**Draft Report of Breakout Group 1**

***Simulation and Modeling at the Exascale for***

***Energy, Ecological Sustainability and Global Security (E3SGS)***

Town hall Meeting, Lawrence Berkeley Laboratory, 17--18 April 2007

Town hall Meeting, Oak Ridge National Laboratory, 17--18 May 2007

Town hall Meeting, Argonne National Laboratory, May 31--June 1, 2007

# 1. Introduction

*How can we improve our understanding of complex biogeochemical cycles
that underpin global ecosystems functions and control the sustainability
of life on Earth?*

The urgency of developing a science of global ecosystems is common for
several key questions. The US Climate Change Science Program and
Intergovernmental Panel on Climate Change (IPCC) have concluded that
climate change will accelerate rapidly during the 21^st^ century unless
there are dramatic reductions in greenhouse emissions \[1\]. The
assessments by the IPCC and the US Military Advisory Board \[2\] suggest
that global warming could have serious implications for the natural and
social fabric in many parts of the world. Fortunately sensible policies
to reduce greenhouse emissions could be formulated using reliable
climate forecasts and development of next generation of ESM models that
include processes and mechanisms to represent the most likely mitigation
strategies that depend on ecological and biological process over land,
ocean and below ground. . In order to develop these forecasts,
scientists must address two major challenges. First, how well can we
forecast with increased certainty the committed climate change over the
next few decades resulting from historical emissions? Second, how well
can we forecast the longer-term climate change including interactions
and feedbacks between all the components of the ESM and at spatial
scales that are of relevance to communities? The latter question is
particularly difficult to answer given our rather limited and
rudimentary knowledge of biogeochemical cycles and feedbacks. Meeting
these challenge will require a qualitatively different level of
scientific understanding and modeling capability and computational
infrastructure than that represented in the current studies of global
average quantities\[3 -- NAS report?\]. Achieving this capability may be
impossible without a concentrated effort over the next 5-10 years in
developing the detailed process models that are demanded by increased
spatial resolution driven by societal needs. It is our belief that
accelerating scientific development, through targeted attack and
application of exascale simulation, is the best way to make a difference
in the limited time during which key decisions must be made.

The town hall discussions have identified ten key scientific questions
that address major unsolved issues and represent targets of opportunity
for computationally intensive simulation. Resolution of these questions
will yield a much better and defensible scientific grounding for policy
and political discourse. Rather dramatic advances in the science will be
required, however, to provide robust answers accompanied by quantitative
estimates of uncertainty. After describing the urgent questions, we
discuss the scientific advances that are necessary to address these
issues. Common to many of the scientific advances are more accurate
multi-scale models that integrate the physical, chemical, and biological
processes comprising the climate system.

A new type of multi-disciplinary research program is urgently required
to advance the state of our knowledge, to address the attendant
scientific challenges, and to project the future of the Earth's
environment from the local to the global scale. This is beyond the scope
of the current limited and piece meal approach to climate modeling
adopted by several US agencies. A focused and well scoped investment in
developing rapidly the process scale science related to biological and
ecological processes of the earth system, new methodologies and software
tools that can integrate these branches of the earth system science to
the existing ESM's and a significantly larger computing resources, such
as those proposed by the exascale program, are required over the next
5-10 years. The opportunity to positively impact the outcome of the
current global change debate is restricted by the current inability of
the models to address these regional and local scale impacts effectively
and a significant investment made over the next few years can lead to a
quantitative impact on this process. Climate science is largely
data-limited, and the success of this research program is contingent
upon basic measurements and observations necessary to validate, verify,
and constrain Earth system models . Quantifying the uncertainties in
predictions is expected to require a new level of integration between
modeling and observational science. New mathematical methods and
algorithmic techniques will also be required to address the fundamental
challenges of multiscale and multi-physics coupling. Even with exascale
computing, approximations and assumptions must be made. Computing power
has been and will continue to be a key factor in making these advances
possible. This initiative will allow the science community to accelerate
the development and hopefully to answer these ten questions in a timely
manner.

# 2. A Prioritization of Urgent Earth System Questions 

## 2.1 Development of Carbon sequestration process models 

*Decisions on land use and carbon capture and storage technologies will
have to be made over the next few decades. Development of a new and
coupled models representing the microbial, ecological and physiological
processes for methods that are currently under consideration in oceans,
land and subsurface will need to be developed. ?*

If we had five years to come up with a sequestration strategy, we would
have to use reduced-form models to do this. Major factors are neglected
in such models so more detailed models are desired to take account of
carbon allocation under large perturbations, of plant mortality, species
migration and change rates. The ability to do detailed and operational
forecasting of the carbon cycle and climate in the 20-50 year range
would put sequestration strategies on a firm scientific ground and be a
valuable tool in helping the society adapt to decadal climate change and
seasonal transients.

The predictability of short-term carbon-climate models must be
rigorously assessed through evaluation with historical data sets=. A
significant emphasis of this theme will be to incorporate measurements
and observations (**Figure 1**) to develop more mechanistic based models
of the various ecological and biological processes at scales ranging
from a single tree/plant to scales of ecological systems. For example
starting with 1870 (pre-industrial conditions) modeled carbon budgets
driven by land use change and increasing atmospheric concentration can
be performed with some certainty. Since predictability in the decadal
range is expected to be low (based on theoretical results, **Figure 1**)
the use of data assimilation techniques for carbon will be required to
constrain these hindcast predictions. Soil and litter carbon due to slow
decomposition of frozen soils in high latitudes, have resulted in
greater carbon storage in that part of the global ecosystem, almost
twice as concentrated in the boreal and tundra regions as in temperate
regions. With significant changes to the precipitation in high latitudes
(up to 20% increase for IPCC scenarios), the possibility of abrupt
changes and release of large stored carbon pools needs to be
investigated. This requires model development and data collection to
understand the processes that form the foundation for further model
development work and ultimately integrating into a ESM. In many cases,
we currently do not know the sign of the carbon flux signal for many
parts of the earth under climate change scenarios.

## 2.2 Characterizing and bounding the coupled Earth system

*A systematic understanding of the mathematics of the climate system has
yet to be discovered. Can we develop a theory of internal and forced
modes of multi-physics, multiscale components that interact as a coupled
system?*

What do we know about the stability of the carbon cycle? From the paleo
record, there is evidence that it is well bounded, especially in the
interglacial periods; but there are no asymptotic analyses of the cycle
based on the mathematics of carbon--climate models. A more rigorous
systems approach, utilizing control theory for systems of partial
differential equations, is needed. Such a theory will allow model
developers to diagnose if important factors and processes are missing
from current models and pinpoint model components that contain errors
when compared with the historical climate record.

Abrupt climate change, and the potential for rapid shifts from one
climate equilibrium state to another, could be understood in the context
of this system theory. The ability to develop accurate models that
incorporate multiscale phenomena from process studies would be greatly
advanced by such a theory.

## 2.3 Probability of Extreme Weather Events and significant shifts in regional climates 

*As climate change accelerates, questions regarding the frequency and
occurrence of extreme weather, shifts in regional climate patterns need
attention. How can the climate models be adapted to meet these
challenges? Higher spatial resolution and improved subgrid scale process
models to capture the range of possibilities in the current climate and
hence the future climate are on solution.*

The study of extreme events using Earth system models is just beginning,
but this is just the kind of information that the impacts community
needs. Some of the largest impacts of climate change are associated with
changes in relatively rare but extreme localized phenomena, e.g., more
intense hurricanes, violent rainstorms, flash floods, and heat waves.,
this also includes low frequency extremes, such as droughts. More
temporal and spatial specificity at scales relevant for agriculture,
industry, and society is not yet feasible from a computational
viewpoint. The ability of existing models to accurately simulate extreme
temperature and precipitation events is severely limited by

horizontal resolution constraints. Furthermore, since extreme events are
rare by definition, adequate statistical characterization of the tails
of the distribution of weather events is required to make quantitative
statements about changes in their behavior. It is likely that
downscalingDownscaling methods will still be needed to reach the local
scale, even with exascale computing power. Engaging stakeholders to
iteratively define the interface and the important metadata needed to
interpret/interpolate between analysis tools, such as GIS or
GoogleEarth, is an important part of this challenge. How do we tell what
is important? Can priorities be model-based?

Using models, we should be able to identify key triggerstriggering
mechisms for extreme weatherclimate events and identify open scientific
issues that introduce first-order uncertainties in climate forecasts.
Since statistics are important for extreme events, a computational
challenge arises in characterizing the tails of the distributions where
extreme events occur.

Petascale computing will allow increases of horizontal resolution to the
10 - 25km scale permitting reasonable simulation of tropical cyclones.
This magnitude of computing power should also permit moderate increases
in ensemble size from of the current state of the art of 10 realizations
to about 50 realizations. Exascale computing will permit a combination
of further resolution increases to better resolve individual storms as
well as increased ensemble size to better capture extreme value
statististics. This will offer a great advance in characterizing the
uncertainty of climate models and provide the impacts community with
reliable expectations of models.

## 2.4 Sustainability of the tropical rain forest

*Precipitation in the tropics is a leading-order factor governing the
carbon cycle. What is the magnitude and stability of the carbon-climate
feedback for tropical ecosystems?*

The Amazon rain forest plays a pivotal role in the climate and in
particular, the carbon cycle. If this ecosystem were to collapse, a
large amount of carbon from decaying plants would be released into the
atmosphere Since climate change simulations indicate that precipitation
will decrease in the tropics under warming scenarios and that less
snowmelt will feed the Amazon River basin, a dryer Amazon could
represent a positive feedback for global warming. Attempts to forecast
this situation highlight the possibility of large changes in the next 50
years.

New methods and models with increased details and more elaborate,
significantly more species diversity of plant and microbial life,
mechanistic process based models of below and above ground ecology are
urgently needed.

## 2.5 Stability of the Polar caps and Greenland and Antarctic ice sheets

*Melting or breakup of either the Greenland ice sheet or of significant
portions of the Antarctic ice sheet could cause a sea level rise of six
meters.  What is the likelihood of this happening and on what
timescale?*\
\
It is currently impossible to exclude the possibility of over a meter of
cumulative sea level rise over the next century due to partial collapse
of the major ice sheets, in addition to the half meter of rise expected
due to thermal expansion. This problem is coupled not\
only to the climate system but also to more directly to energy and
population security. Coastal areas are densely populated and would be
severely impacted by advance of the sea. Critical infrastructure is
often concentrated around coastlines, including oil refineries (80% of
U.S. refining capacity is at 1.5 meters or less above sea level),
nuclear power stations, ports and industrial facilities. Coastal biomes,
many of which are implicitly part of coastal infrastructure designs, may
be severely impacted worldwide. Over the past 15 years there has been an
unanticipated acceleration of ice flow into the ocean from individual
catchments of the Greenland and Antarctic ice sheets. If these
accelerations are sustained, even larger portions of the polar ice
sheets could become vulnerable to mass wasting, with potentially grave
consequences for society. Sea level rise is likely the greatest
uncertainty in evaluating climate change impacts.\
\
Exascale simulation is a key tool in predicting the likely course of ice
sheet dynamics which dominates sea level rise uncertainty. Previously
unanticipated complexity in ice sheet dynamics is emerging, and new
observational techniques are providing a wealth of data on past and
present controls on ice sheet change. A new dynamic ice sheet\
model must incorporate new processes, including ice-stream flow regime
change, production and transport of basal fluids, surface-melt to bed
lubrication, fracturing, grounding line physics, and ice-shelf/ocean
interactions.  It would represent scales appropriate for slow creep
deformation and fabric formation within the vast ice sheet interior;
fast plug flow and water redistribution beneath ice streams and their\
tributaries; and flow acceleration and divergence into an ice shelf.
Successive development will couple this ice sheet simulator to models of
the dynamic earth, atmosphere, and ocean for predictions of future sea
level change.  Systematic methods of constraining the model against the
available data sets using inverse modeling have very high computational
demands but will help yield robust results.

## 2.6 Release of methane hydrates

*As warming occurs in the oceans and on land, frozen deposits of methane
will be released into the atmosphere. What is the potential in the next
20--50 years for a sudden increase in warming due to melting of Arctic
and ocean shelf deposits of methane hydrates?*

Historic records need to be further quantified, along with new
observations to quantify this potential for a sudden release and
positive feedback. High latitude peatland regions are rapidly warming
and as permafrost melts, the shift from anaerobic to aerobic conditions
will need to be part of ESM land surface schemes. Ocean shelf methane
hydrate deposits similarly will release methane as a threshold
temperature is exceeded. Models of methane hydrate deposits need to be
developed and coupled to both the land surface process models anddeep
ocean circulation models.

## 2.7 Sustainability of sea life

*The increasing concentration of atmospheric CO~2~ is changing the pH of
the ocean. What level of change will trigger coral reef collapse,
impacts on fisheries, mega fauna changes, and a change in the ability of
ocean organisms to take up CO~2~?*

## 2.8 Sustainability agricultural ecosystems

*The shift from agricultural or forest production to production of crops
for biofuel could be a significant change in land use patterns in 10--20
years. How might this change the climate's hydrologic cycle and affect
the potential for carbon sequestration of the biosphere?*

Biofuels offer an attractive alternative fuel source that reduces the
net emissions of CO2 into the atmosphere and enhances our independence
from declining and potentially unstable sources of petroleum.
Development of other biofuel sources also looks promising (see Figure
3). The benefits and drawbacks need to be considered in the context of a
carbon sequestration strategy, pollution control, and the hydrological
cycle. This would require fairly detailed models of agricultural
ecosystems at the level of each crop and associated landuse. Such models
at present are fairly simple and parametric (Reference) and a
significant investment and larger computational resources are urgently
needed to advance the development of these models and their integration
into a ESM.

## 2.9 Changes in Precipitation Patterns and hydrology

*?A significant concern for the future are regional scale shifts in
climate patterns that stress surface and groundwater resources and lead
to a disruption of current levels of agriculture production and overall
economic sustainability. What is the extent of these changes, where do
they occur, how often, and what do we need in the ESM's to predict these
changes with some certainty?*

The intelligence community is calling climate change a serious threat to
global security. Competition over water resources under stress and
regional scale shifts in precipitation patterns could further affect
security and political stability in many areas of the world. An increase
in the confidence in the ESM predictions at these scales for
precipitation and hydrology to effectively address these issues over the
next couple of decades. is urgently needed. Model development focused on
methodologies for dealing with the stochastic nature of precipitation
and developdevelopment of more hydrological basin-scale based approaches
for predicting river flow and underground water resources are urgently
needed. New approaches with remote sensed GRACE (Gravity Recovery and
Climate Experiments) water levels and data aassimilation techniques will
reduce these groundwater model uncertainties.

. The actions of nations and populations are not easily quantifiable,
although methods based upon agent models can be used. When these are
teamed with large ensembles of climate change scenarios, the stresses on
the social fabric should be quantifiable and predictable.

**[2.10 Dyamically linking socio-economic response to climate and
climate to socio-economics]{.mark}**

**Summary:** For each of these ten questions, there are significant
scientific challenges on the way toward an adequate answer. The state of
scientific understanding and the challenges in advancing that
understanding further are discussed below (§3 and §4). In some cases,
these challenges can be overcome by basic research into processes,
better observation networks, deeper theoretical understanding, and more
advanced modeling approaches. In all cases, the path will be more direct
and progress accelerated if we can take advantage of petascale and
exascale computational power (§5 and §6). As the demand is amplified for
accurate and reliable predictions of the causes and effects of climate
change (§7), the best approach that scientists can take is to continue
the development of comprehensive Earth system models that can be used as
scientific tools to determine the safe concentration levels for carbon
dioxide and other greenhouse gases in the atmosphere. While the
scientific community is engaged in expanding our theoretical knowledge
and improving our observational depth, we are committed to exploit our
new understanding to address the societal challenges posed by climate
change.

# 3. State-of-the-art

The science surrounding the biogeochemical coupling of climate has
become central to answering these questions as we learn more about how
the coupled carbon cycle has changed in the fossil record, how it is
changing in the present day, and how it might change in response to
global climate change. Addressing the science issues will require new
observations and methods of analysis, new theoretical understanding of
the carbon cycle, and new models of the Earth system that include the
interactions between human society and the environment. These models
play pivotal roles in interpreting the paleoclimate records, in
synthesizing and integrating measurements to study the current carbon
cycle, and in projecting the future responses of human society and the
natural world to evolving climate regimes.

One of the most promising pathways to improving our understanding has
been to develop models that represent the complexity of interactions in
the Earth system as accurately as possible. Over the last 30 years,
these models have advanced considerably in spatial and temporal
resolution and in the representation of key processes. However,
forecasts of environmental and societal responses to climate change
remain highly uncertain. The principal challenges are to quantify the
sources of uncertainty, to reduce the level of uncertainty at all scales
using observations and fundamental theory, and to understand the natural
and anthropogenic feedbacks in the climate system. New,
multidisciplinary teams of physical, biological, and social scientists
could accelerate progress on these challenges with transformational
levels of computing.

The carbon cycle has been characterized using observations from ships,
land surface sites, and aircraft. The amounts of CO~2~ in and exchanges
of CO~2~ among the atmosphere, ocean, and land have been estimated to
the first order. Representations of the carbon cycle have been
introduced into a first generation of Earth system models (ESMs). In
contrast to earlier atmosphere-ocean general circulation models
(AOGCMs), ESMs can simulate the coupled physical, chemical, and
biogeochemical state of the Earth system. Modern AOGCMs operate on
terascale systems, realistically reproduce the historical record of
global warming, and consistently attribute this warming to human-induced
changes in atmospheric chemistry.

One method of assessing our state of understanding is to compare the
process and system level simulations from multiple ESMs for a single
scenario for anthropogenic emissions in hindcast and forecast modes for
integration periods ranging from seasons to centuries. It is evident
from recent studies that the simulated carbon cycle interacts with
climate change to increase, not decrease, the uncertainty in these
forecasts. This uncertainty is caused by many factors, but one of the
most important is the large range of projections for tropical
precipitation. This illustrates that better understanding of
biogeochemical cycles is, to a large degree, contingent on better
understanding and simulation of the physical climate. Systematic error
reduction of the physical climate system needs to progress concurrent to
the advancement of ESMs with BGCM processes and ultimately
socio-economic/energy and emissions feedbacks In addition, the simulated
carbon cycle tends to amplify global warming, although this
amplification is also quite uncertain. The feedback is caused by changes
in the terrestrial carbon cycle that are difficult to test empirically
using our limited observational network and limited process models. The
feedbacks could become important and could therefore confound efforts to
mitigate climate change in the latter part of the 21^st^ century.

Human society has been measurably perturbing the natural carbon cycle
since the mid-18^th^ century. Thanks to comprehensive data regarding the
production and use of fossil fuels, we can quantify the emission of
CO~2~ from these fuels and the disposition of this CO~2~ throughout the
climate system. However, it is unclear if we have socioeconomic models
capable of hindcasting or predicting emissions of CO~2~ with sufficient
accuracy for policy formation. This is especially true since these
models can only be evaluated using historical data, whereas the economic
transformations required to mitigate climate change are without
historical precedent.

In summary, significant advances in understanding biogeochemical cycles
will follow from

-   integration of models and observations of the carbon cycle

-   process-level modeling of biogeochemical cycles across space and
    time scales

-   accurate models of the coupled physical and biogeochemical system

-   robust economic models hindcasting and predicting climate-changing
    pollutants

Attaining these new capabilities requires new approaches that extend
across the traditional disciplines of geophysics, biology, and ecology.
It will require major advances in observational, theoretical, and
computational studies of our environment. Since breakout group B6 is
exploring methods to "incorporate the human dynamic into large-scale
global change analysis," the issues in economic modeling will not be
considered further here.

# 4. Major Technical challenges in understanding biogeochemical cycles

## 4.1 Integration of models and observations of the carbon cycle

Meteorological and oceanic analyses have become an important tool for
studying the mean state and variability of the current physical climate.
These analyses are constructed using a model that is adjusted by
incorporating observations during its integration. These analyses have
proved particularly useful for understanding the relationship between
observations and the underlying dynamics of the climate system. It would
be especially valuable to have a comparable analysis of biogeochemical
cycles that could relate local and global biogeochemical processes.

However, there are no extant analyses that encompass the physical,
chemical, and biogeochemical processes in the climate system.
Development of these analyses will require significant investment in
assimilation systems for chemical and biogeochemical observations from
in situ and satellite platforms. It will also require much more advanced
models to understand the error characteristics of the analysis system.

##  4.2 Process-level modeling of biogeochemical cycles

Simulation of biogeochemical cycles requires detailed understanding of
terrestrial and oceanic ecosystems; the exchange of organic and
inorganic carbon compounds with other parts of the climate system; and
the fluxes of energy, water, and chemical compounds (e.g., nutrients)
that affect these ecosystems. The critical nutrient cycles for ocean and
land ecosystems span over time scales ranging from a few days (such as
nitrogen) to over thousand years (eg. Fe). Modeling over these large
time constants to fully evaluate the couplings between biogeochemical
cycles and ecology will be a significant computational challenge. The
spatial heterogeneity in the biosphere, below ground and above ground
ecology is a fundamental issue overlying much of this science.
Development of new models that can develop sensible volume/area/mass
averaged and mass conserving idealizations that preserve the
heterogeneity of the process and still allow for a degree of
conceptualization are needed. Some other major open challenges are the
sophistication of the ecological representations, the effects of
high-frequency spatial and temporal variability on the carbon cycle
(e.g., fronts and eddies); and the behavior of the biogeochemical cycles
in coastal zones (Doney et al, 2003).

The ecosystem representations tend to be formulated as paradigms of
ecological functions. The field certainly needs more mechanistic models
of these ecosystems constructed at the level of individual organisms. It
also needs much more detailed understanding of the nutrient networks and
how these networks affect the carbon cycle. The effects of sharp
gradients or rapid changes in the physical environment of the components
of the carbon cycle are not well understood. With the advent of
ultra-high-resolution ESMs over the next decade, it should become
possible to probe the effects of rapid variability on scales much
smaller than the mesoscale. Finally, the biogeochemistry in coastal
zones has not been adequately studied. These regions have been
challenging to simulate in global models with insufficient resolution to
resolve the coastal regions, the discharges of river sediments into the
regions, and other related features.

## 4.3 Accurate models of the coupled physical and biogeochemical system

As discussed in the introduction global models of the Earth system are
irreplaceable tools for studying the past, present, and future climate.
The accuracy of these models can, for some processes, be determined
through comparisons with fundamental theory, with observations, or with
benchmark computational models. There are many processes for which there
is currently no practical fundamental theory, for example, the formation
and evolution of clouds and convection. These processes are represented
in AOGCMs and ESMs using simplified statistical representations known as
*parameterizations*. There is also no mathematical theory for the
derivation of parameterizations from either observations or benchmark
computational models. As a result, the parameterizations in ESMs
represent a primary source of uncertainty both in the reliability of the
models as predictive tools and in the fidelity of models to the actual
processes in nature. This uncertainty is manifest in the uncertainties
regarding the sign of cloud feedbacks on climate change, the sign of
convective feedbacks on water vapor, etc.

While it is relatively easy to evaluate the simulations produced using
parameterizations using observations and benchmark calculations, it has
proved extremely difficult to determine how to improve the
parameterizations based upon these evaluations. It has also proved
difficult to quantify accuracy---in a basic sense, it is not clear what
level of accuracy is attainable. It is well known that weather cannot be
predicted accurately beyond roughly one week because of the fundamental
sensitivity of fluid evolution to the initial conditions of the fluid.
However, there is no analogous theory for seasonal, interannual,
decadal, or centennial prediction.

At a minimum, enhanced computing capability should make it possible to
replace parameterizations selectively with computationally-intensive
representations at the limit of our present theoretical understanding.
For example, with exascale computing it may be possible to replace
conventional parameterizations of the carbon cycle (over limited
domains) with mechanistic models that represent individual organisms. It
should also become possible to replace conventional cloud
parameterizations with models of cloud formation based upon the
fundamental physics of condensation.

Ocean models will make better use of the placement of grid points
through unstructured and adaptive mesh technologies that allow for
eddy-resolving simulations with dynamic coasts, sea level rise, detailed
boundary currents, and refinement of critical areas of the bathymetry
such as sills and overflows. Details of tidal mixing---as tides move
over ice melts and enhance melting---will couple with sea and land ice
sheet models for accurate prediction of sea level rise. At the
petascale, we will simulate for centuries; and at the exascale, the
millennial time scales of the deep circulation will be simulated.

# 5. Feasible objectives over the next decade

## 5.1 Integrated models and measurements of biogeochemical cycles

This objective follows from the challenge to synthesize models and
observations of the Earth system. The integrated analysis should include
new measurements of the carbon cycle from planned deployments of
automated oceansondes and aerosondes and from new satellites such as
Orbiting Carbon Observatory and Earthcare. These new observational data
streams will give total column CO~2~ measurements. They should enable
studies critical for detection and attribution of changes in the carbon
cycle, for example, the characterization of the natural variability in
the coupled carbon cycle, the response of biogeochemical sources and
sinks to natural variability in physical climate, and how natural
disturbances such as fires and volcanoes perturb the biogeochemical
cycles.

The coupling of ocean pH with atmospheric CO~2~ will allow a closer
examination of the ability of sea life to adapt to and tolerate climate
change. The complexity of the ocean ecosystem suggests that the carbon
cycle is only the first step in coupling the terrestrial biosphere with
climate. For example, isoprene emissions from Pacific Ocean algae appear
to have an effect on cloud formation. Models of secondary organic
aerosols (SOAs), when compared with the best field measurements,
underestimate SOA by a factor of ten. The treatment of cloud aerosol
interactions, and particulates in general, will be important for
predicting radiation changes as well as nutrient cycles for land and
ocean ecosystems. These treatments require development of much better
microphysical models of multi-component aerosols for the full
multi-decade range of particle sizes observed in the atmosphere.
Physically based and computationally demanding models based upon the
aerosol general dynamic equation should be reconsidered. A comprehensive
methodology for generating secondary organic aerosols from the
potentially numerous organic compounds in the atmosphere from
anthropogenic and biogenic emissions should be developed. This would
help in delineating the differences between the various degrees of BC in
a biomass burning plume, a cause for much uncertainty for calculating
radiative forcing in the current models. Cloud resolving models at very
high spatial resolution would need CCN and droplet activation models
that go beyond the current parametric representation and that can
account for multi-component aerosols with surfactants, inert and
hydrophilic particle nuclei. The impacts of biomass burning on air
quality give urgency to addressing the scientific challenge of
understanding these processes and ensuring that the model is doing the
right thing for the right reasons.

## 5.2 Models for the Earth, environment, and society

The core of these models would simulate the physical, chemical, and
biogeochemical Earth and its environment. This objective follows from
the challenges to develop both process-level and global models of the
physical and biogeochemical climate. These systems would incorporate
subsystems for the impacts of climate change on plants, animals, and
society and for new strategies in adaptation and mitigation of climate
change. Past and present atmosphere-ocean general circulation models
(AOGCMs) have been designed primarily for scientific applications. ESMs
one decade from now should be designed both for scientific
investigations and for societal decision support. These models should
simulate weather and climate from the local to the global scale with
coupling of ecosystems and economic systems. They should solve the
evolution equations in a highly flexible manner that permits refinement
of the dynamics, processes, and chemical state as needed for scientific
or policy-related objectives.

## 5.3 Better theory for and quantification of uncertainty

This objective is possible if there are major new approaches to error
attribution and in the mathematical theory for complex model system.
This objective follows from the challenge to develop demonstrably more
accurate models. If we understand the sources of uncertainty, we may be
able to make models so good in particular areas that we are at the
limits of what we can learn from observation. Conversely, where models
are uncertain, we may be able to suggest observations or experiments
that would significantly add to our knowledge of the climate system.

The propagation of uncertainty through a coupled model is particularly
challenging, as nonlinear and non-normal interactions can amplify the
forced response of a system. New systematic theories about multiscale,
multi-physics couplings are needed to quantify relationships better.
This will be important as ESM results are used to couple with economic
and impact models. The science of the coupling and the quantification of
uncertainties through coupled systems is necessary groundwork to support
complex decisions that will be made over the few next decades.

# 6. Approaches to accelerate development and understanding

As we have emphasized (§3), advances in our understanding require
improved observations, theory, and computationally based models of the
climate system. For the purposes of this discussion, we regard better
observations and theory as "externalities"---our focus is on the
approaches that could benefit from transformational computing.

The Berkeley town hall largely focused on the scientific research
programs that are needed to carry Earth system modeling to the exascale,
whereas the Oak Ridge town hall defined some of the key questions and
challenges that scientists must answer to provide input to policy makers
and decision makers. The Argonne meeting provided more extensive
comments on the strategy to accelerate development in the following
areas.

## 6.1 Focused model development teams with dedicated resources

At present, climate modelers develop ESMs in a mode suitable for large
scientific enterprises. However, assessing the impacts and mitigation of
climate change requires ESMs that have been designed from the outset to
couple to models for ecology, biology, society, and the economy. The
design and exploitation of these models would be greatly enhanced if the
climate community would begin direct collaboration with ecologists,
biologists, social scientists, and experts in public policy.

The design of mitigation strategies that adapt to the changing climate
and our understanding of those changes requires new combinations of
econometrics and game theory. The climate community should collaborate
directly with mathematical economists to incorporate and study the
behavior of interactive mitigation modules in ESMs.

Some of the topics to be addressed by the development teams are

-   high-resolution ESMs with massive assimilation of satellite and
    other data

-   development of hierarchical unit testable model with requirements
    for accuracy in the ESMs

-   detailed modeling of controlled and modified ecosystems to fit the
    environmental envelope in which future climate changes will occur

-   greater scalability and identification of greater degrees of
    parallelism

-   Development of process scale mechanistic models for biogeochemical,
    ecological and aerosol processes.

In short, advancement in the field requires developing collaborations
with

-   ecologists and biologists

-   social scientists and economists

-   applied mathematicians and systems experts

## 6.2 Applied mathematics and computer science collaborations

The climate community needs to force much closer collaboration with
applied mathematicians to address the complexity of climate models.
Areas in which such collaborations could be useful are theoretical
studies of climate models as dynamical systems, new approaches to
quantify and reduce uncertainty, new methods to synthesize models and
data, and techniques to parameterize very complicated processes.

Some of the crosscutting developments that are needed:

-   new applications of new algorithms in the physical climate model

-   new software architectures and rapid development environments to
    facilitate code reformulation and refactoring

## 6.3 High-end simulation capability for climate change experiments and analysis

The new class of ESMs for the environment and society could produce
truly prodigious amounts of model data. Individual ESMs in the next IPCC
assessment will produce on the order of a petabyte of output. Data
volume of this magnitude is already taxing traditional (and usually
serial) analysis techniques and database systems. Extraction of
information critical for impact studies, e.g., systematic shifts in
precipitation extremes and natural modes of variability, will require
new approaches in data archival, data mining, and feature extraction.
Figure 4 shows the balance of modeling investments that result from the
availability of terascale, petascale, and exascale computers. Factors
such as model complexity are traded for resolution, given limited
computational resources.

Some of the specific needs that have been identified as we move toward
exascale are

-   rational design and analysis of computer experiments to navigate
    very large parameter space with very large outputs

-   advances in analysis tools with parallelized capabilities, and the
    ability to explore the full climate solution space using climate
    experiments based on data mining, objective and repeatable metrics
    (e.g. Taylor diagrams), and expert pattern recognition and learning
    capabilities.

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-   increased computational capacity and capability with dedicated
    cycles for large climate change studies

Given the urgency of finding answers to key questions, and the added
complexity of the modeling enterprise in the exascale environment, the
staffing and training of the next generation of earth and computational
scientists are limiting factors. The exascale machines will require a
new level of engagement from the algorithmic point of view. In Table 1,
the shift in algorithmic focus is shown. Multiscale problems are forcing
us to consider many new algorithmic approaches; in Table 2, the changing
emphasis on memory latency and mean time to failure are highlighted.
Computer hardware components and the underlying operating systems will
also be subject to unprecedented demands. The climate community will
have to form even closer collaborations and alliances with hardware
vendors and systems developers to address these major issues.

# 7. Outcomes of accelerated development and understanding

The most important outcome of accelerated development and understanding
will be quicker answers to the key questions that the climate science
community is being asked. At the same time, we will be building momentum
for stronger leadership and depth in climate research and ability to
produce reliable climate forecasts.

These activities should enable the development of entirely new methods
for the attribution of errors in environmental simulation and
understanding. These methods will be based upon a hierarchy of ESMs
running at various ultra-high resolutions combined with a hierarchy of
process-level models of varying complexity. It should become feasible to
separate the uncertainties in the model into terms associated with the
frontiers of understanding at the process and observational level. At
present, the errors are frequently caused by the relatively simple
reductions of these processes and observations incorporated in current
AOGCMs. The accelerated development should make it feasible to separate
model error into

-   Asymptotic process uncertainties: These are the errors remaining in
    the limit of the greatest process fidelity (e.g., incorporation of
    full-complexity cloud models) based on fundamental theory that can
    be constrained by observations. These uncertainties are also caused
    by the interactions of errors among process representations.

-   Asymptotic scale uncertainties: These are the errors in the mean
    state and uncertainties in its high-order statistics (e.g.,
    extremes) remaining in the limit of highest-possible spatial and
    temporal resolution. These are due to couplings between the
    processes, state, and dynamics out of the reach of modern
    observational systems.

-   Asymptotic state uncertainties: These are the errors in the
    constituents of the system (the mixture of condensed and gaseous
    species) remaining in the limit of the most detailed possible
    constituent treatments. Representative treatments include aerosol
    modules that track huge ensembles of individual aerosol particles,
    master chemical mechanisms, etc.

It should also become feasible to attribute uncertainties in studies of
impacts, adaptation, and mitigation to uncertainties in observation,
theory, and computation. For example, this type of attribution could be
facilitated using massive ensembles of ESMs with perturbed physics
coupled to models for ecology, biology, and society. These could yield

-   detailed and comprehensive information regarding the probabilistic
    risks of climate change for the environment and society

-   better engagement of stakeholder communities to define information
    interfaces that inform decision processes

# 8. Major risks to accelerated development and understanding

The major risks to this enterprise are similar to the impediments that
have historically impeded better understanding of climate and
biogeochemical cycles. One of the major risks is the lack of sufficient
observational data to develop, test, and evaluate new process models For
example, there are essentially no routine observations of the vertical
velocity of the atmosphere, and the observations that are available are
collected at isolated surface sites. The absence of data on vertical
velocities has made it much harder to understand and model the
interactions of aerosols and clouds, the dynamics of the boundary layer,
and the vertical mixing of chemical compounds.

It may prove quite difficult to reduce the uncertainties in hindcasting,
forecasting, or present-day simulation of the coupled climate system For
example, the range of equilibrium climate sensitivity has remained
essentially unchanged over the last 30 years of model development. While
the transformation of computing over this time has enabled the
development of much more realistic climate models, estimates of
sensitivity from ensembles of AOGCMs have still not converged. While
this lack of convergence is usually attributed to the rapid increase in
process complexity, it is primarily caused by uncertainties in basic
processes that have been studied intensively for decades, including
cloud evolution and convection.

The climate community has not developed methods to link errors in
climate simulation to errors in process representation, forcing, or
initial conditions. The major difficulty is the difficulty in
attributing error in highly nonlinear systems with huge numbers of
degrees of freedom, e.g. AOGCMs. Conversely, the absence of a basic
theory of error attribution complicates efforts to understand the
connections between process-level realism and the fidelity of the entire
model system. This risk is also related to the lack of proper diagnostic
tools suitable for analysis of complex ESMs.

Finally, there is a risk that the rapid development of ESMs will produce
models too complicated or too expensive for general use.

Thus the major risk factors are

-   lack of sufficient data to constrain key climate processes

-   slow reduction of model uncertainty due to highly intractable or
    more complicated climate processes

-   Absence of adequate diagnostic frameworks connecting forcing,
    response, and initial conditions

-   Development of models too complicated for adoption by the academic,
    impacts, and/or mitigation communities

> **Figures**
>
> ![](media/image1.pct){width="4.986111111111111in"
> height="3.736111111111111in"}

Figure 1. Biofuel production will entail land use changes that interact
with the climate system. National, regional, and local impacts could be
modeled in an exascale Earth system model.

> ![](media/image2.pct){width="4.986111111111111in"
> height="3.736111111111111in"}

Figure 2. The predictability of models is high on the short time scales
and in the long, asymptotic scales. Since many of the questions to be
answered are targeted to the 20--50 year range, the ability of models to
provide reliable forecasts will be challenged.

> ![](media/image3.pct){width="4.986111111111111in"
> height="3.736111111111111in"}

Figure 3. Climate prediction covers the time scales of months to decades
and has great relevance to threat assessment in hydropower, ecosystems,
and energy sectors. This chart shows the relation between weather
prediction and climate change studies.

> ![](media/image4.pct){width="4.986111111111111in"
> height="3.736111111111111in"}

Figure 4. Exascale and petascale computational resources can be invested
in several aspects of a simulation: spatial resolution, simulation
complexity, ensemble size, etc. Each red pentagon represents a balanced
investment at a compute scale.

> ![](media/image5.pct){width="4.986111111111111in"
> height="3.736111111111111in"}

Figure 1. The terrestrial biosphere is a consumer and producer of
chemicals in the atmosphere. As chemicals cycle through plants, soil,
and atmosphere, the long-term feedbacks affect where they are stored.
Nutrients such as nitrogen stimulate plant growth.

Table 1. The "seven Dwarves" extended for atmosphere, ocean, land, and
sea ice models. New developments are highlighted.

  ------ ------------ -------------- ----- --------- --------- ----------- ------- -------------- --------
  Code   Structured   Unstructured   FFT   Dense     Sparse    Particles   Monte   Data           Agents
         grids        grids                linear    linear                Carlo   assimilation   
                                           algebra   algebra                                      

  CAM    X                           X               X         X           X       X              

  POP                 X                              X         X           X                      

  CLM                 X                                                    X       X              X

  CICE                X                              X         X           X       X              
  ------ ------------ -------------- ----- --------- --------- ----------- ------- -------------- --------

Table 2. The climate model stresses computer systems and would run
faster if performance of key subsystems were stronger. On an exascale
computer, emphasis will shift up or down compared with present-day
concerns at the terascale.

> ![](media/image6.pct){width="5.922916666666667in"
> height="4.441666666666666in"}