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CHAPTER NINE
Strategy for Operational
Climate Modeling and
Data Distribution
T
he ability to dynamically simulate climate has existed for a little over 50 years
(Phillips, 1956), even less if climate simulation is viewed as requiring coupled
ocean-atmosphere models (Manabe and Bryan, 1969). Attempts to apply cli-
mate simulation to the problem of seasonal to interannual climate prediction, using
dynamical ocean-atmosphere models with initial conditions based on observations,
have been made for only the past quarter century (Cane et al., 1986). In contrast to
the relative youth of climate prediction, the demand for weather forecast informa-
tion in the United States officially dates to the 1870s when a national weather service
was called for by Congress during the Grant Administration. Since then, the weather
service mission has grown to include a multitude of products, including climate moni-
toring and outlooks, which are used daily by citizens, companies, and researchers. User
sophistication has grown as well from a rudimentary expectation for advance warning
of impending storms to the ability to ingest and interpret gigabytes of raw data from
numerical models of the atmosphere and ocean (Chapter 12). Within the past two
decades, the demand for future climate information has grown to include long-lead
forecasts, seasonal outlooks, and climate change projections. These products are valu-
able to a wide range of sectors and regions.
Given the growing demand for climate prediction products, and the maturation of
climate simulation and prediction to demonstrably useful levels of skill, a strategy for
operational climate prediction is needed. Furthermore, given the sophistication of the
user community and the rapidly growing number and complexity of potential climate
prediction data products based on climate models with ever-increasing complexity
and resolution, the strategy needs to take into account the distribution of data to the
user community for application in a variety of socioeconomic sectors and for basic
research.
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A N AT I O N A L S T R AT E G Y F O R A D VA N C I N G C L I M AT E M O D E L I N G
CLIMATE MODEL DEVELOPMENT FOR OPERATIONAL PREDICTION
For the past several decades, increasingly complex climate models of increasing spa-
tial resolution were developed as research tools to study scientific questions regard-
ing the processes responsible for climate variability, change, and predictability (e.g.,
Delworth et al., 2006; Gent et al., 2011; Kiehl et al., 1998). Other than the development
of seasonal prediction tools, the motivation for these advancements was primarily, un-
til recently, to improve understanding of processes and reduce biases, not to address
any particular societal need for climate predictions, although researchers realized
that the results might have societal implications (NRC, 1979). More recently climate
model development has been driven more by a desire to better understand the gen-
eral impacts of anthropogenic climate change, and several recent reports (e.g., NRC,
2010b, Advancing the Science of Climate Change) and the U.S. Global Change Research
Program 2012-2021 strategic plan (USGCRP, 2012) have noted that both scientific
advancement and addressing specific societal needs should be viewed as drivers of
climate model development.
The user community needs easily accessible and comprehensible climate information
updated on a regular basis. One resource for users interested in decadal and longer
time scales has evolved from a series of climate model comparison (or intercompari-
son) projects (MIPs), organized by the international research community primarily
for the purpose of advising international assessments of climate change that are
conducted periodically by the Intergovernmental Panel on Climate Change (IPCC,
2007a,b,c). These MIPs are described in more detail in Chapter 8. They encourage the
participating model development groups to conduct a series of numerical climate
change simulations that conform to a prescribed protocol, with standardized outputs
placed in a distributed quasipublic archive. These simulations are increasingly used
not only by IPCC and the research community but by a broad range of users as source
material for assessments of climate variability and change and as inputs to other mod-
els specialized to particular applications.
A second resource for users is “operational” climate forecasts (see Box 9.1) for lead
times of months to a few years. Several weather services around the world have de-
veloped climate models specifically to provide scheduled, real-time, forecast products.
For example, the U.S. National Weather Service has developed the Climate Forecast
System (Saha et al., 2006, 2010) to produce operational climate predictions with lead
times of up to 9 months. The second generation of this system went into operation
in March 2011. The European Centre for Medium-Range Weather Forecasts has de-
veloped a seasonal climate prediction system, soon to be in its fourth generation
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Strategy for Operational Climate Modeling and Data Distribution
BOX 9.1 OPERATIONAL PREDICTION
As in operational numerical weather prediction, several characteristics of operational climate
prediction make it distinct from climate model research and development. First, the goals of
operations are driven by a user community rather than scientific advancement. There is no value
judgment implied by this, but the implication is that the needs of the user community have to be
assessed regularly, operational products must respond to users’ needs, and there is an expecta-
tion for improvement over time in various aspects of users’ experience. Second, operations must
conform to a specified schedule of generation and delivery of products. Users expect products to
be available in time, on time, every time, which requires a mindset and a working protocol that
is not necessarily appropriate in a research and development setting. Third, operational predic-
tion requires dedicated resources and contingency (failsafe) planning. Model developers often
work with resources that have been obtained through a competitive process, on an ad hoc basis,
or through windfall opportunities, but those modes are far too undependable for operational
requirements. Operations must have a platform for product generation that is fully functional
when needed and a plan in place for utilizing backup resources when that platform is out of order.
Finally, operational computer code should conform to rigorous standards of software engineer-
ing that may or may not apply to research codes. While many climate prediction research groups
are shifting to a more formal software engineering approach (Chapter 10), primarily motivated by
the need for including the input from a wide community of researchers and model developers,
there remains a more informal methodology in most model development groups that enables
and even encourages risk taking, as is appropriate in a research and development enterprise.
(System41), and other nations have similarly developed seasonal climate prediction
systems that include a climate model developed specifically for this purpose.
There is a desire within the research community to migrate experimental climate
prediction models into operational use (e.g., the National Oceanic and Atmospheric
Administration [NOAA] Climate Test Bed effort to build a multimodel ensemble
[NOAA, 2011]) and to improve on operational models by transitioning model com-
ponents and/or parameterization schemes from experimental models developed in
the broader community, motivated by the growing expectation for governments to
provide climate services (e.g., Dr. Jane Lubchenco’s testimony before Congress during
the hearings to confirm her as Undersecretary of Commerce for Oceans and Atmo-
sphere and Administrator of the National Oceanic and Atmospheric Administration
1 http://www.ecmwf.int/products/changes/system4/ (accessed October 11, 2012).
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[Lubchenco, 2009]). This migration of experimental models into operational use has
the potential of efficiently leveraging the U.S. climate research community to provide
more skillful and comprehensive climate predictions. Effecting this transition is dif-
ficult, because of gaps between research goals and operational imperatives (e.g., that
changing an operational model requires a more careful and elaborate process than for
a research model) and mismatches between resource requirements needed to main-
tain an operational model and the current distribution of resources between research,
development, and operations. There is clearly a need for adequate support for re-
search on climate modeling, operational climate prediction, and an effective interface
between the two.
Finding 9.1: Some operational seasonal-to-interannual prediction efforts are al-
ready under way, and there are archives of model output from research-oriented
international climate model intercomparisons focused on multidecadal to cen-
tennial climate simulation; these archives do not cover all of the needs of climate
information users.
ISSUES RELATED TO OPERATIONAL CLIMATE MODELING
The current practice of configuring and running climate models is primarily done by a
relatively small number of developers and programmers with insufficient support for
robust code development and support. Many aspects of climate model development
and usage (e.g., setting up model experiments and “tuning” climate model parameter-
izations) cannot be made routine when model configuration and execution demands
such large efforts from a small number of people. This practice also does not facilitate
rigorous attention to reproducibility, which is needed to ensure credibility. Finally, sin-
gular efforts such as are the current practice do not adequately support the sustained
two-way conversation that must take place between developers and user communi-
ties2 regarding requirements, expectations, use cases, etc. Interactions between model
developers and other communities of researchers, practitioners, and decision makers
are beginning to be encouraged; for example, the Community Earth System Model
project recently added a working group on societal dimensions.3
Finding 9.2: The current collection of efforts for research in climate model devel-
opment is not well positioned to perform operational climate modeling.
2 Individualsand groups interested in applying climate model outputs to the management of the
societal effects of climate change.
3 http://www.cesm.ucar.edu/working_groups/Societal/ (accessed October 11, 2012).
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Strategy for Operational Climate Modeling and Data Distribution
For decades there has been a mismatch between the expectations of the opera-
tional numerical weather prediction (NWP) and climate prediction community and
the model research and development community. The principal measure of success
of work that is supported by a typical short-term (e.g., 3-year) research grant is the
number, quality, and impact of the research publications that result from any project.
Researchers receive no reward for developments that become “operational,” so there
is little incentive to do what is viewed as extra work to transform research results into
operational methods or procedures. There is a view that the scholarly publications
speak for themselves, which has been described as a “loading dock” approach—the
research results are made available to the operational prediction community via peer-
reviewed publications (left on the loading dock), and it is up to users to figure out how
to use the results. There are some nascent efforts in which the transition to operations
is the objective rather than a by-product of research, e.g., the NOAA Climate Test Bed
activity.4
From the operational community point of view, there are a great many constraints
imposed by operations that should be taken into account by the researchers who seek
to improve the operational predictions. In order to effect a transition from research to
operations, they argue, the research community needs to modify its developments to
conform to the constraints of operations so that their results can become useful, and
the operational center needs to provide infrastructure support for the research com-
munity to use the operational model to conduct its research. The mismatch between
the two communities’ expectations has been called the “valley of death,” that is, a com-
munication and interaction gap. There is clearly a need to better align the two com-
munities and provide adequate resources so that good ideas can be more rapidly and
effectively transformed into operational practice.
Finding 9.3: The expectations of the research community and the operational
prediction community are not well aligned.
As indicated throughout this report (Chapters 1 and 10), a market for climate model
information already exists. Given the growing need for information about future cli-
mate from climate models, involvement of the private sector could be beneficial. The
private sector is already engaged through consulting companies that provide custom-
ized and downscaled climate information. A number of private companies successfully
sell climate information that depends on climate models. Examples include Prescient
Weather, Ltd.,5 Atmospheric and Environmental Research Inc.,6 Risk Management
4 http://www.cpc.ncep.noaa.gov/products/ctb/ (accessed October 11, 2012).
5 http://www.prescientweather.com/ (accessed October 11, 2012).
6 http://www.aer.com/ (accessed October 11, 2012).
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Solutions Inc.,7 Stratus Consulting,8 and ICF International.9 An important question to
resolve is the appropriate balance between the private sector and government organi-
zational structures, such as a national climate services operation.
Is there a potential benefit of involving the private sector more directly in climate
modeling? Could a market be created for climate model information? How much is
happening already?
Such a balance has been struck in the weather community. Since the inception of
the weather enterprise in the 1800s, it evolved to include three sectors: the National
Weather Service, academia, and the private sector. Each plays a vital and unique role in
weather services, and the competition between the sectors led to a flourishing and ex-
tensive set of valuable weather services. However, friction and conflict also abounded.
Policies were initiated in the latter part of the 20th century to try to identify the roles
and missions of the various sectors, but the boundaries were never as crisp as some
wanted and the conflicts continued.
A 2003 NRC report, Fair Weather: Effective Partnerships in Weather and Climate Services,
concluded that it is more effective to define a process for evaluating and adjusting the
roles of the private, public, and academic sectors than to rigidly define such roles. Such
a process entails the American Meteorological Society (AMS)—a neutral party—host-
ing a forum for civil interactions and discussion among the three sectors. AMS formed
the Commission on the Weather and Climate Enterprise, which consists of three
boards and several committees devoted to the various aspects of the partnership. The
existence of the commission has resulted in a significantly reduced atmosphere of
conflict among the three sectors and has helped introduce an era of cooperation. The
joint participation of the private, public, and academic sectors has resulted in better
data coverage, wider information dissemination, more realistic and scalable models,
increased infusion of cutting-edge technology, and a greater number of specialized
products (NRC, 2003).
Finding 9.4: The private sector already has at least some role in providing cli-
mate modeling information. There is precedent for this, for example, in how the
National Weather Service interacts with the private sector.
As described in Chapter 10, standardized model outputs from the leading interna-
tional climate models are routinely combined through the Coupled Model Intercom-
parison Project (CMIP) efforts. These CMIP outputs have heavily contributed to the
7 http://www.rms.com/ (accessed October 11, 2012).
8 http://www.stratusconsulting.com/ (accessed October 11, 2012).
9 http://www.icfi.com/markets/climate (accessed October 11, 2012).
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Strategy for Operational Climate Modeling and Data Distribution
IPCC assessments, as well as provided accessible climate model simulations to a wider
community of users and researchers. A centralized data archive was initially developed
by the Program on Climate Model Diagnostics and Intercomparison at Lawrence Liver-
more National Laboratory in the United States, but as the volume of model output has
grown, an internationally coordinated collaboration was developed, referred to as the
Earth System Grid. As described in Chapter 2, self-organized grassroots efforts such
as the Global Organization of Earth System Science Portals (GO-ESSP)10 and the Earth
Systems Grid Federation (ESGF),11 as well as short-term grant-funded projects such as
Metafor12 and ExArch,13 are responsible in large part for building the data infrastruc-
ture underlying CMIP5. These CMIP efforts are a vital backbone for efficiently provid-
ing current climate model output to diverse user communities. However, despite these
efforts, the data sets are growing in size and complexity, and users require even more
sophisticated access methods than are currently available. The demands being placed
on these networks far exceed the capacity of volunteer energy: it is high time that the
global data infrastructure was recognized as “operational” and resourced as such.
Finding 9.5: The global infrastructure for modeling and data distribution is cur-
rently a community-owned federation without formal governance.
The climate modeling community increasingly recognizes the need to focus on the
reproducibility of results, and of traceability from results back to the methods and
models used to produce them. For instance, the CMIP5 project records model prov-
enance using a questionnaire developed by the Metafor project. It is in fact quite pos-
sible to record provenance, provide codes that produce specific results, etc., such that
third parties can in fact attempt to reproduce or vary them (Balaji and Langenhorst,
2012), aided by software tools such as OLEX,14 but these methods still need enthusias-
tic adoption by the community.
Finding 9.6: Climate modeling groups have not universally put a high prior-
ity on workflow provenance, i.e., documented steps from model configuration
to a given output data set or graphic that make the process transparent and
reproducible.
10 http://go-essp.gfdl.noaa.gov/ (accessed October 11, 2012).
11 http://esgf.org/(accessed October 11, 2012).
12 http://metaforclimate.eu/trac (accessed October 11, 2012).
13 http://proj.badc.rl.ac.uk/exarch (accessed October 11, 2012).
14 http://olex.openlogic.com (accessed October 11, 2012).
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THE WAY FORWARD
Taking the best advantage of research findings for operational climate prediction re-
quires a tight linkage, with shared goals, shared decision making, and shared resource
allocation, between the research and development community and the operational
prediction community. The transition of research advances to operations requires
dedicated resources for external scientists to work on operational models and for the
operational center to accelerate the transition to operations. There is a large benefit of
confronting models with observations (e.g., through operational data assimilation and
prediction) for advancing and potentially transforming climate model development.
A strategy for enabling a more rapid and effective transition from research to opera-
tions that can take best advantage of recent research advances and model develop-
ments in the academic community will require more sophisticated interactions among
climate model developers, climate simulators, data assimilation experts, and climate
analysts. This strategy should include a closer alignment of the goals and expectations
of the research and development community with the goals and expectations of the
operational prediction community and changes in the rewards system that recognize
the value of contributions to operational climate prediction. A systems approach is
needed that takes into account (1) the rigor needed for scientific advancement, (2)
society’s needs for information from climate models, (3) the complexity and volume of
data generated by climate models, and (4) the complex relationships among govern-
ment laboratories, university research groups, the private sector, and potential users of
climate model information. A meaningful and robust personnel exchange, whereby re-
search scientists spend a significant period of time visiting operational centers to help
advance prediction science, would be a beneficial part of such a strategy. Operational
centers would best promote these advances by providing operational models, sup-
porting data sets, and a user-friendly model testing environment that allows external
researchers to test experimental parameterizations and/or model components in an
operational setting.
Closing the gap between research and development and operational prediction will
require the capability to establish workflow provenance and automate analysis where
feasible and reasonable, for which research and development are needed.
This committee judges that data management of the large and complex data sets
that are regularly produced by climate modeling research and operations should be
viewed as on par, in terms of importance and resource provisioning, with the research
and development, simulation, and prediction efforts themselves. Data distribution,
including robust technical support for remote analysis to the large and diverse stake-
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Strategy for Operational Climate Modeling and Data Distribution
holder community, needs to be viewed as an operational imperative rather than a
research project, with appropriate management and resource allocation.
The vision of the committee is that over the next 20 years climate modeling for inter-
annual to decadal to centennial time scales will develop a stronger operational com-
ponent. The model predictions will be substantially more robust and provide a consid-
erably richer and more comprehensive set of products that are closely tied to recent
scientific research developments in climate modeling. Although there have been
previous calls for the United States to commit to the production of operational climate
data products for model-based global climate projections (Chapter 2), the committee
feels that it is too soon to make such a commitment for decadal to centennial predic-
tion. Considerable research and dialogue among stakeholder communities is needed
to determine if there is any overlap between what can be predicted and what needs
to be predicted, in particular for decadal time scales.
Recommendation 9.1: To better address user needs for short-range climate
predictions, the U.S. and international modeling communities should continue
to push toward a stronger operational component for prediction of seasonal
climate and regular experimental simulation of climate change and variability on
decadal time scales.
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