Decadal Oscillation (PDO). Recent advances in climate forecasting have
enabled forecasters to predict streamflows in the Columbia River basin
six months earlier than forecasts that rely on actual snowpack
measurements. This added lead time will allow more spot market energy
sales to be made in late summer and early fall. Therefore, benefits were
measured as the value of spot market energy sales that could result with
improved streamflow estimates. The authors estimated that these sales
could increase annual revenue by approximately \$161 million per year
(2004 dollars). To put this in perspective, this represents
approximately 0.075% of GDP from electric, gas, and sanitary services in
the United States.

Maurer and Lettenmaier (2004) examined the benefits provided by improved
estimates of climate, snow, and soil moisture states in the Missouri
River Basin. Using 'realistic' streamflow predictability, they estimated
that improvements in hydropower benefits associated with improvements in
understanding the links between remote sea surface temperatures and
regional climate as well as land surface moisture states had a value of
\$6.8 million, which is approximately 0.003% of GDP from US electric,
gas, and sanitary services. The greatest portion of these benefit are
from knowledge of the climate state (\$3.4 million). Knowledge of soil
moisture states in the winter and spring account for \$2.1 million in
benefits, and snow water content accounted for the remaining \$1.3
million in hydropower benefits. The authors also noted that systems with
more storage capacity such as the Missouri basin will have smaller
benefits from long lead time climate and weather information. This is
because systems with larger storage capacity can take advantage of high
flows to store water for future low flow states, where as smaller
systems have to closely monitor their storage capacity to maintain
minimum and maximum flow requirements.

Yeh et al. (1982) estimated the potential benefits of long-range (one
month to one year) streamflow forecasts for the operation of the
Oroville-Thermalito reservoir system of the California State Water
Project. Benefits included hydropower generation, as well as, water
conservation for irrigation (and other beneficial uses), and decreased
seepage damage to crops. The hydropower benefits were estimated between
\$0.2 and 0.4 million annually in 1983 dollars, which would be
approximately equal to \$0.4-0.8 million in 2004 dollars, or 0.04% of
California's GDP from electric, gas, and sanitary services.

5.  **Water Resource Management**

Weather and climate forecasts, especially long term forecasts, are
extremely important for water resource management in terms of flood
management, agricultural and urban water use management, and reservoir
management for power generation, recreation, navigation, and endangered
or threatened species. Early, accurate, and timely weather and climate
forecasts allow managers to predict streamflows that are necessary for
efficient management of water systems. Inaccurate information and
information that is not timely force managers to manage water systems
more conservatively, which can increase the cost of water supplies and
decrease flood protection.

Floods such as the Red River flood of 1997 exemplify the importance of
accurate and timely weather forecasts for flood water management. At an
estimated cost of US\$4 billion (1998 dollars) the Red River flood of
1997 was the most costly flood , on a per capita basis, for a major
metropolitan area in the U.S. history (Todhunter, 2001). Nearly 1,300
homes were destroyed and 55,000 area residents evacuated their homes.

Early snowmelt estimates in February predicted the potential for severe
spring flooding, allowing the U.S. Army Corps of Engineers to construct
temporary dikes in a timely manor. However, in April a series of five
sequential forecast revisions, over a period of 50 hours, made the
implementation of additional emergency response measures unfeasible. The
rapid succession of revised forecasts coupled with communication and
transportation difficulties associated with a blizzard in the area, as
well as overconfidence in previous forecasts, and misjudged risk
assessments, lead to poor decisions about how to fight the flood
(Pielke, 1999).

One of the key factors contributing to the size of the damages from the
Red River flood of 1997 was the limited accuracy of the early streamflow
predictions. Studies by Hamlet et al. (2002), Pagano et al. (2001), and
Yeh et al. (1982) provide examples of economic benefits associated with
long-lead (one month to one year) hydrologic predictability and
reservoir management. Hamlet et al. (2002) concentrated on the benefits
of streamflow predictions associated with predictions of future
precipitation. Pagano et al. (2001) concentrated on the relationship
between El Niño Southern Oscillation (ENSO) and winter climate, and its
corresponding impact on snowpack and summer streamflows. Pagano et al.
(2001) also note that the operational water management community
currently underutilizes climate information and suggest ways in which
this could be remedied. Lastly, Yeh et al. (1982) concentrated on the
benefits associated with knowledge about climate state, snow, and soil
moisture content associated with streamflow predictability. Some
specifics from each of these studies are provided below and summarized
in Table 3. Due to the close relationship between water resource
management in streams and energy generation, most of these benefit
estimates are also presented in the previous section on the energy
sector.

Hamlet et al. (2002) estimated that recent advances in climate
forecasting, especially those associated with ENSO and PDO have enabled
forecasters to predict streamflows in the Columbia River basin six
months earlier than forecasts that rely on actual snowpack measurements.
This allows more spot market energy sales to be made in late summer and
fall. These sales are estimated to increase annual revenues from
Columbia River basin electricity sales by approximately \$161 million
per year, in 2004 dollars (additional discussion also presented in the
energy sector description above). The authors note that, under their
model assumptions, other uses in the Columbia River, such as meeting
streamflow targets for salmon protection, and irrigation water would not
change.

In the Salt River Project of Central Arizona, El Niño-related climate
information allowed reservoir managers to make long-lead decisions
regarding the need for groundwater pumping to augment reservoir water
supplies that saved the project \$1 million (Pagano et al., 2001). The
El Niño-related climate information predicted the absence of January
precipitation followed by late season rains that would increase
streamflows. Under normal operations, anticipated precipitation is not
realized by March 1, groundwater would be pumped to augment reservoir
supplies for increased streamflows during the dry period. The long-lead
forecast for future rains in the late season allowed the managers to
avert the expensive contingency plan of pumping groundwater. This
example illustrates how advanced knowledge of climatic conditions can
help reservoir and water managers prepare for various changes in
climatic conditions and reduce the potential economic costs of regional
changes in water availability and potential costs of lost production by
adapting to the changing conditions in advance.

Pagano et al. (2001) observed that although climate forecasts are a
useful tool for managers, the operational water management community
currently underutilizes the forecasts. This is probably also true for
other resource managers as well. To remedy this situation they
suggest 1) the formation of closer relationships between forecasters and
users, 2) introducing forecast use demonstration projects, 3) increasing
institutional flexibility, and 4) creating a regional forum to
facilitate information transfer between the hydro-climatic research
community and operational water managers. These recommendations would
also be useful for other resource managers as well.

Yeh et al. (1982) estimated the potential benefits of long-range (one
month to one year) streamflow forecasts for the operation of the
Oroville-Thermalito reservoir system of the California State Water
Project. Benefits included hydropower generation, water conservation for
irrigation and other beneficial uses, and decreased seepage damage to
crops. As mentioned in the previous section, the hydropower benefits
were estimated between \$0.4-0.8 million in 2004 dollars. Water
conservation benefits were estimated at 22-24 percent increase in flow
releases or approximately 200,000 acre-feet of water. The value of this
water savings will depend on the beneficial use for which it will go to.
It may go to a low valued crop, a high valued crop, or in-stream flows
for fishing or recreation. Each of these uses will have different
economic values associated with them. However, using a relatively low
value of water to agriculture of \$50 per acre foot, this annual water
savings from one project would amount to \$10 million per year. Another
less tangible benefit is the reduction in crop and orchard damage from
seepage that occurs whenever streamflows above 8,500 CFS are sustained
for several days.

6.  **Fisheries/Marine**

Few studies of the value of forecasts to the commercial fishing industry
have been conducted that estimate the value of weather forecasts, even
though short term forecasts are important to safety, and long-term
forecasts could enhance fishery management decisions. Two such examples
of fishery management benefits are presented in Table 4. One gives an
estimate of the value of forecasts to a specific fish species (Costello
et al., 1998), while the other estimates the value of forecasts to the
entire US fisheries sector (NOAA, 2003).

Costello et al. (1998) estimated the value of improved El Niño forecasts
in the management of salmon in the Pacific Northwest. This study
estimated the value of El Niño forecasts in terms of annual welfare
gains obtained from changes in management of the fishery. Through the
use of more accurate El Niño forecasts that provide information about
ocean conditions within the salmon fishery, salmon fisheries managers
can make less conservative fishery management decisions, allowing for
larger harvests, and larger hatchery releases. They can also lessen the
restrictions on wild fish escapement. Both of these factors can lead to
increases in producer and consumer surpluses. For example, the authors
found that a perfect El Niño forecast resulted in an annual welfare gain
of approximately \$1 million, and an imperfect forecast was valued at
approximately \$0.4 million, or approximately 4% of the value from
salmon landings in the Pacific Northwest.

In a draft report by NOAA (2003), value of improvements in NOAA's
satellite imager and sounder is estimated for the US fisheries sector.
In particular, improvements in the satellite imager and sounder will
provide temperature boundaries where many species congregate and provide
this data at high spatial resolution with frequent coverage of the same
locations. This information can in turn be used to improve the accuracy
of biomass survey results. It is believed that with more accurate
population estimates and more targeted management practices, there can
be an increase in catch while still protecting stocks and endangered
species. Their case study estimated that this could increase the catch
weight of commercial landings by 1/100 of 1 percent, a value of nearly
\$3.2 million, or approximately 0.10 % of the value of landings for all
US fisheries.

7.  **Forestry**

Meteorological information, such as historical weather and climatic
patterns can be very useful to the forestry sector. For example,
researchers at the US Forest Service's Pacific Northwest Research
Station are currently producing maps linking weather conditions and fire
occurrence since 1895, and using this information to develop 3- to
12-month fire forecasts. These long-range predictions will allow
agencies to better anticipate firefighting needs, and improve fire
preparedness. Similarly, researchers at the Southern Research Station
are looking at the relationships between major climatic patterns and
various environmental indicators of severe wildfire conditions. The
information generated will help managers in wildfire preparedness
efforts and will also be useful for the management of prescribed fires.
The economic value of this type of information, however, has yet to be
evaluated[^1].

Knowledge about future climatic conditions is valuable because it allows
timber managers to adapt to future growing conditions by shifting wood
processing capital to regions with economic advantages in timber growth,
and by planting new species that are adapted to the new climate
conditions in various regions. Contrary to the situation with the value
of short-term weather forecasts, there are several publications in the
biological and social science literature that have estimated the
ecological and economic impacts of future climatic conditions on the
forestry sector. Studies by Aber et al. (2001), Dale et al. (2001),
Hansen et al. (2001), and McNulty and Aber (2001), have suggested that
climate change will have significant affects on the distribution,
condition, species composition and productivity of forests. Information
regarding expected climatic conditions allows for adjustments in the
forest sector, such as interregional migration of production,
substitution in consumption, and altered stand management, that can
mitigate many of the negative economic impacts of climate change (McCarl
et al., 2000).

It is generally believed that future climate change will result in
increases in CO~2~ levels. This increase in CO~2~ levels, along with
regional adjustments in the forestry sector are expected to boost forest
productivity by 20 percent (Aver et al., 2001). Studies by Joyce et al.
(1995), Perez-Garcia et al. (1997), Burton et al. (1998), and Sohngen
and Mendelsohn (1998), each predict that the climate change related
increases in forest inventories will result in a decrease in timber
prices. Due to the inelasticity of demand (insensitivety to price) for
timber products, this increase in forest productivity translates into a
slight negative economic impact for producers and a more positive impact
for consumers, resulting in a positive net economic welfare effect (Van
Kooten et al., 1990; Perez-Garcia et al., 1997; Sohngen and Mendelsohn,
1998; and Alig et al., 2002). Sohngen and Alig (2000) expect this
welfare effect to range between \$1.9 and \$10.7 billion per year (in
2004 US dollars), by the middle of the next century (Sohngen and Alig,
2000). This represents approximately 2-9% of GDP from lumber and wood
products and paper and allied products (Table 5).

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8.  **Human Health and Social Services, Disaster Management**

We have nothing specific to add to this section.

9.  **Policy**

We have nothing specific to add to this section.

10. **Transportation (air, land, water)**

The transportation sector is another sector that benefits greatly from
climate and weather forecasts, because these forecasts can help minimize
traffic delays. The air transportation sector is particularly vulnerable
to weather induced traffic delays because bad weather in cities with
major air transport hubs can have impacts that affect the whole system.
The Air Transport Association estimates that the direct cost to airlines
of flight delays (either mechanical or weather induced­) exceeds \$3.2
billion annually, in 2004 dollars (Hayhurst, 2001). Though the benefit
studies presented below estimate the benefits from short-term weather
forecasts rather than long-term forecasts, they do indicate that weather
forecasts are valuable to the transportation sector. Also information
from such short term weather forecasts can be used to determine
long-range weather patterns, which in turn, could be used to make
management decisions regarding seasonal transportation routs.

Most of the recent studies that estimate the value of weather services
to the transportation sector have been for air transportation. For
example, studies by Paull (2001), and NOAA (2002) measured benefits from
reductions in accidents; studies by Allan et al. (2001), Evans et al.
(1999), NOAA (2002), Sunderlin and Paull (2001); and Rhoda and Weber
(1996) measured benefits from reductions in flight delays, and Leigh
(1995) measured benefits from reductions in fuel costs. Estimated
benefit values range between \$25 million and \$590 million per year.
This represents between 0.02% and 0.62% of the US Gross Domestic Product
from air transportation. There have been relatively few studies in the
economic literature that have evaluated the benefits to land and water
transportation. Three of which are discussed here (NOAA 2002, 2003
draft, and Smith and Vick, 1994). Details regarding the particulars of
each of these transportation studies are presented below, along with a
summary of these values presented in Table 6.

**Air transportation values**

Paull (2001) estimated the safety benefits of reducing the risk of
in-flight icing accidents, which are estimated at an annual cost of
\$106 million, or 0.1% of GDP from air transportation. Paull (2001)
estimated that air temperature and freezing point information used in a
icing diagnostic and forecast algorithms can reduce the risk of
in-flight accidents with an annual benefit of \$34 million. This
represents approximately 0.04% of US GDP from air transportation in the
U.S. However, the author also notes that these benefits will not be
fully realized if dissemination of the information to pilots is not done
in a timely manner.

NOAA (2002) estimated the benefits of improvements in the Geostationary
Operational Environmental Satellite System in terms of both accident
reductions (due to better detection of volcanic ash plumes), and
reductions in flight delays. Improvements in the satellite system in the
form of increased detail and accuracy of weather information, such as
intensity and rate of development of convective weather (thunderstorms),
will allow air traffic control to make more efficient use of air space,
thus reducing flight delays. The benefits were estimated at \$58 million
per year, representing approximately 0.06% of GDP from air
transportation. Most of these benefits (\$40 million) will come from
avoided delays, with the remainder coming from the avoidance of volcanic
ash plumes.

Similarly, benefits of the Terminal Convective Weather Forecast product
being developed as part of the Federal Aviation Administration Aviation
Weather Research Program were estimated by Sunderlin and Paull (2001).
Benefits were measured in terms of delay reductions at Dallas-Fort
Worth, Orlando, Jacksonville, and New York airports and extrapolated to
the national level. The improved weather forecasts and the associated
reductions in delays were estimated to have an annual value of \$580
million. This represents approximately 6% of U.S. weather delay costs
and 0.6% of Gross Domestic Product from air transportation. An earlier
report by Rhoda and Weber (1996) estimated that the benefits of delay
reductions from use of the Weather Systems Processor, which included the
ITWS, was \$25 million per year, based on expected year 2000 traffic
counts at planned WSP airports.

Alan et al. (2001) estimated the value of an Integrated Terminal Weather
System (ITWS) to New York City airports. Predictions of convective
storms were extended to include 30-60 minute predictions. These
predictions allowed traffic managers to reduce airport gridlock by
releasing additional departures each hour and correctly setting arrival
rates. This improved decision making capability resulted in more than
\$176 million in benefits per year, which is approximately 0.19% of US
GDP from air transportation.

In addition, a study by Evans et al. (1999) estimated the value of
terminal wind information based on improved merging and sequencing of
aircraft and the use of simultaneous operation with independent
approaches. They estimated the benefits from three West Coast airports
that are not currently scheduled to receive an Integrated Terminal
Weather System (ITWS). These annual benefits were projected to be worth
\$25.7 million at Los Angeles (LAX), \$16.7 million at Seattle (SEA),
and \$119 million at San Francisco (SFO), representing approximately
0.03% of US GDP from air transportation.

Improvements in terminal aerodrome forecast information were also
estimated for Quantas Airlines at the Sydney airport in Australia
(Leigh, 1995). Rather than estimating the value in terms of flight
delays however, Leigh (1995) estimated the value in terms of avoided
costs of carrying extra fuel. For example, Leigh estimated the value of
a 1% increase in accuracy to have an approximate value of \$11 million
per year (US 2004 dollars). This value was derived from the airline's
ability to avoid the cost of carrying extra fuel that may be required to
divert to alternate airports in bad weather conditions. Leigh also noted
that this was a conservative estimate because it excludes the value of
potential reductions in air pollution.

**Ground and water transportation values**

It is expected that the ground and water transportation industry will
also benefit from improved weather forecasts, but there are few extant
studies in the economic literature. Table 6 contains a summary of three
studies that estimated the value of weather forecasts on trucking,
shipping, and general transportation, all in 2004 US dollars.

Each study estimated the value of weather forecasts in terms of avoided
costs and increased efficiencies gained through improvements in weather
forecasts. For example, NOAA (2002) estimated that with improvements in
forecasts for short-term ice formation and fog conditions (with at least
two hours notice), the trucking industry in the US could save \$29
million per year by rerouting trucks, which is approximately 0.02% of
GDP from trucking and warehousing. Similarly, a draft report by NOAA
(2003) estimated that improvements in marine forecasts, which will allow
vessels to decrease transit times and reduce cargo losses due to severe
weather conditions throughout the year, could save the industry \$95
million per year, or approximately 0.66% of US GDP from water
transportation. In Scotland, a study by Smith and Vick (1994) estimated
that daily precipitation predictions from radar imagery could help
reduce road maintenance costs by \$35,000 per year in just one Regional
Council in Scotland by reducing the need for de-icing materials.

11. **Commerce, Industry, and Manufacturing**

We have nothing specific to add to this section.

12. **Tourism, Sports and Leisure**

Climatological services can potentially provide significant economic
benefits to the tourism, sports, and leisure sector. However, very few
studies in the economic literature have concentrated on estimating the
value of weather services to this sector. A draft report by NOAA (2003)
does however indicate that information from their geostationary
operational environmental satellite system (GOES), which allows for
intense monitoring of sea surface temperatures, can be valuable in the
management of recreational fisheries. For example, improvements in the
satellite imager and sounder will provide more frequent sea surface
temperature readings that will identify boundaries where many species
congregate. This information can in turn be used to improve the accuracy
of biomass survey results (see discussion of the draft report by NOAA
(2003) in the Fisheries/Marine section above). It is believed that the
improvements in biomass estimates for the commercial fishery, will allow
for more targeted management practices, and make it possible to increase
catch without harm to the populations. NOAA (2003) estimated the
increase in recreational catch from the Atlantic, Gulf, and Pacific
coasts, could result in a value to recreational fishing of \$0.41
million, representing an additional 0.0001% of US GDP from amusement and
recreation services.

13. **Finance, Insurance and Reinsurance**

We have nothing specific to add to this section.

14. **Weather Modification (Climate Change)**

Several studies have estimated the economic impacts of climate change,
many of which have been in the agricultural sector and have been
mentioned previously in section 1 on Agriculture. There have also been
several in the tourism sector, which we will concentrate on here.

As illustrated in Loomis and Crespi (1999), the impact of climate change
will have both positive and negative impacts on the tourism sector,
depending on the location and type of activity. For example, Loomis and
Crespi (1999), estimate that increases in CO~2~ will increase recreation
visitation days for beach recreation and stream recreation such as
fishing, kayaking, and rafting. However, it is expected to decrease
visitation days for forest-based recreation, such as, camping, hiking,
picnicking, and downhill and cross-country skiing.

Event hough Loomis and Crespi (1999) expect fishing recreation days to
increase, in some regions where specific types of cold water fisheries
are susceptible to climate change, there will be a negative impact from
climate change. For example, a study of the recreational trout fishery
in the Appalachian Mountains region of North Carolina estimated that
climate change would severely impact cold water trout fisheries in the
region resulting in annual economic losses of \$61-584 million (in 1995
dollars), or \$75-722 million in 2004 dollars (Ahn et al. 2000).

The economic impact of climate change for winter tourism, and the ski
industry in particular has been estimated for several countries around
the world (Australia: Galloway 1998, Konig 1998; Austria: Breiling et
al. 1997; Canada: McBoyle and Wall 1992, Lamothe and Periard 1988;
Scotland: Harrison et al. 1999; Switzerland: Konig and Abegg 1997,
Elasser and Burki 2002; United States: Bloomfield and Hamburg 1997). All
of these studies projected negative consequences for the industry (Scott
2003). In Australia for example, which is the most tourism dependant
developed country in the world and where 50% of the country's tourism
income comes in the winter, the warming trend between 1960 and 1990,
caused the loss of 15 winter sport season days each year (Breiling,
1998). These lost winter sport days cost the country an estimated 1.5%
drop in GNP, and as much as a 6% drop in gross local product (Breiling,
1998).

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Such losses however, can be mitigated by adapting to climate change. The
economic impact of such changes will vary greatly depending on each
regions willingness and ability to adapt to long term changes in weather
patterns.

**References**

Abawi, G.Y., R.J. Smith, and D.K. Brady. "Assessment of the value of
long range weather forecasts in wheat harvest management." Journal of
Agricultural Engineering Resources, 62:39-48, 1995.

Aber, J., R. P. Neilson, S. McNulty, J. M. Lenihan, D. Bachelet, and R.
J. Drapek. "[Forest Processes and Global Environmental Changes:
Predicting the Effects of Individual and Multiple
Stressors.](http://www.meteor.iastate.edu/gccourse/issues/forestry/bioone3.pdf)"
BioScience, 51:735-752, 2001.

Adams, R., L. Houston, and R. Weiher. "The Value of Snow and Snow
Information Services." Report prepared for NOAA's National Operational
Hydrological Remote Sensing Center, Chanhassen, MN, under contract
DG1330-03-SE-1097, 2004.

Adams, Richard M., Laurie L. Houston, Bruce A. McCarl, Mario Tiscareno
L., Jaime Matus G., Rodney F. Weiher. "The Benefits to Mexican
agriculture of an El Niño Southern Oscillation (ENSO) Early Warning
System." Agricultural and Forest Meteorology, 115:183-194, 2003.

Adams, Richard M., Chi-Chung Chen, Bruce A. McCarl, and Rodney F.
Weiher. "The

Economic Consequences of ENSO Events for Agriculture." Climate Research,
13:165-172, 1999.

Adams, R.M., K.J. Bryant, B.A. McCarl, D.M. Legler, J.J. O'Brien, A.R.
Solow, and R. Weiher. "Value of Improved Long-Range Weather
Information." Contemporary Economic Policy, 13:10-19, 1995.

Ahn, S. De Steiguer, J. Palmquist, R. and Holmes, T. "Economic analysis
of the

potential impact of climate change on recreational trout fishing in the
southern Appalachian Mountains." Climate Change, 45:493-509, 2000.

Alan, S.S., S.G. Gaddy, and J.E. Evans. "Delay Causality and Reduction
at the New York City Airports Using Terminal Weather Information
Systems." Lexington, MA:MIT Lincoln Laboratory. 2001.

Alig, R.J., D.M. Adams, B.A. McCarl. "Projecting Impacts of Global
Climate Change on the US Forest and Agriculture Sectors and Carbon
Budgets." Forest Ecology and Management, 169:3-14, 2002.

Bloomfield J., and S. Hamburg. "Seasons of change: global warming and
New England's White Mountains." Washington D.C.: Environmental Defense
Fund. 1997.

Burton, D.M., B.A. McCarl, N.M. De Sousa, D.M. Adams, R.A. Alig, S.M.
Winnett. "Economic Dimensions of Climate Change Impacts on Southern
Forests." The Productivity and Sustainability of Southern Forest
Ecosystems in a Changing Environment. Mickler, R., and S. Fox, eds., pp
777-794. New York: Springer Press, 1997.

Breiling M, P. Charamza, and O. Skago. "Klimasensibilitat
osterreichischer bezirke mit besonderer berucksichtigung des
wintertourismus." Report 97:1. Institute for Landscape Planning, Alnarp,
Austria, 1997.

Chen, C.-C., B. McCarl, and H.Hill. "Agricultural value of ENSO
information under alternative phase definition." Climatic Change,
54:305-325, 2002.

Chen, Chi-Chung, Bruce A. McCarl, and Richard M. Adams. "Economic
Implications of Potential ENSO Frequency and Strength Shifts." Climatic
Change, 49:147-159, 2001.

Costello, C.J., R.M. Adams, S. Polasky. "The Value of El Niño Forecasts
in the Management of Salmon: A Stochastic Dynamic Assessment." American
Journal of Agricultural Economics, 80:765-777, 1998.

### Dale, Virginia H.; Joyce, Linda A.; McNulty, Steve; Neilson, Ronald P.; Ayres, Matthew P.; Flannigan, Michael D.; Hanson, Paul J.; Irland, Lloyd C.; Lugo, Ariel E.; Peterson, Chris J.; Simberloff, Daniel; Swanson, Frederick J.; Stocks, Brian J.; Wotton, Michael. "Climate Change and Forest Disturbances." BioScience, 51(9):723-734, 2001.

Evans, J.E., T.J. Dacey, D.A. Rhoda, R.E. Cole, F.W. Wilson, E.R.
Williams. "Weather Sensing and Data Fusion to Improve Safety and Reduce
Delays at Major West Coast Airports." Lexington, MA:MIT Lincoln
Laboratory. 1999.

Galloway, R.W. "The potential impact of climate changes on Australian
ski fields." In: Pearman Gl (ed) Greenhouse: Planning for climatic
change. CSIRO, Melbourne, Australia, 428-437, 1988.

Hamlet, Alan F., Daniel Huppert, and Dennis P. Lettenmaier. 2002.
"Economic Value of Long-Lead Streamflow Forecasts for Columbia River
Hydropower." Journal of Water Resources Planning and Management, 91:101,
March/April 2002.

Hansen, Andrew J.; Neilson, Ronald P.; Dale, Virginia H.; Flather,
Curtis H.; Iverson, Louis R.; Currie, David J.; Shafer, Sarah; Cook,
Rosamonde; Bartlein, Partick J. "Global Change in Forests: Responses of
Species, Communities, and Biomes" BioScience, 51(9): 765-779, 2001.

Harrison, S.J., S.J. Winterbottom, C. Sheppard. "The potential effects
of climate change on the Scottish tourist industry." Tourism Management,
20:203-211, 1999.

Hayhurst, John. Air Traffic Management News Room and Resources,
"Developing the Next Generation Air Traffic Management System."
Testimony of John Hayhurst President, Air Traffic Management, The Boeing
Company before the U.S. House of Representatives Committee on Science
Space and Aeronautics Subcommittee July 19, 2001.
http://www.boeing.com/atm/resources/testimony.html, accessed June 23,
2004.

Hill, H.S.J., J.W. Mjelde, W. Rosenthal, and P.J. Lamb. "The Potential
Impacts of the Use of Southern Oscillation Information on the Texas
Aggregate Sorghum Production." Journal of Climate, 12:519-530, 1999.

Hill, H.S.J., J. Park, J.W. Mjelde, W. Rosenthal, H.A. Love, and S.W.
Fuller. "Comparing the value of Southern Oscillation Index-Based Climate
Forecast Methods for Canadian and US Wheat Producers." Agricultural
Forest Meteorology, 100:261-272, 2000.

Houston, Laurie L. Richard M. Adams, and Rodney F. Weiher. "The Economic
Benefits of Weather Forecasts: Implications for Investments in
Rusohydromet Services." Report prepared for the World Bank under NOAA
contract OG 1330-04-SE-0052, 2004.

Joyce, L.A., J.R. Mills, L.S. Health, A.D. McGuire, R.W. Haynes, R.A.
Birdsey. "Forest Sector Impacts from Changes in Forest Productivity
under Climate Change." Journal of Biogeography, 22:703-713, 1995.

Katz, R.W. and A.H. Murphy, Economic Value of Weather and Climate
Forecasts. Cambridge University Press, 222 pp, 1997.

Konig, U. "Tourism in a warmer world: implications of climate change due
to enhanced greenhouse effect for the ski industry in the Australian
Alps." Wirtschaftsgeorgraphie und Raumplanung, Vol. 28, University of
Zurich, Zurich, Switzerland, 1998.

Konig, U. and B. Abegg. "Impacts of climate change on tourism in the
Swiss Alps." Journal of Sustainable Tourism, 1:46-58, 1997.

Leigh, R.J. "Economic Benefits of Terminal Aerodrome Forecasts (TAFs)
for Sydney Airport, Australia." Meteorological Applications, 2:239-247,
1995.

Loomis, J. and J. Crespi. Estimated Effects of Climate Change on
Selected Outdoor Recreation Activities in the United States." In
Mendelsohn, R., Neumann J.E., eds. The Impact of Climate Change on the
United States Economy. Cambridge (UK): Cambridge

Maurer, E. P. and D. P Lettenmaier. "Potential Effects of Long Lead
Hydrologic Predictability on Missouri River Main-Stem Reservoirs."
Journal of Climate, 17(1): 174-188, 2004.

McBoyle, G.R. and G. Wall. "Great lakes skiing and climate change." In:
Gill, A. and R. Hartmann (eds) Mountain resort development. Centre for
Tourism Policy and Research, Simon Fraser University, Burnaby, Canada,
pp 70-81, 1992.

McNulty, Steven G.; Aber, John D. "U.S. National Climate Change
Assessment on Forest Ecosystems: An Introduction." BioScience, 51(9):
720-722, 2001.

Messina, C.D., J.W. Hansen, and A.J. Hall. "Land Allocation Conditioned
on ENSO Phases in the Pampas of Argentina." Agricultural Systems,
60:197-212, 1999.

Mjelde, J.W. and J.B. Penson. "Dynamic Aspects of the Impact of the use
of Perfect Climate Forecasts in the Corn Belt Region." Journal of
Applied Meteorology, 39:67-79, 2000.

Mjelde, J.W., T.N. Thompson, and C.J. Nixon. "Government Institutional
Effects on the Value of Seasonal Climate Forecasts." American Journal of
Agricultural Economics. 78:175-188, 1996.

Mjelde, J.W. Thompson, F.M. Hons, J.T. Cothren and C.G. Coffman. "Using
Southern Oscillation Information for Determining Corn and Sorghum
Profit-Maximizing Input Levels in East-Central Texas." Journal of
Production Agriculture, 10:168-175, 1997.

Nicholls, J.M. "Economic and Social Benefits of Climatological
Information and Services: a Review of Existing Assessments." World
Meteorological Organization, Geneva, Switzerland. WMO/TD-No. 780. 38 pp.
1996.

NOAA, Geostationary Operational Environmental Satellite System (GEOS)
GOES-R Sounder and Imager Cost/Benefit Analysis (CBA) -- Phase III.
Prepared for the Department of Commerce by National Oceanic and
Atmospheric Administration (NOAA), National Environmental Satellite,
Data, and Information Service (NESDIS), Office of Systems Development.
2003 Draft.

NOAA, Geostationary Operational Environmental Satellite System (GEOS)
GOES-R Sounder and Imager Cost/Benefit Analysis (CBA). Prepared for the
Department of Commerce by National Oceanic and Atmospheric
Administration (NOAA), National Environmental Satellite, Data, and
Information Service (NESDIS), Office of Systems Development. November
15, 2002.

NOAA, 2001. "Economic Value for the Nation: NOAA's National
Environmental Satellite Data and Information Service." Silver Spring,
MD. September 2001.

Pagano, Thomas C., Holly C. Hartmann, and Soroosh Sorooshian. "Using
Climate Forecasts for Water Management." American Water Resources
Association,
7(5):[1139-1153](http://www.awra.org/jawra/toc/toc_37_5.html), 2001.

Paull, G. "Integrated Icing Diagnostic Algorithm (IIDA) Safety Benefits
Analysis Accident Case Studies." Prepared for the FAA by MCR Federal,
Inc. Bedford. MA. 2001

Perez-Garcia, J. L.A. Joyce, A.D. McGuire, C.S. Binkley. "Economic
Impact of Climatic Change on the Global Forest Sector. In: R.A. Sedjo,
R.N Sampson, and J. Wisniewski (eds) Economics of Carbon Sequestration
in Forestry. :ewis Publishers, Boca Raton, FL. 1997.

Pielke, Jr., Roger A., "Who Decides? Forecast and Responsibilities in
the 1997 Red River Flood." Applied Behavioral Science Review,
7(2):83-101, 1999.

Rhoda, D.A. and Me. Weber. "Assessment of Delya Aversion Benefits of the
Airport Surveillance Radar (ASR) Weather Systems Processor (WSP) --
Project Report." Lexington, MA:MIT Lincoln Laboratory. 1996.

Scott, Daniel. "Climate change and tourism in the mountain regions of
North America." Presented at the 1^st^ International Conference on
Climate Change and Tourism, Djerbga, Tunisia, 9-11, April 2003.

Smith, K. and Vick, S.D. "Valuing Weather Radar Benefits for Winter Road
Maintenance: A Practical Case Example." Meteorological Applications,
1:173-177, 1994.

Sohngen, Brent and R. Alig. "Mitigation, Adaptation, and Climate Change:
Results from Recent Research on US Timber Markets." Environmental
Science and Policy, 3:235-248, 2000.

Sohngen, R., and R. Mendelsohn. "Valuing the Market IImpact of Large
Scale Ecological Change in a Market: the Effect fo Climate Change on US
Timber." American Economic Review, 88:686-710, 1998.

Solow, Andrew R., Richard M Adams, Kelly J. Bryant, David M. Legler,
James J. O'Brien, Bruce A. McCarl, William Nayda, and Rodney Weiher.
"The Value of Improved ENSO Prediction to U.S. Agriculture." Climatic
Change, 39: 47-60, 1998.

Stratus Consulting Inc., "Benefit Analysis for National Oceanic and
Atmospheric Administration High-Performance Computing System for
Research Applications." Report Prepared by Lazo, J.K., M.L. Hagenstad,
K.P. Cooney, J.L. Henderson, and J.S. Rice for: Joan M. Brundage, Chief
Information Officer, National Oceanic and Atmospheric
Administration/Office of Oceanic and Atmospheric Research, Aeronomy
Laboratory. Stratus Consulting Inc, Boulder, CO. 2003.

Sunderlin, J. and G. Paull. "FAA Terminal convective Weather Forecast
Benefits analysis." Prepared for the FAA by MCR Federal, Inc. Bedford,
MA, 2001.

Todhunter, Paul E. "A Hydroclimatological Analysis of the Red River of
the North Snowmelt Flood Catastrophe of 1997." Journal of the American
Water Resources Association, 37(5):1263-1278, 2001.

Van Kooten, G.C., C.S. Binkley, G. Delcourt "Effect of Carbon Taxes and
Subsidies on Optimal Forest Rotation Age, and Supply of Carbon
Services." American Journal of Agricultural Economics, 77(5):365-374,
1995.

Yeh, William W-G., Leonard Becker, and Robert Zettlemoyer. "Worth of
Inflow Forecast for Reservoir Operation." Journal of Water Resources
Planning and Management Division, ASCE, October 1982, 108-WR3:257-269,
1982.

+-----------------+--------------------------+----------+--------------+
| **Table 1.      |                          |          |              |
| Agriculture:    |                          |          |              |
| value of        |                          |          |              |
| long-term       |                          |          |              |
| weather         |                          |          |              |
| forecasts**     |                          |          |              |
+-----------------+--------------------------+----------+--------------+
| **Type of       | **Value of Weather       | **% of   | **Source**   |
| Information**   | Information**            | Sector** |              |
|                 |                          |          |              |
|                 | **(2004 US dollars)**    |          |              |
+-----------------+--------------------------+----------+--------------+
| Precipitation   | Imperfect: \$11/ha-yr    |          | Abawi et al. |
| forecasts       |                          |          | (1995)\*     |
|                 | perfect: \$19/ha-yr      |          |              |
|                 | derived from             |          |              |
|                 | implementing wheat       |          |              |
|                 | harvest strategies such  |          |              |
|                 | as early harvesting,     |          |              |
|                 | drying, and contract     |          |              |
|                 | harvesting               |          |              |
+-----------------+--------------------------+----------+--------------+
| ENSO early      | Imperfect: \$20-31       | 0.3-2%   | Adams et al. |
| warning system  | million/yr               | of crop  | (2003)       |
|                 |                          | pr       |              |
|                 | Perfect: \$59-79         | oduction |              |
|                 | million/yr from 5        | value in |              |
|                 | important agricultural   | the 5    |              |
|                 | states in Mexico         | states   |              |
|                 | (excluding livestock     |          |              |
|                 | production)              |          |              |
+-----------------+--------------------------+----------+--------------+
| ENSO            | Imperfect: \$168         | 1-2% of  | Adams et al. |
| predictions     | million/yr               | net      | (1995)\*     |
|                 |                          | income   |              |
|                 | Perfect: \$254           | of U.S.  |              |
|                 | million/yr from          | farmers  |              |
|                 | Southeast U.S.           |          |              |
|                 | agricultural region      |          |              |
|                 | (including livestock)    |          |              |
+-----------------+--------------------------+----------+--------------+
| Precipitation   | Imperfect: \$848-\$2,276 |          | Bowman et    |
| and temperature |                          |          | al. (1995)\* |
| forecasts       | Perfect: \$1,314-\$2,800 |          |              |
|                 | derived from wool        |          |              |
|                 | producers in Victoria    |          |              |
|                 | Australia                |          |              |
+-----------------+--------------------------+----------+--------------+
| Changes in ENSO | \$482-\$592 million per  |          | Chen et al.  |
| frequency and   | year from US             |          | (2001)       |
| strength        | agriculture's use of an  |          |              |
|                 | ENSO monitoring and      |          |              |
|                 | early warning system,    |          |              |
|                 | depending on frequency   |          |              |
|                 | and strengths of ENSO    |          |              |
|                 | events.                  |          |              |
+-----------------+--------------------------+----------+--------------+
| ENSO            | Imperfect:               | Up to 2% | Chen et al.  |
| predictions     | \$507-\$959/yr million   | of GDP   | (2002)\*     |
|                 |                          | from US  |              |
|                 | Perfect: \$1,768         | agr      |              |
|                 | million/yr from US       | iculture |              |
|                 | agriculture (including   |          |              |
|                 | livestock and secondary  |          |              |
|                 | products)                |          |              |
+-----------------+--------------------------+----------+--------------+
| Southern        | Imperfect: \$178 million |          | Harrison and |
| African         |                          |          | Graham       |
| seasonal        | Perfect:\$0.72 billion   |          | (2001)       |
| forecasts for   |                          |          |              |
| drought         | (value of forecast to an |          |              |
|                 | international aid        |          |              |
|                 | organization)            |          |              |
+-----------------+--------------------------+----------+--------------+
| Precipitation,  | Imperfect                |          | Hill et al.  |
| temperature,    | \$0-\$102/ha-yr from     |          | (1999)\*     |
| and radiation   | Texas sorghum producers  |          |              |
| forecasts       |                          |          |              |
+-----------------+--------------------------+----------+--------------+
| Precipitation,  | Imperfect: \$0-11/ha-yr  |          | Hill et al.  |
| temperature,    |                          |          | (2000)\*     |
| and radiation   | Perfect:\$10-57/ha-yr    |          |              |
| forecasts       | from planning fertilizer |          |              |
|                 | applications on US and   |          |              |
|                 | Canadian wheat fields    |          |              |
+-----------------+--------------------------+----------+--------------+
| Precipitation   | Imperfect:               |          | Jochec et    |
| and temperature | \$-159-\$5/section-yr    |          | al. (2001)\* |
| forecasts       |                          |          |              |
|                 | Perf                     |          |              |
|                 | ect:\$-49-129/section-yr |          |              |
|                 | from livestock ranchers  |          |              |
|                 | in Texas                 |          |              |
+-----------------+--------------------------+----------+--------------+
| El Nino         | Perfect: \$272 million   |          | McNew (1999) |
| forecasts       | from reduced costs of    |          |              |
|                 | stockpiling corn.        |          |              |
+-----------------+--------------------------+----------+--------------+
| Maximum and     | Imperfect \$6-\$17/ha-yr |          | Messina et   |
| minimum         | from corn, soybean,      |          | al. (1999)\* |
| temperature,    | sunflower, and wheat     |          |              |
| precipitation,  | producers in Argentina   |          |              |
| and radiation   |                          |          |              |
| forecasts       |                          |          |              |
+-----------------+--------------------------+----------+--------------+
| Precipitation,  | Perfect: \$1.4-\$3.2     |          | Mjelde and   |
| temperature,    | billion over 10 years    |          | Penson       |
| and radiation   | from making fertilizer   |          | (2000)\*     |
| forecasts       | application decisions in |          |              |
|                 | the Corn Belt region     |          |              |
+-----------------+--------------------------+----------+--------------+
| Precipitation   | Imperfect:               |          | Mjelde et    |
| forecasts       | \$1,170-14,520/farm      |          | al. (1996)\* |
|                 |                          |          |              |
|                 | Perfect: \$19,900/farm   |          |              |
|                 | from crop type, nitrogen |          |              |
|                 | application, Federal     |          |              |
|                 | Farm Program             |          |              |
|                 | participation, and crop  |          |              |
|                 | insurance decisions      |          |              |
+-----------------+--------------------------+----------+--------------+
| Precipitation   | Imperfect:               |          | Mjelde et    |
| forecasts       | \$1.2-2.3/acre from      |          | al. (1997)\* |
|                 | fertilizer application   |          |              |
|                 | level, planting date and |          |              |
|                 | seeding rate decisions   |          |              |
+-----------------+--------------------------+----------+--------------+
| ENSO            | Imperfect: \$297-\$329   | 1-2% of  | Solow et al. |
| predictions     | million/yr               | net      | (1998)\*     |
|                 |                          | income   |              |
|                 | Perfect: \$400           | of U.S.  |              |
|                 | million/yr from US       | farmers  |              |
|                 | agriculture              |          |              |
+-----------------+--------------------------+----------+--------------+
| \*Values were   |                          |          |              |
| obtained from   |                          |          |              |
| Exhibit A.2. of |                          |          |              |
| Stratus         |                          |          |              |
| Consulting      |                          |          |              |
| report by Lazo  |                          |          |              |
| et al. 2003.    |                          |          |              |
|                 |                          |          |              |
| Note: For ease  |                          |          |              |
| of comparison,  |                          |          |              |
| all values were |                          |          |              |
| converted to    |                          |          |              |
| 2004 US dollars |                          |          |              |
| using the CPI   |                          |          |              |
| from the        |                          |          |              |
| Economic        |                          |          |              |
| Research        |                          |          |              |
| Service and     |                          |          |              |
| historical      |                          |          |              |
| exchange rates  |                          |          |              |
| from            |                          |          |              |
| http://ww       |                          |          |              |
| w.x-rates.com/. |                          |          |              |
+-----------------+--------------------------+----------+--------------+

+-----------------+----------------------+------------+--------------+
| **Table 2.      |                      |            |              |
| Energy: value   |                      |            |              |
| of weather      |                      |            |              |
| forecasts**     |                      |            |              |
+-----------------+----------------------+------------+--------------+
| **Type of       | **Value of Weather   | **% of     | **Source**   |
| Weather         | Information**        | Sector     |              |
| Information**   |                      | Value**    |              |
|                 | **(2004 US           |            |              |
|                 | dollars)**           |            |              |
+-----------------+----------------------+------------+--------------+
| Improved        | \$161 million/year   | 0.075% of  | Hamlet et    |
| long-range      | derived from more    | GDP from   | al. (2002)   |
| weather         | efficient reservoir  | US         |              |
| forecasts of    | operations in the    | electric,  |              |
| ENSO and PDO    | Columbia River Basin | gas, and   |              |
|                 | and hydropower sales | sanitary   |              |
|                 | on spot markets      | services   |              |
+-----------------+----------------------+------------+--------------+
| Improved        | \$6.8 million in     | 0.003% of  | Maurer and   |
| streamflow      | hydropower benefits  | GDP from   | Lettenmaier  |
| forecasts based | in the Missouri      | US         | (2004)       |
| on improved     | River Basin          | electric,  |              |
| knowledge of    |                      | gas, and   |              |
| links between   |                      | sanitary   |              |
| tropical sea    |                      | services   |              |
| surface temps   |                      |            |              |
| and regional    |                      |            |              |
| climate, snow,  |                      |            |              |
| and soil        |                      |            |              |
| moisture        |                      |            |              |
| conditions      |                      |            |              |
+-----------------+----------------------+------------+--------------+
| Precipitation   | \$0.4-0.8 million in | 0.04% of   | Yeh et al.   |
| data relating   | hydropower benefits  | CA GDP     | (1982)       |
| to long-range   |                      | from US    |              |
| (one month to   |                      | electric,  |              |
| one year)       |                      | gas, and   |              |
| streamflow      |                      | sanitary   |              |
| forecasts       |                      | services   |              |
+-----------------+----------------------+------------+--------------+
| Note: For ease  |                      |            |              |
| of comparison,  |                      |            |              |
| all values are  |                      |            |              |
| converted to    |                      |            |              |
| 2004 US dollars |                      |            |              |
| using the CPI   |                      |            |              |
| from the        |                      |            |              |
| Economic        |                      |            |              |
| Research        |                      |            |              |
| Service. GDP    |                      |            |              |
| percents are    |                      |            |              |
| based on 1999   |                      |            |              |
| GDP figures     |                      |            |              |
| from U.S.       |                      |            |              |
| Census Bureau,  |                      |            |              |
| Statistical     |                      |            |              |
| Abstract of the |                      |            |              |
| United States:  |                      |            |              |
| 2001, p. 418    |                      |            |              |
+-----------------+----------------------+------------+--------------+

  ------------------ ----------------------- ------------- ---------------
  **Table 3. Water                                         
  Resource                                                 
  Management: value                                        
  of weather                                               
  forecasts**                                              

  **Type of Weather  **Value of Weather      **% of Sector **Source**
  Information**      Information**           Value**       

  Improved           \$161 million/year      No sector     Hamlet et al.
  long-range weather derived from more       value         (2002)
  forecasts of ENSO  efficient reservoir     available for 
  and PDO            operations in the       water         
                     Columbia River Basin    resources     
                     and hydropower sales on               
                     spot markets                          

  El Nino related    \$ 1 million from       No sector     Pagano et al.
  climate            avoiding groundwater    value         (2001)
  information        pumping to augment      available for 
  related to         reservoir supplies      water         
  precipitation                              resources     
  forecasts                                                

  Precipitation data 2000 AF of water,       No sector     Yeh et al.
  relating to        dollar value varies     value         (1982)
  long-range (one    with use. Assuming a    available for 
  month to one year) \$50/AF would yield an  water         
  streamflow         annual benefit of \$10  resources     
  forecasts          million                               

  Note: For ease of                                        
  comparison, all                                          
  values are                                               
  converted to 2004                                        
  US dollars using                                         
  the CPI from the                                         
  Economic Research                                        
  Service.                                                 
  ------------------ ----------------------- ------------- ---------------

+------------------+----------------------+------------+--------------+
| **Table 4.       |                      |            |              |
| F                |                      |            |              |
| isheries/Marine: |                      |            |              |
| Value of weather |                      |            |              |
| forecasts**      |                      |            |              |
+------------------+----------------------+------------+--------------+
| **Type of        | **Value of Weather   | **% of     | **Source**   |
| Weather          | Information**        | Sector     |              |
| Information**    |                      | Value**    |              |
+------------------+----------------------+------------+--------------+
| Improved El Nino | Perfect forecast:    | app        | Costello et  |
| forecasts        | \$1 million          | roximately | al. (1998)   |
|                  |                      | 4% of the  |              |
|                  | Imperfect forecast:  | value from |              |
|                  | \$0.4 million to the | salmon     |              |
|                  | salmon fishery in    | landings   |              |
|                  | the Pacific          | in the     |              |
|                  | Northwest            | Pacific    |              |
|                  |                      | Northwest  |              |
+------------------+----------------------+------------+--------------+
| Improved         | \$3 million from     | app        | NOAA, (2003  |
| satellite imager | increased fish       | roximately | Draft)       |
| and sounder      | landings             | 0.10 % of  |              |
| which will       |                      | the value  |              |
| provide sea      |                      | of         |              |
| temperature data |                      | landings   |              |
| and increase     |                      | for all US |              |
| accuracy of      |                      | fisheries  |              |
| biomass survey   |                      |            |              |
| results          |                      |            |              |
+------------------+----------------------+------------+--------------+
| Note: For ease   |                      |            |              |
| of comparison,   |                      |            |              |
| all values are   |                      |            |              |
| converted to     |                      |            |              |
| 2004 US dollars  |                      |            |              |
| using the CPI    |                      |            |              |
| from the         |                      |            |              |
| Economic         |                      |            |              |
| Research         |                      |            |              |
| Service.         |                      |            |              |
+------------------+----------------------+------------+--------------+

  ------------------ ----------------------- ------------- ---------------
  **Table 5.                                               
  Forestry: Value of                                       
  weather forecasts                                        
  (climate change)**                                       

  **Type of Weather  **Value of Weather      **% of Sector **Source**
  Information**      Information**           Value**       

  Climate change     Average annual net      2% of GDP     Perez-Garcia et
  (CO~2~ lievels)    welfare effect \$1.9    from lumber   al. (1997)
                     billion                 and wood      
                                             products and  
                                             paper and     
                                             allied        
                                             products      

  Climate change     Average annual net      9% of GDP     Sohngen and
  (CO~2~ levels)     welfare effect \$10.7   from lumber   Mendelsohn,
                     billion                 and wood      (1998)
                                             products and  
                                             paper and     
                                             allied        
                                             products      

  Note: For ease of                                        
  comparison, all                                          
  values are                                               
  converted to 2004                                        
  US dollars using                                         
  the CPI from the                                         
  Economic Research                                        
  Service.                                                 
  ------------------ ----------------------- ------------- ---------------

+----------------+--------------------+--------------+----------------+
| **Table 6.     |                    |              |                |
| Transportation |                    |              |                |
| (air, land,    |                    |              |                |
| water): value  |                    |              |                |
| of weather     |                    |              |                |
| forecasts**    |                    |              |                |
+----------------+--------------------+--------------+----------------+
| **Type of      | **Value of Weather | **% of       | **Source**     |
| Weather        | Information**      | Sector       |                |
| Information**  |                    | Value**      |                |
+----------------+--------------------+--------------+----------------+
| [Air           |                    |              |                |
| transportatio  |                    |              |                |
| n]{.underline} |                    |              |                |
+----------------+--------------------+--------------+----------------+
| Integrated     | \$176 million/yr   | A            | Allan et al.   |
| terminal       | from improved      | pproximately | (2001)\*       |
| weather system | decision making    | 0.19% of US  |                |
| services       | resulting in       | GDP from air |                |
|                | reduced gridlock   | tr           |                |
|                | and reduced delays | ansportation |                |
+----------------+--------------------+--------------+----------------+
| Improved       | \$12.3 million per |              | Anaman et al.  |
| weather        | year               |              | (1998)         |
| forecasts in   |                    |              |                |
| the form of    |                    |              |                |
| terminal       |                    |              |                |
| aerodrome      |                    |              |                |
| forecasts      |                    |              |                |
+----------------+--------------------+--------------+----------------+
| Weather        | \$162 million/year | A            | Evans et al.   |
| sensing and    | from delay         | pproximately | (1999)\*       |
| forecasting    | reduction benefits | 0.03% of US  |                |
|                | from LAX, SFO, and | GDP from air |                |
|                | SEA (i.e. \$16.7   | tr           |                |
|                | million SEA,       | ansportation |                |
|                | \$25.7 million     |              |                |
|                | LAX, \$119 million |              |                |
|                | SFO)               |              |                |
+----------------+--------------------+--------------+----------------+
| Improvement of | \$10 million per   |              | Leigh (1995)\* |
| Terminal       | year for all       |              | and            |
| Aerodrome      | Quantas            |              | [              |
| Forecasts      | international      |              | http://www.esi |
|                | flights\*          |              | g.ucar.edu/HP_ |
|                |                    |              | rick/esig.html |
|                | [impe              |              | Accessed March |
|                | rfect:](http://www |              | 1](http:       |
|                | .esig.ucar.edu/HP_ |              | //www.esig.uca |
|                | rick/attributes.ht |              | r.edu/HP_rick/ |
|                | ml#imperfect)\$6.8 |              | esig.html Acce |
|                | million/year       |              | ssed March 1), |
|                |                    |              | 2004           |
|                | [perfect:](htt     |              |                |
|                | p://www.esig.ucar. |              |                |
|                | edu/HP_rick/attrib |              |                |
|                | utes.html#perfect) |              |                |
|                | \$8.4              |              |                |
|                | million/year\      |              |                |
|                | derived from the   |              |                |
|                | Quantas Airlines   |              |                |
|                | avoidance of       |              |                |
|                | carrying extra     |              |                |
|                | fuel               |              |                |
+----------------+--------------------+--------------+----------------+
| Improvements   | \$58 million/year  | A            | NOAA (2002)\*  |
| in satellite   | from reduced       | pproximately |                |
| imager and     | delays and         | 0.06% of US  |                |
| sounder        | accidents. (\$40   | GDP from air |                |
|                | million derived    | tr           |                |
|                | from reduced       | ansportation |                |
|                | flight delays, and |              |                |
|                | \$18 million       |              |                |
|                | derived from       |              |                |
|                | avoiding volcanic  |              |                |
|                | ash plumes)        |              |                |
+----------------+--------------------+--------------+----------------+
| Integrated     | \$33.7 million/yr  | A            | Paull (2001)\* |
| Icing          |                    | pproximately |                |
| Diagnostic     | from reduced       | 0.04% of US  |                |
| Algorithm      | accidents          | GDP from air |                |
|                |                    | tr           |                |
|                |                    | ansportation |                |
+----------------+--------------------+--------------+----------------+
| Weather        | \$25 million/year  | A            | Rhoda and      |
| systems        | from reduced       | pproximately | Weber (1996)\* |
| processor      | flight delays as a | 0.026% of US |                |
| (WSP)          | result of national | GDP from air |                |
| modifications  | deployment of WSP  | tr           |                |
|                |                    | ansportation |                |
+----------------+--------------------+--------------+----------------+
| Terminal       | \$580 million/yr   | 6% of        | Sunderlin and  |
| Convective     |                    | weather      | Paull (2001)\* |
| Weather        | derived from delay | delay at the |                |
| Forecast       | reductions         | Terminal     |                |
|                |                    | Convective   |                |
|                |                    | Weather      |                |
|                |                    | Forecast     |                |
|                |                    | sites (0.6%  |                |
|                |                    | of GDP from  |                |
|                |                    | air          |                |
|                |                    | tra          |                |
|                |                    | nsportation) |                |
+----------------+--------------------+--------------+----------------+

+-----------------+----------------------+----------------+-----------+---+
| **Table 6       |                      |                |           |   |
| (continued).    |                      |                |           |   |
| Transportation  |                      |                |           |   |
| (air, land,     |                      |                |           |   |
| water): value   |                      |                |           |   |
| of weather      |                      |                |           |   |
| forecasts**     |                      |                |           |   |
+-----------------+----------------------+----------------+-----------+---+
| **Type of       | **Type of Weather    | **Type of      | **Type of |   |
| Weather         | Information**        | Weather        | Weather   |   |
| Information**   |                      | Information**  | Info      |   |
|                 |                      |                | rmation** |   |
+-----------------+----------------------+----------------+-----------+---+
| [Ground and     |                      |                |           |   |
| water           |                      |                |           |   |
| transportati    |                      |                |           |   |
| on]{.underline} |                      |                |           |   |
+-----------------+----------------------+----------------+-----------+---+
| Improved        | \$29 million/year    | 0.02% of GDP   | NOAA      |   |
| satellite       | derived from         | from trucking  | (2002)    |   |
| imager and      | rerouting            | and            |           |   |
| sounder which   | efficiencies in the  | warehousing    |           |   |
| will improve    | trucking industry    |                |           |   |
| predictions of  |                      |                |           |   |
| short-term ice  |                      |                |           |   |
| formation and   |                      |                |           |   |
| fog conditions  |                      |                |           |   |
+-----------------+----------------------+----------------+-----------+---+
| Improved        | \$95 million/year to | 0.66% of GDP   | NOAA      |   |
| satellite       | commercial shipping  | from water     | (2003)    |   |
| imager and      | from transit time    | transportation | Draft     |   |
| sounder which   | savings and cargo    |                |           |   |
| will improve    | loss reductions      |                |           |   |
| marine          |                      |                |           |   |
| forecasts of    |                      |                |           |   |
| winds and waves |                      |                |           |   |
+-----------------+----------------------+----------------+-----------+---+
| Daily           | > [imperfect:]       |                | Smith and |   |
| precipitation   | (http://www.esig.uca |                | Vick      |   |
| predictions     | r.edu/HP_rick/attrib |                | (1994)    |   |
| from radar      | utes.html#imperfect) |                |           |   |
| imagery         | > \$35,000 per year, |                |           |   |
|                 | > derived from       |                |           |   |
|                 | > deciding whether   |                |           |   |
|                 | > to apply de-icing  |                |           |   |
|                 | > materials for a    |                |           |   |
|                 | > single Regional    |                |           |   |
|                 | > Council in         |                |           |   |
|                 | > Scotland           |                |           |   |
+-----------------+----------------------+----------------+-----------+---+
| \*Values were   |                      |                |           |   |
| obtained from   |                      |                |           |   |
| Stratus         |                      |                |           |   |
| Consulting      |                      |                |           |   |
| report by Lazo  |                      |                |           |   |
| et al. 2003,    |                      |                |           |   |
| Exhibit A.2.    |                      |                |           |   |
|                 |                      |                |           |   |
| Note: For ease  |                      |                |           |   |
| of comparison,  |                      |                |           |   |
| all values were |                      |                |           |   |
| converted to    |                      |                |           |   |
| 2004 US dollars |                      |                |           |   |
| using the CPI   |                      |                |           |   |
| from the        |                      |                |           |   |
| Economic        |                      |                |           |   |
| Research        |                      |                |           |   |
| Service and     |                      |                |           |   |
| historical      |                      |                |           |   |
| exchange rates  |                      |                |           |   |
| from            |                      |                |           |   |
| <http://www     |                      |                |           |   |
| .x-rates.com/>. |                      |                |           |   |
| Percent of GDP  |                      |                |           |   |
| is based on     |                      |                |           |   |
| 1999 GDP        |                      |                |           |   |
| figures from    |                      |                |           |   |
| U.S. Census     |                      |                |           |   |
| Bureau,         |                      |                |           |   |
| Statistical     |                      |                |           |   |
| Abstract of the |                      |                |           |   |
| United States:  |                      |                |           |   |
| 2001, p. 418    |                      |                |           |   |
+-----------------+----------------------+----------------+-----------+---+

[^1]: More detailed information regarding these research projects can be
    obtained from the following web site:
    (<http://www.fs.fed.us/r1/nfp/research/projects.shtml>).