Acknowledgements

The Department of Energy’s Office of Biological and Environmental 

Research would like to thank all participants in the Climate Research 

Roadmap Workshop for their scientific contributions before, during, 

and after the meeting. The workshop would not have been possible 

without the scientific vision and leadership of the writing group leads: 

Tony Janetos, Richard Rood, Peter Thornton, and Dave Turner. The 

writing teams did an exceptional job of stimulating a productive 

discussion and capturing new ideas and concepts that emerged. 

We also are thankful for the excellent work of the staff from Oak 

Ridge National Laboratory’s Biological and Environmental Research 

Information System, who developed and managed the blogs for the 

workshop and formatted this report.

Suggested citation for this report: U.S. DOE. 2010. Climate Research Roadmap Workshop: Summary Report, May 13–14, 

2010. DOE/SC-0133, U.S. Department of Energy Office of Science (www.sc.doe.gov/ober/BER_workshops.html).

Image credits: Climate modeling image on cover and in Chapter 5 courtesy of Oak Ridge National Laboratory. Sea-ice image on 

cover courtesy of Ted Scambos, National Snow and Ice Data Center. Atmospheric science image in Chapter 3 courtesy of DOE’s 

Atmospheric Radiation Measurement Climate Research Facility. 
Climate Research Roadmap Workshop: Summary Report U.S. Department of Energy Office of Science i

Office of Biological and Environmental Research

Office of Biological and Environmental Research

This workshop report is available at www.sc.doe.gov/ober/BER_workshops.html. 

DOE/SC-0133

Climate Research Roadmap Workshop: 

Summary Report

May 13–14, 2010

Report Publication Date: September 2010
ii U.S. Department of Energy Office of Science Climate Research Roadmap Workshop: Summary Report

Office of Biological and Environmental Research
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Office of Biological and Environmental Research

Contents

Acknowledgements Inside Front Cover

1. Introduction 1

2. Workshop Summary 5

3. Atmospheric Science Discussion Paper 15

4. Terrestrial Science Discussion Paper 21

5. Climate Modeling Science Discussion Paper 27

6. Integrating Science Discussion Paper 35

Appendix A: Workshop Program 41

Appendix B: Workshop Participants and Observers 42

Appendix C: BER Climate Websites 44

Acronyms Inside Back Cover
iv U.S. Department of Energy Office of Science Climate Research Roadmap Workshop: Summary Report

Office of Biological and Environmental Research
1. Introduction

Climate Research Roadmap Workshop: Summary Report U.S. Department of Energy Office of Science 1

Office of Biological and Environmental Research

The U.S. Department of Energy (DOE) 

Office of Science works at the interface 

of climate and energy with the recognition that energy production and use have led to an 

increase in anthropogenic greenhouse gas emissions 

and changes in Earth’s climate. DOE has a long 

history of characterizing the interplay of climate 

and energy. For example, it developed the first general circulation models (GCMs), the forerunners of 

today’s climate models, to understand and predict 

the atmospheric movement of radioactive particles 

from nuclear weapons testing. It also supported 

critical large-scale manipulative ecological studies 

to understand the role of terrestrial ecosystems in 

climate change. DOE’s energy mission led to the 

first federally funded program to study the relationship between atmospheric carbon dioxide and 

climate change. Subsequent collaborative efforts 

produced the first terrestrial carbon cycle models. 

DOE developed and deployed the Atmospheric 

Radiation Measurement program (ARM) to understand the relationships between solar radiation and 

clouds, still one of the greatest uncertainties in 

climate science. DOE is responsible for the Program for Climate Model Development and Intercomparison (PCMDI), which creates improved 

methods and tools for diagnosing and intercomparing GCMs that simulate the global climate. DOE 

also develops models of individual climate system 

components, including the leading ones for oceans 

(Parallel Ocean Program; POP) and sea ice (Community Ice Code; CICE) and, in cooperation with 

the National Science Foundation, the Community 

Land Model (CLM). These DOE-supported activities have contributed to international efforts to 

understand and predict Earth’s changing climate.

1.1 BER’s Major Research Areas 

for Climate

Within DOE’s Office of Science, the Office of 

Biological and Environmental Research (BER) is 

responsible for an integrated program of basic science that is developing a predictive, systems-level 

understanding of the coupled Earth system. Today, 

DOE’s basic climate science programs are providing unique, world-leading capabilities in climate 

modeling and cloud, aerosol, and ecosystem/carbon 

cycle research. These programs also are improving 

our understanding of the effects of greenhouse gas 

emissions on Earth’s climate and the biosphere and 

building foundational science to support effective 

energy and environmental decision making. BER 
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Chapter 1. Introduction Office of Biological and Environmental Research

carries out this scientific mission by tightly coupling 

theory, observations, experiments, and models and 

simulations with scientific emphases in three major 

areas: (1) Atmospheric System Research, (2) Environmental System Science, and (3) Climate and 

Earth System Modeling.

Atmospheric System Research (ASR). This 

research seeks to better understand two major 

uncertainties in climate change projections: the role 

of clouds and the effects of aerosol emissions on 

Earth’s radiation balance. This program is coupled 

to and benefits from observational data generated by 

the Atmospheric Radiation Measurement (ARM)

Climate Research Facility. A multiplatform national 

scientific user facility, the ARM Climate Research 

Facility has instruments at fixed and varying locations around the globe for obtaining continuous, 

long-term field measurements of climate data. The 

facility promotes the advancement of atmosphericprocess understanding and climate models through 

precise observations of atmospheric phenomena and 

is tied closely to ASR research activities.

Environmental System Science. This research 

seeks to understand the terrestrial fate, transport, 

and impacts of energy-related environmental contaminants, including carbon dioxide. Within this 

broad scope, the Terrestrial Ecosystem Science activity develops scientific understanding of how climate 

change affects terrestrial ecosystems and what role 

they play in global carbon cycling.

Climate and Earth System Modeling. This 

research seeks to improve our understanding of current and future climate by: 

1. Developing, evaluating, and using regional and 

global models to obtain more accurate projections of future climate at higher resolution. 

2. Developing improved representations of specific 

model components (e.g., atmosphere, ocean, 

land, sea ice, and ice sheets)—along with better 

coupling mechanisms—and integrating them 

into Earth System Models that take advantage of 

DOE’s high-performance computing capabilities. 

3. Developing and using Integrated Assessment 

Models to determine the impacts from and possible mitigation of climate change. This research 

activity includes exploring the complex interactions of human and natural Earth systems. 

BER’s climate research communities are coordinated 

with those of other federal agencies through the U.S. 

Global Change Research Program (USGCRP) and 

its subsidiary Interagency Working Groups (IWGs).

1.2 Workshop Approach and Goals

In 2008, the BER climate research program developed a strategic plan, which since has prompted a 

number of organizational and scientific advances. 

These include further consolidation of climate 

research activities within BER; an updated program-specific plan for ASR; and several topical 

workshops and associated reports, such as Scientific 

Grand Challenges: Challenges in Climate Change Science and the Role of Computing at the Extreme Scale; 

Science Challenges and Future Directions: Climate 

Change Integrated Assessment Research; and Carbon 

Cycling and Biosequestration: Integrating Biology 

and Climate Through Systems Science. In addition, 

the USGCRP and research programs at its member 

agencies continue to evolve. 

In recognition of the ongoing advances and challenges of climate change research, BER organized 

a workshop asking the scientific community to 

identify the current state of climate science. The 

goal of the workshop was to determine the research 

challenges important for developing a predictive 

understanding of global climate. Participants were 

asked to focus on interdisciplinary research that 

capitalized on BER’s scientific strengths in Atmospheric System Research, Terrestrial Ecosystem 

Science, and Climate and Earth System Modeling. Approximately 50 scientists representing these 

three areas were asked to identify desired outcomes 

for the next 10 years. Goals were identified for the 

near (1–3 years), mid (4–7 years), and long term 

(8–10 years). Discussions were focused by discipline 

(atmospheric, terrestrial, and modeling) and by 

latitude (high, temperate, and tropical). In addition, 

opportunities and needs for integration across disciplines and latitudes were identified with a specific 

focus on crosscutting challenges and outcomes. BER 

will use this workshop output to update its strategic 

plan for climate research.
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Science leads were identified for each of the three 

disciplinary areas (atmospheric, terrestrial, and 

modeling). These leads assembled writing teams who 

drafted discussion papers for each area. To stimulate 

thinking prior to the workshop, the three discussion 

papers were posted on a blog, and all participants 

were invited to read and comment on descriptions 

of current situations; inputs and resources; desired 

outcomes; and near-, mid-, and long-term goals outlined for each science area. On the first day of the 

workshop, discussions were organized by these three 

scientific disciplines. In addition, workshop participants charged with identifying crosscutting challenges and opportunities circulated among the three 

disciplinary breakout groups soliciting input on 

opportunities and needs for integration. The second 

day focused on research challenges from a latitudinal perspective (high, temperate, and tropical) to 

encourage integrative, cross-disciplinary thinking. 

Following the workshop, the leads and their writing 

teams updated the discussion papers and drafted an 

integration paper. The four discussion papers (atmospheric science, terrestrial science, climate modeling, 

and integrating science) are included as chapters 

in this workshop summary report. BER program 

staff also used workshop presentation materials 

and detailed staff notes of workshop discussions to 

develop this report. 
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The Department of Energy (DOE) brings 

a unique and critical blend of research 

resources, infrastructure, and policy to the 

U.S. effort to understand and project the impacts of 

energy production and use on climate change. As 

one of the leaders of U.S. climate change research, 

DOE’s Office of Biological and Environmental 

Research (BER) strives to improve the effectiveness 

of its climate science programs. The BER climate 

science portfolio includes world-leading capabilities 

in research and climate modeling that address major 

knowledge gaps in our understanding of climate 

change, such as clouds and aerosols and the carbon 

cycle. BER is the major U.S. supporter of groundbased observations of clouds and their processes, 

ocean and sea-ice modeling, Integrated Assessment 

Modeling, and large-scale ecosystem manipulation 

experiments. BER leverages climate research investments by other federal agencies, such as remote 

sensing capabilities of the National Aeronautics and 

Space Administration (NASA) and ship-based ocean 

programs of the National Oceanic and Atmospheric 

Administration (NOAA). BER also collaborates 

with the U.S. Department of Agriculture and the 

National Science Foundation (NSF) on the development of decadal and regional climate prediction 

using Earth System Models (ESMs). Other synergies 

include international programs that provide additional data and observations for developing state-ofthe-art climate models. 

2.1 Climate Research 

Recommendations

Workshop participants acknowledged BER’s vital 

contributions to national and international efforts 

in climate science, particularly in climate modeling, clouds and aerosols, and ecosystem research. 

In workshop discussions, seven overarching recommendations emerged for the next 10 years of BER 

climate research.

1. Build upon BER Strengths in Integrated, 

Model-Inspired Science to Understand 

Complex Earth Systems

The goal of the BER Climate and Environmental 

Sciences program is to understand the behavior 

of coupled Earth systems. The inherent complexity of these systems and our limited ability to 

observe processes and interactions as they occur 

have proven to be major challenges to predictive climate simulations at the global scale and 

over extended time frames. Understanding such 

2. Workshop Summary

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complex systems will require a strong component 

of integrated, multidisciplinary research. The 

concept of coupling and integration was a recurrent theme during the workshop and is reflected 

throughout this document. Future integrated 

studies of Earth system dynamics should be 

designed to focus on sensitive parts of the system, 

as suggested by analysis of modeling results. Integrated studies then are designed to take a systems 

approach to understanding a complex system 

(e.g., the carbon cycle) and to apply the disciplines and scales necessary to answer the specified 

questions. Results of such integrated research are 

well aligned to inform both process and fully 

coupled models. Spatial scaling from molecular 

to regional to global then can be reflected in 

corresponding models, with critical information 

and process understanding translated from one 

scale to another. This approach to understanding 

complex physical, biological, and environmental 

systems is unique to BER and important to supporting DOE mission needs.

2. Foster Balanced Program of Discovery 

and Use-Inspired Research

Within BER, there is a need to balance and 

connect the integrated, research campaign 

approach described below with discovery research 

focused on specific processes within the climate 

system. BER’s research is closely connected to 

DOE mission needs, and being able to address 

those needs in both the near and long term 

requires a continuing evaluation to balance the 

longer-term investments associated with discovery 

research with the shorter-term investments 

associated with use-inspired research. Discovery 

research is a hallmark of DOE’s Office of Science 

and provides the scientific foundations for future 

advances in understanding. Within BER’s climate 

program, discovery research includes studies on 

the chemical evolution of atmospheric aerosols, 

cloud microphysics, the role of microbial processes 

in soil carbon cycling, ecosystem community 

succession under changing climate scenarios, and 

improved numerical techniques for representing 

complex systems in global models. An area that 

illustrates BER’s use-inspired research is the 

integrated assessment of climate change—where 

knowledge gained from discovery science is 

synthesized, subjected to further basic research, 

and ultimately expressed in the form of leading 

Integrated Assessment Models (IAMs) that are 

used by the scientific community, planners, 

and decision makers alike. For example, IAMs 

are used by the science community to drive 

scenarios, to make climate model assumptions 

that underpin Intergovernmental Panel on 

Climate Change studies, to provide insights 

on energy technology options and implications 

for greenhouse gas stabilization options, and, 

increasingly, to help reveal multisectoral impacts 

and vulnerabilities and decision options at 

the mitigation-adaptation interface. A careful 

balance of discovery science and use-inspired 

research provides important scientific advances to 

address the needs of the broader climate research 

community while also advancing the basic 

sciences underpinning DOE missions.

3. Develop and Support Targeted 

Scientific Research Campaigns

The concept of scientific research campaigns 

arose independently in several workshop sessions 

as one approach that can be applied to a range 

of scales and scientific challenges. This approach 

would direct the research community to focus 

on a few tractable challenges over short periods 

of time and to structure its research around the 

delivery of scientific “products.” BER has a history of deploying targeted research campaigns 

to understand and predict large, coupled complex systems and currently applies this approach 

to specific process science areas (e.g., Intensive 

Operational Periods for Atmospheric Radiation Measurement (ARM) and Free-Air CO2 

Enrichment experiments). Future research campaigns could be conducted within a particular 

discipline—involving, for example, the synthesis 

of existing knowledge regarding carbon flux or 

efforts to improve the representation of tropical 

precipitation in global climate models. Research 

campaigns also could span disciplines, such as 

improved understanding and model representation of critical Arctic processes. The research 

campaign approach identifies specific scientific 

challenges and applies the necessary efforts in 
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data collection, experimentation, and synthesis 

and modeling to resolve the issue. This approach 

is well aligned with BER’s scientific capabilities 

and strengths. 

4. Understand and Quantify Uncertainty 

in Climate Projections

Quantifying the uncertainty of model results not 

only will provide critical information needed to 

inform science-based policy decisions, but also 

will support the integration of model and process 

research. Research is needed to understand and 

characterize three types of model uncertainty: 

(1) internal, in which multiple runs of the same 

model give different or divergent results because 

of the stochastic nature of some model parameters or the mathematical representations within 

the model; (2) intermodel, which involves using 

ensemble runs to determine if different models 

give different or similar results; and (3) model 

observations, which address the divergence 

between model projections and recorded observations. Each of these uncertainties is important 

for understanding climate simulations and will 

require different approaches to resolve. In addition 

to ensemble comparisons, models need to be challenged with actual observations. This will require 

targeted research that provides information 

compatible with the spatial and temporal scales 

of current models and increased integration in 

the planning, execution, and analysis of climaterelated research programs. 

5. Understand Sign (+ or –) of the Carbon 

Feedback and How It Changes over Time

Global-scale modeling efforts have been used to 

simulate potential effects of climate change on 

ecosystems and their role in the global carbon 

cycle because of insufficient direct evidence 

describing such effects across multiple spatial 

and temporal scales. In general, model results 

show a wide range of terrestrial carbon cycle 

responses to warming. Direct observational 

and experimental evidence shows that processes 

controlling the storage and flux of carbon 

among the atmosphere, terrestrial, and ocean 

biospheres are sensitive to temperature, but 

the mechanisms are only partially understood. 

Changing climate—particularly as it relates to 

increasing temperature—is expected to alter 

the cycling and storage of carbon, resulting in 

feedbacks that further impact climate change. 

Unfortunately, there is not a broad consensus 

on whether this feedback has a positive or negative value. This leads to major uncertainty in 

our ability to project future climate. DOE has a 

long history of contributing to both the process 

and modeling components of understanding the 

carbon cycle and its feedbacks. DOE’s ongoing contributions to the resolution of existing 

uncertainty require improving process-level 

understanding of terrestrial and atmospheric 

systems—including ecosystem-scale observations and experimentation—and incorporating 

these new insights into coupled models. 

6. Understand Role of Natural and 

Anthropogenic Disturbances in 

Earth Systems and Incorporate This 

Information into Model Projections 

Disturbance, both natural and anthropogenic, 

was recognized as a major factor in altering the 

balance of all Earth systems. Disturbances range 

from extreme natural events like fire, drought, 

major storms, and volcanic eruptions to anthropogenic land-use change such as urbanization, 

afforestation, and shifts in agriculture. Disturbances often dominate the long-term response of 

natural systems. However, the role of disturbance 

is neither well understood nor incorporated 

robustly into current ESMs. Experiments and 

modeling suggest that interactions with disturbance history, nutrient supply, and changes 

in temperature and soil moisture all can play 

significant roles in determining the magnitude 

and timing of carbon uptake by land ecosystems 

under increased CO2. Among these factors, the 

role of disturbance is the least characterized and 

is thought to play an important role in ecosystem 

changes. The ability to distinguish among different disturbance types for differing ecosystems is 

critical for accurately incorporating these important factors into models. 
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7. Understand and Incorporate the 

Complete Water Cycle into Regional, 

Climate, Earth System, and Integrated 

Assessment Models

Water was recognized throughout the workshop 

as a critical integrating factor for climate projections and is an integrating factor for many Earth 

systems. The need to understand and incorporate 

the complete water cycle in regional, climate, 

Earth System, and Integrated Assessment models 

was identified as an important goal for the 

climate research community. Known challenges 

exist in representing water cycles in models of the 

physical climate system. Although there is some 

agreement among models on predicting largescale patterns of future precipitation changes, 

regional shifts in water availability and hydrologic 

extremes still are difficult to predict. Nonetheless, precipitation and soil moisture are critical to 

human systems, ecosystem community structure, 

and carbon balance. Understanding and predicting water availability, seasonality, extreme events, 

and use for irrigation are important for more 

integrated insights into coupled Earth systems. 

In addition, subsurface water (i.e., groundwater) 

is poorly represented in most models and can be 

a controlling factor in many ecosystems. Incorporating water cycle considerations across BER’s 

programs will enhance the systems approach to 

climate change research.

2.2 Summaries of Breakout Session 

Discussions

The following section provides summaries of the 

written and oral results of the workshop. The summaries are organized according to the workshop 

structure: disciplinary discussions in atmospheric 

science, terrestrial science, and climate modeling, as 

well as discussions of high, temperate, and tropical 

latitudes. Additional details may be found in subsequent chapters containing the full discussion papers.

Atmospheric Science

The Atmospheric Science group emphasized that the 

impacts of aerosols and cloud-aerosol interactions 

on climate, cloud responses, and associated radiative 

effects are among the greatest uncertainties in our 

ability to predict climate change. These processes 

and their impacts on the climate system also vary 

with the climate regime and are, in turn, impacted 

by interactions with the rest of the Earth system 

depending on latitude and geography.

Desired Outcome. Understand and quantify the 

interplay among aerosols, clouds, and climate 

and determine the mechanisms responsible for 

cloud feedbacks in order to improve the reliability 

of future climate change predictions, with particular attention to the impact on Earth’s radiative balance and precipitation.

Approach. The Atmospheric Science breakout group 

identified three related but distinct areas of research in 

which advances are needed to achieve this outcome:

1. Aerosols and their interactions with clouds.

The interactions between aerosols and clouds 

represent a critical area of uncertainty in climate 

change studies. A more precise understanding 

of these interactions is needed to interpret the 

impact that changing emissions resulting from 

shifts in energy sources and land use have on the 

chemical and physical properties of aerosols and 

on the combined influence on cloud properties.

2. Cloud feedbacks. Better characterization and 

quantification are needed of the mechanisms, 

radiative impacts, and signs of cloud feedbacks. 

Also needed are estimates of the regional magnitude and uncertainty of both shortwave and 

longwave cloud feedbacks.

3. Improved strategies for high-resolution modeling. Higher-resolution simulations, evaluated 

with observations on a similar scale, need to be 

performed. These simulations are necessary to 

expose the sensitivity of the energy and hydrological cycles and their interactions with clouds and 

aerosols to the resolution of models.

Near-Term Goals (1–3 years). Efforts should be 

continued to refine our understanding of and ability 

to predict atmospheric processes that control radiative flux between the top of the atmosphere and the 

boundary layer (i.e., clouds, aerosols, and related processes). Collection and use of long-term observational 

records from the ARM Climate Research Facility 
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Chapter 2. Workshop Summary Office of Biological and Environmental Research

should be continued, expanded, and exploited. The 

resulting improvements in understanding need to be 

incorporated into process and global models. These 

models, in turn, must be evaluated iteratively against 

observational datasets, including those from the ARM 

Climate Research Facility.

Mid-Term Goals (4–7 years). Enhanced sets of 

observational data and process information should be 

used to test the fidelity of improved candidate physics 

modules (as well as current Community Earth System 

Model modules) in hindcast and case study mode. The 

ability of these modules to reproduce physical cloud 

properties and radiative effects for different aerosol 

and dynamical conditions needs to be documented, 

and predicted cloud feedbacks need to be determined 

over multiple scales. These test case results and refined 

observational and experimental process studies should 

be used to develop next-generation refinements to these 

model components. The sign, magnitude, and uncertainty of global shortwave cloud radiative feedbacks 

also need to be estimated.

Long-Term Goals (8–10 years). Models should be 

developed that are capable of representing aerosols, 

clouds, their interactions, and their impacts on 

precipitation and radiative budgets. Observational 

datasets specifically designed to evaluate the planned 

model advances must be developed and used. A series 

of simulations needs to be conducted with these 

models over a range of temporal and spatial scales to 

demonstrate and determine their improved ability to 

represent cloud properties. 

Terrestrial Science

The Terrestrial Science group noted that terrestrial 

ecosystems store roughly four times more carbon 

than the atmosphere and that annual carbon fluxes 

between the terrestrial biosphere and the atmosphere are nearly seven times greater than annual 

anthropogenic carbon emissions from fossil-fuel 

combustion and land-use change. Thus, the carbon 

stored in long residence–time terrestrial sinks (e.g., 

wood and soil organic matter) has the potential, over 

time, to significantly impact the carbon balance in 

atmospheric reservoirs. The potential for rapid and 

large perturbations to these systems supports the 

need for quantitative understanding of the responses 

and feedbacks so that future climate change can be 

accurately predicted.

Desired Outcome. Understand and quantify 

regionally specific effects of ongoing and future 

climate changes on the structure and functioning of terrestrial ecosystems, including feedbacks 

between ecosystems and the climate system, so 

that society can better understand, predict, and 

plan for climate change.

Approach. The primary focus should be to obtain 

a detailed mechanistic understanding of ecosystem 

dynamics—including net land carbon flux—and 

the relationship to climate change factors. Important 

processes to include are net primary production, 

autotrophic respiration, heterotrophic respiration, 

and ecosystem changes due to disturbance. Focused 

research is needed on mechanisms that have strong 

interactions with climate change and are poorly 

constrained by theory and observation. This breakout group recognized that different mechanisms are 

likely to play dominant roles in different climate 

zones and ecosystem types. The group also noted that 

a key to deconvoluting these challenges is to target 

critical processes in different climate zones, and they 

identified key questions associated with specific latitudinal systems. Subsurface ecosystem mechanisms 

(e.g., biogeochemistry) also were discussed as particular areas of need well aligned with BER capabilities. 

Near-Term Goals (1–3 years). Basic research and 

multifactor ecosystem manipulation experiments 

need to be conducted in the most climatically 

important and sensitive terrestrial ecosystems (e.g., 

the Arctic). Long-term observational programs such 

as AmeriFlux should be continued, expanded, and 

leveraged. Importantly, all of these efforts must be 

carried out in coordination with the needs and perspectives of the modeling community.

Mid-Term Goals (4–7 years). Model projections 

should be tested against initial results from the outlined experiments. The results can be used to improve 

process models and next-generation ESMs. As Arctic 

experiments progress, corresponding experiments 

should be initiated in a second priority ecosystem 

using lessons learned and scientific and modeling 

advances made in the interim. Observational datasets 
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need to be expanded by enhancing the AmeriFlux 

network with model-informed data and analysis infrastructure to improve the synthesis, evaluation, and 

transfer of knowledge to ESMs. Interactions between 

ESM and process science communities should be 

continued to identify priority research needs in each 

discipline, with an emphasis on making regional estimations of ecosystem function and response.

Long-Term Goals (8–10 years). Results from Arctic 

experiments must be synthesized to improve process 

representations in state-of-the-art ESMs. Model projections should be tested against initial results from 

experiments in the second priority ecosystem. The 

resulting knowledge needs to be incorporated into 

relevant process models and next-generation ESMs. 

The AmeriFlux network should be updated based 

on the needs of and results from model sensitivity 

analyses and scientific and technological advances. A 

major synthesis of results from process research and 

ESM projections should be conducted to estimate 

ecosystem response at regional and global spatial 

scales and at decade to century time scales. 

Climate Modeling

Models of Earth’s climate system are mathematical 

tools that provide an integrated view of the planet’s 

current and future climate. Improved through better 

understanding of relevant physical, chemical, and 

biological processes, these models increasingly represent and incorporate previously unaccounted physical, chemical, and biological mechanisms. However, 

the size, complexity, and sophistication of models 

of Earth’s physical systems already exceed current 

computational capabilities. Additionally, demand has 

increased for reliable climate information on regional 

scales for a variety of aspects relevant to society.

Desired Outcome. Develop and test an application-focused comprehensive Earth System Modeling capability and analysis environment that 

includes natural and human Earth systems, information on climate change at decade to century 

time scales and local to global spatial scales, and 

descriptions and quantification of uncertainties.

Approach. The Climate Modeling breakout group 

identified four related but distinct areas of research 

that need to advance to achieve this outcome:

1. Development of a comprehensive ESM. Such a 

model would include physical, biogeochemical, 

and human-system components. The integrated 

model also would incorporate resolved dynamics; 

parameterizations of unresolved physical, chemical, and biological processes; and algorithms to 

couple submodels and component models. 

2. Development of a comprehensive, multiscale 

modeling capability that spans regional to 

global scales. This effort is important because 

critical aspects of global climate change emerge 

through the rectification of processes occurring at 

spatial and temporal scales well beyond current 

modeling capabilities.

3. Development of applications and environments 

for model evaluation. Model projections must 

be evaluated by independent researchers. This 

requires participation of diverse groups of scientists who have expertise in observations and their 

quality and who are familiar with the applications 

of models and quantitative assessment of model 

strengths and weaknesses.

4. Description and quantification of uncertainty 

in climate models. Uncertainty needs to be 

described and quantified in each aspect of process 

understanding. The resulting understanding needs 

to be incorporated into models (at all scales) and 

into the projections of those models. BER research 

also should address model uncertainty as both an 

explicit and implicit part of determining priorities 

in the model development path. 

Developing an ESM that can be used for projecting 

future climate change will involve a series of steps in 

which new processes and methods are continuously 

and incrementally added and tested. This includes 

informing model development with a cyclical process of observation, measurement, and experiment as 

part of the progression of model development, evaluation, and release. 

Near-Term Goals (1–3 years). The Climate Modeling group identified a need to implement and 

evaluate the latest generation of ESMs while increasing emphasis on regional-scale modeling and the 

associated methodologies necessary for producing 

regional-scale resolution. Efforts should continue to 

provide physics-based representations of currently 
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parameterized critical processes and to develop firstgeneration integrated ESMs. To advance our ability 

to evaluate models and assess their state of the art, 

the community should support organized simulation 

and analysis efforts, such as the Intergovernmental 

Panel on Climate Change (IPCC) and the Coupled 

Model Intercomparison Project (CMIP). Efforts 

should be made to identify and resolve critical biases 

identified by the analyses. An identified overarching need is to describe and quantify uncertainty 

in model projections. This will require developing, 

evaluating, and implementing new approaches to 

compare uncertainty from numerous perspectives.

Mid-Term Goals (4–7 years). Additional and 

improved process models need to be incorporated into coupled models (e.g., ESMs). Near-term 

advances in regional-scale modeling will be incorporated into ESMs, including improvements in time 

integration for managing multiple resolutions in the 

same model and better approaches for initializing 

simulations. Biases identified in previous evaluations 

should be addressed by improving existing process 

models or developing new models as appropriate. 

Next-generation ESMs using multiscale approaches 

that allow the regional evaluation of, for example, 

clouds and ocean eddies should be run and evaluated. Model evaluation should provide feedback 

to drive improvements in underpinning models. 

Previously developed approaches to uncertainty 

quantification should be applied to current simulations. Improvements in such quantification need to 

include a framework for evaluating regional-scale and 

comprehensive ESMs. 

Long-Term Goals (8–10 years). State-of-the-art 

capabilities in process, regional, Integrated Assessment, and Earth System Modeling should be used 

to create a comprehensive and integrated ESM. This 

will include using state-of-the-art approaches that 

provide multiscale capabilities (nested regional to 

global modeling scales) and quantify uncertainty. 

This new capability should be used to initiate, evaluate, and iterate grand challenge simulations.

Latitudinal Opportunities for Integrated 

Research Efforts

High Latitudes. Discussions about challenges 

and opportunities for high-latitude research 

predominately centered on the current lack of 

long-term observational datasets and the important, but poorly understood, potential impacts of 

disturbance. The major conclusion was that most 

high-latitude systems are insufficiently observed, 

understood, and represented in global models. The 

concept of polar amplification was stressed (i.e., 

alterations of climate-related, high-latitude systems 

may have significant global implications, such as 

sea-level rise from melting ice or massive carbon 

release from thawing permafrost). This is complicated by the fact that understanding of temperate-latitude systems does not translate well into 

understanding of high latitudes (e.g., clouds and ice 

nucleation and interactions among sea ice, land ice, 

and snow). There are also unique biomes and interfaces, such as biogenic activity around permafrost 

or sea ice, that can be studied only in high-latitude 

systems. Identifying and investigating major disturbance regimes (e.g., changing snow and ice coverage, fire, and warming) were seen as the best ways 

to prioritize research efforts. Positive feedbacks 

with likely significant impacts include: albedo–

temperature–water cycle feedbacks from warming; 

the magnitude and nature of carbon releases from 

melting permafrost; and external (anthropogenic) 

radiative forcing exerted on the Arctic, including 

cloud cover, black carbon, ozone, sulfate, greenhouse gases, and organic carbon aerosols. 

Examples of global-scale, overarching research challenges in high-latitude systems include:

•	 Determining the consequences throughout the 

system of a seasonally ice-free Arctic, including 

impacts on terrestrial and marine ecosystems, 

permafrost thaw, atmospheric circulation, cloud 

formation, and aerosol availability and type.

•	 Assessing the potential predictability (and how to 

realize that predictability) of the Arctic system on 

different time scales.

•	 Identifying and characterizing tipping points and 

abrupt changes. 

•	 Understanding the role of extreme events on the 

trajectory of the system. 

In addition, group members acknowledged that 

process research in areas currently not part of the 
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BER program is needed because such studies will 

inform BER modeling efforts. These include cryospheric and oceanographic processes associated with 

the Arctic, particularly in light of the emergence of 

ice-free water. Such research would require engaging 

other federal agencies that support climate science. 

Overall, high-latitude research efforts are challenged 

by the remoteness of these systems, the extreme conditions under which observations and experiments 

need to be performed, and the international nature 

of the Arctic system in particular. 

Temperate Latitudes. Because they have the largest 

populations, temperate latitudes were highlighted as 

the locations where humans will be impacted most 

by climate change. Temperate regions also are home 

to major sources of greenhouse gas emissions, are the 

most studied and observed areas, and thus are the 

latitudes of greatest understanding and data density. 

A high priority is to synthesize existing temperatelatitude ecosystem and climate process studies—an 

effort that will advance Earth System Modeling. The 

role of the water cycle and the impacts of a changing 

climate on the water cycle also were seen as important research objectives. Although not unique to this 

latitude, the importance of the water cycle was highlighted as an integrating approach that encompasses 

many critical aspects of climate research: ecology, 

cloud processes, and aerosols. The modeling community sees temperate latitudes as crucial for the 

movement of water from tropical to high latitudes. 

Temperate latitudes are where human impacts are 

generated and where changes probably will have 

the greatest direct effects (e.g., water availability 

and agriculture). The richness of available datasets 

from this region argues for intensive analysis and 

evaluation of regional-scale prediction capabilities 

with optimal science input. An example would be 

to conduct sensitivity studies of process climate 

modeling over a continent. The concentration of 

human impacts in this latitudinal band points to 

the potential for the human dimension to dominate 

other forcing factors, including the ability to predict 

how the entire Earth system would respond to 

anthropogenic interventions such as geoengineering 

or cap-and-trade measures.

Examples of cross-disciplinary research emphasized 

for temperate latitudes include: (1) aerosol fertilization 

of extensive temperate forests and generation of secondary aerosols from these forests, (2) aerosol indirect 

effects and cloud feedback effects, and (3) the need to 

close the temperate-latitude carbon budget. 

Tropical Latitudes. The tropics (the area between 

± 23.5 degrees latitude) represent approximately 

40% of Earth’s land surface area. However, this geographical location is thought to be responsible for 

a disproportionately large number of known model 

biases. Coordinated efforts are needed to understand 

and incorporate the roles of tropical ecosystems into 

models and to determine how these roles are likely 

to change as a result of human activities. Major 

model deficiencies with regard to tropical processes 

include El Niño–La Niña-Southern Oscillation 

(ENSO), Inter Tropical Convergence Zone (ITCZ), 

Madden–Julian Oscillation (MJO), and monsoon 

and precipitation features. Workshop participants 

noted that these deficiencies are the same areas the 

research community would have listed a decade 

ago—an observation highlighting the scientific 

challenges inherent in such processes.

The water cycle was seen as a critical factor for 

understanding these and other integrated tropical 

processes. This cycle also is a dramatically understudied aspect of diverse systems, including cloud 

processes, rainfall seasonality, and soil moisture. 

Because it links terrestrial and atmospheric processes, 

the water cycle is a potential integrating factor for 

BER climate research programs. From a modeling 

perspective, efforts to link models across scales are 

another mechanism to integrate process and modeling science. Scaling and information exchange also 

are viewed as critical research challenges. 

There is a need to reconcile the various components 

of the global carbon budget and to be able to predict 

the future of the tropical carbon sink. This includes 

important integrating research topics such as understanding and modeling forest and savannah transitions with respect to water and nutrient cycling, 

aerosol production, and biogenic organics. A coupled 

carbon-water tropical research program could engage 

the full spectrum of BER climate science and provide 

critical advances to process science and ESMs. Fire, 
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although not unique to tropical systems, is a critical and often underrepresented factor in modeling 

ecosystem structure, carbon cycling, and aerosol production. The impact of land-use change (particularly 

deforestation and irrigation) is a major crosscutting 

issue with broad climate implications for the tropics. 

Opportunities exist to establish common research 

infrastructures in the tropics, including coordinating the establishment of long-term observational 

platforms with large-scale manipulative experiments. 

Individual-investigator research in tropical systems 

should be encouraged as a step toward potential 

larger-scale efforts in the future.
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3. Atmospheric Science Discussion Paper

Understanding and Quantifying the Interplay among Aerosols, Clouds, and Climate

3.1 Current Situation

General circulation models (GCMs) are complex, integrative tools that rely heavily on 

parameterizations of the physical processes 

of the climate system. Although today’s GCMs are 

far superior to those from a decade ago, the range of 

climate sensitivity values from these models has not 

decreased significantly with time. A major challenge in current climate research is understanding 

the effects of aerosols and cloud-aerosol interactions 

on climate as well as the cloud response and radiative impact associated with changing greenhouse gas 

forcing and aerosols. As documented in assessments 

by the Intergovernmental Panel on Climate Change 

(IPCC) and others, these two areas represent the 

greatest uncertainties in quantitatively predicting 

climate changes and their impacts, such as shifts in 

regional precipitation. For example, although there is 

some agreement among models on predicting largescale patterns of future precipitation changes, regional 

shifts in water availability and hydrologic extremes 

still are difficult to predict.

Cloud extent and lifetime, as well as precipitation amount and intensity, are strong yet complex 

functions of the environment in which clouds and 

precipitation form. These environmental factors 

include aerosol loading, composition, and particle 

size as well as the small- and large-scale dynamic and 

thermodynamic state of the atmosphere. The uncertainty in cloud response to external forcings impedes 

the ability to understand how climate has and will be 

influenced by changes in the emission of greenhouse 

gases, aerosols, and their precursor gases and by the 

associated cloud feedbacks. 

Our understanding of atmospheric processes has 

increased greatly over the last decade because of 

improved observational and laboratory capabilities and 

the use of high-resolution process models (e.g., large 

eddy simulation models and cloud-resolving models). 

However, this new knowledge has been incorporated slowly into GCMs because of the complexity of 

the processes and the difficulty in representing that 

complexity in models that can treat the processes only 

parametrically. Moreover, our knowledge is incomplete 

in many areas, particularly (1) the interactions between 

clouds, small-scale dynamics, and the large-scale atmospheric state; (2) processes occurring at the molecular 

scale that influence aerosol and cloud-droplet formation and ice nucleation; and (3) aerosol-chemistrycloud interactions and precipitation scavenging. 

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The large-scale atmospheric state has strong regional 

dependence, especially as it relates to aerosol and 

cloud properties. Cloud feedbacks and aerosol 

effects—including direct light scattering and 

impacts on clouds—then may be expected to be 

climate regime dependent and will interact with the 

rest of the Earth system in complicated ways at different latitudes and over different types of surfaces.

3.2 Inputs and Resources

The atmospheric research programs within the 

Department of Energy’s (DOE) Office of Biological 

and Environmental Research (BER) have a proven 

history of providing cutting-edge science for understanding anthropogenic influences on climate. BER 

atmospheric research is focused on developing a 

sufficiently detailed and accurate understanding of 

aerosols, their interactions with clouds, and the subsequent impact on climate so that models incorporating 

these processes will have substantially less uncertainty 

in their predictive capabilities than do current ones.

BER has a wide range of resources being used to 

address the outstanding questions in atmospheric 

systems research. These include:

•	 Complex physically based atmospheric models spanning a wide range of scales, from the local to global.

•	 Extensive atmospheric datasets on aerosols, clouds, 

radiation, and precipitation acquired in the past 1 

to 2 decades. These datasets include ground-based 

remote sensing data, satellite data, and in situ data 

from aircraft, unmanned vehicles, and balloons.

•	 The ability to collect additional data for targeted 

problems through long- and short-term field 

experiments using Atmospheric Radiation Measurement (ARM) Climate Research Facility fixed 

sites, ARM Mobile Facility campaigns, and ARM 

Airborne Facility missions.

•	 New techniques for measuring broad categories of 

the composition of aerosols and their precursors.

•	 New experimental laboratory systems and approaches for studying the formation and fates of 

aerosols under atmospheric conditions and their 

role as cloud condensation nuclei (CCN) and ice 

nuclei (IN).

•	 A proven capability to integrate the results of laboratory and field studies into state-of-the-art models 

on a variety of geographical and temporal scales.

These resources—both intellectual and physical 

or infrastructural—are distributed among many 

entities, including DOE national laboratories 

such as Pacific Northwest National Laboratory 

(and the DOE Environmental Molecular Sciences 

Laboratory), Argonne National Laboratory, Oak 

Ridge National Laboratory, Brookhaven National 

Laboratory, Lawrence Berkeley National Laboratory, Lawrence Livermore National Laboratory, 

Sandia National Laboratories, Los Alamos National 

Laboratory, and the National Renewable Energy 

Laboratory. Additional participants are other 

governmental agencies like the National Oceanic 

and Atmospheric Administration, the National 

Aeronautics and Space Administration (NASA), 

and Environment Canada; U.S. and international 

universities; and other research institutes (e.g., the 

National Center for Atmospheric Research). This 

breadth of participants and resources provides a 

unique and powerful springboard to generate new 

and transformative insights into aerosols, clouds, 

and their interactions, as well as cloud dynamics 

and their impacts on climate.

3.3 Desired Outcome

DOE, as the steward of U.S. energy policy, has 

sponsored research aimed at improving the representation of a wide range of atmospheric processes 

in GCMs to advance the fidelity of these models. 

In the next decade, BER will focus on understanding and quantifying the interplay between aerosols, 

clouds, and climate and the mechanisms responsible for cloud feedbacks. This knowledge will help 

improve the reliability of predictions of future 

climate change, with particular attention to impacts 

on radiative balance and precipitation. To accomplish this goal, the problem will be separated into 

three subobjectives:

1. Aerosols and Their Interactions with Clouds.

The interaction between aerosols and clouds is a 

critical area of uncertainty in climate change 

studies (past and future). A more precise 

understanding of these interactions is needed 
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to interpret how changing emissions (of aerosols, their precursors, and greenhouse gases) 

resulting from shifts in energy sources and land 

use affect the chemical and physical properties 

of aerosols and the combined influence on 

cloud properties.

2. Cloud Feedbacks. Better characterization and 

quantification of the mechanisms and radiative impact of cloud feedbacks are needed. This 

includes the signs and estimates of the regional 

magnitude and uncertainty of both shortwave 

and longwave cloud feedbacks. 

3. Improved Strategies for High-Resolution 

Modeling. Such strategies will involve performing higher-resolution simulations, evaluated with 

observations on a similar scale, to expose the sensitivity of the energy and hydrologic cycles—and 

their interactions with clouds and aerosols—to 

the model resolution. These studies will include 

land surface models of vegetation, storage, and 

run-off; atmosphere-cryosphere interactions; 

processes affecting changes in extreme weather 

events; and interactions between cloud- and 

large-scale circulations.

The near-, mid-, and long-term goals associated with 

these subobjectives are given below to provide one 

possible roadmap for how each could be accomplished. Although clearly interrelated with multiple 

connections, these subobjectives are discussed separately in this chapter to maintain roadmap clarity.

Aerosols and Their Interactions 

with Clouds

Aerosols and their interactions with clouds are 

responsible for major uncertainties in characterizing 

climate change over the last 150 years. Anthropogenic sources for aerosols and their gaseous precursors have changed dramatically over this period 

as emissions associated with fossil fuel, land use, 

and agriculture have changed. Uncertainty in the 

response of clouds to aerosol changes (in terms of 

changes in cloud reflectivity, extent, and lifetime 

and in precipitation amount and intensity) has complicated our understanding of how the Earth system 

has responded to changes in greenhouse gases during the past century. The uncertainties introduced 

in interpreting this driving agent of climate change 

impede our ability to understand how changes in 

other such agents (like greenhouse gas emissions and 

cloud feedbacks) have and will influence climate. 

For this reason, the climate research community 

needs to intensify its efforts to (1) provide a quantitative understanding of aerosol influence on clouds 

and climate and (2) reduce uncertainties associated 

with the impacts of anthropogenic emissions of 

aerosols (and their precursors) on climate.

Near-Term Goals (1–3 years)

•	 Identify a set of case studies (with boundarycondition and evaluation datasets) for large 

eddy simulation (LES) modeling appropriate for 

studying aerosol-cloud interactions in different 

synoptic and climatic regimes. While several previous Global Energy and Water Cycle Experiment 

(GEWEX) Cloud System Study (GCSS) activities 

have attempted to compare the behavior and realism of LES models, these studies have not attended 

sufficiently to the role of aerosols in LES response.

•	 Analyze output from the IPCC’s Fifth Assessment 

Report (AR5) for the range of described emission scenarios to characterize and understand the 

various responses to aerosols from the different 

climate models employed.

•	 Improve our understanding of aerosol impacts on 

clouds using a mixture of ARM advanced data 

products and fixed-site observations (augmented 

by instrumentation funded by the American 

Recovery and Reinvestment Act), ARM Mobile 

Facility and ARM Airborne Facility deployments, 

laboratory studies, and satellite data. This goal 

includes ascertaining the important chemical, 

physical, and structural properties of aerosols that 

determine their CCN/IN activity.

•	 Conduct laboratory and field experiments to 

elucidate the major precursors of aerosols and the 

important oxidants and mechanisms leading to 

aerosol formation.

•	 Design field and laboratory experiments to 

measure the indirect effect of aerosol changes on 

cloud fields, including perturbations introduced 

into the aerosol field in the vicinity of clouds. 
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Mid-Term Goals (4–6 years)

•	 Comprehensively characterize aerosol formation 

and chemical evolution. Such characterizations 

will require demonstrating an understanding of 

the chemistry involved and an ability to predict, 

for example, aerosol size distribution, optical 

properties, and CCN/IN activity given a set of 

precursor gases and aerosol emissions and a set of 

meteorological conditions in the vicinity of the 

emission region (prior to cloud processing).

•	 Statistically describe the macro- and microphysical 

properties of clouds for different aerosol, dynamical, and thermodynamical conditions—separating 

the statistics by some sort of classification scheme 

and different climate regimes.

•	 Demonstrate that LES models correctly simulate 

cloud and precipitation response to clean versus 

polluted conditions in different dynamical and 

thermodynamical environments.

•	 Determine the interplay among gases, aerosols, 

and aqueous chemistry that occurs in clouds and 

the subsequent impact these interactions have on 

aerosol properties (i.e., cloud effects on aerosols).

Long-Terms Goals (7–10 years)

•	 Construct an observationally based dataset of cloud 

and aerosol properties over a range of dynamical 

and thermodynamical conditions. This dataset 

must allow separation of aerosol impacts from the 

influence of meteorological variability on cloud 

properties to understand the relative importance of 

these factors. 

•	 Demonstrate that models can properly simulate 

cloud properties in both aerosol-perturbed and 

-unperturbed conditions. In other words, show 

that a model can separate the meteorological 

versus aerosol influence on clouds in short-term 

episodes over a range of different meteorological 

regimes and cloud types.

•	 Implement parameterizations of appropriate 

aerosol-cloud interactions in climate models. 

•	 Produce models capable of projecting aerosol influence on clouds and precipitation and the impact 

on the radiative budget that is relevant for climate 

change studies.

Cloud Feedbacks

Approximately 20 years ago, cloud feedbacks were 

identified as fundamental drivers of differences among 

simulations of climate change produced by different 

models subject to the same climate forcing. Today, we 

have accurate, decade-long, top-of-atmosphere (TOA) 

fluxes from CERES (NASA’s ongoing Clouds and 

the Earth’s Radiant Energy System experiment) and 

accurate surface fluxes from a collection of surface 

sites. The computed atmospheric shortwave absorption 

values—when TOA and surface fluxes are matched 

in space and time for short time periods of an hour to 

a day—are wildly variable and inconsistent with our 

theoretical understanding of atmospheric radiative 

transfer. Clouds and their interaction with shortwave 

radiation are deemed to be the causes of this problem. 

Understanding and resolving this challenge represent 

a fundamental step toward knowing how clouds affect 

Earth’s radiation budget.

Given the relatively short period of satellite observations and the natural variability of the climate system, 

observing cloud feedbacks associated with slow 

climate forcing—such as the increase in greenhouse 

gas concentrations—is not yet possible. However, we 

can observe changes in cloud properties and associated changes in cloud radiative impacts in response to 

observed shifts in short-term internal climate dynamics (such as the El Niño-Southern Oscillation and 

Madden–Julian Oscillation in the tropics) or seasonal 

changes in ice cover in the polar regions. Gaining 

this information requires explicitly retrieving cloud 

optical and microphysical properties and relating the 

variability in these properties within a climate regime 

to the thermodynamic and dynamic variability in 

atmospheric circulation on the regional scale. The 

climate research community needs to develop appropriate methodologies—usable in both the current 

atmosphere and in model simulations of the current 

atmosphere—for evaluating the ability of GCMs to 

adequately represent cloud radiative effects and their 

response to circulation changes on a short time scale. 

Additionally, the climate modeling community needs 

to coalesce behind a methodology (or possibly a few 

methodologies) to assess cloud feedbacks in models 

so that (1) each model can be subjected to the same 

analysis and (2) comparisons among model representations of cloud feedbacks can be simplified.
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Near-Term Goals (1–3 years)

•	 Improve our understanding of the relationship between observed TOA and surface radiative fluxes 

and their dependence on cloud structure and optical properties.

•	 Converge on a methodology to assess cloud feedbacks in GCMs and to relate observed changes 

in the properties and radiative effect of clouds to 

changes in circulation properties.

Mid-Term Goals (4–6 years)

•	 Observationally relate the radiative effect of clouds, 

in both the shortwave and longwave, to variability 

in cloud properties and environmental conditions. 

This evaluation must be carried out as a function 

of climate regimes, paying particular attention 

to the ones where clouds play a dominant role in 

determining the regional radiation budget, such as 

the Arctic and marine stratocumulus regimes.

•	 Assess cloud radiative impacts and their response 

to short-term (annual to decadal) circulation variability in the AR5 models and provide a quantitative evaluation of these quantities compared to 

observations of the current climate as a function 

of climate regime. 

Long-Term Goals (7–10 years)

•	 Determine the sign and magnitude, within a factor 

of 2, of the global shortwave cloud feedback.

•	 Demonstrate that global climate models can simulate changes in cloud structure, optical properties, 

and radiative effects consistent with the variability 

and change in circulation and sea surface temperature over the last 20 years.

Improved Strategies for HighResolution Modeling

Because cloud and cloud-aerosol interaction processes operate on small scales, higher-resolution 

regional models, cloud-resolving models (CRMs), 

and LES models increasingly are being used to 

address specific questions about cloud processes. 

In most case studies, models have disagreed markedly among themselves and relative to observations 

because of different parameterizations of ice-phase 

and mixed-phase microphysics, turbulence on 

subgrid scales, and the resolution and domain size 

used. Climate models produce information at coarse 

scales, but many applications of practical interest require smaller-scale information on statistics 

of quantities like precipitation. Various statistical 

and dynamical downscaling techniques have been 

developed for this purpose, with no approach being 

clearly preferable. A different category of downscaling is the multiscale modeling framework approach 

that embeds a coarse CRM within each gridbox of 

a global climate model to replace the parameterizations of clouds and convection. These models have 

shown some skill in deep convective regimes, but 

their usefulness in other climate regimes must be 

explored more fully. A single approach to downscaling likely will not be ideal in all situations 

(e.g., simulating hurricanes versus simulating local 

precipitation statistics to drive a hydrologic model). 

An optimized approach to downscaling might 

allow some fundamental questions to be addressed 

about cloud feedbacks and their impact on general 

circulation and regional climate change before conventional GCMs or global CRMs are able to tackle 

such issues. The community needs to undertake a 

systematic study of high-resolution models and their 

relationship to global models to accelerate progress 

toward realistic climate change prediction.

Near-Term Goals (1–3 years)

•	 Develop observationally based datasets—including 

both boundary conditions and observations of 

atmospheric, aerosol, and cloud properties—that 

can be used to drive and evaluate CRM simulations of clouds and precipitation. Specific attention needs to be paid to developing datasets in 

currently underrepresented climate regimes such as 

tropical land areas and polar regions. 

•	 Elucidate the precursors responsible for new particle formation and growth in air to evaluate how 

dependent these processes are on region and scale.

Mid-Term Goals (4–6 years)

•	 Improve representation of cloud microphysics, 

cloud-turbulence interactions, and surface fluxes 

in CRMs to the point that these models faithfully 

reproduce cloud and precipitation features in 

observed case studies.
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•	 Carry out long-term (seasonal to multiannual) 

CRM simulations at fixed ARM Climate Research 

Facility or ARM Mobile Facility deployment sites 

to test the ability of CRMs to simulate cloud 

processes and surface-atmosphere interactions accurately for a diverse set of meteorological conditions.

•	 Conduct comparison studies of downscaling techniques and models for a set of regions spanning 

different climatic zones (e.g., tropics to Arctic) and 

evaluate the relative and absolute skill of each.

•	 Identify the time and geographical scales over 

which aerosol properties determining CCN/IN 

activity change in different climate regimes to 

provide data relevant to higher-resolution models.

Long-Term Goals (7–10 years)

•	 Assess downscaling techniques and—based on 

climate regime and requirements of different user 

communities—converge on the accuracy and utility of various approaches.

•	 Carry out long-term (decadal to century) simulations for the past and current centuries for different climate regimes to understand the efficacy of 

these approaches. Also address outstanding questions about determinants of regional climate—

including aerosols and their interactions with 

clouds—emphasizing impacts on the hydrologic 

cycle and extremes.
4. Terrestrial Science Discussion Paper

4.1 Current Situation

Terrestrial ecosystems provide irreplaceable 

services to humanity through production 

of food, fiber, and fuel. Understanding and 

predicting the effects of climate change on terrestrial ecosystems are therefore critically important 

to society. Terrestrial ecosystems also play several 

fundamental roles in the dynamics of Earth’s climate 

system and its response to changes in atmospheric 

composition, radiative forcing, and land surface 

properties. They store about four times more carbon than the atmosphere, and annual carbon fluxes 

between the terrestrial biosphere and atmosphere are 

nearly seven times greater than annual anthropogenic 

carbon emissions from fossil-fuel combustion and 

land-use change. Since terrestrial ecosystems react 

rapidly to perturbation, the response of land surface 

properties and land carbon pools and fluxes to climate change can generate important feedbacks that 

could accelerate such change on short (decadal to 

century) time scales. Thus, a quantitative and predictive understanding of these responses and feedbacks 

is necessary for accurately predicting future climate 

change. Understanding climate system feedbacks and 

their influence on the ability of terrestrial ecosystems to continue providing essential services under a 

changing climate is necessary for informing effective 

climate change mitigation and adaptation policies.

Climate change encompasses a broad spectrum 

of factors, many of which can generate significant 

ecosystem responses. The most important climate 

factors—in terms of influence on unmanaged 

ecosystems and the carbon cycle—are changes in 

carbon dioxide (CO2) concentration; temperature; 

the frequency, intensity, seasonal patterns, and annual 

mean accumulations of precipitation; cloud cover 

and downwelling radiation; humidity; atmospheric 

inputs of reactive nitrogen; near-surface ozone concentrations; and atmospheric inputs of black carbon 

aerosols. These factors, however, do not operate in 

isolation. Experimentation and modeling studies 

suggest that ecosystem responses depend strongly on 

interactions among forcing factors. They also depend 

on interactions within ecosystems among carbon and 

nutrient cycles, hydrology, disturbance dynamics, 

age-class distributions, community structure, physiology, and physiological adaptation.

Observation, experimentation, modeling, and synthesis all have contributed to our understanding of 

how climate change influences terrestrial ecosystems 

and the carbon cycle. Our most complete knowledge relates to impacts that single factors of climate 

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change forcing have on ecosystems in the temperate 

zone. The responses of tropical and high-latitude 

ecosystems to individual climate change factors still 

are poorly characterized, and the integrative effects 

of multiple, simultaneous climate forcings are not 

well understood even for the best-studied ecosystems 

in the temperate zone.

Experiments on ecosystem response to elevated CO2

concentration consistently show short-term increases 

in net primary production, with less-consistent 

evidence that longer-term responses are mediated 

by carbon-nutrient interactions. Modeling studies hypothesize that global-scale response of land 

ecosystems to rising CO2 is regulated strongly by 

carbon-nutrient interactions—a result with important consequences for predicting mean global CO2

concentrations under a range of future scenarios. 

Experiments and modeling suggest that interactions with disturbance history, nutrient supply, and 

changes in temperature and soil moisture all can play 

significant roles in determining the magnitude and 

timing of carbon uptake by land ecosystems under 

increased CO2. These interactions also are expected 

to have varying effects depending on the type of 

climate zone and vegetation community involved. 

Different plant species respond differently to elevated 

CO2, leading to possible changes in the presence 

and abundance of species and subsequent effects on 

surface energy balance, hydrology, and productivity. 

Although rare, some temperature and rainfall 

manipulation experiments also have explored the 

effects of climate change on terrestrial ecosystems. 

For example, temperature manipulations (e.g., using 

passive and active heating technologies and soil or 

whole-system heating) have shown significant (albeit 

not always consistent) responses in both primary 

production and heterotrophic respiration—two dominant processes controlling the net rate of ecosystem 

carbon exchange. In general, these results highlight 

the importance of soil moisture status and nutrient dynamics in regulating ecosystem response to 

warming. However, since most such work has been 

conducted in the temperate zone, virtually no data 

exist from tropical or high-latitude ecosystems. 

Similarly, data from precipitation manipulation 

experiments have shown variable results. For example, 

data from mature temperate forests suggest that these 

ecosystems may be resistant to long-term drought, but 

meta-analyses from tropical forests indicate that their 

net carbon exchange is sensitive to large-scale and 

long-term drought. Together, these studies underscore 

potential variations in the effects of climate change 

among different ecosystems and climate zones.

Observations of carbon, water, and energy fluxes and 

associated measurements of ecosystem processes currently are being conducted at dozens of U.S. locations 

as part of the AmeriFlux network, which is supported 

by the Department of Energy’s Office of Biological and Environmental Research (BER) and other 

governmental agencies. The longest-running stations 

now have gathered nearly 20 years of observations, 

and many others have records longer than 10 years. 

Recent synthesis efforts have focused on multisite and 

multimodel evaluation exercises, resulting in important improvements in the consistency of station data 

records and the identification of critical knowledge 

gaps and prediction deficiencies in several models.

Mechanistic studies are explaining internal ecosystem 

feedbacks connecting carbon and ecosystem dynamics, but a more comprehensive understanding of the 

relevant processes is necessary to inform prediction 

and policymaking. For example, in all ecosystems, 

nutrient availability influences leaf physiology, plant 

carbon-allocation patterns, and decomposition 

dynamics, but significant interactions among nutrient cycles remain unresolved. Nitrogen fixation, 

for instance, has important mechanistic links to 

the phosphorus cycle, but no hypothesis testing of 

these interactions has yet been conducted. Similarly, 

both the type of disturbance and the time since its 

occurrence influence nutrient dynamics and carbonnutrient interactions. Allocation patterns show age 

dependencies and pseudoperiodic behavior, but the 

mechanisms involved are not yet well understood. 

Mortality is connected to climate change factors 

through physiological stress; damage from insects and 

disease; and severe weather events such as drought, 

hurricanes, windstorms, and ice damage. Plants are 

adaptable to changes in their growth environment, 

and acclimation might occur under climate change, 

but mechanistic understanding of these dynamics is 

still weak. As a final example, carbon balance and 

ecosystem processes below ground are not understood 

as well as those above ground. 
Climate Research Roadmap Workshop: Summary Report U.S. Department of Energy Office of Science 23

Chapter 4. Terrestrial Science Discussion Paper Office of Biological and Environmental Research

New insights on microbial community composition 

and function are emerging, but knowledge still is 

limited about mechanistic relationships to decomposition rates, nutrient dynamics, and soil organic matter 

stability. Acclimation of microbial activity and decomposition in response to climate change is increasingly 

well documented, but our understanding of the factors 

underlying thermal adaptation in soil microbial communities is lacking. In addition to new mechanistic 

studies, synthesis and meta-analysis efforts urgently 

are needed to organize and consolidate new knowledge 

gained from previous scientific investments.

Global-scale modeling efforts have been used to 

simulate potential effects of climate change on ecosystems because of insufficient direct evidence describing such effects across multiple spatial and temporal 

scales. In general, model results show a wide range of 

terrestrial carbon cycle responses to warming. Within 

a given model, variations arise by climate zone. 

Among models, they emerge because of the balance 

of factors influencing production and respiration and 

the influence of predicted climate change–induced 

shifts in plant communities. Models that include 

carbon-nutrient dynamics predict much different 

responses to climate change than those that do not, 

with the former suggesting less carbon release or even 

modest carbon uptake in response to warming. In all 

cases, however, ecosystem response to climate change 

appears to be strongly modulated by changes in soil 

moisture or nutrient dynamics. Today’s models also 

tend to overestimate the severity of drought response 

in temperate and tropical forests. Current modeling 

results have helped to frame and refine a set of critical science questions regarding interactions between 

ecosystems and the climate system: 

•	 Which factors control changes in CO2 fertilization 

response on decadal to century time scales? 

•	 How do carbon-nutrient interactions modulate the 

response of different ecosystems to warming? 

•	 What role does changing soil moisture status play 

in the expression of long-term climate-ecosystem 

feedbacks? 

Answers to these questions should be sought with 

new manipulative experiments and associated process studies.

In summary, a combination of observational, experimental, modeling, and synthesis efforts has greatly 

improved our understanding of the potential effects 

of climate change on terrestrial ecosystems. Despite 

this progress, our current understanding suffers from 

two very significant shortcomings. First, most experimental studies have focused on ecosystem effects of 

single factors of climate change forcing. Nearly all 

the work completed to date, however, highlights the 

importance of interactions among global change drivers, suggesting that investigating the effects of single 

drivers in isolation is insufficient. Second, most efforts 

have concentrated on ecosystems in the temperate 

zone. Tropical and high-latitude ecosystems, though, 

dominate global terrestrial carbon pools and fluxes, 

and their potential responses to even individual climate change factors still are poorly characterized. 

An integrated research program is seen as the most 

effective means of advancing our understanding 

of land ecosystem response to climate change and 

of the associated climate-ecosystem feedbacks. 

Such a program would combine new, multifactor 

experimentation; novel mechanistic studies at high 

latitudes and in the tropics; synthesis of observations 

and experimental results; and continued evaluation 

and improvement of predictive models. 

4.2 Inputs and Resources

The terrestrial ecosystem research community has 

decades of experience conducting field-scale experiments on ecosystems and the climate system and 

developing and evaluating models of their dynamics for application at multiple spatial and temporal 

scales. Systematic measurements of carbon, water, 

and energy fluxes and associated ecosystem variables 

have been made at multiple locations for more than 

a decade. Such long-term measurements are critical to understanding the dynamics of ecosystems 

and their potential responses to and feedbacks with 

climate change on decadal and longer time scales. 

BER supports a research community capable of 

carrying out complex, goal-oriented ecosystem 

research and modeling activities necessary to inform 

Earth System Models (ESMs) and decision makers 

concerned with the potential ecological effects of 

climate change.
24 U.S. Department of Energy Office of Science Climate Research Roadmap Workshop: Summary Report

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First-generation ESMs are now operational. In addition 

to the traditional representation of physical climate system dynamics, the most sophisticated of these models 

include a predictive representation of carbon cycling 

in the terrestrial biosphere, oceans, and atmosphere. 

They also incorporate dynamic fire and biogeography, 

the land nitrogen cycle, and multiple trophic levels and 

nutrient limitations in oceans. Currently under development, second-generation ESMs introduce the dynamics 

of changes in land use and land cover in response to 

physical and biogeochemical factors associated with 

climate change.

4.3 Desired Outcome

Objective of the terrestrial science research activity:

To understand and quantify regionally specific 

effects of ongoing and future climate changes 

on the structure and functioning of terrestrial 

ecosystems—including their feedbacks with the 

climate system—so that society can better understand, predict, and plan for climate change and 

its effects on ecosystems.

Detailed mechanistic understanding of ecosystem 

dynamics (including the net flux of land carbon) 

and the relationship of these dynamics to climate 

change factors is a primary focus for this research. 

Important processes are net primary production, 

autotrophic and heterotrophic respiration, and ecosystem changes arising from disturbance. Focused 

research directed toward ecosystem mechanisms that 

interact strongly with climate change and are poorly 

constrained by theory and observation will result in 

improved understanding and more accurate prediction. Some of these critical mechanisms identifiable 

today include the influence of multifactor climate 

change on plant and microbial physiology, regional 

understanding of carbon-nutrient dynamics and 

coupled biogeochemical cycles, interactions between

plants and microbial communities, disturbance 

dynamics, and biogeographical shifts in species distributions. Other critical mechanisms may emerge 

over the coming decade, and BER’s Terrestrial 

Ecosystem Science activity should be configured to 

respond flexibly to new knowledge and insights. 

Differing mechanisms likely will play dominant 

roles in different climate zones and ecosystem types. 

Research efforts must capture these critical variations, 

emphasizing the climate zones and ecosystem types 

that have the greatest impact on the global climate 

system. High-latitude systems with large stocks of 

soil carbon and tropical forests with large stocks 

and with high gross fluxes of carbon are the highest 

priority. This emphasis is needed partly because of our 

poor understanding of the critical climate-ecosystem 

mechanisms in these systems. In high-latitude systems, we must improve understanding of permafrost 

and thermokarst dynamics and how they relate to 

CO2 and methane (CH4) release under a changing 

climate. Seasonal freeze-thaw and snow cover dynamics in these systems also generate important climate 

effects through changes in surface energy fluxes and 

albedo. In tropical forests, a better understanding 

is needed of ecosystem sensitivity to rising CO2, 

warming, and drought and how multiple nutrient 

limitations might constrain these responses. Climate 

change also could impact the energy balance in tropical forests, with potentially significant feedbacks to 

regional cloud cover and precipitation. Another high 

priority is improved understanding of interactions 

among disturbance history, CO2 fertilization, nitrogen deposition, and climate change in temperate-zone 

forests. Some critical uncertainties extend across latitudinal boundaries. For example, plant-microbe interactions and soil organic matter dynamics, acclimation 

of plant physiological processes to climate change factors, predictive representation of plant mortality, and 

variation in allocation patterns in response to multiple 

forcings all have important sensitivities to climate 

change and are poorly understood or understudied. 

Near-Term Goals (1–3 years)

•	 Design, prototype, and deploy a multifactor CO2

× warming × nutrient manipulation study in a 

high-latitude, high-carbon permafrost environment. Perform a priori modeling.

•	 Perform feasibility studies for a new suite of 

moderate-cost tropical forest experiments focusing 

on multiple nutrient limitations and manipulations of CO2, precipitation, and soil-only warming. Identify the most tractable experiments for 

deploying on short time frames, perform a priori

modeling, and initiate experiments.
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•	 Conduct comprehensive syntheses and metaanalyses of results from prior observational (e.g., 

AmeriFlux) and experimental (e.g., Free-Air 

CO2 Enrichment) studies. Use results for multimodel evaluation exercises and for designing 

a formal, long-term network for ecosystem flux 

measurements. 

•	 Evaluate the influence of disturbance history on 

net carbon exchange using existing flux networks 

as starting points. Use modeling to control for the 

large number of other factors that also contribute 

to variability in measured fluxes.

•	 Redesign the AmeriFlux network to introduce a 

manipulation component and to be driven by 

science questions relevant to BER. Add value to 

the national effort through coordination with the 

National Ecological Observatory Network.

•	 Design and initiate new process studies focused on 

soil organic matter dynamics and plant-microbe 

interactions.

•	 Devise and conduct model application studies 

focused on identifying single and multifactor 

climate–carbon cycle responses and feedbacks. 

Mid-Term Goals (4–7 years)

•	 Continue the high-latitude, multifactor experiment. Perform an early evaluation of a priori

model predictions against results.

•	 Select and initiate moderate-cost tropical forest 

experiments and synthesize their early results. 

Design, prototype, and implement coupled 

nitrogen-phosphorus models based on early 

hypotheses and experimental results. Perform 

a priori modeling.

•	 Incorporate into ESMs new process knowledge 

on the influence of ecosystem manipulation and 

disturbance history and on ecosystem interactions 

with climate and biogeochemistry. This knowledge 

will be gained through synthesis of experimental 

results and flux network activities.

•	 Implement the new AmeriFlux network focused 

on critical science questions and expanded to 

include experimental manipulations. A data and 

analysis infrastructure should be in place from 

the outset to facilitate data-model fusion, model 

evaluation and improvement, and effective acquisition and transfer of new knowledge to ESMs. 

•	 Design and implement for ESMs a new generation 

of soil organic matter submodels based on synthesis efforts and new process studies.

•	 Continue model application studies focused 

on identifying single and multifactor climateecosystem responses and feedbacks. Initiate highresolution simulations driven by the best available 

retrospective and future scenario datasets to explore 

regional details of ecosystem response and climate–

carbon cycle feedbacks.

Long-Term Goals (8–10 years)

•	 Synthesize results emerging from the high-latitude, 

multifactor experiment and integrate them as new 

algorithms and improved parameterizations into a 

high-resolution ESM. 

– Position the program to answer the questions: How significant might effects of climate 

change be on Arctic tundra, and how important might feedbacks mediated through CO2

and CH4 releases and albedo changes be to 

regional and global climate?

•	 Synthesize results from moderate-cost tropical 

forest experiments and integrate the findings as 

new algorithms and parameterizations into a highresolution ESM. 

– Position the program to answer the question: 

What effect does multinutrient limitation have 

on the response of tropical forest ecosystems to 

climate change?

•	 Update the long-term ecosystem flux measurement 

network to include new process measurements 

and manipulations, as suggested by model sensitivity analyses and synthesis of new observational 

and experimental work. 

– Answer the question: What are the quantitative bounds of uncertainty for predicting ecosystem structure (states) and function (fluxes) using 

our best ecosystem models, as evaluated against 

observations across a range of ecosystem types and 

climate zones?
26 U.S. Department of Energy Office of Science Climate Research Roadmap Workshop: Summary Report

Chapter 4. Terrestrial Science Discussion Paper Office of Biological and Environmental Research

•	 Develop and distribute a synthesis statement on 

ecosystem response to climate change and climate 

system feedbacks estimated at global and regional 

scales. This statement will be based on full analysis 

of results from process research and projections 

from improved ESMs informed by the research. 

– Answer the question: How do feedbacks between 

terrestrial ecosystems and climate change factors 

influence predicted climate states on decadal and 

century time scales?
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Office of Biological and Environmental Research

5.1 Current Situation

Models of the Earth’s climate system are 

among the most important tools scientists have for improving understanding 

of climate variability and change, as well as their 

societal impacts. Climate system models are based 

on quantitative methods and are built on a broad 

spectrum of measurement programs that allow 

examination of possible futures. Physical models 

that combine processes and interactions of the atmosphere, oceans, land surface, and ice easily exhaust 

current computational resources. Moreover, even 

as models advance via improved understanding, 

they increasingly are representing and incorporating previously unaccounted physical, chemical, and 

biological mechanisms. The resulting computational 

challenges are formidable and compounded further 

by the need to integrate knowledge of the physical 

climate with biological and anthropogenic influences such as vegetation, energy use and technology, 

economics, land use, hydrology, and agronomy. 

The demands on this evolving predictive capability are intensifying and progressively more varied. 

Reliable climate information—usable by public 

and private decision makers—is needed on regional 

scales and for a variety of aspects relevant to society, 

including U.S. energy policy, of which the Department of Energy (DOE) is the steward. This need is a 

key rationale for establishing an integrated modeling program, as many scientific challenges can be 

addressed with similar models or with components 

of a comprehensive portfolio of models developed 

in coordination. This chapter provides a strategic 

foundation to motivate, organize, and support development of such a program.

5.2 Resources and Capabilities

DOE, in partnership with the National Science 

Foundation, has supported and released the Community Climate System Model (CCSM), Version 

4. Previous versions of this model have been used 

to simulate 20th century climate and perform 

climate change projections for various national and 

5. Climate Modeling Science Discussion Paper

Developing Predictive Capabilities to Understand the Sensitivity of Earth’s Climate 

System to Natural and Human Influences
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Chapter 5. Climate Modeling Science Discussion Paper Office of Biological and Environmental Research

international assessments. Version 4 will be used 

in similar applications. A variety of development 

activities continues on a suite of modeling capabilities ranging from (1) process-level models (i.e., those 

that quantify an isolated process such as cloudaerosol interactions) to (2) component models (e.g., 

atmosphere, ocean) to (3) climate system models, 

which are composites of component models. DOE 

has supported the development of Integrated Assessment Models (IAMs) that include human activities, 

economics, and environmental policy. 

DOE also has begun to develop Earth System Models 

(ESMs) that project the interactions of human activities and the physical and biological climate. A state-ofthe-art analysis environment for climate modeling now 

exists, but description and quantification of uncertainties are at an early phase. Fundamental research 

supported by DOE’s Office of Biological and Environmental Research (BER) is developing an Earth System 

Modeling capability and analysis environment. The 

program supports the following major capabilities:

•	 Physical component models that have been integrated into global and regional models. These 

include:

– Ocean circulation models [Parallel Ocean 

Program (POP) and a Hybrid coordinate 

POP (HYPOP)].

– A sea-ice model [Community Ice Code 

(CICE)].

– Community Ice Sheet Model (CISM).

•	 Process models that have been integrated into 

ESMs. These include: 

– Coupled carbon- and sulfur-cycle models.

– Atmospheric chemistry models.

– Aerosol models (modal schemes).

– Dynamic vegetation models.

•	 A coupled climate model (the CCSM 4).

•	 IAMs to support the determination of safe levels of 

greenhouse gas concentrations in the atmosphere. 

IAMs include representations of various determinants 

of greenhouse gas emissions; economic, energy, and 

land-use decision making; and the atmosphere, climate, oceans, and climate impacts and adaptation.

•	 Continued development of:

– Nonhydrostatic and global cloud-resolving 

models.

– New dynamical cores.

– Representations of human activities in integrated ESMs.

– Processes that facilitate the understanding 

of magnitudes and probabilities of abrupt 

climate change.

•	 Exploration and evaluation of regional-scale methodologies and techniques.

•	 Improvement of computational throughput 

through increased scalability and performance.

•	 Federated data repositories providing leading 

technology to facilitate the research community’s 

access to national and international model data.

•	 Model diagnostics, including:

– Advanced methods and tools for climate 

model diagnosis and intercomparison [e.g., 

Coupled Model Intercomparison Project 

(CMIP)].

– Testbeds to evaluate cloud and aerosol

processes.

– Methods focused on identifying and reducing model errors.

– Performance metrics for gauging model 

improvement and identifying relative 

strengths and weakness of models. 

– Detection and attribution analysis for understanding climate variability and change.

– Efforts to assess climate sensitivity and natural 

and forced climate variability, including 

analysis of multimodel projections.

– Nascent efforts to quantify the uncertainties 

and feedbacks in Earth system processes.

5.3 Desired Outcome

ESMs integrate our research-based knowledge of 

human activities and the climate and are essential 

tools to quantify the interplay between energy use 

and climate. Over the next decade:
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Chapter 5. Climate Modeling Science Discussion Paper Office of Biological and Environmental Research

BER will develop and test an application-focused 

comprehensive Earth System Modeling capability 

and analysis environment, which includes natural and human Earth systems, information on 

climate change at decade to century time scales 

and local to global spatial scales, and descriptions and quantification of uncertainties.

Developing an ESM that can be used for projecting future climate change is a continuous series of 

steps in which new processes and methods are added 

and tested. This suggests cyclic model development in which the application of a model from one 

development cycle serves to identify the strengths 

and weaknesses that describe uncertainties to be 

addressed in the next model released in the cycle. 

The 5-year interval between Intergovernmental 

Panel on Climate Change (IPCC) assessments and 

the approximately 2- to 3-year cycle for model development provide typical time scales for the release of 

new model code. Regular releases of model cycles 

assure integration, testing, and evaluation of modeling capabilities to be used to meet DOE Office of 

Science needs, including participation in the federal 

climate-science community and in national and 

international assessments. 

Development cycles will take place through campaigns focused on answering science questions. Two 

ultimate scientific goals are to understand and quantify the coupled Earth system. Hence, scientifically 

investigating the coupling of processes also is a focus. 

Since other objectives are describing and quantifying 

uncertainty, the applications need to address a set of 

issues whose uncertainty is discerned as important. 

Within this context, we endorse the following as 

appropriate examples of ambitious, important, and 

integrative application areas:

•	 Determine the sign of the carbon cycle feedback 

and how it changes over time.

•	 Identify the impacts of warming in the Arctic and 

the global implications of Arctic change.

•	 Quantify the rate and magnitude of sea-level rise.

•	 Quantify the rate and magnitude of global climate 

response to land-use change, including impacts of 

deploying new energy systems. 

To manage the complexity of developing this modeling capability, we propose a strategy organized under 

four broad, interrelated themes and goals. These 

themes address implementation, process, analysis and 

evaluation, and uncertainty—all essential elements 

of the program.

1. Developing a Comprehensive and Integrated 

Earth System Model. A comprehensive and 

integrated ESM includes physical components 

(e.g., atmosphere, ocean, land surface, sea ice, 

and ice sheets), biogeochemical components (e.g., 

atmospheric chemistry; land and ocean biogeochemistry; and carbon, nitrogen, sulfur, and 

other cycles), and IAMs of human decisions (e.g., 

energy use and emissions, land-use and landcover change, economics, and policy decisions). 

Such a model also captures a variety of space and 

time scales, each represented by submodels that 

describe the behavior of observed phenomena. 

The comprehensive and integrated ESM will 

incorporate resolved dynamics, parameterizations 

of unresolved physical and biological processes, 

and algorithms to couple submodels and component models. Further progress requires: 

•	 Incorporating and improving processes in 

global models, including missing physical 

components (e.g., ice sheets) and dynamic 

vegetation, new biogeochemical components, 

and improvements in process representations 

needed to reduce model bias.

•	 Adding the effects of human decisions by coupling IAMs with an ESM.

•	 Performing and using ESMs at the highest 

level possible on current and future computer 

architectures.

2. Developing a Comprehensive, Multiscale 

Modeling Capability. One of the defining 

characteristics of the climate system is the strong 

interactions across time and space scales that 

occur within and between physical and biochemical components. Many dominant modes of 

variability in the climate system (e.g., El Niño–

Southern Oscillation) exist through interactions 

across broad spatial and temporal scales. The 

most relevant aspects of global climate change 
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(such as shifts in temperature, precipitation, 

and sea level) emerge through the rectification 

of processes occurring at spatial and temporal 

scales well beyond current modeling capabilities. 

Modeling research often advances by focusing on 

quantitatively describing the processes that make 

up these interactions. Robust, comprehensive, 

and integrated models require explicit attention 

to the physics of coupling between spatial and 

temporal scales—that is, between submodels and 

component models. Building a comprehensive, 

multiscale modeling capability requires:

•	 Developing new algorithms and methods 

for capturing multiple spatial and temporal 

scales within a single model simulation. This 

includes investigating varying equation sets 

at different spatial and temporal scales.

•	 Emphasizing the construction of multiscale 

physical parameterizations in each physical 

system component and the ways these parameterizations interact within and between system 

components to produce dominant features of 

the observed climate system.

•	 Developing the ability to capture spatial 

and temporal scales—along with associated 

physical processes—at different resolutions in 

different parts of the global domain, especially 

related to the simulation of regional climate.

3. Establishing Applications and Frameworks 

for Model Evaluation. Applying projections 

from integrated ESMs to scientific investigations 

and decision making is, ultimately, of primary 

interest. Scientific rigor requires full evaluation 

of the quality of projections by independent 

researchers. Formally, this is the validation step 

of the scientific method and requires the participation of scientists who are expert in observations and their quality. A dedicated group of 

scientists familiar with the applications of models 

and quantitative assessment of their strengths 

and weaknesses is needed. Fully evaluating 

simulation results for impacts assessment requires 

developing and maintaining flexible and customizable analysis tools and the computational 

environment to implement them. DOE has been 

responsible for distributing all such assessment 

results for IPCC’s Fourth Assessment Report 

(AR4) and will continue to do so for AR5. 

With the increasing model complexity, growing 

diversity of simulation types, and use of information from several modeling centers, the logistics, 

methods, and complexity of full model evaluation are expanding. An extensive simulation, 

analysis, and distribution effort requires:

•	 Participating in national and international 

assessments of climate change and its impacts.

•	 Developing advanced tools for distributing 

and visualizing data and for performing 

detailed analyses.

•	 Developing and deploying information technology–based environments, performance 

metrics, and diagnostics for model validation 

and intercomparison and using these tools to 

describe and analyze uncertainties of climate 

change projections. 

4. Describing and Quantifying Uncertainty. 

Uncertainty of the knowledge produced from 

climate change projections is an essential product 

of scientific investigation. Addressing uncertainty is both explicit and implicit in determining priorities in the development path of BER’s 

integrated ESM capability. Uncertainty must be 

described and communicated in a form meaningful to scientists and usable by public and 

private decision makers. This presents a fundamental challenge to climate modeling scientists 

and programs because the use of uncertainty 

spans many disciplines. Simply striving to reduce 

uncertainty is not necessarily the primary hurdle 

in assuring the usability of uncertainty estimates. 

More tractable and potentially useful objectives 

are describing sources and types of uncertainty; 

analyzing the interaction of these sources and 

types; and, when possible, quantifying uncertainty. A particular focus is identifying high-risk, 

low-probability events. The following list organizes uncertainties by type and serves to motivate development paths for a modeling program.

•	 Quantifying primary variable uncertainty 

(e.g., temperature) and integrated responses 

(e.g., sea-level rise) and presenting this 
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information in a way suitable for risk 

analysis. 

•	 Addressing uncertainty related to biases and 

misrepresentation of the variability of multiscale, coupled processes and phenomena in 

climate models (e.g., mean state tropical biases 

and sea ice).

•	 Addressing uncertainty related to mechanisms and processes known to be missing 

from climate models (e.g., ice sheet models 

and groundwater flow).

•	 Exploring uncertainty related to specification 

of emission scenarios and, more generally, 

human enterprise.

Developing a Comprehensive and 

Integrated Earth System Model

The development of an ESM that can be used for 

projections of future climate change is a continuous 

process by which new processes and methods are 

added and tested. Here we identify goals for developing new methodologies to improve and create new 

models over the next decade. 

Near-Term Goals (1–3 years)

•	 Incorporate new component and process models. 

In this time frame, several new models under 

development will require implementation and 

evaluation, including those for dynamic vegetation, ice sheets, indirect aerosol effects, and 

the carbon and sulfur cycle (with methane). 

Improvements in all other component models—

such as new atmospheric dynamical cores—will 

continue, as will incorporation of better process 

representations in all components. In addition, 

one or two persistent and outstanding model 

biases should be chosen for focused reduction. 

All these improvements will form the basis of 

next-generation models for assessment activities 

beyond IPCC AR5.

•	 Continue current efforts toward the first generation of integrated ESMs. This project is centered 

on coupling climate and ESMs with IAMs and is 

based on the CCSM, Community Land Model, 

and Global Change Assessment Model. This effort 

initially will be focused on changes in land use and 

land cover; associated carbon feedbacks, water use, 

and hydrology; and biofuels.

•	 Deploy new computer architectures with multicore and hybrid processors. A concerted effort 

on model algorithms, programming models, and 

code design will be needed to adapt to these new 

architectures. Such a focused effort, similar to the 

transition in the early 1990s, will require collaborations with the DOE Office of Advanced Scientific Computing Research.

Mid-Term Goals (4–7 years)

•	 Incorporate new component and process models. 

As in the near-term, new models under development will require implementation and evaluation during this time frame. New ocean and ice 

components and atmospheric dynamical cores are 

scheduled to be completed in this period. Surface 

and subsurface hydrology models are likely to be 

important additions also. Model biases identified 

in IPCC AR5 will provide targets for additional 

process improvements. Further chemical and 

biogeochemical enhancements will be ready too. 

These will form the basis of the next two generations of models for any assessment activities 

beyond IPCC AR5.

•	 Begin to implement and optimize advanced time 

integration techniques into ESMs. Further exploration of initial state issues for decadal prediction 

with convergence on appropriate methodologies 

will continue.

•	 Conduct initial simulations and evaluations of 

first-generation integrated ESMs. Development 

of next-generation integrated ESMs will begin, 

coupling more processes and identifying important feedbacks.

•	 Deploy new computer architectures. Some convergence and decisions on programming models 

likely will occur during this period, and detailed 

performance analysis will lead to further performance tuning.

Long-Term Goals (8–10 years)

•	 Begin to integrate ESMs, integrated ESMs, regional 

modeling, and other improvements into our goal: 

the comprehensive and integrated ESM. Such an 
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integration should start to be possible during this 

time and likely will extend beyond 10 years.

•	 Begin initial grand challenge simulations of 

cloud-resolving models for short time integrations. 

•	 Continue required physical and computational 

improvements. These will be determined by analyses of model performance.

Developing a Comprehensive, 

Multiscale Modeling Capability

The goals outlined in this section relate to the desire 

of achieving higher-fidelity simulations of the coupled climate system while recognizing that models 

will always be under-resolved. In other words, there 

will always be relevant spatial and temporal scales 

present in the observed climate system that cannot 

be accommodated in our globally uniform, highresolution grand challenge simulations. 

Near-Term Goals (1–3 years)

•	 Increase focus on regional modeling and evaluate 

methodologies for regional climate models. This 

short-term evaluation will include global highresolution models, variable-resolution global 

models, and nested regional models.

•	 Test and evaluate new methods for treating 

multiple time scales of interest in a single model. 

This is required because high-, nested-, and 

variable-resolution models introduce difficulties 

with time integration. Also needed is examination 

of appropriate initial states for short-term prediction, including predictability experiments using 

both model and data-assimilated initial states.

•	 Continue longer-term research toward models with 

“direct” simulation capability of clouds, ocean 

eddies, land ice streams, and other relevant physical processes. Along with this effort will be the 

exploration of scale-aware parameterizations for use 

within a global, multiscale modeling environment.

Mid-Term Goals (4–7 years)

•	 Based on near-term evaluation of regional modeling, identify appropriate approaches and integrate 

them into the integrated ESM framework. During 

this time period, simulations and evaluation will 

take place. Research will continue toward the 

“direct” regional simulation of computationally 

challenging physical processes (clouds, ocean 

eddies, and land ice streams) within a global, 

multiscale modeling environment.

•	 Develop physical and biochemical process models. 

These models will use different model equations 

and physical parameterizations as functions of 

local spatial and temporal resolution.

•	 Perform the first fully coupled climate simulations using a multiscale approach across numerous 

physical and biogeochemical components. This 

approach will allow the regional study of clouds, 

ocean eddies, ice streams, and other physical 

processes not able to be accommodated in globally 

uniform, high-resolution grand challenge simulations. These regional studies in turn will inform 

the configuration of grand challenge simulations.

Long-Term Goals (8–10 years)

•	 Make available integrated ESMs with multiscale 

capability. Issues regarding the lack of robust, 

scale-aware parameterizations likely will remain.

Establishing Applications and 

Frameworks for Model Evaluation

These goals are focused on the actual application 

of a model for mission needs. Participation in any 

national or international assessment will occur as 

necessary. Such assessments and related multimodel 

analyses will require computational infrastructure 

for distributing, analyzing, and visualizing model 

projections and output. New tools and techniques 

will be developed to analyze the set of campaigns 

focused on essential, coupled, multiscale processes. 

A strong concentration on uncertainty description 

and quantification is needed so that decision makers 

can assess risk and devise mitigation and adaptation 

strategies. Additional requirements are environments 

for model validation that provide both test problems 

and gridded observational datasets.

Near-Term Goals (1–3 years)

•	 Perform simulations for IPCC AR5 and create a distributed archive for the CMIP Phase 5 

(CMIP-5) using Earth System Grid (ESG) tools 

and infrastructure. Also during this time, conduct 

multimodel analyses on CMIP-5 data to evaluate how realistically CCSM 4 and other current 
Climate Research Roadmap Workshop: Summary Report U.S. Department of Energy Office of Science 33

Chapter 5. Climate Modeling Science Discussion Paper Office of Biological and Environmental Research

models simulate the recent past. Analyze projections 

of future climate change on two time scales: near 

term (to about 2035) and long term (to 2100 and 

beyond). The range of scenario experiments will permit further examination of initial state and predictability, carbon cycle feedbacks, and other factors.

•	 Explore causes of and solutions to persistent model 

biases. Despite ongoing model improvements, wellknown biases continue to persist in most models. 

For example, tropical biases are common, as are 

systematic errors in fields of sea ice. Identifying 

the root causes for such errors has proven difficult 

and generally is best accomplished with a strategy 

that enables additional (sensitivity) experiments. 

Persistent errors will continue to be identified in 

current models (CMIP-5) and be further diagnosed 

in existing and new experimental testbeds.

•	 Begin developing the next-generation ESG. 

Features will include new analyses, parallel and distance visualization capabilities, and new interfaces 

for nonexperts to interact with climate model data.

•	 Identify new model metrics for regional evaluation, 

statistics for extreme events, integrated ESM 

output, and other analyses required for model 

improvement and needs of decision makers.

Mid-Term Goals (4–7 years)

•	 Perform simulations for future assessments and distribute data and multimodel analyses to the climate 

community. Make new projections with new model 

additions (e.g., ice sheets, sea-level rise, dynamic 

vegetation, methane) and incorporate changes identified to reduce bias in the last assessments. Projections will include sea-level rise and other quantities 

now available, with further explorations of decadal 

predictability and the impact of initial state.

•	 Continue exploring causes and solutions to persistent model biases. Characterize and investigate new 

modes of variability that arise because of introducing, for example, dynamic vegetation and ice sheet 

models, indirect aerosol effects, and carbon- and 

sulfur-cycle models (with methane).

•	 Deploy the next-generation ESG and continue 

developing future generations with increasing 

usability beyond the climate community.

•	 Complete and evaluate the regional-scale effectiveness of initial simulations of robust, ultra–high resolution models. Similarly, evaluation of other nesting 

and variable-resolution approaches should be complete, and initial projections using these approaches 

can begin. Focus regional simulations on regions of 

importance. Create a framework and datasets for 

evaluating regional projections with new regional 

models and ultra–high resolution global models. 

Also begin analyzing climate extremes in simulation 

data with high spatial and temporal resolution.

•	 Develop the tools and methods to evaluate and validate the integrated ESM. This will require not only 

datasets for physical models, but also data needed to 

validate integrated assessment components.

Long-Term Goals (8–10 years)

•	 Perform simulations and assessments using fully 

integrated ESMs that can simulate global and 

regional changes, incorporate human decisions, 

and include physical and biogeochemical processes. This integrated capability will enable DOE 

researchers and decision makers to examine a wide 

range of scientific issues, climate change scenarios, 

and important feedbacks. 

Describing and Quantifying Uncertainty

Describing and quantifying uncertainty require 

documentation of the types of uncertainties being 

addressed. This is needed at each step of the previously identified goals that support model development, multiscale process coupling, and application 

analysis. An overarching consideration for uncertainty is ensuring that scientific knowledge can be 

better used to assist decision makers with risk assessment needs. This will require scientifically investigating uncertainty and its use. BER is uniquely 

positioned for this research because its programs 

include IAMs and social scientists.

Near-Term Goals (1–3 years)

•	 Test existing and emerging ensemble-based uncertainty specification methodologies for climate 

models. Initial experiments should be performed 

with component models or simplified models (e.g., 

atmosphere-only models or with coupling to a 

simplified “slab” ocean).
34 U.S. Department of Energy Office of Science Climate Research Roadmap Workshop: Summary Report

Chapter 5. Climate Modeling Science Discussion Paper Office of Biological and Environmental Research

•	 Assess the utility of different methodologies and 

identify approaches to be actively aligned with 

model development and applications.

•	 Initiate research on how uncertainty is used by 

decision makers.

Mid-Term Goals (4–7 years)

•	 Apply selected uncertainty quantification methods 

to available model simulations and use these results 

to identify new areas for model development.

•	 Further develop tools for improved robustness 

and explore possible avenues to reduce some 

model biases.

•	 Integrate research on the use of uncertainty by 

decision makers with uncertainty specification of 

model projections.

Long-Term Goals (8–10 years)

•	 Distribute projections generated by the modeling 

capability that include uncertainty estimates, both 

quantitative and descriptive. Concurrently with 

actual model results, ESG will provide a robust 

and easy interface to uncertainty information for 

experts and nonexperts.
6.1 Challenges and Expected Outcomes

The challenges of synthesis and integration 

for the Office of Biological and Environmental Research’s (BER) overall climate 

change research program are somewhat different 

than for the program’s individual components. 

These overall challenges are:

•	 Identify internal scientific links among the different elements of BER research activities on 

climate change.

•	 Identify external scientific links needed for BER’s 

climate research activities to succeed.

•	 Identify gaps that need to be filled or research 

areas that do not take full advantage of existing 

capabilities.

The expected outcome of a successful synthesis and 

integration effort is a scientific program that is:

•	 Internally consistent, has appropriate links among 

process research activities, and supports modeling 

tasks in multiple disciplines.

•	 Appropriately linked to measurement, experimental, 

and modeling programs outside the Department of 

Energy (DOE) but nonetheless necessary for success 

of its climate activities.

•	 Supportive of and relevant to the appropriate BER 

program metric.

•	 Capable of adapting to changing circumstances 

and new discoveries.

Continued synthesis and integration might prove 

useful in many areas of the overall climate research 

program. For example, Earth System Models 

(ESMs) do not represent some important terrestrial 

ecological processes (e.g., disturbance) or functional 

processes (e.g., methane generation and consumption within soils) particularly well. Considering this, 

what are the appropriate lessons for experimentalists 

within the program? Which experiments are the 

most important to conduct? Which observational 

programs could contribute to improvements in these 

model representations? What existing datasets might 

be exploited to improve representation of these processes in ESMs?

ESMs are beginning to incorporate aspects of the 

energy system, land use, and climate dynamics in 

interactive ways, but which of these might come into 

play first, and why? How will the program choose 

Climate Research Roadmap Workshop: Summary Report U.S. Department of Energy Office of Science 35

Office of Biological and Environmental Research

6. Integating Science Discussion Paper
36 U.S. Department of Energy Office of Science Climate Research Roadmap Workshop: Summary Report

Chapter 6. Integrating Science Discussion Paper Office of Biological and Environmental Research

new parameterizations for physical processes as 

models change substantially in scale and scope and as 

new experimental data become available? How then 

will models be evaluated and challenged with observational data, and how will various forms of model 

uncertainties be identified and begin to be quantified?

6.2 Integration and Synthesis 

Working Group 

During the workshop, a small working group was 

organized to focus particularly on these challenges 

and on outcomes for BER’s overall climate program. 

Members of the working group circulated among 

the three disciplinarily oriented breakout sessions 

(atmospheric science, terrestrial science, and climate 

modeling) and solicited the participation of the chairs 

and several members from each group. At the end of 

the first day, a discussion was held specifically on the 

topics of synthesis and integration, and results from it 

were presented in the plenary session the next morning. At the end of the second day, a final summary 

was prepared to reflect the overall discussion and 

conclusions among the integration working group 

members and the ensuing plenary sessions.

6.3 Goal of Climate Program

Integration

The overall scientific goal of the synthesis and 

integration component of climate change research 

is to understand the behavior of the coupled Earth 

system, not just its individual components. Figure 6.1, below, depicts system components and their 

coupling, which illustrate the breadth of this goal, 

especially as represented in models.

Several implications are inherent in such a goal. 

Achieving it requires the explicit identification and 

quantification of all the various forcings on the 

climate system, including those that are the consequences of human decisions. These are the most 

rapidly changing forcings of climate and its interaction with physical, biological, and human components of the Earth system. Ignoring them thus 

would lead to a scientifically biased understanding 

of Earth system dynamics.

This goal also forces a research program to focus 

explicitly on the complexity of the overall coupled 

system, not just on that of its individual components. Increasing our understanding of these 

components is necessary but by no means sufficient 

for attaining the program’s overall goal. Clearly, 

this requirement makes model evaluation and the 

identification and, ultimately, quantification of 

uncertainty both important and daunting.

Finally, this goal implies that the scientific problems of most interest should be those that cut 

across these different components of the Earth 

system (i.e., those that require examining more 

than one component to test hypotheses or answer 

scientific questions).

6.4 Programmatic Challenges

Although this notion of scientific integration and 

synthesis generally is appealing intellectually in 

addressing questions about the coupled Earth system, it presents several implementation challenges 

for the overall program. For example, how is the 

desire to address the coupled Earth system balanced 

with the necessity of increasing our understanding 

of individual components? How do we deal with the 

tension between discovery science, without regard 

to potential utility, and science that is both tangibly 

useful and intellectually interesting and challenging? 

What sort of program management steps might be 

taken to ensure balance, and how does the program 

avoid a problematic image of attempting to build a 

“model of everything”?

Fig. 6.1. Components and Interactions of the Earth 

System and Their Representation in Models.
Climate Research Roadmap Workshop: Summary Report U.S. Department of Energy Office of Science 37

Chapter 6. Integrating Science Discussion Paper Office of Biological and Environmental Research

Pragmatically, the most productive path forward 

would be for the overall program to adopt a philosophy of working in “campaign” mode. This idea—

which arose independently in several working 

groups—implies that the program should not necessarily seek to make progress on all possible problems 

in climate science simultaneously. Instead, it should 

seek to focus the community’s effort on a few tractable challenges over shorter periods of time and to 

structure research around the delivery of “products” 

in manageable time frames. No single campaign need 

involve all research communities at once; the BER 

climate program certainly is large enough to support 

several campaigns simultaneously. To avoid jumping 

from issue to issue, DOE will need to periodically 

evaluate scientific progress and resist the temptation 

to continue pursuing problems simply because they 

have been studied historically and there is more to 

learn (which, after all, will always be true in science).

The program also will have to recognize that some 

problems are so difficult to resolve, either theoretically or empirically, their campaigns might need to 

be structured over longer time periods than others. 

The general view of workshop participants is that 

thinking programmatically in terms of campaigns 

will enable DOE to identify problems of special 

importance—not only to Earth science, but to the 

missions of DOE, its unique assets, and the experience of its research community.

6.5 Potential Examples

The synthesis and integration group discussed 

several scientific issues and questions that would 

meet the criteria described above and might serve as 

examples of the kinds of problems the BER climate 

program could address. In this section, each example is outlined without seeking to describe all possible details or competing hypotheses. Note that these 

are examples and do not represent a comprehensive 

list by any means; nor does listing them imply that 

they have been assigned priorities. Nevertheless, the 

following example questions provide a reality test of 

the concepts described above (i.e., that such problems in the science of the Earth system are amenable 

to the campaign mode of operation and are relevant 

to DOE missions, experience, and expertise).

Example 1: What is the Sign of the Carbon Cycle 

Feedback and Its Changes over Time?

Some good evidence from modeling studies suggests 

that the carbon cycle feedback to the physical climate system has the potential to be positive and to 

amplify the surface temperature response to a given 

amount of anthropogenic greenhouse gas emissions. 

The importance of testing this phenomenon is clear: 

if the positive feedback is large, or long-lasting, then 

the Earth system could be much more sensitive 

to a given amount of emissions than we currently 

understand. There is also no reason to think that the 

carbon cycle feedback itself is constant over time, 

since it depends on the reaction of many biological 

processes and on human decisions about emission 

trajectories—each of which interacts with various 

changes in the physical climate system.

Addressing the different facets of this problem will 

require greater process-level sophistication in terrestrial system models that simulate various aspects 

of the carbon cycle. These include demographic and 

disturbance processes resulting from fire and harvesting regimes (i.e., factors associated with human 

land use). Also needed is process-level ecophysiological understanding that can be obtained only from 

experimenting on and observing intact ecosystems. 

Coupling terrestrial and human-factor models with 

models of atmospheric composition and the physical 

climate system also will be necessary, as will understanding the enormous human perturbation of the 

carbon cycle and the role energy technologies play in 

this occurrence.

Example 2: What are the Impacts and Feedbacks Associated with Changes in Arctic 

Regions?

The unexpected and rapid reduction in summer sea 

ice in the Arctic has revealed a physical phenomenon 

that we clearly do not understand completely. This 

situation has important implications both for the 

ocean-ice-albedo feedbacks in the climate system 

and for marine and terrestrial Arctic ecosystems. 

The ice phenomenon raises the potential not only for 

changes in internal physical feedbacks in the climate 

system, but also for positive feedbacks from changes 

in Arctic biogeochemistry.
38 U.S. Department of Energy Office of Science Climate Research Roadmap Workshop: Summary Report

Chapter 6. Integrating Science Discussion Paper Office of Biological and Environmental Research

Addressing the various aspects of this problem will 

require significant modeling and observational studies of Arctic terrestrial and oceanic environments 

and of the range of couplings they exhibit with 

the atmosphere. Also necessary is a much better 

understanding of the interaction of Arctic hydrology and climate variability with the biogeochemical 

processes occurring in the very deep pool of biological carbon locked in permafrost and other Arctic 

landscapes. In its climate modeling program, DOE 

already has projects exploring some features of this 

overall issue, so clearly this presents an opportunity 

to use DOE’s unique assets and experience.

Example 3: What are the Future Rates and 

Magnitudes of Sea-Level Rise?

The practical and programmatic importance of 

understanding future rates and magnitudes of 

sea-level rise is obvious. Scientifically, though, this 

research arena also is a source of both empirical and 

modeling uncertainties. These uncertainties range 

from the physical dynamics of ice sheet expansion 

and loss to the physics and mechanics of ice flow and 

calving into the ocean to the coupling of the fate of 

land glaciers and thermal expansion of the oceans. 

Ultimately, models of these physical processes need 

to be detailed enough to make some judgments 

about how changes in mean sea level may be linked 

with coastal dynamics so that model results can be 

localized and interpreted more broadly. Interdisciplinary studies of the consequences of sea-level rise 

also would be necessary—in terms of understanding 

both the vulnerability of the energy infrastructure 

(especially important from a DOE perspective) and 

potential adaptation measures.

This example also clearly illustrates another feature of such an interdisciplinary challenge for the 

BER climate program: DOE cannot be expected to 

pursue all aspects of this problem alone. Effectively 

carrying out this sort of end-to-end, interdisciplinary science will require cooperation with other agencies and entities (e.g., states and the private sector for 

information on vulnerability of the energy infrastructure and the National Aeronautics and Space 

Administration (NASA) for remote measurements 

of ice sheet dynamics and mean sea-level rise from 

satellite topographic missions).

Example 4: What are the Future Potential Rates 

and Magnitude of Changes in Land Cover and 

Use as Drivers of Global Changes?

Changes in land cover and land use currently 

contribute about 20% of the annual flux of anthropogenic carbon dioxide (CO2) to the atmosphere. 

However, this figure is subject to substantial 

scientific uncertainties in terms of measurement 

and depends on processes that are the direct 

consequences of human decisions linked in part 

to the energy system. At the same time, changes 

in land cover and use are linked quite directly 

to the physical climate system through shifts in 

albedo, sensible and latent heat fluxes, and other 

biogeochemical and hydrologic changes—all 

of which together result in both regional and 

local consequences.

Nearly every aspect of the BER climate program 

involves understanding not only how today’s landuse changes contribute to shifts in the physical 

climate system, but also what the potential future 

rates and magnitudes of such changes are as well as 

their implications for climate. These program components include flux measurements and climate, 

carbon cycle, and Integrated Assessment Modeling. 

Successfully resolving this problem would contribute to understanding the rate, magnitude, and 

potential changes to the apparent current terrestrial 

carbon sink and its interaction with terrestrial 

hydrology. Moreover, the fact that future terrestrial 

ecosystems also will respond directly to increases 

in atmospheric CO2 concentrations and to climate change itself raises significant challenges for 

which there is no substitute for understanding the 

coupled Earth system.

Example 5: How Might Representation 

of the Hydrologic Cycle be Improved in 

Future Generations of Earth System Models?

An integrated and much improved representation 

of the hydrologic cycle in ESMs is a critical goal 

of the modeling community. Achieving this would 

enable greater regional fidelity of the models, 

a more accurate accounting of Earth’s energy 

budget, and a fuller understanding of both climate 

impacts and their feedbacks to the climate system.
Climate Research Roadmap Workshop: Summary Report U.S. Department of Energy Office of Science 39

Chapter 6. Integrating Science Discussion Paper Office of Biological and Environmental Research

Addressing this problem requires substantial interdisciplinary interactions within the climate community. 

These collaborators would include, for example, 

atmospheric physicists interested in measuring and 

modeling cloud and precipitation processes; land 

hydrologists concerned with representing the effects 

of land use, topography, and soil characteristics on 

runoff, groundwater, soil moisture, and streamflow; 

and researchers focused on understanding the climate 

feedbacks of changes in soil moisture and irrigation.

As with the other listed challenges, improving 

model representation of the hydrologic cycle should 

involve not just DOE, but other collaborating agencies and institutions such as the U.S. Geological 

Survey, NASA, the National Science Foundation, 

and the National Oceanic and Atmospheric Administration, among others. Such complex, interdisciplinary problems demand that collaborations be 

sought to augment DOE resources.

6.6 Suggestions for Implementation

Discussions from the integration and synthesis 

group resulted in several suggestions, including the 

four below, for processes to implement campaigns 

addressing either the aforementioned questions or 

similar ones about the integrated Earth system.

1. Use existing advisory mechanisms within BER to 

help identify and prioritize an appropriate series 

of challenges that the climate program could seek 

to undertake.

2. Recognize that interdisciplinary research of 

the sort outlined here requires interdisciplinary 

funding mechanisms to succeed. This may seem 

obvious, but the critical point is that funding 

opportunities that are problem-based and cut 

across current program boundaries likely will be 

necessary. Some experience with this mode of 

operation already has produced several excellent 

interdisciplinary proposals and collaborations.

3. Realize that difficult, interdisciplinary problems 

are unlikely to be fully resolved in 3-year funding cycles and consider lengthening the funding 

cycle in some cases.

4. Recognize that such activities may be challenging, both scientifically and programmatically, but 

some experience already has been gained and is 

beginning to move in this direction. This should 

serve as a reminder of the potential for success.
40 U.S. Department of Energy Office of Science Climate Research Roadmap Workshop: Summary Report

Chapter 6. Integrating Science Discussion Paper Office of Biological and Environmental Research
Climate Research Roadmap Workshop: Summary Report U.S. Department of Energy Office of Science 41

Office of Biological and Environmental Research

Appendix A: Workshop Program

Climate Research Roadmap Workshop Agenda

May 13–14, 2010

Thursday, May 13

8:30 a.m. Welcome and program goals

•	 Bill Brinkman, Director, Office of Science

•	 Anna Palmisano, Associate Director of Science for Biological and Environmental Research (BER) 

8:45 a.m. Workshop objectives, agenda, output (Mike Kuperberg, BER) 

9:00 a.m. Atmospheric Science (Dave Turner, University of Wisconsin) 

9:30 a.m. Terrestrial Science (Peter Thornton, Oak Ridge National Laboratory) 

10:00 a.m. Break 

10:30 a.m. Modeling (Ricky Rood, University of Michigan) 

11:00 a.m. Integration (Tony Janetos, Pacific Northwest National Laboratory) 

11:15 a.m. Charge to breakout groups (Mike Kuperberg) 

11:30 a.m. Breakouts 

12:30 p.m. Lunch provided 

1:30 p.m. Breakouts continue 

3:30 p.m. Reconvene in plenary—Report from breakouts 

4:30 p.m. Convene writing group to summarize breakout results

5:00 p.m. Integration group meets

Friday, May 14

8:00 a.m. Integration of climate research (Tony Janetos) 

8:30 a.m. Latitudinal challenges and needs (Jerry Melillo, Marine Biological Laboratory) 

9:00 a.m. Breakouts—Roadmap for climate research needs in: 

•	 High latitudes (Phil Jones, Los Alamos National Laboratory)

•	 Mid latitudes (Rob Wood, University of Washington)

•	 Low latitudes (Steve Oberbauer, Florida International University) 

12:00 p.m. Lunch provided

12:30 p.m. Reconvene in plenary session—Reports from breakouts 

1:30 p.m. Presentation from Integration group and final comments 

2:00 p.m. Adjourn 

2:00 – 5:00 p.m. Steering committee and writing team to remain and draft summary
42 U.S. Department of Energy Office of Science Climate Research Roadmap Workshop: Summary Report

Appendices Office of Biological and Environmental Research

Appendix B: Workshop Participants and Observers 

Participants

Ackerman, Steven

University of Wisconsin, Madison

Ackerman, Thomas

University of Washington

Brodie, Eoin

Lawrence Berkeley National Laboratory

Catania, Ginny

University of Texas

Cleveland, Cory

University of Montana

Collins, William (Bill)

Lawrence Berkeley National Laboratory

Daum, Peter

Brookhaven National Laboratory

Del Genio, Anthony

NASA Goddard Institute for Space Studies

Diffenbaugh, Noah

Stanford University

Edmonds, James A. (Jae)

Joint Global Change Research Institute

Finlayson-Pitts, Barbara

University of California, Irvine

Finzi, Adrien

Boston University

Fisher, Rosie

Los Alamos National Laboratory

Flanner, Mark

University of Michigan

Gleckler, Peter

Lawrence Livermore National Laboratory

Goulden, Michael

University of California, Irvine

Hack, James

Oak Ridge National Laboratory

Haine, Thomas

Johns Hopkins University

Hanson, Paul

Oak Ridge National Laboratory

Hilbert, David

CSIRO Tropical Forest Research Centre

Hurrell, Jim

National Center for Atmospheric Research

Hurtt, George

University of New Hampshire

Jacoby, Henry

Massachusetts Institute of Technology

Janetos, Tony

Joint Global Change Research Institute

Jones, Philip

Los Alamos National Laboratory

Knapp, Alan

Colorado State University

Kushner, Paul

University of Toronto

Leung, Lai-yung (Ruby)

Pacific Northwest National Laboratory

Mahowald, Natalie

Cornell University

McDowell, Nathan

Los Alamos National Laboratory

McGuire, Anthony David

University of Alaska, Fairbanks

Melillo, Jerry

Marine Biological Laboratory

Nenes, Athanasios

Georgia Institute of Technology

Oberbauer, Steven

Florida International University

Ojima, Dennis

Colorado State University

Persson, Ola

University of Colorado

Pinto, James

National Center for Atmospheric Research

Rasch, Philip

Pacific Northwest National Laboratory
Climate Research Roadmap Workshop: Summary Report U.S. Department of Energy Office of Science 43

Appendices Office of Biological and Environmental Research

Ringler, Todd

Los Alamos National Laboratory

Rood, Richard

University of Michigan

Shepson, Paul

Purdue University

Sobel, Adam

Columbia University

Teixeira, Joao

NASA Jet Propulsion Laboratory

Thornton, Peter

Oak Ridge National Laboratory

Turner, David

University of Wisconsin, Madison

Verlinde, Johannes

Pennsylvania State University

Weyant, John

Stanford University

Wilbanks, Thomas

Oak Ridge National Laboratory

Wood, Robert

University of Washington

Zak, Donald

University of Michigan

Zhang, Minghua

State University of New York at Stony Brook

Ziemann, Paul

University of California, Riverside

Observers

Alapaty, Kiran

U.S. Department of Energy

Amthor, Jeff

U.S. Department of Energy

Anderson, Todd

U.S. Department of Energy

Binkley, Steve

U.S. Department of Energy

Bownas, Jennifer

Oak Ridge National Laboratory

Brinkman, Bill

U.S. Department of Energy

Carruthers, Julie

U.S. Department of Energy

Christen, Kris

Oak Ridge National Laboratory

Ferrell, Wanda

U.S. Department of Energy

Graber, Joe

U.S. Department of Energy

Joseph, Renu

U.S. Department of Energy

Kundu, J. D.

U.S. Office of Management and Budget

Kuperberg, Mike

U.S. Department of Energy

Lesmes, David

U.S. Department of Energy

Mansfield, Betty

Oak Ridge National Laboratory

Mills, Marissa

Oak Ridge National Laboratory

Neely, Debra

Oak Ridge Institute for Science and Education

Palmisano, Anna

U.S. Department of Energy

Stodolsky, Marv

U.S. Department of Energy

Weatherwax, Sharlene

U.S. Department of Energy

Williamson, Ashley

U.S. Department of Energy
44 U.S. Department of Energy Office of Science Climate Research Roadmap Workshop: Summary Report

Appendices Office of Biological and Environmental Research

BER Climate Programs

Atmospheric System Research 

www.sc.doe.gov/ober/CESD/asr.html

Atmospheric Radiation Measurement Climate Research Facility

www.sc.doe.gov/ober/CESD/acrf.html

Terrestrial Ecosystem Science

www.sc.doe.gov/ober/CESD/ter.html

Regional and Global Climate Modeling

www.science.doe.gov/ober/CESD/regional.html

Earth System Modeling 

www.science.doe.gov/ober/CESD/esm.html

Integrated Assessment Modeling 

www.science.doe.gov/ober/CESD/ia.html

Climate Models and Tools Referenced in This Report

Program for Climate Model Development and Intercomparison 

www-pcmdi.llnl.gov

Parallel Ocean Program

climate.lanl.gov/Models/POP/

Community Ice Code

climate.lanl.gov/Models/CICE/

Community Land Model

www.cgd.ucar.edu/tss/clm/

BER Climate Reports and Plans

DOE Climate Change Research Program: Strategic Plan (2008)

www.sc.doe.gov/ober/Climate%20Strategic%20Plan.pdf

BER Atmospheric System Research Science and Program Plan (January 2010)

www.sc.doe.gov/ober/Atmospheric%20System%20Research%20Science%20Plan.pdf

Scientific Grand Challenges: Challenges in Climate Change Science and the Role of Computing at the 

Extreme Scale (November 2008)

www.sc.doe.gov/ober/ClimateReport.pdf

Science Challenges and Future Directions: Climate Change Integrated Assessment Research (June 2009)

www.sc.doe.gov/ober/IA%20Workshop_06-25-09.pdf

Carbon Cycling and Biosequestration: Integrating Biology and Climate Through Systems Science 

(March 2008) 

genomicscience.energy.gov/carboncycle/

Appendix C: BER Climate Websites
Acronyms

ARM Atmospheric Radiation Measurement

program

AR5 Fifth Assessment Report of the IPCC

ASR Atmospheric System Research

BER DOE Office of Biological and

Environmental Research

CCN cloud condensation nuclei

CCSM Community Climate System Model

CERES Clouds and the Earth’s Radiant

Energy System

CICE Community Ice Code Model

CISM Community Ice Sheet Model

CLM Community Land Model

CMIP Coupled Model Intercomparison Project

CMIP-5 Coupled Model Intercomparison Project

Phase 5

CRM cloud-resolving model

DOE U.S. Department of Energy

ESG Earth System Grid

ESM Earth System Model

ENSO El Niño–La Niña-Southern Oscillation

GCM general circulation model

GCSS GEWEX Cloud System Study

GEWEX Global Energy and Water Cycle 

Experiment

HYPOP Hybrid coordinate Parallel Ocean

Program

IAM Integrated Assessment Model

IN ice nuclei

IPCC Intergovernmental Panel on Climate

Change

ITCZ Inter Tropical Convergence Zone

IWG Interagency Working Group

LES large eddy simulation

MJO Madden-Julian Oscillation

NASA National Aeronautics and Space 

Administration

NOAA National Oceanic and Atmospheric

Administration

NSF National Science Foundation

PCMDI Program for Climate Model

Development and Intercomparison

POP Parallel Ocean Program model

TOA top-of-atmosphere

USGCRP U.S. Global Change Research Program

Printed with soy ink on recycled paper