University of Missouri
_______
A ‘regionalized’ climate scenario engine
for … climate change adaptation strategies (?)
Response to NOAA Solicitation: OAR-CPO-2011-2002561
MU PI:
Anthony R. Lupo
Department of Soil, Environmental and Atmospheric Sciences
University of Missouri
Columbia, MO 65211
MU Project Manager:
Verne Kaupp
Project lead: Earth science coordinator
ICREST/University of Missouri
Columbia, MO 65211
ABSTRACT
The overall objective of this work is to develop a “regionalized” climate scenario engine to
predict near-term regional and national eco-climate risks and to identify potential adaptation
strategies focusing on the traditional cornerstone triad of Policy-Science-Technology.
The proposed effort will fill the nation’s needs for near-term regional and national climate
predictions for the next couple of seasons, and for estimating conditions expected over the next
ten years and beyond to enhance decision-making in the attempt to minimize negative impacts on
the economic health and vitality of the nation, and maximize positive results by adapting to
expected changing climate conditions.
Many sectors (e.g., agriculture) of the regional economy are influenced strongly by weather and
climate. The ability to anticipate climatic variables such as temperature and precipitation, and
then drought or excess precipitation as much as one to two seasons in advance is a crucial issue.
The inter-seasonal and interannual variability of climate in the region are strongly influenced by
the tropical Pacific region phenomenon known as El Nino. While great advances have been
made in understanding this phenomenon, the ability to forecast its evolution is still an area that
needs attention. Additionally, the impact of El Nino is greatly influenced by longer-term climatic
variations and, of course climate change, which may be the result of anthropogenic activities.
Using the results of previously published studies, long-range forecasts can be made and these can
be made available to water resource decision-makers to use in order to help them formulate
policy or decide which activities to undertake. Thus, this work will have four basic objectives: a)
to examine the issue of interannual, inter-decadal, and climate change in the mid-west region
over the course of a century and use this information to generate seasonal forecasts, b) to use
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global and regional modeling capability to supplement these forecasts. The models will be
evaluated for their own capability to make seasonal forecasts in the region, c) to create a
decision-making tool, which can be accessed by water resource policy-makers who make
economically related decisions throughout the region. This will also include integrating the use
of satellite information to supplement the forecast process, and d) to create a web-based interface
that will be accessible and useful to that local community in support of their activities.
Additionally, this system will be made adaptable for other regions as long as the background data
is provided.
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TABLE OF CONTENTS
(1)RESULTS FROM PRIOR RESEARCHError! Bookmark not defined.
(2) STATEMENT OF WORK ................. Error! Bookmark not defined.
REFERENCES………………………………………………………………………….14
(3) BUDGET .................................................................................... 19
(4) BUDGET JUSTIFICATION ......................................................... 19
CURRENT/PENDING SUPPORT .......... Error! Bookmark not defined.
VITAE .................................................. Error! Bookmark not defined.
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(1) Results from prior research
(This section should not exceed two pages)
In the middle part of North America, the sea surface temperature patterns (SST) as forced by
ENSO in the tropical Pacific region correlates quite well with seasonal temperature and
precipitation regimes (e.g., Kung and Chern, 1995; Park and Kung 1998; Lee and Kung 2000;
Berger et al. 2002; Lupo et al. 2007), especially during the cold season. For example, Lupo et al.
(2005) (Berger et al. 2003) demonstrate that winters tend to be snowier in the southern (northern)
part of Missouri during El Nino (La Nina) years. Ratley et al. (2002) and Lupo et al. (2007)
demonstrate that La Nina years tend to correlate with very dry summers and fall seasons within
this region. Using these results can produce simple seasonal forecasts that show skill beyond
climatology (see Changnon et al. 1999; Lupo et al. 2008b). Long Range Forecasts for
temperature and precipitation two seasons ahead can be evaluated two seasons ahead in terms of
percent, where 0% is a forecast that is the same as climatology and 100% is a perfect forecast
(Lupo et al. 2008b).
Using the results from above, observed data provided by NOAA and satellite data provided
by NOAA and NASA, for example, the MODIS (Moderate Resolution Imaging Spectrometer),
such as vegetation indexes and land cover indexes, would be able to be correlated with
temperature, precipitation, tropical Pacific Ocean region SSTs (ENSO phase), and Palmer
Drought Index. This kind of information could be used by the forecasting group in order to
supplement long range forecasting tools by providing a view of the ground surface conditions.
This would give the forecaster information on how current weather conditions are already
influencing the biosphere. This data would be available through MODIS
(http://modis.gsfc.nasa.gov/about/), and vegetation indexes use visible and near-infrared
channels to derive these. The MODIS instrument is aboard the Terra (a morning flyover vehicle)
and Aqua (an evening flyover vehicle) satellites.
The NASA GISS climate models are General Circulation Models (GCM) and several have
been developed for use by the research community. Examples of the GISS model in use can be
found in Schmidt et al. (2006) or Hansen et al. (1984). The GISS GCMs are cartesian which can
be run at a variety of horizontal and vertical resolutions. However, this work will use output
provided on grids with a resolution of 2°×2.5° in the horizontal (latitude × longitude) and 31layers in the vertical. The dynamics are based on generally the "Arakawa B" grid scheme.
Scheme B uses no horizontal viscosity and is particularly suitable for coarse resolution models.
The newest GISS GCM is the ModelE, which was released in 2004. Our work will use the basic
set of inputs, and utilize surface temperature, precipitation, sea surface temperatures, and
standard level heights/pressures.
The University of Missouri possesses in-house a regional scale model which can be used
either as a forecast model, or to simulate climate on a regional scale. The Mesocale Atmospheric
Simulation System (MASS) is a limited-area terrain-following sigma-coordinate model. It was
developed, maintained and improved by MESO, Inc. The latest version of MASS has interactive
multiple-nest capability, nonhydrostatic dynamics allowing simulations on the order of 1 km or
greater, a four-dimensional data-assimilation capability, four levels of microphysics and several
convective parameterization schemes. The physics of the model are similar to that of the GISS
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GCM with the major difference being that the horizontal resolution can be run as low as 3 km,
and there are more than 50 layers in the vertical. The same outputs as the GISS will be used here.
(2) Statement of work
1) Introduction and background
In the Midwestern region of the United States, weather and climate play critical roles in the
economic vitality of the region (e.g., Changnon 1999; Changnon et al. 1999), and especially in
the management of water resources. Thus, the decisions made by those who formulate and then
implement policy or who make economic decisions must take weather and climate into account
(Changnon and Kunkel 1999). For example, in agriculture, which crops to plant or when and if a
particular field should be fertilized will depend critically on the forecast of temperature and/or
precipitation up to two seasons in advance. Also, as drought and excess precipitation impact
water supplies critically, it is important for us to understand the frequency of drought and it’s
associated climatic regimes. To complicate matters, droughts can be considered climatologically,
agricultural,
and/or
hydrological,
and,
at
times,
these
do
not
coincide
(http://www.drought.unl.edu/dm/). Additionally, two of the most important climatic issues
influencing this region of the country are a) the interannual (and interdecadal) variability of
temperatures and precipitation, and b) climate change.
The former are largely influenced by physical phenomenon, such as El Nino and Southern
Oscillation (ENSO) (e.g., Kunkel and Angell 1999, Changnon 1999, Berger et al. 2002, Lupo et
al. 2005). The degree to which ENSO influences weather and climate in a region can be modified
over the course of several decades. Recently, it has been shown that the Pacific Decadal
Oscillation (e.g., Mantua et al. 1997; Gershunov and Barnett 1998; Lupo et al. 2005, 2007) can
modify the influence of the ENSO cycle in this part of the country. In popular culture, these
issues and concepts are most readily understood by the general public in the discussion of
hurricane frequencies (Gray et al. 1997; Lupo and Johnston 2000; Lupo et al. 2008a).
Climate change is the other issue that will have a critical role in the long-term decision
making processes (Barnston et al. 2005). While there is wide-spread agreement among scientists
that climate change is constantly occurring and that over the last several decades, the climate has
become warmer globally (e.g. IPCC 2001, 2007), but there is some debate as to the degree to
which there may be human contribution (e.g., IPCC 2007). Also, while most of the IPCC
projections suggest that a monotonic increase in temperatures can be expected globally, an
important paper was recently published that demonstrated that interannual and interdecadal
variability, such as that forced by the ENSO and PDO, would still occur, although their
frequency and interaction may change (e.g., Tsonis et al. 2007). Some research has suggested
that ENSO has been occurring more frequently over the last part of the 20th century (e.g.,
Mokhov et al. 2004), and the IPCC (2007) states that one of the key questions to be answered by
research is how climate change will influence the occurrence and / or strength of ENSO. Thus,
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these issues must be taken into account when trying to project future weather and climatic
conditions.
For some economic decision-making, such as in agriculture or energy usage, the use of longrange weather forecasts is already occurring, and has been for some time (e.g. Changnon and
Kunkel 1999). While the reliability of these forecasts is still low, great advances have occurred
over the last two decades (Barnston et al. 1994; Anderson et al. 1999). These forecasts generally
attempt to predict whether or not temperatures and precipitation will be above, near, or below
normal (e.g. http://www.cpc.ncep.noaa.gov/products/predictions/) for one month to two seasons
in advance. In some cases, energy usage can be forecast one year in advance (e.g., Changnon et
al. 1999). Thus, these forecasts are generally probabilistic, but should be better than that of
climatology if they are to have any value. Long range forecasting generally uses a statistical
approach (e.g., Barnston et al. 1994; Anderson et al. 1999), and includes such procedures as
contingency tables and analogues. Contingency tables use climate classifications, and analogue
techniques look to seek out similar conditions from the past, and both are a crude form of what
are called neural networks (e.g., Roebber et al. 2003).
In the middle part of North America, the sea surface temperature patterns (SST) as forced by
ENSO in the tropical Pacific region correlates quite well with seasonal temperature and
precipitation regimes (e.g., Kung and Chern, 1995; Park and Kung 1998; Lee and Kung 2000;
Berger et al. 2002; Lupo et al. 2007), especially during the cold season. For example, Lupo et al.
(2005) (Berger et al. 2003) demonstrate that winters tend to be snowier in the southern (northern)
part of Missouri during El Nino (La Nina) years. Ratley et al. (2002) and Lupo et al. (2007)
demonstrate that La Nina years tend to correlate with very dry summers and fall seasons within
this region. Using these results can produce simple seasonal forecasts that show skill beyond
climatology (see Changnon et al. 1999; Lupo et al. 2008b, and Table 1 here). Table 1 shows the
skill of long range forecasts issued by the global change research group at MU
(http://weather.missouri.edu/gcc) for temperature and precipitation two seasons ahead in terms of
percent, where 0% is a forecast that is the same as climatology and 100% is a perfect forecast.
However, caution must be taken when looking at these results as they may have applicability
only within their regions of study (e.g., Palecki and Leathers 2000). Additionally, the skill for
long range forecasts here will not be as high as those for short range (0 – 3 days) forecasts over
climatology, but will be higher than those where a model replaces climatology for the baseline
(Market and Lupo 2002).
Climate change must also be taken into account and it is recognized that, even if the globe
warms monotonically by a large amount, the impacts of climate change on temperature and
precipitation regimes will be unevenly distributed (e.g., IPCC, 2001, 2007; Semenov and
Bengtssen, 2002) across the globe. Thus, it is important to be able to not only monitor the
climate but to model it regionally. This is a key question that also needs resolution, as it is not
necessarily true that accounting for climate change in a forecast is as simple as adding the trend.
Climate change also impacts interannual and interdecadal variability as well (e.g., IPCC, 2001,
2007; Semenov and Bengtssen, 2002). Additionally, while progress has been made in the last
two decades in climate prediction (Reichler and Kim 2008) and the forecasting of ENSO related
interannual variability (Wittenberg et al. 2006; IPCC 2007), more progress needs to be made in
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order to more faithfully replicate ENSO, and it is still not possible to forecast interdecadal
variability.
Table 1. Skill Scores for the long range forecast generated here versus climatology (taken from
Lupo et al. 2008b).
Forecast period
Skill Score (%)
Total (Temperature + Precipitation)
20%
Temperature
34%
Precipitation
0%
Total Summer Season
38%
Temperature
40%
Precipitation
33%
Total Winter Season
0%
Temperature
19%
Precipitation
-14%
2) Objectives
The overall objective of this work is to develop a “regionalized” climate scenario engine to
predict near-term regional and national eco-climate risks and to identify potential adaptation
strategies focusing on the traditional cornerstone triad of Policy-Science-Technology.
The proposed effort will fill the nation’s needs for near-term regional and national climate
predictions for the next couple of seasons, and for estimating conditions expected over the next
ten years and beyond to enhance decision-making in the attempt to minimize negative impacts on
the economic health and vitality of the nation, and maximize positive results by adapting to
expected changing climate conditions.
The intent is to examine the near term societal and public policy impacts of climate change
and interannual and inter-decadal variability in the middle of North America on various sectors
of economy, and then develop decision support materials which will be used by decision and
policy makers in order to supplement their economic decision making. Additionally, the system
we are proposing here would be easily transferrable to any region in North America or globally.
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Within this framework, there are four issues, which will require detailed investigation:
1) To quantify the interannual variability of climate in the middle of North America
over a longer period of time.
Lupo et al. (2005) and Lupo et al. (2007) and others have examined the interannual
variability of temperature and precipitation in relation to ENSO for the middle of North
America from 1955. They examined data acquired from the Missouri Climate Center and
from the National Center for Atmospheric Research (NCAR) / National Centers for
Environmental Prediction (NCEP) archives. We are proposing here to extend their
investigations back further, to about 1900. This will provide for a longer data base which
will contribute to the decision making tools used to generate long range weather
forecasts. This would be accomplished during phase 1 and 2 of the work.
Additionally, this group needs to generate a longer record of scored, real-time long range
forecasts than currently available (five years – see Lupo et al. 2008b). Evaluation of these
will determine how much real progress can be made by including model data, satellite
data, or a longer observational record. In Table 1, it is clear that there is more room for
improvement in the winter season, while the summer season forecasts have been good.
This portion of the work would be implemented during the first three phases of the work,
and be operational by phase 4. Additionally, even longer term or true climate projections
(several years out) would be developed and implemented during phase 4 based on what
was learned in the first three phases.
2) To model climate change and climate variability.
Using the results of Lupo et al. 2007, 2008b as a guide, we will examine the ability of the
NASA GISS model (see methodology section) to capture regional interannual variability
and climate. This is a key research problem as identified by the recent IPCC report. We
will use hindcasting (back-forecast) in order to determine whether or not the model can
produce results that are similar to the published results found in Lupo et al. (2008b), who
evaluate the model performance using a modified Brier Skill score taken from Lupo and
Market (2002). The ability of models to replicate the current state of the climate as well
as internnual variability has improved, but capturing interdecadal variability is still
difficult. This work would be carried out during phase 1 and 2.
Also, we will use the regional modeling capability available in-house at the University of
Missouri. The regional model would be used to “fine tune” the GISS model results and
would be part of the work plan during phase 2 and 3 of the work, and be operational for
phase 4.
3) To create a decision making tool that includes information such as satellite imagery
and model forecasts.
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Using the results from above, satellite data provided by NASA the MODIS (Moderate
Resolution Imaging Spectrometer), such as vegetation indexes and land cover indexes,
would be able to be correlated with temperature, precipitation, tropical Pacific Ocean
region SSTs (ENSO phase), and Palmer Drought Index. This kind of information could
be used by the forecasting group in order to supplement long range forecasting tools by
providing a view of the ground surface conditions. This would give the forecaster
information on how current weather conditions are already influencing the biosphere.
Additionally, correlations of the Midwest land cover to tropical SSTs has not been
performed before, and this would be a new and unique aspect of this work. This data
would be available through MODIS (http://modis.gsfc.nasa.gov/about/), and vegetation
indexes use visible and near-infrared channels to derive these. The MODIS instrument is
aboard the Terra (a morning flyover vehicle) and Aqua (an evening flyover vehicle)
satellites.
3) Impacts on user communities?? [placeholder at this time]
[Tony, we need to discuss at the next meeting how we use the text below…]
With adequate public and private-sector policy-level decision-making, not only will negative
impacts of climate change be minimized, but positive results from the changing conditions can
be optimized. Input for decision-making on a variety of policy issues will result from Climate
Modeling that includes the impacts of global climate change. Model simulation results are of
potential value to a variety of individual, local, regional and national decision-makers. Following
are brief explanations of some of the potential user groups.
Agricultural uses of these model simulation results can include managing cropping patterns
to optimize production based on climate forecasts, and changing livestock species or managing
living conditions for those species being raised. Air pollution control permitting will be
impacted, because warmer temperatures increase in the kinetics of air pollutant formation; if
climate modeling predicts increasing temperatures in a given region, air pollution permit writers
will have the information necessary to make more informed permitting decisions. Biofuels and
electric power production decisions will be optimized using more accurate temperature and
precipitation forecasts. Flood mitigation and response will be improved by providing better
input for targeting locations and regions that will require assistance. Indigenous aquatic species
maintenance can be improved by identification of areas in which water bodies will be impacted
by higher or lower stream flows. Public water supplies at the local and regional levels can be
better managed if more accurate forecasts of excessive precipitation and drought are available.
Air, land, and water transportation system management will benefit from more accurate
forecasting of storms, road flooding, and river levels. Wastewater treatment is impacted by
ambient air temperatures, and precipitation that impacts infiltration and inflow to sewer systems
and retention time in treatment basins; climate forecasts will assist treatment plant planners,
designers, and managers. Wetland management for surface water quality maintenance can be
improved with better forecasting of seasonal temperatures and precipitation.
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Each policy subject has its own unique set of decision-makers. Some of these are individual
facility owners e.g., farmers, others are local agency personnel, others are state or regional
agency staff, and still others are members of national organizations or federal agencies. Each set
of decision-makers is a potential user of the results of climate modeling simulation results, and
should be identified and targeted as a client. This is the target population that the project team
will identify. Information will be transmitted and the target population will be defined by
presentations at regional and national seminars, and organizational and industry conferences and
meetings. During Phases 3 and 4, this population will be contacted to make them aware of the
availability of and methods to use the model simulation results, and included in the
dissemination of the website use instructions. Specifics on the use of the Climate Forecast
Website and the results posted thereon will be provided by on-line training on the University of
Missouri Distance Learning Website.
Climatological impacts on the water economic sector (public water supplies) are well known.
Both drought and excessive precipitation cause changes in both water quality and water quantity.
These changes vary with climatological conditions and geographical location. As the federal
regulations limiting chemical and microbial substances in drinking water increase, both of
these changes make providing the necessary quantity of adequately treated water for public
consumption more difficult for water supplies. As variation in weather and regulatory conditions
increase, it is becoming apparent that better forecasting of water availability and treatment
requirements is needed by the nation’s public water supplies.
An example of the impact of climate change on water quality (water chemistry) is the
increase in regulated disinfection byproduct formation potential in surface water sources during
the first several seasons of normal precipitation following a drought period. The formation of
these carcinogenic chemicals increases because of natural organic materials in the storm water
runoff into surface water bodies.
One example of the impact of climate change on water quantity is the need to modify the
water intake drawoff levels because of the depth of flow in rivers or water level in
reservoirs. Another is the need to raise wellheads in alluvial well fields when forecasting
indicates wells will be subject to increased flood elevations.
Changes in water flow and chemistry require changes in water treatment chemical feed
dosages, or the treatment chemicals, used throughout water treatment processes. The
necessary changes must be determined by laboratory analyses, associated cost changes
must be budgeted, and then they must be implemented. So the longer the lead time from
identification of potential change to implementation, the better the results. This is why
the proposed improvements in climate forecasting are becoming critical for the water
economic sector.
The major water treatment plants that provide water to the major urban areas are
required, and have the experience and databases necessary, to relate climate forecasts to
the various
management actions necessary to provide adequate supplies of properly
treated water during all climate conditions. To optimize their ability to meet these
requirements, they need high quality, short and near-term climate forecasts so they have
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time to test, budget for and implement the management efforts that are required to
address the impacts of changing climatological conditions. Climatological forecasting is
not currently being used
in water supply resource planning and management
because the current forecasting does not incorporate current weather pattern trends
coupled with climate change modeling. This project is designed to mitigate that major
deficiency.
Success in the application of improved climate forecasting to the water supply economic
sector will be impacted by a variety of factors. These include the forecast results (i.e.,
climate forecast model simulations) being in the correct units for convenient use by
water supply managers, timeliness of the forecast periods and data presentation,
knowledge of the water economic sector and its terminology by the researchers and
staff of the delivery organization, and training of the water economic sector
organizations’ staffs in the use of the forecast data. The proposed project team includes
members whose collective experience adequately addresses these issues.
The University of Missouri Water Resources Research Center (MU WRRC) has been
involved in theoretical and applications research in water supply for many years. It is
part of several national networks of sister agencies, and is well acquainted with the public
water supplies in Missouri. It is a well-respected member of the water supply industry’s
national and state organizations. It has been involved in research and related
projects with the federal and state agencies involved in water resources work; these
include the Missouri Department of Natural Resources (MDNR), US Environmental
Protection Agency (USEPA) and US Geological Survey (USGS) for many years.
Because of these relationships, it is the preferred delivery mechanism for the
products of this project at both the state and national levels.
The decision support tools to be developed are the improved climate forecasts provided
in a timely manner to facilitate management decision-making, and training modules on
the use of the forecast data. Pilot projects will involve presentation of the forecast data to
major municipal water economic sector members in Missouri, tracking their use of the
data in management decision-making, and the water quality and quantity results of the
management decisions. The results of these projects will be published in water economic
sector journals, incorporated in university training courses, and shared with the major
federal and state water resource agencies. The delivery mechanism will be the MU
WRRC. The water resource agencies are expected to identify other uses of the results of
this proposed project; those uses will be specific to the agencies’ various missions
With adequate public and private-sector policy-level decision-making with respect to
water supplies, not only will negative impacts of climate change be minimized, but
positive results from the changing conditions can be optimized. Input for decisionmaking on water use and policy issues will result from Climate Modeling that includes
the impacts of global climate change. Model simulation results are of potential value to a
variety of individual, local, regional and national decision-makers. Following are brief
explanations of some of the potential user groups.
- 11 -
Flood mitigation and response will be improved by providing better input for targeting
locations and regions that will require assistance. Indigenous aquatic species maintenance
can be improved by identification of areas in which water bodies will be impacted by
higher or lower stream flows. Public water supplies at the local and regional levels can
be better managed if more accurate forecasts of excessive precipitation and drought are
available. Air, land, and water transportation system management will benefit from more
accurate forecasting of storms, road flooding, and river levels. Wastewater treatment is
impacted by ambient air temperatures, and precipitation that impacts infiltration and
inflow to sewer systems and retention time in treatment basins; climate forecasts will
assist treatment plant planners, designers, and managers. Wetland management for
surface water quality maintenance can be improved with better forecasting of seasonal
temperatures and precipitation.
Water resource decision-makers are potential users of the results of climate modeling
simulation results, and should be identified and targeted as a client. This is the target
population that the project team will identify. Information will be transmitted and the
target population will be defined by presentations at regional and national seminars, and
organizational and industry conferences and meetings. During Phases 3 and 4, this
population will be contacted to make them aware of the availability of and methods to use
the model simulation results, and included in the dissemination of the website use
instructions. Specifics on the use of the Climate Forecast Website and the results posted
thereon will be provided by on-line training on the University of Missouri Distance
Learning Website.
4) Methodologies
[Are these still valid?]
The methodologies for the objectives above borrow from previous work (Lupo et al. 2007,
2008b), which were modified from the earlier studies of Kung and Chern (1995), Lee and Kung
(2000), and Mokhov et al. (2004). These can be briefly described below.
Observational and Model Data
The analyses used in Lupo et al. (2007, 2008b) were the global monthly mean and
reconstructed SSTs and SST anomalies compiled by the NCEP and available through the
National Oceanic and Atmospheric Administration (NOAA) online archive available online at
(http://www.cdc.noaa.gov/cdc/reanalysis/). Monthly SSTs and anomalies are also available and
these can be found in the monthly Climate Diagnostics Bulletin online at
(http://www.cpc.ncep.noaa.gov). The mean SST anomalies in the ENSO region are available
from 1864 to the present through the Center for Ocean and Atmospheric Prediction Studies
(COAPS –http://www.coaps.fsu.edu), and the phase of the ENSO is found also at the COAPS
site. These data would be used to meet objectives #1 and 2 and extend the observational record
back to 1948 in phase 1 and phase 2 of the work.
The ENSO definition is used in many studies (e.g., Lupo et al. 2005; 2007 and references
therein). In summary, the index classifies years as El Niño (EN), La Niña LN, and neutral (NEU)
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based on 6-month running-mean Pacific Ocean basin sea surface temperatures (SST) anomaly
thresholds bounded by the region 5o N, 5o S, 150o W, and 90o W. The defined region
encompasses both the Nino 3 and 3.4 regions in the tropical Pacific. The anomaly thresholds
used to define EN years are those greater than +0.5o C, less than -0.5o C for LN years and NEU
otherwise. The ENSO year is defined as beginning on 1 October for the year and ending in
September the next year (following the references above and COAPS). The 500 hPa heights and
height anomalies from the NCEP re-analysis project (Kalnay et al. 1996) were also examined
and are available via the many of the same sources referenced above. Finally, the mean monthly
temperature and precipitation records for the Midwest will be taken from the Missouri Climate
Center and the Midwestern Regional Climate Center. These are available back to the late 1800s
for many places in the region (e.g., Columbia, MO back to 1890). The ENSO data and the
observed mid-western surface records will be used in each objective and project phases 1 - 3, and
we will use these to extend the work of Lupo et al. (2007) back to 1900.
Making and evaluating Long Range Forecasts
In order to construct long range forecasts, seasonal forecasts can be made and then evaluated
against climatology. Climatology is simply the 30-year mean of temperature or precipitation for
a particular period (a day or a month), and represents the minimal standard that any forecasting
technique needs to exceed to be useful. We will use these techniques in meeting each objective.
The long range forecasts of temperature and precipitation will be made for the winter
(December – February) and summer seasons (June – August), which are the time periods of
interest for the local and regional media and agricultural communities. The forecasts will be
made four to five months in advance of each season. The forecasts will be made based on the
prevalent SST patterns of the previous few months (see Lupo et al. 2008b), and a first guess as to
how these may evolve over the next few months (using past history and/or model forecasts). The
potential for atmospheric blocking (Wiedenmann et al. 2002) and the evolution of large-scale
flow regime will also be factors that contribute to the long-range forecast. Thus, the forecasters
are using contingency and analogue techniques to make forecasts. GISS model and regional
model information will be used as guidance.
Long range forecasts are a mix of probability based techniques and qualitative analysis. To
evaluate them we will use the point scoring scheme shown in Table 7 of Lupo et al. (2008b). The
scheme, which is modeled after Lupo and Market (2002), is based on defining normal as within
+/- 0.5 standard deviations of the mean, and above and below normal as outside that range. Two
points are awarded for a good forecast of seasonal temperature and precipitation (observed
category matched the forecast category), while none are awarded for a poor forecast. For
example, if a forecaster predicted the summer temperatures would be close to normal (-0.5 – 0.5
standard deviations from the seasonal mean) and the observed was within this range, this
constitutes a good forecast. Using the example above, a poor forecast would constitute a situation
where the observed seasonal temperatures were greater than one standard deviation above
normal. We will then examine the skill scores (Table 1) to evaluate the utility of these forecasts,
and these were calculated using the formula taken from Lupo and Market (2002):
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(our forecast – climatology) / (perfect forecast – climatology) * 100% (1)
The long range forecasts issued by Lupo et al. (2008) were better than climatology overall,
but showed no improvement over climatology in the winter season. In Table 1 the numbers are
large, since the number of forecasts issued (and therefore the sample size) is, as of yet, small.
During the summer season, our forecasts were better than climatology by 38%, which compares
to 0% during the winter season. When examining precipitation, our forecasts were significantly
worse than climatology in the winter season (14%), but did not show any difference overall when
considering temperature as well. Thus, the information derived here has the potential for use as a
tool or guidance for long range forecasts in our region (especially during the summer season),
and this information could be constructed for other areas of the United States and applied to long
range forecasts as well.
The GISS Model
The NASA GISS climate models are General Circulation Models (GCM) and several have
been developed for use by the research community. These models use numerical techniques to
solve the fundamental equations of geophysical fluid dynamics, which describe such physical
principles as the conservation of mass, momentum, and energy. Mass can be separated into dry
and moist processes. Physical processes that do not have an analytical expression can be
parameterized, and examples of this include incoming and outgoing solar radiation, cloudiness,
albedo, etc. Examples of the GISS model in use can be found in Schmidt et al. (2006) or Hansen
et al. (1984).
The GISS GCMs are Cartesian, which can be run at a variety of horizontal and vertical
resolutions. However, this work will use output provided on grids with a resolution of 2°×2.5° in
the horizontal (latitude × longitude) and 31-layers in the vertical. The dynamics are based on
generally the "Arakawa B" grid scheme. Scheme B uses no horizontal viscosity and is
particularly suitable for coarse resolution models. The newest GISS GCM is the ModelE, which
was released in 2004.
The MASS Regional Model
The University of Missouri possesses in-house a regional scale model which can be used
either as a forecast model, or to simulate climate on a regional scale. This model is available
through the Atmospheric Science Wav Laboratory, and resides on a UNIX based platform using
a SUN Blade-2000 workstation.
The Mesocale Atmospheric Simulation System (MASS) is a limited-area terrain-following
sigma-coordinate model. It can be run in either hydrostatic or non-hydrostatic mode. It was
developed, maintained and improved by MESO, Inc. The latest version of MASS has interactive
multiple-nest capability, non-hydrostatic dynamics allowing simulations on the order of 1 km or
greater, a four-dimensional data-assimilation capability, four levels of microphysics and several
convective parameterization schemes. There are three available map projections, and we can
globally re-locate the model easily. The physics of the model are similar to that of the GISS
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GCM with the major difference being that the horizontal resolution can be run as low as 3 km,
and there are more than 50 layers in the vertical.
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(3) Budget
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(4) Budget Justification
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