Scaling Studies in 

Arctic System Science 

and Policy Support

A Call-to-Research

A Report from the 

U.S. Arctic Research Commission
COVER PHOTOS

Top left to bottom right | Belugas in the Bering Sea seen during the April 2006 

U.S. Fish and Wildlife Service (USFWS) Walrus Survey (photo by USFWS/Brad Benter). 

| Polar Sea and Louis S. St. Laurent at end of Arctic Ocean Crossing, north of Svalbard 

(photo by Knut Aagaard). | Sampling across ice field on the Arctic ocean (photo by 

Knut Aagaard). | Sunset (photo by Leopoldo Llinas). | Sitting on top of submarine after 

breaking through the ice (photo by Bernard Coakley).

Background | Iceberg in the Svalbard Islands, Norway (iStockphoto).

About the U.S. Arctic research commission

The U.S. Arctic Research Commission is a small, independent federal agency 

established in 1984 by the Arctic Research and Policy Act (Public Law 98-373, 

July 31, 1984; amended as Public Law 101-609, November 16, 1990). The 

Commission’s principal duties are: (1) to establish the national policy, priorities, 

and goals necessary to construct a federal program plan for basic and applied 

scientific research with respect to the Arctic, including natural resources and 

materials, physical, biological and health sciences, and social and behavioral 

sciences; (2) to promote Arctic research, to recommend Arctic research policy, 

and to communicate research and policy recommendations to the President 

and the Congress; (3) to work with the National Science Foundation as the 

lead agency responsible for implementing the Arctic research policy and to 

support cooperation and collaboration throughout the Federal Government; 

(4) to give guidance to the Interagency Arctic Research Policy Committee to 

develop national Arctic research projects and a five-year plan to implement 

those projects; and (5) to interact with Arctic residents, international Arctic 

research programs and organizations, and local institutions, including regional 

governments to obtain the broadest possible view of Arctic research needs.
Scaling Studies in 

Arctic System Science 

and Policy Support

A Call-to-Research

A Report from the U.S. Arctic Research Commission

Edited by Charles J. Vörösmarty, 

A. David McGuire, and John E. Hobbie
Acknowledgements

The content of this report relied on input from a broad spectrum of experts who contributed

substantial time and personal energy into its preparation. The U.S. Arctic Research Commission 

greatly appreciates the efforts of the following collaborators:

*Waleed Abdelati, University of Colorado, Boulder (waleed.abdalati@colorado.edu)

Dominique Bachelet, Oregon State University (bachelet@fsl.orst.edu)

*C.J. Beegle-Krause, Research4D (cjbk@research4d.org)

*James Berner, Alaska Native Tribal Health Consortium (jberner@anthc.org)

*Lawson Brigham, University of Alaska Fairbanks (lwbrigham@alaska.edu)

Kathy Farrow, U.S. Arctic Research Commission (k.farrow@arctic.gov)

*S. Craig Gerlach, University of Alaska Fairbanks (ffscg@uaf.edu)

*Jackie Grebmeier, University of Maryland Center for Environmental Science (jgrebmei@umces.edu)

*Lawrence Hamilton, University of New Hampshire (lawrence.hamilton@unh.edu)

*Larry Hinzman, University of Alaska Fairbanks (lhinzman@iarc.uaf.edu)

*John E. Hobbie, Marine Biological Laboratory, Woods Hole, MA (jhobbie@mbl.edu)

*Peter Larsen, University of California, Berkeley (phlarsen@lbl.gov)

*Philip A. Loring, University of Alaska Fairbanks (ftpal@uaf.edu)

*Carl Markon, U.S. Geological Survey (markon@usgs.gov)

*A. David McGuire, U.S. Geological Survey (admcguire@alaska.edu)

Fritz Nelson, University of Delaware (fnelson@udel.edu)

Ramakrishna Nemani, NASA Ames Research Center (rama.nemani@nasa.gov)

*James Overland, National Oceanic and Atmospheric Administration (james.e.overland@noaa.gov)

*Edward Rastetter, Marine Biological Laboratory, Woods Hole, MA (erastett@mbl.edu)

*Mike Steele, University of Washington (mas@apl.washington.edu)

Marc Stieglitz, Georgia Institute of Technology (marc.stieglitz@ce.gatech.edu)

*Charles Vörösmarty, The City College of New York/CUNY (cvorosmarty@ccny.cuny.edu)

*John Walsh, University of Alaska Fairbanks (jwalsh@iarc.uaf.edu)

Mathew Williams, University of Edinburgh (mat.williams@ed.ac.uk)

*Daniel White, University of Alaska Fairbanks (dmwhite@alaska.edu)

Rebecca Woodgate, University of Washington (woodgate@apl.washington.edu)

*Report authors

Editing and design services were provided by Ellen Kappel and Johanna Adams 

of Geosciences Professional Services, Inc.
Letter from the Chair.................................................................................................................................. 1

Executive Summary .................................................................................................................................... 3

1. Introduction............................................................................................................................................. 5

2. Scaling Challenges Within Arctic Science: A Disciplinary Perspective...................................... 7

2.1. Introduction...................................................................................................................................... 7

2.2. Physical Systems............................................................................................................................... 8

 2.2.1. Weather and Climate Prediction......................................................................................... 8

 2.2.2. Glaciers, Ice Caps, and Continental Ice Sheets............................................................... 12

 2.2.3. Permafrost and Hydrology ................................................................................................ 16

 2.2.4. Arctic Ocean and Sea Ice.................................................................................................... 21

2.3. Ecosystems and Biology................................................................................................................ 25

 2.3.1. Marine Ecosystems.............................................................................................................. 25

 2.3.2. Terrestrial Ecosystems Including Freshwater................................................................. 29

2.4. Human Systems.............................................................................................................................. 33

 2.4.1. Arctic Communities............................................................................................................ 33

3. The Role of Scaling in Interdisciplinary Arctic and Earth System Science .............................. 39

3.1. Introduction.................................................................................................................................... 39

3.2. Tipping Points in the Arctic System............................................................................................ 39

3.3. Changing Arctic Terrestrial Ecosystems and Their Impacts on the Earth System ............. 41

4. The Role of Scaling in Societal Applications.................................................................................. 44

4.1. Introduction.................................................................................................................................... 44

4.2. Arctic Human Health Research Issues in Alaska ..................................................................... 45

4.3. Climate Change Mitigation.......................................................................................................... 47

4.4. Public and Private Infrastructure Vulnerabilities.................................................................... 50

4.5. Subsistence Harvest and Commercial Fisheries........................................................................ 52

4.6. Non-Renewable Resource Extraction ......................................................................................... 55

4.7. Ice Navigation in a Changing Arctic........................................................................................... 58

4.8. Oil Spill Preparedness, Response, and Restoration.................................................................. 61

5. Synthesis of Key Findings.................................................................................................................... 64

Appendix 1. A Primer on Scaling .......................................................................................................... 68

Appendix 2. Sources and Background Reading ................................................................................ 72

Contents

1

Dear Colleague,

As a non-scientist student of the Arctic, I have had the opportunity to meet and hear from the 

best polar scientists in the world, working in many disciplines. We share common views: the 

Arctic is changing, the change is important to the world, and how fast it changes—and exactly 

how—will have great impacts. As citizen taxpayers, many of these changes will require us to make 

large investments. Some changes, such as accelerated permafrost thawing or more frequent forest 

fires, will constrain the way we live and operate in the Arctic. Others, such as new sea routes or 

new fisheries, will bring economic opportunity to the North and greater value to the world.

These realizations are fueled by a highly dynamic and expanding body of scientists—scientists 

who are contributing fundamental knowledge about how the Arctic works and how it is changing. 

Part of this community’s success results from the incredibly broad scope of their studies, 

measurements, and models that have been developed over the past two decades.

Scientists study the North from various perspectives using various platforms. Satellites give us 

a better-than-birds-eye view of receding ice conditions in the aggregate, for example, but a scientist with a drill is still required to confirm the thickness of the ice at any given point. Observation 

of the fluxes of gases to or from plots of ground, a few square meters in area, may tell us how 

Arctic tundra is “breathing” at a detailed level, but the value of these collected data to climate 

scientists—and policymakers—may have meaning only when the information is extrapolated 

over vast expanses.

For example, some have recently suggested that the amount of methane out-gassing from 

the tundra is sufficiently large, on a pan-Arctic scale, that it needs to be accounted for when 

compiling global carbon inventories and fluxes. To meet the needs of Arctic Ocean fisheries regulators and shippers, we will need to improve our sea-ice models so that they can more accurately 

predict ice cover over the entire basin. The models must be able to forecast conditions in any 

given port, at any given time, where fishing or shipping may be based. We can estimate changing 

weather patterns on a grand scale, but we can’t say whether more or less precipitation will impact 

the habitat or productivity of a local fishery.

Macro and micro? Lump your observations or split them? Small scale or large? When it comes to 

understanding processes in the Arctic, what does the very small tell us about the very large? What 

does the aggregate dynamics tell us about the smaller pieces that make up the full system? What 

Letter from

the chair
2

does the region tell us about the locality? How many buoys are needed? How many sensors from the 

bottom of the sea to the upper atmosphere are necessary, to be sure we know what we think we know 

about changing climate in the North? These and many other questions constitute the essence of 

the scaling problem.

While these issues are hardly restricted to Arctic science, they become particularly relevant in 

the North. Within a global climate context, this region is recognized as constituting a strongly 

coupled and highly sensitive system. To understand the role of the Arctic in global change—

both as generator and recipient of impacts—requires knowledge of the behavior of many interacting components that traditionally have been sampled, simulated, and studied at relatively 

fine scales. To me, “scaling” issues must be better understood if science will be able to deliver 

on its goal of better understanding and predicting the state of the Arctic with a high degree of 

accuracy and precision.

Two USARC Commissioners, Dr. John Hobbie and Dr. Charles J. Vörösmarty, both scientists, 

brought these many issues to our attention. The aims of this report are to: (i) increase awareness of the great diversity of approaches to Arctic research, with respect to scales of time and 

space, (ii) identify impediments to progress should scale-related issues not be addressed, and 

(iii) identify opportunities that will improve the status quo and spark new innovations across 

otherwise isolated parts of the research spectrum. We also discuss how scaling issues should be 

considered in the public policy domain. If not, we will fail a generation of human beings who 

are relying on our work—in climate, biodiversity, the humanities and other sciences, but also in 

energy, engineering, and a broad swath of decisions that have social and economic relevance. 

A typical audience for a U.S. Arctic Research Commission report is the membership of 

Interagency Arctic Research Policy Committee (IARPC). These agencies carry out research goals 

set by the Commission, but it is the science community itself that, in adhering to the scientific 

method, sets standards of measurement and analysis—as ultimately judged by peer review—that 

give us confidence that the “facts” we’re finding are truly facts. In this report, we call on the 

science community, as well as its sponsors, to pay closer attention to the many aspects of scaling 

that present themselves today—as both challenges and opportunities—that should become part of 

the larger Arctic research agenda.

Mead Treadwell, Chair

U.S. Arctic Research Commission

Anchorage, Alaska, June 3, 2010
3

Executive

Summary

A goal of the U.S. Arctic Research Commission is to assist 

U.S. agencies in establishing a national Arctic research 

program. In 2003, the Commission recognized the changes 

already happening in the Arctic environment because of 

climate warming and the need to synthesize available information, regardless of the scale at which they were collected, 

to predict impacts on the whole Arctic. The first workshops 

to discuss this required data synthesis were held in Seattle 

in 2003 and Woods Hole in 2004, and they brought together 

six experts in scaling up to the regional level through models 

of hydrology and of plant response to climate change. The 

next workshop, held in Seattle in 2008, included more than 

20 experts in Arctic environmental processes and scaling in 

the fields of atmospheric modeling, ocean physics, biology, 

river hydrology, terrestrial ecology, and the interactions of 

human populations with climate change. This report, organized at the Seattle meeting, is not intended to be a comprehensive synthesis of existing data and models but instead is 

a call for action to fill the gaps in the knowledge necessary 

to reach the goal of developing an understanding of the 

effects of climate and environmental changes at the scale of 

the whole Arctic environment including their atmospheric, 

marine, terrestrial, and human components.

Synthesis Finding 1. Scaling issues and even the definitions of scale are so varied across individual disciplines 

that they hinder interdisciplinary research. There exists 

a great breadth of spatial and temporal scales that characterize any one Arctic disciplinary science and its applications, which is matched by an equally broad admixture of 

spatial and temporal scales when comparing disciplines. 

Such diversity arises due to the differences in the historical 

development of individual disciplines and the resulting 

unique nomenclatures regarding scale—for example, the 

microscale means something radically different to a microbial ecologist than to an Arctic Ocean sea ice modeler. 

Discipline-specific approaches to scaling have made it 

difficult for different disciplines to effectively integrate. 

One way forward is to cast grand challenge research questions built around transdisciplinarity as well as multiscale 

perspectives to understand the current and future states of 

the fully coupled Arctic system involving all key natural 

and human components. 

Synthesis Finding 2. Scale incongruities among 

components of the Arctic system give rise to opportunities to study intermediate scales. The existing body of 

research has focused traditionally on measurements made 

at local scales, which are important for understanding the 

inherent dynamics of discipline-specific processes. These 

same disciplines have also relied on coarse-scale models to 

achieve understanding over the broad domain. In contrast, 

intermediate spatial and temporal scales have received relatively less attention, yet it is precisely along the interface of 

intermediate scales that systems are often critically defined, 

for example, through boundary layer fluxes linking the 

highly heterogeneous Arctic land surface to a well-mixed 

overlying atmosphere. Intermediate scales, or mesoscales, 

provide an important context through which coarse-scale 

dynamics become useful in setting the bounds of key 

phenomena and fine-scale dynamics can be generalized. 

Difficult numerical stability challenges face the modeling 

community across scales. These challenges must be solved 

to ensure stable numerical “handshakes” across contrasting 

time/length scales. These stable handshakes set the stage 

for robust Arctic systems models that can then be useful for 

informing policy decisions.

Synthesis Finding 3. Thresholds are scale-sensitive 

and important, yet prove difficult to detect, study, and/

or predict. Threshold responses occur at the point where 

there is an abrupt change in a system quality, property, or 
4

phenomenon, or where small changes in a driving variable 

of the system produces large, persistent, and potentially 

irreversible responses. Thresholds represent tipping points, 

and involve time and space “edge effects.” A key to identifying thresholds is proper representation of interactions of 

processes across a spectrum of scales. Again, within this 

spectrum, the intermediate scale may be critical; more work 

needs to be focused within this space-time domain. 

Synthesis Finding 4. Scales of human perception are 

much different than those associated with the study of 

natural systems. Arctic human systems are complex and 

multifaceted, encompassing both indigenous and industrial 

societies that vary greatly in their domains of perception 

and human footprints. Traditional societies have evolved 

the capacity to detect and understand the implications of 

changes in Arctic systems and have adapted using strategies through which they can cope with local shortages 

in renewable resources. For example, they may utilize a 

higher level of mobility to make use of a much larger spatial 

domain for hunting and gathering. Viewed as a scaling 

issue, native populations have developed strategies to 

effectively reduce the impact of high-frequency “noise” in 

the landscape by integrating their interactions over a wider 

domain, which tends to dampen such variations. Decisionmaking in industrial societies also spans many spatial 

scales. Modern-day institutional and legal frameworks can 

be found at individual village, provincial, national, and 

international levels. In an age of globalization, macro-level 

decisions on the Arctic can easily fail to establish links to 

processes operating in the Arctic itself and of relevance 

to people and livelihoods. Studying the perceptions of 

space-time domains and Arctic system change by traditional as well as modern Arctic communities will help to 

better understand our society’s readiness to adapt to Arctic 

environmental change. 

Synthesis Finding 5. Information has not been well 

structured to facilitate cross-scale studies. Given the 

reality of a diverse treatment of space-time issues within 

and across disciplines, it is not surprising that coherent 

information systems are not yet in place to reconcile or 

deal with these incongruities. Social and natural scientists organize information over very different accounting 

units (e.g., administrative units versus watersheds) further 

impeding a unified system-level picture. Jointly developing 

models and integrated data compendia, with a broad range 

of thematic data sets that are spatially and temporally 

harmonized, will allow cross-disciplinary research to be 

more easily executed. 

Synthesis Finding 6. Science conclusions and uncertainties require better translation into information for 

policymakers. Expected change in the Arctic system will 

be complex, multifaceted and multiscale. Decision-makers 

and managers must therefore recognize that a particular 

action that targets one scale may not be adequate—and in 

some cases detrimental—at another. Uncertainties in scientific knowledge can be compounded when moving across 

scales, yet these uncertainties have rarely been quantified or 

conveyed to decision-makers. Fostering a dialogue through 

which the decision-making community clearly articulates 

the space and time domains over which they require policyactionable scientific information and through which the 

science community can assess their readiness to provide 

this knowledge will constitute an important step forward in 

the effective transfer of science to policy.
5

1. Introduction

The scientific community today faces mounting demands 

to provide reliable and policy-actionable information on 

the state and trajectory of change across the Arctic system. 

Understanding the decline of sea ice, polar bear and other 

wildlife population changes, Greenland ice sheet dynamics, 

fluctuations in terrestrial carbon reservoirs, and environmental impacts on Arctic social systems requires a coherent 

and comprehensive view of regional and pan-Arctic 

dynamics as well as linkages to the lower-latitudes. 

The scientific community has invested heavily in the 

intellectual, experimental, and logistical infrastructure that 

supports researchers focused on in situ and local placebased studies. Although these investments are a critically 

important foundation for the scientific understanding of 

the Arctic, there exists a growing number of techniques and 

approaches that enable researchers to observe, simulate, and 

analyze trends over much larger spatial scales, including 

the full pan-Arctic. At the same time, Arctic scientists have 

yet to develop a coherent way of tackling such fundamental 

questions like: Are the measurements that plant physiologists make at the leaf, plant, or plot scale relevant across 

the entire Arctic domain? or the arguably more complicated question: At what spatial and temporal scales do the 

signatures of climate change and variability translate into 

impacts on land- or ocean-based food webs that in turn affect 

indigenous harvests?

Relatively little research has been dedicated to the 

design of strategies that coherently link place-based studies 

with Arctic dynamics across the spectrum of scales. Each 

scale along this spectrum is valuable in its own right, yet 

the synergies across scales are not yet fully understood 

or appreciated. Tangible strategies to bridge the scales 

and to ensure a consistent quality of conclusions across 

different scales have not been adequately developed. As 

a result, we have an incomplete understanding of how 

dynamics observed at well-studied, local sites are ultimately 

transformed into the behavior of the full Arctic system. 

How can we create a coherent picture of the system and its 

dynamics across all relevant scales? The ability to answer to 

this question requires improvement of scaling approaches 

for specific aspects of the system being evaluated by each 

community of researchers.

The need for research to improve scaling approaches in 

the Arctic is the central focus of this U.S. Arctic Research 

Commission report. Although scaling issues are hardly 

unique to the Arctic sciences, by addressing scaling in an 

Arctic context, the Commission hopes to shed light on an 

approach to research that merits an elevated level of attention within federal agencies. A review of the state of the 

art in scaling across some important research themes is 

timely, especially in light of elevated interest in the Arctic as 

evidenced by recent agency investments in the 2007–2009 

International Polar Year and Arctic Observing Network. 

The principal aims of this report are to:

• Review approaches to scaling across a variety of disciplines and applications

• Identify opportunities for the use of scaling to improve 

our capacity to understand the state and trajectory of the 

Arctic system

• Articulate the role of scaling in accommodating the 

needs of the environmental policy and management 

communities for scientifically sound information

• Develop a Call-to-Research around which the research 

community and U.S. federal agencies can identify promising new arenas of scientific enterprise

The purpose of this report is not to conduct scaling 

studies per se, but instead to identify research needs, challenges, and opportunities associated with the role of scaling 

in the Arctic research agenda. This goal will be achieved by 

providing some key examples drawn from several thematic 

areas. The report is strategic in nature, and is illustrative as 

opposed to being fully comprehensive. In this context, the 

targeted audiences are scientists, practitioners, and, importantly, agency program managers within the auspices of the 

Interagency Arctic Research Policy Committee (IARPC), 
6

the State of Alaska, and others interested in how integrative 

scaling research can improve science that supports policy 

and public information about the Arctic. 

This report is structured to be both informative yet 

also practical, given the wide-ranging set of issues that 

will be presented. To this end, it begins with an overview 

of research challenges across major thematic arenas, with 

several illustrations on the use of scaling to forward the 

research agenda in particular disciplines. Both biogeophysical and human dimension issues are discussed. There 

is then a treatment of several synthesis issues drawn from 

these examples, indicating how scaling can be used to 

formulate an improved vision of the state and trajectory 

of the Arctic system, turning attention to research that is 

more interdisciplinary in nature. The report next addresses 

scaling issues in the domain of societally important applications. The report ends with a synthesis of key findings, 

followed by a set of specific recommendations for research, 

targeted at the IARPC agencies.

For those interested, Appendix 1 presents a more technical background on scaling.
7

2. Scaling Challenges

Within Arctic Science

 A Disciplinary Perspective

2.1. Introduction

With several new and important scientific studies, major 

international assessments, and high-profile media reports 

documenting dramatic environmental changes, it is no 

surprise that the Arctic continues to command the attention of scientists, policymakers, and the public. Although 

the paleographic record demonstrates the region to be 

dynamic and susceptible to major climatic shifts, there 

is growing evidence that today the Arctic’s environment is changing at an unprecedented rate by modern 

standards—as evidenced by broad-scale increases in air 

temperature, rapid gains/losses in lake area associated with 

permafrost degradation, major “greening” and geographic 

shifts in vegetation, reductions in sea ice cover, melting of 

ice sheets and smaller glaciers, heating and loss of permafrost, changing river flows, lengthened ice-free period in 

lake and rivers, and reduction in snow cover. There is also 

concern about how recently observed increases in freshwater supply to the Arctic Ocean and the North Atlantic 

could reduce thermohaline circulation, with potentially 

global-scale climate change consequences. 

Although a fundamental feature of these changes is their 

coherence, the driving mechanisms and ultimate effects 

of these many changes are still poorly understood. The 

transformation of the pan-Arctic to a seasonally ice-free 

state that is likely to persist for centuries is an emerging 

consensus view. The consequences of these dramatic 

changes to the Arctic and global climate systems have thus 

become critical problems requiring further investigation.

Arctic research increasingly requires interactions across 

traditional disciplines and must address issues that are not 

necessarily driven by curiosity-based science. The overall 

demand for knowledge is often driven by policy imperatives. Section 3, which is dedicated to interdisciplinary 

discussions, highlights feedbacks among the disciplines 

and provides examples. Here, we focus on advances made 

within the traditional disciplines through which the Arctic 

has been examined. As will be demonstrated below, these 

disciplinary boundaries and approaches are not absolute, 

and are increasingly giving way to the new requirements of 

Arctic systems thinking, which focus fundamentally on the 

issue of how the component parts of the Arctic—physical, 

chemical, biological, and human—are united and produce 

behaviors that emerge from their interactions.

This section of the report is divided into three main 

parts: (1) the physical domain, composed of the atmosphere, 

cryosphere, land-based permafrost and hydrologic systems, 

and ocean; (2) marine, terrestrial, and freshwater biology 

and ecosystems; and (3) major scaling issues drawn from 

the research on human systems.
8

2.2. Physical Systems

2.2.1. Weather and Climate Prediction 

Recommendations

• Optimize observational programs and process studies 

to collect data that directly address the needs of Arctic 

weather and climate modelers.

• Refine remote-sensing and other strategies that upscale 

in situ point measurements, providing key weather 

and climate model parameters that can be applied 

over the full Arctic spatial domain.

• Build high-resolution GIS infrastructure and databases of variables (e.g., vegetation, soil properties, 

topography, slope orientation) to help downscale 

coarse-resolution climate model outputs onto the land 

mass through dynamical and statistical approaches.

• Unite upscaling and downscaling methodologies to 

ensure the appropriate “handshakes” across subdomains of Arctic weather and climate models that 

depict land, ocean, and atmospheric interfaces.

Science Context

Weather and climate predictions are vital to understanding 

the Arctic Earth system and to planning human activities 

over a range of temporal scales. In the scientific realm, the 

Arctic is like much of the rest of the planet. It comprises 

atmosphere, land, and oceanic components with their 

characteristic dynamics and storage capacities for energy, 

water, and constituents, with characteristic spatial-temporal 

domains of key processes (Figure 2.1). However, as a relatively small, well-contained, coupled air-land-ocean system 

with sharp seasonal contrasts in energy flows and phase 

changes of water, the Arctic serves as an important testing 

ground for developments in coupled modeling. Simulating 

the exchange of numerical information or “handshakes” 

of component models across the major domains (air-landocean), each with their unique time constants at their 

interfaces (e.g., air masses flowing over near-stationary, 

though not immobile, sea ice), have proven difficult to 

firmly establish due to numerical incongruities. Further, 

establishing strategies that make the best use of point-scale 

measurements from weather stations or experimental 

instruments at process study sites, and merging these data 

sets with satellite remote sensing covering the entire panArctic domain, has yet to be worked out.

On the societal front, short-term weather forecasts are 

vital to transportation, many outdoor activities, and, in the 

case of severe weather, human safety and the protection of 

property. At monthly to seasonal time scales, the potential 

benefits of climate forecasts for planning in the industrial 

and commercial sectors are substantial. At decadal and 

longer time scales, robust climate forecasts would be highly 

valuable for improved understanding of Arctic climate 

change trajectories and thus design strategies for societal 

adaptation in the Arctic and elsewhere. A changing climate 

will almost certainly bring consequential changes in highlatitude weather. Temperature and precipitation extremes 

will change with climate, and these extremes will impact 

humans and ecosystems. However, forecasts of extreme 

weather events and a changing climate will be effective only 

if they are sufficiently site-specific in ways that enable planning and preparation on the local jurisdictional scale. The 

scale challenge to the Arctic weather and climate science 

community is thus substantial, spanning many spatial 

and temporal scales depending on the scientific question 

or application need. 

Scaling Issue 

Weather and climate dynamics span an enormous range 

of physical processes, and spatial and temporal scales 

(Figure 2.1). Weather and climate predictions are based 

largely on models that divide the atmosphere (and ocean, 

land, and ice) into grid cells of finite volume. Typical 

horizontal dimensions of atmospheric grid cells range 

from about 10 km in regional weather prediction models 

to 100–200 km in global climate models. Other model 

components, such as those depicting the ocean and land 

surfaces, are sometimes divided into smaller grid cells 

when sufficient data exist and when experience shows that 

finer-scale variability exerts an impact on model fidelity. 

The corresponding variables assigned to each grid cell 

still represent averages over finite volumes or areas rather 

than point values. 
9

Given user requirements for 

local rather than broad-area 

forecasts, there is a need for 

models to account for sharp 

local variations. In coastal 

areas, for example, steep gradients of key variables can lead 

to large differences in weather 

(including temperature, the 

presence or absence of precipitation, precipitation type, 

cloudiness, winds) and climate. 

Another example is when 

mountain ranges—known to 

control the spatial distribution 

and intensity of precipitation 

due to variations in elevation, 

wind direction, and atmospheric water vapor—are depicted 

in more detail from high-resolution digital topographic 

data. Such elevation differences cannot be resolved by a 

model with 10–100 km resolution, and the grid-cell average 

of weather variables can represent a seriously deficient 

forecast of weather for a particular point within a grid 

cell. Conversely, the finite size of grid cells and the corresponding reliance on grid-cell averages makes it necessary 

to parameterize processes that occur on scales finer than 

the size of a model’s grid cell in order to make sufficiently 

robust calculations regarding atmospheric dynamics. The 

methods to make these so-called upscaling and downscaling

computations continue to evolve. 

Climate and weather models maintain feedbacks across 

different domains, such that a calculation on the land 

surface has an implication for atmospheric physics, which 

then applies a control over the future state of the land mass. 

The challenge is to maintain mass and physical process 

consistency in this bi-directional context. Imagine the 

upscaling-downscaling challenge as a conversation between 

two people. The importance of a common language 

becomes immediately apparent.

Model Parameterizations and Upscaling

The essence of model parameterization is the effective use 

of one or more numerical constants that serve as input 

into a simulation able to capture the effect of a small-scale 

process without the need for explicitly simulating all of 

the subsidiary physical dynamics. For example, laboratory 

or small plot field measurements of the strength of plant 

leaves to control the evapotranspiration process are used 

to construct the land surface parameter known as stomatal 

resistance. In grid-based models, the grid-cell average of the 

effects of the process is determined by combining (1) gridcell means of variables computed and carried forward in 

time by the model and (2) the parameters that are prescribed 

a priori (ideally on the basis of the experimental data or 

field measurements). Other examples of processes that are 

parameterized in weather and climate prediction models 

are turbulent mixing, radiative transfer, and cloud microphysical processes—all of which are essentially molecularscale processes but have to sensibly be depicted (i.e., scaled) 

across a more macroscale domain. Given the tremendous 

number of molecules in a grid cell of even a fine-resolution 

atmospheric model (~ 10 km × 10 km × 100 m), it will never 

be feasible to formulate explicitly the molecular interactions that underlie these processes. However, it does become 

relevant to ask how experimental values can be used to 

construct effective parameters that can be readily absorbed 

into weather and climate simulations. 

In ocean models, the parameterization of mixing is 

crucial to the evolution of temperature and salinity fields. 

Sea ice models often use parameterizations that treat the 

fraction of leads, the extent of ridging, and the associated 

Hour Day Week Month Season Year Decade Century Millenium

TEMPORAL SCALE

SPATIAL SCALE 

Pan-Arctic

Region

River Basin

Catchment

Hillslope

Plot

Microclimate, slope drainage

Thunderstorm,

ash ood

High and

low pressure systems

Regional

circulation

(NAO, PDO)

Drought,

sea ice extremes

Volcanic

eects

atmospheric

circulation (AO)

Earthsolar

orbital

eects

Anthropogenic

climate

Large change

scale

Figure 2.1. Spatial and temporal scales related to weather and climate dynamics. NAO = 

North Atlantic Oscillation. PDO = Pacific Decadal Oscillation. AO = Arctic Oscillation. 
10

ice thickness distribution. In terrestrial models, evapotranspiration rate is an example of a critical variable that 

is parameterized. The importance of the synergy between 

modeling and observational studies in the Arctic landatmosphere boundary has long been recognized, although 

progress has been slow. Carefully designed observational/

field programs and process studies targeted at modelers’ 

needs will provide the data to more precisely define model 

parameters. Remote sensing has a particularly important 

role to play in this upscaling, insofar as plot-level information can adhere to categories used by remote-sensing 

experts who generate maps of surface conditions that are 

often time-varying. For example, the reflectivity of different 

surfaces (e.g., bare tundra vs. snow, open ocean vs. sea ice) 

has been shown to yield important impacts on atmospheresurface fluxes and site-specific values can be applied to 

the shifting mosaic of surface types over much broader 

domains monitored from space.

There are nevertheless difficulties and uncertainties 

associated with remote-sensing data, directly linked to 

the scaling issue. All satellite instruments have temporal 

and spatial resolution tradeoffs and require ongoing and 

complex calibration against field data. These data are often 

at mismatched scales (e.g., calibrating MODIS NDVI 

[a vegetation “greenness” index at ~ 1 km2] against fieldbased measurements of LAI [leaf-area indices at ~ 1 m2

]). 

The use of classification schemes for land surfaces often 

ignores the fact that there is a continuum of variation, for 

example, within a single tundra land surface class. These 

variations can have significant impacts on the depiction of 

processes. Tundra LAI can vary by an order of magnitude, 

and in models dependent on calculations of evapotranspiration losses from the land surface to atmosphere, substantial biases can arise.

Approaches to Downscaling

Although parameterization captures the upscaling of 

processes whose aggregate effects are important for 

predicting system-level behaviors, downscaling seeks to 

“spatialize” the computed, aggregate means over a grid cell 

into a form that could be useful in many scientific and practical applications. This downscaling is critical, for example, 

in capturing better the two-way interaction of coupled, 

dynamic models. Downscaling is also useful where climate 

model outputs drive so-called “stand-alone models,” for 

example, providing temperature or precipitation drivers for 

a permafrost-hydrology model that later could be used in 

an economic impacts analysis of melting Arctic tundra. 

The two basic approaches to downscaling are dynamical

and statistical. There is also a combined approach. 

Dynamical downscaling is based on using a highresolution regional model nested within (or driven by) a 

coarse-resolution, typically global domain model, which 

in turn provides time-varying boundary conditions. The 

high-resolution model then makes its calculations over its 

domain, but at each computational time step moves some 

of its predictions across the coarse-scale boundary and at 

the same time gets new information from the coarse-scale 

model. Passing information on wind energy fields would 

be one concrete example. Several levels of nesting can be 

used to achieve very high resolution (< 1 km). Such modules 

may even be placed in a coarse-resolution model’s grid cell 

to achieve ultra-fine resolution over a small area. There are 

tradeoffs involving the use of such an approach. On the 

one hand, the higher the resolution, the better the depiction of the physical domain. Indeed, certain processes like 

the dynamics of the atmosphere’s water cycle can only be 

explicitly treated when depicted at higher resolutions. Yet, 

with every reduction in area or volume, great computational demands—and substantially increased run time—are 

placed on the modeling system, and for this reason we do 

not today have many high-resolution global models, but 

many more of the nested variety.

Statistical downscaling is based on—as the name 

implies—statistical algorithms, like multiple regression 

equations, which relate model-computed quantities to 

observational data. This approach, which generally requires 

a priori knowledge of a system’s behavior in order to select 

candidate predictors, is used in weather forecasting, where 

the term “model output statistics” (MOS) describes the 

products. For example, a model’s grid-cell temperature can 

be used as a single predictor of temperature at a specific 

location, building a statistical connection to temperature data from a weather station, but can also be used in 

conjunction with other model variables such as wind, 

humidity, and cloud cover from the same grid cell and/or 

from upstream grid cells to get a more accurate downscaled 

prediction. The success of the MOS approach, which may 
11

be viewed as a statistical enhancement of raw model output, 

has made it one of the staples of the weather prediction 

enterprise generally. There are few published examples of 

the use of this downscaling approach in Arctic climate 

applications, though it has been demonstrated successfully 

through algorithms targeting Svalbard and Norway. 

The third approach to downscaling involves a fusion 

of coarse-resolution model output and higher-resolution 

information. For example, the so-called topoclimate algorithms use climate and weather model outputs together 

with high-resolution topographic information and assumed 

(or model-derived) lapse rates to construct high-resolution 

fields of temperature and precipitation. One can start with 

observational data from surface networks, which provide 

the values that are interpolated and fit to the topography of 

the high-resolution grid. The PRISM database is one such 

example available for the United States, including Alaska. 

The PRISM database includes surface air temperatures at 

1–2 km resolution. The left and right panels of Figure 2.2

are examples of PRISM-derived temperatures. Ongoing 

research activities use such an approach to obtain highresolution projections of climate by superimposing projected 

changes from global climate models onto the high-resolution 

climatologies (e.g., PRISM) of recent decades. The raw model 

output prior to downscaling has the excessively smooth 

character shown in Figure 2.2 (center).

Another combination strategy for constraining downscaled, coarse-resolution model predictions uses means and 

variances evaluated from (1) daily or monthly monitoring 

station data and (2) corresponding climate model outputs. If 

the normalized anomalies of the model output are rescaled 

using the means and standard deviations of the station 

data, one obtains daily (or monthly) values of a particular 

variable. These time series can be constructed to depict 

future scenarios, providing temporally detailed information about extremes and temporal changes thereof. The 

usefulness of this approach depends on the validity of the 

assumption that the statistical distributions of the variables 

do not change over time. In recent uses of this approach 

(e.g., for the 2008 U.S. National Climate Assessment), 

Gaussian and gamma statistical distributions were used for 

monthly temperature and precipitation, respectively. In all 

of these approaches, should the downscaling misrepresent 

the probability density function of, say, temperature across 

a landscape, then any nonlinear functions of temperature 

will be incorrectly modeled. Statistical analyses like Kriging 

can provide estimates of uncertainty by exploiting information on the space-time covariances of error.

What is Required for Progress

• Optimize observational programs and process studies 

to collect data that directly address the needs of 

Arctic weather and climate modelers. Field studies are 

critical to quantifying and understanding land-surface 

and ocean-surface boundary dynamics. In the end, these 

studies yield information about a particular category of 

interface, and in some instances, a particular site. It is 

therefore essential that these experiments be designed to 

Figure 2.2. Left: Climatological average daily maximum temperatures for July 1961–1990. Reds represent values of 15–25°C (60–77°F), blues 

and greens represent values of 5–10°C (40–50°F). Temperatures are from the PRISM database (Daly et al., 2002). Center: Projected changes by 

2040–2060 of surface air temperatures for winter in the A1B emission scenario used by the IPCC. Changes are composited over 20 IPCC models 

and range from about 3°F (yellow) to about 7°F (red). Right: Superposition of left and center panels, showing high-resolution projection for 2050. 

The maps demonstrate a combination technique to reconcile information at two different scales.
12

maximize their utility and generality in providing not 

only parameters for weather and climate models, but also 

validation data sets. 

• Refine remote-sensing and other strategies that 

upscale in situ point measurements, providing key 

weather and climate model parameters that can be 

applied over the full Arctic spatial domain. Synoptic 

and repeated views of the pan-Arctic are afforded by 

a constellation of polar-orbiting satellites. These data 

provide calibration, validation, and operational ingestion 

of key land and ocean surface variables needed by atmospheric models, and provide additional and important 

quantitative information to improve model fidelity. 

• Build high-resolution GIS infrastructure and databases of variables (e.g., vegetation, soil properties, 

topography, slope orientation) to help downscale 

coarse-resolution climate model outputs onto the land 

mass through dynamical and statistical approaches.

Downscaling requires subsidiary geospatial data sets that 

can be used to better “spatialize” the otherwise coarsescale outputs that characterize the current generation of 

climate models. A community-designed, cyber-enabled 

infrastructure and database resource is recommended.

• Unite upscaling and downscaling methodologies 

to ensure the appropriate “handshakes” across 

subdomains of Arctic weather and climate models 

that depict land, ocean, and atmospheric interfaces.

Because of strong links in the dynamics of major 

components of the Arctic Earth system, upscaling and 

downscaling are closely interconnected, and thus a 

coordinated exploration of the approaches should yield 

advances in the manner in which future models are 

configured and linked to observational data programs.

2.2.2. Glaciers, Ice Caps, and 

Continental Ice Sheets

Recommendations

• For ice sheets, encourage detailed process studies of 

the “throttle points” and key interfaces with potential 

to control broad-scale dynamics, like those between 

ice and ocean or lake outlets. 

• For glaciers and ice caps, institute a systematic panArctic inventory of key characteristics to enable 

systematic extrapolations. 

• For ice sheets, ice caps, and glaciers, develop and 

execute a monitoring strategy to identify representative 

sampling targets that can be used to infer the surface 

water and energy balances plus internal dynamics of 

these cryospheric elements as a whole. 

• Support studies that unite modeling and monitoring 

as the essential building blocks for understanding the 

temporal dynamics of these systems, including their 

placement into broader paleo/historical context. 

Science Context

Arctic glaciers, ice caps, and ice sheets (see Box 2.1) are a 

critical component of the Earth system. They store enormous quantities of water in solid form, serving as major 

buffers for heat exchange with the atmosphere. They are also 

important as great reflectors of incoming solar radiation 

and thus are an important controlling factor in contemporary and future climate change. From a societal perspective, they become critical primarily due to their potential 

contributions to sea level (Table 2.1). Unlike floating sea 

ice, the melting of which would have a negligible effect on 

sea level, melting of Arctic land ice has the potential to raise 

sea level by more than 7 m, placing in peril major portions 

of the global coastal zone, where a substantial fraction of 

the world’s population resides and through which much of 

the globalized world’s economic productivity passes. The 

Intergovernmental Panel on Climate Change (IPCC) has 

estimated that sea level will rise in the coming century by 

0.18–0.59 m over the coming century, with a large contribution from shrinking ice sheets, but because the models used 

do not capture rapid dynamic processes adequately and the 

potential for ice sheet instability, the IPCC indicates that sea 

level rise could be substantially higher. Although glaciers 

and ice caps have far less potential to raise sea level than the 

Greenland and Antarctic ice sheets, their current contributions are today greater and may continue to be so through 

the coming century. 

The importance of glaciers and ice sheets is underscored 

by their sensitivity to small changes in climate. Over the 

last decade, there has been increasing evidence that the 

disappearance of these ice masses is accelerating. For the 

Greenland ice sheet, which was considered until only the 

last decade to be a fundamentally frozen environment, 

we are seeing evidence that it is rapidly transforming 
13

from a cryospheric element of the Arctic system into a 

dynamic hydrologic feature that is increasingly characterized by its flows of liquid water. Issues abound with 

respect to the entry of freshwater into the Greenland 

and Nordic seas where important zones of convective 

deep Atlantic water exist. 

The Scaling Issue

Figure 2.3 shows the various scales of 

processes for the different types of land ice 

masses. They span a tremendous range of the 

space/time continuum. The scaling challenges 

associated with understanding glacier and ice 

cap behavior are, by their very nature, more 

“local,” and the implications of their changing 

dynamics on sea level rise are different than 

for ice sheets, which represent a more spatially contiguous 

mass of ice with continental-scale climate, geological, and 

cryospheric dynamics. For glaciers and ice caps, of which 

there are over 200,000 worldwide, the major spatial scaling 

challenge is understanding the behavior of large glacier 

systems from the sampling of a few. Individual glaciers 

Table 2.1: Sea level equivalent of major land-ice masses.

Land-Ice Component

Area

(106 km2)

Ice Volume 

(106 km3)

Potential Sea 

Level Rise (m)

Glaciers and Ice Caps* 0.51–0.54 0.05–0.13 0.15–0.37

Greenland Ice Sheet 1.7 2.9 7.3

Antarctic Ice Sheets 12.3 24.7 56.6

*Does not include the small glaciers and ice caps that surround the Greenland and 

Antarctic ice sheets

Box 2.1. Glaciers, Ice Caps, and Ice Sheets

Glaciers, ice caps, and ice sheets are elements of the cryosphere that 

capture the essence of multiple scales. Their primary distinguishing 

features are size and shape. These ice features also are distinguished by 

scale-dependent dynamics, with glaciers and ice caps showing active 

dynamics today that contribute to sea level rise, and the ice sheets having 

more inertia but unknown thresholds.

• Glaciers are:

– Flowing rivers of ice that are often much longer than they are wide 

– Potentially tens of kilometers long and several kilometers wide, 

but most much smaller

• Ice caps:

– Fall in between ice sheets and glaciers, with scales on the order of 

hundreds to thousands of square kilometers 

– Exhibit some impact on their local climate and Earth deformation 

• Ice sheets are:

– Millions of square kilometers in area, several kilometers thick

– Strongly influence their regional climate 

– Substantially deform the earth on which they rest through their weight 

Ice caps and ice sheets also contain outlet glaciers, which, as their name 

implies, exhibit glacier-like characteristics, but are fed by the large reservoirs of ice contained in the ice caps and ice sheets.
14

are not of much consequence to sea level rise. However, 

consistent behavior across systems of glaciers are detectable 

and have been found to be substantial, as evidenced by the 

large sea level contributions from melting Alaskan glaciers, 

Canadian ice caps, and Patagonian ice fields, further 

enhanced by wastage of glaciers at lower latitudes worldwide. For ice sheets, the challenge is more along the lines of 

relating local processes—particularly at the ice/ocean interface of outlet glaciers—to large-scale behavior as it relates to 

total ice sheet mass balance and ice sheet stability. Glacier 

and ice sheet behavior is a manifestation of processes that 

occur across the full spectrum of temporal scales and legacy 

effects are apparent. The convolution of these processes 

makes unraveling the temporal aspects difficult at best. 

The Greenland Ice Sheet

The primary scaling consideration for the Greenland ice 

sheet is deciphering the coupling between local processes or 

“throttle points” and the affected ice sheet’s drainage basin. 

The greatest challenges are related to ice sheet dynamic 

processes, particularly, understanding how the interactions among floating ice, grounded ice, and seawater at the 

marine-terminating outlet glaciers influence the ice sheets 

and the smaller drainage basins that control their discharge. 

Outlet glaciers are on the order of a few kilometers to a few 

tens of kilometers wide and long, yet collectively may be 

critical in controlling the dynamics of an ice sheet that is on 

the order of thousands of kilometers in length. It is at this 

interface between ice and water, where change in backpressure can occur rapidly with the disappearance of floating 

ice. The effects of those changes propagate well up and into 

the ice sheet, impacting overall ice sheet balance through 

enhanced discharge. 

A further scaling consideration related to accelerated ice 

flow is the similarities and differences among outlet glaciers. 

Each outlet glacier, where dynamic processes dominate the 

local balance, has its own unique dynamic characteristics. 

It is important to understand how representative (or not) 

one glacier may be of others that are similar (or different), 

as we try to infer large-scale behavior from local measurements. Satellite observations are crucial in this area, as they 

can enable the monitoring of unique dynamic processes 

over nearly all of the outlet glaciers. However, relating these 

observations to the detailed mechanisms that control flow 

requires comprehensive measurements on, within, beneath, 

and around these glaciers and their floating tongues. In 

this sense, the scaling challenge is similar to that of alpine 

glaciers: relating the knowledge we obtain in a few to the 

characteristics of an entire system. 

 The impact of meltwater penetration, 

often very localized, on overall ice sheet 

mass balance is poorly understood. It 

has been demonstrated that melt ponds 

on the Greenland ice sheet drain very 

rapidly and cause seasonal acceleration 

in ice flow. But, the spatial scales of this 

acceleration are not well known. Thus, 

it is not now clear whether 100 km of ice 

moves in a slab fashion or whether there 

is local convergence or divergence of the 

ice flow, limiting the impact on overall 

balance. If it is the latter, then the key 

consideration becomes over what scales 

and how it affects ice sheet stability. As 

with outlet glaciers, data remain limited, 

and models are not yet equipped to 

account for this phenomenon. 

TEMPORAL SCALE

SPATIAL SCALE (km2) 

Days Seasons Years Decades

Glacier-ocean interface

processes (flow rates)

Regional (104) Local (102) System (106)

Ice sheet sea level contributions

Accumulation

redistribution

Surface

balance

processes

Centuries

Ice sheet flow response

to climate forcing

Propagation of glacier-ocean

interface processes (flow)

Accumulation

redistribution

Ice sheet sea level contributions

Glacier sea level

contributions

Figure 2.3. Spatial and temporal scales on which glacier, ice cap, and ice sheet 

processes operate.
15

Many questions thus remain unanswered with regard to 

how various processes affect overall ice sheet mass balance, 

and these processes are heavily linked to issues of scale. How 

rapidly and far do the effects of meltwater penetration propagate into the ice? What governs the rate and magnitude of 

that propagation? How long does it take ice sheets and outlet 

glaciers to respond to new boundary conditions following 

floating ice breakup? How vulnerable are ice sheets to rapid 

loss by this mechanism? The major challenges to answering 

such questions stem from the paucity of data on ice sheet 

bottom topography in the vicinity of outlet glaciers, floating 

ice thickness, grounding line processes, ocean temperatures 

beneath floating ice, and other variables. However, as more 

data are acquired, careful attention needs to be paid to the 

temporal and spatial scales over which these processes act. 

Research is needed in this area so that models designed to 

predict the magnitude and rate of sea level contribution 

from the ice sheets in the face of these potential dynamic 

instabilities can operate on several scales. 

Finally, surface balance processes present a somewhat 

different sort of scaling challenge than the nonlinear, potentially unstable, dynamic processes. The issues are not so 

much coupling of the processes, but rather understanding 

the extent to which microscale measurements represent 

macroscale processes. Because we rely on point measurements of surface processes that are spaced hundreds of 

kilometers apart to calibrate and validate models and satellite data, it is critical that we understand whether the local 

processes measured in situ are representative of broader 

domains. Surface energy and mass exchanges are governed 

by small-scale (millimeters to meters) roughness, while 

surface accumulation is heavily influenced by topography 

and wind redistribution on scales ranging from centimeters 

to kilometers. Knowledge of the spatial variability and the 

dependence of surface balance on that variability is an 

important area requiring further research.

Glaciers and Ice Caps

To better understand the impact of glacier and ice cap 

changes we need to: (1) be able to assess the overall behavior 

of a glacier based on information acquired at a few locations 

or across a few transects, and (2) extrapolate information from a limited number of glaciers to larger areas. It 

is instructive to categorize changes due to surface mass 

balance and dynamics separately, and to consider shortand long-term changes (i.e., the response time of a typical 

glacier, which is on the order of several decades). 

Considerable evidence suggests that for short-term 

changes, surface mass balance can be characterized for 

a region from a limited number of benchmark glaciers 

or weather station measurements. Scaling from remotesensing observations, such as airborne laser altimetry 

or satellite-based NASA Gravity Recovery and Climate 

Experiment (GRACE) measurements on regional scales 

(tens of kilometers) to the microscale, can allow us to 

determine which benchmark glacier or weather stations 

(coupled to a mass balance model) are most representative 

of a region; or, if existing data sets are not representative, 

what adjustments need to be made to make such data sets 

representative; or, whether entirely new benchmark data 

sets are needed. One example of successful benchmark 

glacier identification exists in Norway, where measurements 

at a single stake on a single glacier represent changes on 

all 12 glaciers across a region. The task is to find similarly 

representative locations in other regions. An important part 

of determining which glaciers can serve as benchmarks, 

and how those glaciers need to be sampled, is determining 

the spatial structure of surface mass balance, in particular, 

the accumulation component, which tends to have high 

spatial variability.

In addition to these surface balance considerations, 

which are tied to climatic processes, there are the nonlinear 

glacier changes related to the dynamics of marine or laketerminating glaciers that do not appear to follow climatic 

cycles. These changes can affect regional calculations over 

all time scales, but only for regions with these types of 

glaciers. As such, we need to categorize glaciers as being 

land-terminating or water-terminating so that climatic 

versus nonclimatic processes can be appropriately dealt 

with. Moreover, we need to identify the phase of a tidewater 

glacier cycle (advance vs. retreat) from satellite imagery, and 

treat it separately in scaling analysis. There may be simple 

ways to scale elevation change profiles between retreating 

tidewater glaciers, but more work is needed.

Finally, a major uncertainty in assessing worldwide 

glacier contributions to sea level is the limited knowledge 

of glacier areas. Mapping of glaciers has neither been thorough nor systematic, and the fact that many glaciers are 
16

changing so rapidly increases the challenge. Ultimately, we 

cannot effectively scale any condition or parameter if we do 

not know the area to which we are scaling. For this reason, 

a priority needs to be a systematic mapping of glacier areas 

throughout the world.

Temporal Considerations

The remaining scaling challenge for glaciers and ice sheets 

is that of time, more specifically, placing recent change into 

broader context. Our ability to measure glacier and ice sheet 

processes on any kind of meaningful scales is very recent. 

There are some glaciers in Europe for which records exist 

for hundreds of years, but for most glaciers, the records 

are either nonexistent or only span a few decades. For ice 

sheets, the ability to observe large-scale behavior has only 

come about in the last couple of decades with advances in 

satellite remote sensing. Thus, a major challenge remains 

in fitting the processes we are observing today into the 

appropriate temporal context. Ice cores hold a tremendously 

valuable record of past climatic conditions and associated 

surface mass balance conditions, but the same cannot be 

said for understanding flow-related processes, particularly 

at ice sheet margins, or processes in areas where excessive 

melt has contaminated the ice core records. 

There are techniques for crudely estimating past ice 

boundaries from geological signatures, or inverting 

present ice characteristics and structure to determine past 

conditions. The former is limited, however, to areas that 

are currently exposed (i.e., they don’t tell us about past 

conditions in which there was less ice), and they don’t tell 

us much about the rates of change. The latter is limited 

by the amount of data available on the structural conditions within the ice. As a result, we know very little about 

how changes in present ice conditions relate to those 

of the past. For example, we do not have observational 

evidence to determine whether the recently reported flow 

acceleration in much of southern Greenland marks the 

beginning of a rapid decay of the ice sheet, or whether this 

is a process that has come and gone repeatedly through 

time. As we continue to improve models with the collection of more data, we will be able to understand the time 

scales over which dynamic changes occur, and gain a 

better understanding of how today’s processes fit into 

the broader temporal context. For now, however, this 

major challenge remains.

What Is Required for Progress

• For ice sheets, encourage detailed process studies of 

the “throttle points” and key interfaces with potential 

to control broad-scale dynamics, like those between 

ice and ocean or lake outlets. Recent research has 

shown the importance of outlet glaciers, their linked 

drainage systems, and how far up into the ice sheet their 

dynamics penetrate, for example, the excursion of meltwater up into the ice sheet. Given the potential of these 

smaller component systems to control ice sheet dynamics 

over a much larger domain, they merit special attention. 

• For glaciers and ice caps, institute a systematic panArctic inventory of key characteristics to enable systematic extrapolations. Given an incomplete global picture, 

despite their current importance to global sea level rise, 

an inventory should be made immediately to provide a 

benchmark against which future change can be assessed. 

The inventory should include state variables, such as the 

detailed position of fronts, areas, and volumes.

• For ice sheets, ice caps, and glaciers, develop and 

execute a monitoring strategy to identify representative 

sampling targets that can be used to infer the surface 

water and energy balances plus internal dynamics of 

these cryospheric elements as a whole. This recommendation addresses the key challenge of searching for 

general behaviors from incomplete sampling, such as the 

outlet glaciers of the ice sheets to representative glaciers 

and ice caps that are emblematic of regional systems of 

these smaller features of the cryosphere.

• Support studies that unite modeling and monitoring 

as the essential building blocks for understanding the 

temporal dynamics of these systems, including their 

placement into broader paleo/historical context. These 

studies will plug a gap in current data sets and tools for 

understanding how representative current conditions are 

with respect to the past, such as for the advance/retreat 

cycle of tidewater glaciers. 

2.2.3. Permafrost and Hydrology

Recommendations

• Foster development of landscape evolution models 

across the Arctic, focusing on the propagation of 

small-scale processes to sequentially larger domains. 
17

• Support work to design systematic sampling programs 

that can provide guidance in choosing representative 

sites for monitoring key parameters and variables. 

• Improve understanding of the nature of continuous 

and discontinuous permafrost dynamics and the characteristic space-time domains of their change. 

• Continue studies that explore the macroscale behavior 

of water fluxes, focusing on an acceleration of the 

hydrologic cycle. 

Science Context

Water in all of its phases –solid, liquid, and gas—is the 

fundamental “glue” linking virtually all aspects of the 

Arctic system. The importance of water distributed over 

the Arctic landscape is inextricably linked to the state 

of permafrost, that is, the perennially frozen ground 

that undergoes rapid change in response to seasonal 

temperature changes and freeze-thaw, a potentially 

long-term if not permanent reconditioning in response 

to greenhouse warming. 

This landscape is central to the functioning of the panArctic system as a whole, with its centrality defined by fluxes 

in both horizontal and vertical dimensions. For example, 

water and permafrost are important for storing matter and 

energy—snow and rainfall collects on the surface, is stored 

temporarily on vegetation and in soils and surface waters, 

but can ultimately be re-evaporated into the atmosphere. 

Snow is also an important control of surface 

energy fluxes through its high level of reflectivity 

to incoming solar radiation. The hydrology 

of hillslopes is conditioned both horizontally 

and vertically, with important gradients in 

moisture and upslope-downslope redistributions of water and constituents that help to 

define the patterning of vegetation. Excess water 

draining from Arctic terrestrial ecosystems is 

horizontally redistributed through groundwater 

movement and through stream channels, both 

in terms of poorly organized wetland and lake 

complexes in shallow gradient landscapes, and 

through bona fide drainage networks that in 

some cases coalesce into some of the world’s 

largest river systems. 

Some of these large rivers are discharging increasing 

amounts into the Arctic Ocean, a possible harbinger of 

an acceleration of the high northern water cycle, but one 

with unclear interpretations that could involve climate, 

land cover, and water engineering changes. This increasing 

discharge also affects global-scale ocean behavior. Several 

temporal and spatial scales define such interactions, but a 

consistent set of results from field to hemispheric scales is 

absent (see Figure 2.4).

Climate change is modifying the nature of Arctic 

landscapes and how they store, release, and process water 

(Box 2.2). Thermokarst topography forms as ice-rich 

permafrost thaws, either naturally or anthropogenically, 

and the ground surface subsides into the resulting voids 

(Figure 2.5). The important and dynamic processes 

involved in thermokarsting include thaw, ponding, 

surface and subsurface drainage, and surface subsidence 

and related erosion. These processes are capable of rapid 

and extensive modification of the landscape; preventing 

or controlling anthropogenic thermokarsting is a major 

challenge for northern development. Thermal degradation of ice-rich permafrost with coincident subsidence 

of the ground surface has recently resulted in extensive 

thermokarsting and creation of new water-filled surface 

depressions on the Beaufort Coastal Plain in northern 

Alaska. Analysis of aerial photography indicated that 

widespread ice wedge degradation had not occurred before 

Figure 2.4. Characteristic scales of organization for hydrology, permafrost, and 

affiliated watershed processes.

Seconds Minutes Hours Days Months Years

TEMPORAL SCALE

SPATIAL SCALE

Decades Centuries Millennia

Snow and rainfall

Snow drifts

Snowmelt

Active layer

Water tracks

Patterned ground

Stream channels

Thermokarst

Ice wedges

Ice sheets

Glaciers and ice caps

Snow redistribution

Lake drainage

Kilometers Centimeters Meters

Ice lenses
18

The Scaling Issue

Extrapolating field measurements or modeling results 

across spatial and temporal scales remains an important 

technical challenge, acknowledged by several major documents focusing upon research needs in the twenty-first 

century (e.g., The National Academies Arctic Observing 

Network report). Effectively resolving this problem will only 

be achieved through coordinated field studies and complementary modeling analyses targeting a range of scales. 

Current techniques do not effectively consider the losses 

in precision that are incurred as measured or simulated 

variables are expanded over greater and greater areas (see 

discussion of a similar problem in the atmospheric sciences, 

Section 2.2.1). In order to confidently validate hydrological models, predictions should be compared to field 

measurements from the spatial and temporal domain being 

simulated; however, it is only recently becoming possible to 

measure certain field parameters over spatial scales greater 

than a few meters.

Arctic landscapes are complex, and the challenge is one 

of understanding their structure and dynamics, which 

vary greatly over scale. Gradients and fronts, embodying in 

1980. Field observations and sampling showed that ice 

wedge degradation has been relatively recent, as indicated 

by newly drowned vegetation. Despite the relatively cold 

average annual temperature of this northern permafrost, 

thermokarst was widespread on a variety of terrain conditions, but most prevalent on ice-rich centers of old drained 

lake basins and alluvial-marine terraces.

Box 2.2. Dynamics, Function, and Scale in 

 Permafrost-Dominated Hydrologic Systems

Hemispheric-scale changes to climate reverberate to the microscale, exemplified well by the coupling of hydrology 

and permafrost. In response to some imposed disturbance, such as a tundra fire or climatic warming, massive ice 

permafrost may differentially thaw, creating irregular surface topography. Depressions forming on the surface 

soon form ponds, accelerating subsurface thaw through lower albedo (reflectivity of incoming solar radiation) 

and additional heat advected into the pond through runoff. In time, a talik (a layer of unfrozen soil above the 

permafrost and below the seasonally frozen soil) may form below such ponds as the water depth becomes greater 

than the amount that can refreeze during the winter. If the talik grows to a size that completely penetrates the 

underlying soil or connects to a subsurface layer that allows continued drainage, the pond may then begin to drain 

(Figure 2.5). In recent years, numerous studies have documented changes in the size or number of surface water 

bodies. The implications of these analyses are that in regions over thin permafrost (~ < 20 m), surface ponds may 

shrink and surface soils may become drier as the permafrost degrades. This condition depends upon regional 

hydrologic gradients (i.e., whether the region is a groundwater upwelling or downwelling zone). The same mechanisms that allow drying of the ponds may also cause soil drying with significant impacts to latent and sensible heat 

fluxes. In colder regions with thicker permafrost, as the warming proceeds, near-surface ice thaws, the land surface 

subsides, and new water bodies are formed. 

Figure 2.5. Shrinking ponds resulting from degradation of permafrost 

have been documented in Alaska, Canada, and Siberia. Drying of 

these ponds often accompany changes in surrounding soil moisture 

and vegetation, with a months-to-decades time scale.
19

many cases extremely small scales, characterize the waterpermafrost system. The smallest-scale water movement in 

the Arctic relevant to watershed hydrology arguably is the 

microscopic water migration in frozen soil across a thermal 

gradient. Due to the tensions created in thin films of water 

on soil particles, liquid water will be drawn from warmer 

soils toward the freezing front. Depending upon the types 

of soil and amount of water available, ice lenses on the order 

of millimeters to centimeters thick can form parallel to the 

freezing front. These ice lenses play an important role in 

the hydrologic and thermal dynamics of frozen soils, influencing both heave and subsidence. Likewise, water migration toward the permafrost table during the summer months 

maintains an ice-rich layer of very low permeability just 

below the active layer (the surface layer of soil that experiences thaw every summer). 

Although such processes are critical at the microscale, 

their dynamics can be detected using environmental 

sensing techniques over different scales relevant to hydrologic and permafrost dynamics in the field. Wireless 

communications and cheap, autonomous, networked 

sensors enable the development of “sensor nets” as a means 

to monitor landscapes over a range of scales (e.g., patch to 

hillslope to small watershed) for direct input to or testing 

against models. For still larger domains, airborne and satellite remote sensing can be used. In particular, microwave 

radiometer and radar data sets have been used to detect 

freeze-thaw signatures and the onset of runoff events in 

small watersheds and across the entire pan-Arctic. 

Furthermore, local permafrost change propagates 

its impact over much broader dimensions. Permafrost, 

which is at least 100,000 years old in much of the Arctic, 

has acted to both constrain development of drainage 

networks and produce much of the microtopography 

that characterizes large areas of the Arctic. The common 

tussock is a decimeter-scale topographic feature that exerts 

scale-dependent controls on water movements across the 

region. Tussocks exert primary control over small-scale 

soil moisture variations, often being quite dry on top with 

standing water between the tussocks (Figure 2.6). During 

spring runoff events, when the active layer is still very thin, 

the rapid component of hillslope runoff occurs either as 

overland flow, inter-tussock flow, or as pipeflow and matrix 

flow in the highly organic near-surface soil. On hillslopes, 

water moves in circuitous routes around the tussocks until 

entering water tracks, or small first-order channels that 

quickly drain hillsides. Water tracks have a very simple 

structure, tending to form in parallel drainages, straight 

down the hillside, separated by 20 to 50 m. As permafrost 

warms and soil erodes, it is expected and observed that 

drainage networks formed of water tracks will evolve 

toward dendritic drainage patterns commonly observed 

in more temperate regions, thus changing the “rules” and 

scales by which these systems function.

Although water tracks are typical on hillsides, patterned 

ground is the dominant topographic feature in flat areas 

such as the coastal plain (Figure 2.7). The time scales 

of forming such polygonal patterns occurs over tens or 

hundreds of thousands of years. As tundra soil cools quickly 

during cold winters, contraction cracks appear, generally 

in the same location year after year. When the snow melts 

in the following spring, water infiltrates the cracks and 

quickly freezes, expanding upon freezing, and pushing the 

soil farther apart. As this repeats over the centuries, massive 

ice wedges develop into troughs bounded by ridges of soil. 

These intricate spider webs of channels maintain dominant 

control on soil moisture. Although they allow lateral water 

movement, their primary influence results through vertical 

scaling and the control on the water table. Sedge tundra 

typical of the coastal plain is adapted to the hydrophilic 

environment maintained by the low gradients and shallow 

water table. However, with a warmer climate, as permafrost 

degrades and ice wedges melt, the troughs become deeper 

and the depth to the water table increases, drying the 

Figure 2.6. Water movement around tussocks is an important flow 

regime on small scales over much of the Arctic. 
20

Hydrologic drainage patterns have major influence on 

the spatial distribution of soil moisture, and an increase 

in the number of incised channels will result in a reduction in surface area of poorly drained or wet soils. This 

may alter vegetation distribution and subsequently snow 

distribution. Digital elevation models (DEM) have allowed 

unprecedented investigations into the spatial arrangement 

of landforms in recent years, and a new suite of descriptive parameters has emerged in fluvial geomorphology 

(Figure 2.8). Foremost are the fractal dimension and the 

cumulative-area distribution. Several researchers have used 

these parameters to show that mature channel networks 

possess a high degree of similarity in the spatial distribution of channels, regardless of geologic control.

What Is Required for Progress

• Foster development of landscape evolution models 

across the Arctic, focusing on the propagation of 

small-scale processes to sequentially larger domains.

Given the close connection between permafrosthydrologic change and the response dynamics of coupled 

water-vegetation-geomorphological systems, models 

uniting these dynamics are needed over sufficiently long 

time domains to capture the impact of forecast climate 

surface and drastically changing the habitable environment 

for vegetation. Although these processes are fundamentally 

local, they may exert the most important influence on 

regional scales, as a small change in distance to the water 

table may initiate broad-scale drying, which will influence 

regional ecosystems and the regional surface energy balance. 

Figure 2.8. Hydrological simulations of water-table depth across a vegetated drained thaw lake basin near Barrow, Alaska, based upon contrasting 

horizontal DEM resolutions (1 m on left and 5 m on right). Both DEM s represent arithmetic averages of a 0.25-m pixel DEM, which was derived 

from airborne LIDAR measurements. The simulations were initialized with the water table at the ground surface and forced with the meteorological conditions of summer 2006. The figures represent the 75th day in the simulation. Results highlight the need to adequately treat scale in 

representing key landscape characteristics and processes. (Produced by A. Liljedahl)

Figure 2.7. Subtle elevational differences (on the scale of centimeters 

to decimeters) exert the dominant control on hydrological processes 

of snow distribution, runoff, and soil moisture variations. These 

polygonal features consist of intersecting wedges of massive ice, 

and therefore also represent a vulnerable landform that is adversely 

impacted by a warming climate.
21

Science Context

Recent studies have drawn attention to observed changes to 

the Arctic Ocean and its sea ice, the importance of regional 

ocean dynamics, and the implications of these Arctic-based 

changes on the broader global ocean and climate system. 

Predictions for the twenty-first century show that there 

are substantial changes in the form of freshwater export 

from the Arctic Ocean. With a severe decrease in sea ice, 

the total flux becomes dominated by liquid freshwater. 

Synthesis of results from models contributing to the IPCC 

Fourth Assessment Report documents a consistency across 

models in terms of an acceleration of the hydrologic cycle, 

expressed as increases in the fluxes of water through major 

elements of the system. Principal among these fluxes are 

increases in freshwater inputs to the ocean from net precipitation, river discharge, and sea ice melt, and an increase 

in liquid water storage. While the current state-of-the-art 

represented by these models are in qualitative agreement, 

it is noteworthy that the magnitude of trends and some of 

the basic ocean and sea ice budget terms require substantial 

improvement and that the treatment of key ocean dynamics 

and sea ice processes vary considerably across models.

Perhaps most telling is the fact that ice-free conditions in 

the observed contemporary record predate model predictions by decades (Figure 2.9). The future appears to be the 

present. The long-term ramifications could be profound, 

and it has been reasoned that the system has few if any 

“backstops” against the continuation of such changes. 

Particularly important is the so-called sea ice-albedo 

feedback, wherein exposed relatively dark ocean waters 

continue to absorb summertime incoming solar radiation, 

making it more difficult to reestablish sea ice during the 

winter months. Evidence is accumulating that the amount, 

thickness, and ages of sea ice (i.e., the loss of thick, multiyear ice) is moving toward a more seasonally ice-free and 

less-ice-dominated ocean system. Systemic change in sea ice 

will bear important implications on the physics, chemistry, 

and biology of the ocean, as well as upon indigenous livelihoods. The global economy, eager to exploit new sources 

of natural resources and new transportation corridors, 

will also become more dependent on a reconfigured Arctic 

Ocean system and hence the ability of scientists to forecast 

its future state and dynamic trajectories.

change, but recognizing the importance of sufficiently 

treating the simulation of shorter-term dynamics 

(e.g., seasonal freeze-thaw).

• Support work to design systematic sampling programs 

that can provide guidance in choosing representative 

sites for monitoring key parameters and variables. The 

complexity, remoteness, and harshness of the Arctic 

means that deployment of field sampling stations will be 

finite and modest at best. The complexity of the Arctic 

landscape must be met with organized classification 

systems for landscape and permafrost types that can be 

used optimize field data campaigns and later map, using 

remote sensing, the dynamics of these landscapes. 

• Improve understanding of the nature of continuous 

and discontinuous permafrost dynamics and the 

characteristic space-time domains of their change.

Thermokarsting, and the waxing and waning of inundation in lake systems, are important hydrologic responses 

to warming that will change the horizontal and vertical 

connectivity of low-relief landscapes across the panArctic. Reconciling field observations of individual sites 

with regional-perspectives afforded by remote sensing 

will be required. 

• Continue studies that explore the macroscale behavior 

of water fluxes, focusing on an acceleration of the 

hydrologic cycle. Such studies will need to consider a 

host of factors that are often clearly observable for specific 

sites (e.g., logging, fire, permafrost degradation), but have 

yet to be upscaled coherently to help explain progressive 

increases in the discharge of large Eurasian rivers.

2.2.4. Arctic Ocean and Sea Ice 

Recommendations

• Create opportunities for improved understanding of 

the Arctic Ocean and sea ice as a coupled system, 

by uniting monitoring and process studies of its individual components. 

• Support work aimed at articulating how dynamics 

propagate across Arctic Ocean components. 

• Catalyze modeling studies that address contrasting 

space-time scales embodied by a variety of Arctic sea 

ice and ocean processes.
22

Arctic Sea Ice

Sea ice often appears self-similar across a range of geophysical scales. Low-altitude aerial photographs are visually 

similar to high-altitude photographs and satellite imagery. 

Although this is an interesting property of sea ice, for the 

purpose of understanding and modeling sea ice physics, 

there are distinct spatial and temporal scales, as well as 

important transitions, that are particularly relevant, based 

on both its internal structure and external forcing.

Figure 2.10 shows a hierarchy of scales that divides 

sea ice dynamic processes into floe, multi-floe, aggregate, 

coherent, sub-basin and seasonal scales. The rationale 

for identifying these scale differences is that there are, 

in general, distinct model formulations with particular 

variables and parameters used at each scale. The first 

significant change in sea ice behavior appears as an emergent property at the transition from the multi-floe scale to 

the aggregate scale, where a statistical mechanical length 

scale is established and sea ice can be considered a plastic 

continuum. A second important length scale is known as 

the coherent scale where the spatial and temporal scale 

of sea ice mechanics best match the external length scale 

of wind forcing. Winds on the coherent scale in turn 

provide nonlocal forcing to the aggregate scale. At dimensions larger than the coherent scale, spatial and temporal 

averaging of the external forcing occurs in the sea ice 

response. Understanding and modeling sea ice dynamics 

at each of these scales requires formulating the problem 

in terms of the variables and parameterizations at the 

next smaller scale.

The Scaling Issue

Ocean circulation scales are set partly by physical boundary 

conditions (the ocean surface and seafloor bathymetry), 

and partly by internal processes generated by the nonlinear 

equations of fluid motion. The result is that the ocean 

contains important dynamical features at all spatial and 

temporal scales, from the smallest (millimeters, seconds) 

to the largest (global, millennial). Processes at these 

different scales interact in significant yet often poorly 

understood ways. 

The Arctic Ocean is, to first order, a “two-layer fluid,” 

with a fresh, cold upper layer (0–300-m depth) influenced 

by the cold atmosphere and freshwater influx from rivers, 

net precipitation, and relatively fresh inflows from the 

North Pacific Ocean. The addition of sea ice adds a large 

degree of complexity. Sea ice has several roles in the ocean 

system, which play out across multiple scales. Sea ice acts 

as a cap to thermal exchanges, is an important reflector 

of incoming solar radiation in the summer, and acts as 

an energy storage and release mechanism as it undergoes 

seasonal changes in phase. It also interplays in a complex 

way with ocean currents and surface winds. Further, the 

Arctic seas have their own special conditions (e.g., sea 

ice, huge continental shelves, a nearly constant Coriolis 

parameter) that bear impacts on the broader ocean circulation, making for a challenging scaling environment (see 

Figures 2.10 and 2.11).

Figure 2.9. The rates of present-day loss of sea ice rival those of simulations of Arctic sea ice 20 years into 

the future. A possible tipping point beyond which sea ice cannot return could become a present-day reality. 

(Redrawn after Holland et al., 2007).

September Sea Ice Extent

CCSM3 Model Simulation

Observations

Model Drop

1.8 million km2

, 2024–2025

Observed Drop

1.6 million km2

, 2006-2007

1900 1920 1940 1960 1980 2000 2020 2040 2060 2080 2100

Year

Ice Extent (millions km2

) 10

9

8

7

6

5

4

3

2

1

0
23

The above scaling methodology relies primarily on a 

hierarchical approach to scientific inquiry. There are other 

approaches, most notably that recognize fracture mechanics 

as a key organizing process on all scales. This view is still 

compatible with the above hierarchy as the coherent scale 

emphasizes coupling with external forcing, rather than 

internal structure within the medium.

layer has been referred to as a “distillation” process wherein 

salt is seasonally sequestered below a fresh surface layer. 

The overall residence time of this uppermost layer in the 

Arctic Ocean is probably similar to the overlying sea ice 

cover (i.e., 1–10 years). A recent synthesis targeting the 

stocks, flows, and residency time attributes of the entire 

pan-Arctic water cycle demonstrated the value of such 

1 m 10 m 100 m 1 km 10 km 100 km 1000 km

SPATIAL SCALE

TEMPORAL SCALE 

Bottom water renewal

Deep (AW) renewal

Polynya

uxes

Upper (surface + 

halocine) renewal

Synoptic

atmospheric

forcing

Fronts Eddies

Geothermal heating,

double diusion

River-shelf

interactions

Lead uxes

1000 years

100 years

10 years

1 year

1 month

1 day

Floe Multifloe Aggregate

Coherent

Subbasin

Seasonal

1,000

100

10

TEMPORAL SCALE (days)

SPATIAL SCALE (km)

1 10 100 1,000 10,000

Figure 2.11. Dominant temporal and spatial scales in Arctic oceanography. 

AW = Atlantic Water.

Figure 2.10. Dominant temporal and spatial scales in Arctic sea ice studies.

The Arctic Ocean’s Surface Mixed Layer

Surface mixed layer scales (0–50-m depth) 

are set by interactions with the overlying 

atmosphere and sea ice. In particular, sea 

ice presents a unique boundary condition linked to a variety of scales. For 

example, ice formation leads to a negative 

buoyancy flux from brine rejection within 

the smallest-scale molecular sublayer 

at medium-scale ridge keels and leads, 

and in the larger scales of polynyas and 

seasonally ice-free areas. This process 

is analogous to wintertime cooling and 

mixed-layer destabilization in lower 

latitudes, except that salinity plays the 

dominant role in determining density at 

cold arctic temperatures. Moreover, subtle 

details of small-scale convection at the 

sharp boundaries of a lead may in fact 

create a stabilizing influence during brine 

rejection. Conversely, summer brings 

freshwater into the surface layer from 

a variety of sources. Rivers, essentially 

point sources for freshwater and heat, 

often reach maximum flow while the 

ice is still melting at the shore, leading 

to flooding on the ice and temporary 

“impoundments” of freshwater below. 

This influences the residence time on the 

shelf and exchange of freshwater with 

the rest of the Arctic Ocean in poorly 

understood ways. Sea ice meltwater may 

collect in leads and melt ponds during 

light wind conditions, only to be mixed 

downward during wind or melt events. 

The net annual effect on the ocean surface 

1 m 10 m 100 m 1 km 10 km 100 km 1000 km

SPATIAL SCALE

TEMPORAL SCALE 

Bottom water renewal

Deep (AW) renewal

Polynya

uxes

Upper (surface + 

halocine) renewal

Synoptic

atmospheric

forcing

Fronts Eddies

Geothermal heating,

double diusion

River-shelf

interactions

Lead uxes

1000 years

100 years

10 years

1 year

1 month

1 day

Floe Multifloe Aggregate

Coherent

Subbasin

Seasonal

1,000

100

10

TEMPORAL SCALE (days)

SPATIAL SCALE (km)

1 10 100 1,000 10,000
24

integrative measures, for example, a newly recognized 

importance of Bering Strait inflow variations on the 

overall system dynamics. 

The Pycnocline

Between 50 and 300 m below the sea surfaces lies the 

pycnocline, composed of a variety of water masses formed 

both on shallow shelves and within deep basins. The Arctic 

Ocean pycnoclines develop their characteristics by modification of Pacific Water (PW), Atlantic Water (AW), and 

river waters through interaction with some combination 

of the cold atmosphere, melting or growing sea ice, and 

benthic conditions on the shelves. Formation regions can 

be relatively small scale (e.g., a shore polynya) but by lateral 

intrusion into the layered pycnocline they can influence 

the entire Arctic Ocean and even the North Atlantic Ocean 

via southward currents. Cooling and/or salinization on the 

shelf produces dense water that forms benthic boundary 

currents. These currents acquire properties (e.g., high 

nutrient signals) that are communicated to the central 

Arctic Ocean when they leave the benthos at the shelf break. 

Details of this shelf-basin exchange are still unclear, but 

certainly involve eddies and other episodic phenomena. The 

net result of these disparate halocline water mass processes 

is a pycnocline that is quite strong in much of the Arctic 

Ocean, suppressing all but the smallest vertical scales. 

The Intermediate and Bottom Layers of the Arctic Ocean

The Arctic Ocean’s intermediate or AW layer (300–1700 m) 

is relatively warm and salty, influenced strongly by inflow 

from the North Atlantic Ocean. Circulation of this AW 

layer is tightly constrained by bathymetry, which follows 

from conservation of angular momentum in the special 

Arctic case of a nearly constant Coriolis parameter. 

However, this is not the whole story, because in fact AW fills 

the entire lower layer of the Arctic Ocean. The spreading 

of this water laterally across isobaths is still not well 

understood, although it is thought that small-scale double 

diffusive mixing plays an important role. The signature of 

this mixing has been used to diagnose AW age and circulation. AW exits the Arctic Ocean cooler than it entered by 

several degrees, implying upward heat loss through the 

pycnocline. Also, weak dynamical forcing of AW within 

the Arctic Ocean leads to relatively long residence times of 

several decades. However, recent work indicates that pulses 

of anomalously warm AW have been and may continue to 

flow around the Arctic Ocean, forced by climate changes 

within the Nordic Seas or perhaps even further south. The 

bottom waters below 1700 m are relatively homogeneous, 

although they display subtle spatial variability that may 

indicate renewal by dense shelf waters, geothermal mixing, 

and boundary currents. In fact, relatively little attention has 

been given to this part of the Arctic Ocean.

Modeling Challenges

These many diverse boundary forcings and internal 

processes ensure that numerical modeling of the Arctic 

Ocean presents special challenges. The unique momentum 

and buoyancy forcings from the overlying sea ice cover 

are particularly important to capture in simulations. One 

aspect of this cover is that it suppresses internal gravity 

wave generation, leading to low mixing rates that must be 

accounted for in order to accurately simulate the largescale circulation. There is also the challenge of resolving in 

models narrow inflow/outflow straits of 30–50-km width 

and unusually shallow and broad continental shelves 

(50–100-m depth for several hundred kilometers north 

of the Russian Arctic coast), while devoting a sufficient 

number of grid points to represent the deep (4 km or more) 

basins and large-scale circulation. Finally, high stratification and a large Coriolis parameter together lead to the 

smallest geostrophic length scales (~ 10 km or less) in 

the global ocean, which sets the scale for eddies, fronts, 

jets, and many other phenomenon in both the real world 

and in simulations.

What Is Required for Progress

• Create opportunities for improved understanding of 

the Arctic Ocean and sea ice as a coupled system, 

by uniting monitoring and process studies of its individual components. Unique spatial and temporal scales 

characterize the sea ice, surface mixed layer, pycnocline, 

and deep water elements of the Arctic Ocean system, yet 

these remain poorly quantified and strategies for linking 

the components need to be better developed. Major 

steps forward could be made by analyzing seemingly 

simple concepts and integrative measures like that of the 

residency time for water masses in these subdomains, 
25

2.3. Ecosystems and Biology

2.3.1. Marine Ecosystems

Recommendations

•	 Foster baseline studies of key biotic processes, 

including better quantification of the growth, reproductive, and survival rates of key marine organisms. 

•	 Initiate biological and chemical time-series studies, 

as an important repository of information for understanding the temporal dynamics of marine ecosystems, 

and assessing the importance of trends, episodic 

events, and response times to change. 

•	 Design optimal Arctic system biological and chemical 

sampling strategies, with scales matched to the 

physical and chemical dynamics to which ecosystems 

are most closely linked. 

•	 Unite physical, chemical, and biological simulations 

at the inherent scales of control on key processes, 

both under contemporary and future climate 

scenario conditions.

Science Context

Marine system dynamics across the Arctic are characterized by enormous seasonal and interannual changes in 

ocean circulation, summer-winter light contrasts, and phase 

changes of water from liquid to ice, which in turn propagate 

effects into marine biogeochemistry, productivity, and the 

shifts in distribution of oceanic life forms, including those 

of commercial value. In this context, the present and future 

state of Arctic marine ecosystems is dictated by climate variability and the shadow of climate change. 

Climate warming will result in changes to marine 

biological processes that are complex and interconnected on 

various temporal and spatial scales. Global climate change 

simulations for the marine environment predict ocean 

seawater warming, changes in oceanic stratification, circulation, and convective overturning, as well as changes in sea 

level and cloud cover, the latter influencing light supply to 

the surface ocean. These predicted physical changes would 

directly impact primary production at the base of the food 

web, which would then likely change the overall marine food 

web trophic structure in polar regions. 

Modeling studies have begun to uncover the potential 

responses of marine ecosystems to some of these changes. 

According to one study assessing the sensitivity of six 

biomes in the world ocean to climate changes, climate 

warming could substantially reduce the productive phytoplankton zones of the Northern Hemisphere, specifically 

because of a reduction in the marginal sea ice biome. 

which have yet to be sufficiently established. Studies of 

the “breakpoint” at which individual elements, such as 

ice floes, can be considered a contiguous plastic medium 

or when fracture mechanics can be applied across scales, 

will define a new way of thinking about the dynamics of 

these complex entities as unified physical systems.

• Support work aimed at articulating how dynamics 

propagate across Arctic Ocean components. These 

interconnections occur at a variety of temporal and 

spatial scales, from the episodic to the long term. 

Knowledge about how disturbances, such as quasiperiodic atmospheric pressure anomalies like the Arctic 

and North Atlantic Oscillations amplify or dampen the 

transport of freshwater plumes and sea ice from the local 

domain, and interact with ocean eddies and larger forces 

such as the Coriolis or the movement of North Atlantic 

waters within the intermediate-depth Arctic Ocean, are 

fundamentally scaling issues. 

• Catalyze modeling studies that address contrasting 

space-time scales embodied by a variety of Arctic 

sea ice and ocean processes. Simulating multiscale 

processes that include sharp contrasts of temperature, salinity, inflow/outflows, stratification, fronts/

jets, and Coriolis forces constitutes a numerical 

stability challenge.
26

Another study focused on the extreme retreats of the sea 

ice in the Pacific Arctic beginning in 2007 and predicted a 

strong increase in open-water production of phytoplankton. 

A recent international workshop on this region concluded 

that chlorophyll concentrations from satellite views did not 

follow this prediction, as the recent sea ice retreat does not 

coincide with a clear shift over time in primary production. 

This contradiction points out the immaturity of current 

ecosystem models. The high uncertainty associated with 

biological process models in combination with the high 

uncertainties of today’s coupled climate models make it 

difficult to accurately determine ecosystem response to a 

changing climate. 

Other studies focusing on the physics of tipping points 

and positive feedbacks that could transform the Arctic 

Ocean into a more or less seasonally ice-free system as the 

norm raises the notion that Arctic Ocean ecosystems may 

undergo similarly profound transformations. Do changes in 

sea ice harbor a set of one-way, local-scale impacts or do the 

physical changes create synergies that restructure the entire 

ecosystem? Or, does the entire ecosystem adapt in some way 

to these changes? It is currently unknown how the impact of 

these disturbances on individual components of the marine 

ecosystem could be carried up to the scale of the entire panArctic, an outcome that in turn becomes a critical global 

change question. Thus, a high research priority is to identify 

and prioritize the key temporal and spatial scales pertinent 

to biological response related to sea ice reduction. This is a 

necessary precursor to forecasting ecosystem response to 

climate warming.

The Scaling Issue

The marine biological system is complex, with time scales 

for biological processes ranging from seconds to years and 

spatial scales ranging from millimeters to thousands of 

kilometers (Figure 2.12). Additionally, one needs to understand the linked physical, chemical, and biological interactions in order to interpret observed biological changes that 

are occurring and projected as a result of environmental 

change. The marine carbon cycle is a good example of a 

biologically essential phenomenon intimately tied to the 

physical forcings associated directly with global change. 

Spatial and temporal scales can range from small (meters 

to kilometers, days to weeks for local-scale productivity), 

to intermediate (tens to hundreds of kilometers, weeks to 

months associated with upwelling and stratification), to 

large (thousands of kilometers, year to decadal in response 

to long-term losses of sea ice and warming). Within these 

broad categories, biological scaling issues are complex for 

marine ecosystems due to varying daily, seasonal, and 

interannual biological processes. The science 

thus needs to focus on the combination of scales 

(small, medium, and large) that will provide key 

insight into the function of the marine ecosystem 

if we are to adequately understand, model, and 

forecast marine biological ecosystem change 

under a warming climate. Most of the Arctic 

research on marine ecosystems has been shipborne observations of organisms and biological 

processes or satellite-based estimates of chlorophyll. A first step toward scaling, that is, synthesis 

of the biotic data and associated chemical and 

physical concentrations and processes affecting 

open water photosynthesis, has just begun.

One good example of a scaling issue is the 

heterogeneity of benthic infaunal populations, 

a key prey base for higher migratory trophic 

animals such as gray whales, walrus, and spectacled eiders. Although there is uncertainty in 

Upwelling

stratification

Sea ice loss,

seawater warming,

resource exploitation

Photosynthesis,

microbial 

cycling,

plankton 

growth

Pelagicbenthic

coupling

Plankton

vertical

migration,

fish

schooling

Marine

mammals,

seabirds

Benthic

population

dynamics

Fish

Migration and life cycles

Marine paleorecord as 

index of marine processes

Hour

10000 1000 100 10 1

Day Week Month Season

TEMPORAL SCALE

SPATIAL SCALE (km)

Year Decade Century

Figure 2.12. Schematic space-time plot of key marine biological processes 

that influence ecosystem structure, with key physical forcing functions 

identified in italics.
27

the relationship between rapid sea ice 

retreat and the interannual primary 

production in the Pacific marine 

sector, there are clear changes being 

observed during the summer period 

in species ranges for benthic fauna, 

zooplankton, and fish. Additionally, 

changes in sea ice conditions have a 

direct impact at the habitat scale for 

marine mammals using sea ice as a 

resting platform. Thus, to understand 

biological and ecosystem processes, 

there is a need for benthic sampling 

from the 3–5-km scale (to study 

patch dynamics), to the local scale 

(10–20 km), to the regional scale 

(100–1000 km), and ultimately to the 

pan-Arctic scale (10,000 km). This 

broad range of scales used for sampling will permit scientists to compare and contrast forcings and assess impacts 

over the entire pan-Arctic domain.

Another example of a scaling issue is the uniformity of 

net primary production (per unit area) across the five major 

regions of the Arctic Ocean, 1998–2007. This uniformity 

is based on observations only, but is this uniformity at this 

scale of 10,000 km caused by similar physical and chemical 

processes? There are data on depth of the mixed layer and 

nutrient chemistry that accompany the primary production 

measurements from ships. It will be a good test of the scientific understanding of the processes involved if models now 

under construction are able to reproduce both the similarity and variability in primary production in the data sets. 

Eventually, satellite data will allow us to monitor whether 

or not the homogeneity of each region is maintained when 

marine environments change, and therefore may used to 

scale productivity measurements to the pan-Arctic level. 

Satellite data on the spatial distribution of chlorophyll 

concentrations over the Arctic Ocean could be incorporated 

into the models (Figure 2.13) to reach the goal of achieving 

a pan-Arctic projection of planktonic primary productivity.

The Unique Role of Sea Ice

The time-space plot developed in Figure 2.12 identifies many biological marine processes that are important to Arctic marine ecosystems, along with potential 

perturbations. Sea ice loss, discussed Section 2.2.4 as a 

key multiscale physical change in the Arctic Ocean, also 

controls the very nature of food and energy pathways 

through the Arctic marine ecosystem. Both lower and 

higher trophic levels in Arctic ecosystems are closely keyed 

to the timing of the expansion and retreat of seasonal 

sea ice and thus the inherent scales of sea ice processes 

(Figure 2.10). Arctic ecosystems are anchored by ice algae 

or by early stabilization of the water column by melting 

sea ice. On shallow northern Arctic shelves, the retreating 

spring ice and cold water temperatures result in a shift of 

energy to the benthos. In comparison, in more sub-Arctic 

seas in regions or years with little or no Arctic sea ice, 

primary productivity is delayed until solar heating provides 

thermal stratification. In these latter systems, zooplankton 

are better matched in time to primary production and thus 

the energy pathways emphasized in the pelagic zone. A shift 

toward a more open-water-dominated food system could 

mimic processes at lower latitudes and shift the scales at 

which the current ice-dominated system operates. 

Detailed studies of sardine larvae at lower latitudes 

illustrate the importance of identifying the most important 

scale to measure. In this example, broad-scale processes 

(hundreds of kilometers; e.g., water column stability, 

phytoplankton and zooplankton stocks) had a significant 

impact on sardine larval abundance, whereas processes at 

Figure 2.13. Annual observed (a) and predicted (b, c) primary production in surface 

waters of the Arctic Ocean. (a) Shipboard measures of primary production from many 

sources. (b) Estimates of primary production that use ship-based measures of chlorophyll 

to spatially extend estimates of primary production based on a regression of primary 

production on chlorophyll. (c) Data are estimated from the same regression model and a 

satellite estimate of surface chlorophyll across the Arctic Ocean. This approach to scaling 

illustrates the power of incorporating satellite estimates to extrapolate over the domain of 

the pan-Arctic. The resulting estimates give a rather limited first approximation of the total 

primary production. The calculations do not include processes, such as algal response 

to nutrients, nutrient availability, or depth of the mixed layers that are necessary for 

projecting responses of primary production to environmental changes caused by climate 

change. (Unpublished synthesis data from P. Matrai)
28

the medium scale (10 km; e.g., community level) and small 

scales (meters; e.g., sardine larval feeding condition, growth 

rates) were not significantly related. In contrast, benthicdominated food webs of the Arctic Ocean show great 

heterogeneity of infauna and should be studied at a range of 

scales (3–20 km) specifically identified as important to key 

predators of interest, the large mammals and seabirds. This 

identification of the important scales to study and model 

would constitute a testable hypothesis on the evolving 

scales of key ecosystem change processes.

For marine mammals, there are different adaptation 

strategies in response to sea ice dynamics and its potential 

change. Ice-obligate species, such as ringed seal, walrus, 

and polar bears, require a sea ice habitat for survival. 

Ice-associated species, such as ribbon seal, use ice for 

molting and weaning pups, but after the short springtime 

spend their time foraging over long sub-Arctic distances. 

Seasonally migrating species take advantage of the high 

summer productivity and generally arrive after the sea ice 

has retreated northward. Thus, sea ice is critical over a wide 

variety of temporal and spatial scales for organisms that 

migrate thousand of kilometers for food and reproduction.

Modeling Challenges

Key to gaining a pan-Arctic understanding of marine 

ecosystems will be robust ecosystem models that incorporate the major trophic components and physical forcing 

parameters across a range of temporal and spatial scales 

(Figure 2.12). Successful models must be anchored by 

field data to evaluate forecasting skill in depicting changes 

in the regional and pan-Arctic marine ecosystem; shortterm responses to environmental variability, seasonal to 

years, can be used in this context. For pelagic ecosystems, 

community production models are needed to translate 

concentrations of nutrients before and after spring and 

summer into net production of plankton. Food web models 

of the plankton will allow predictions of the impact of 

lengthening of the period of open water on zooplankton 

and fish, including commercial species. For benthic food 

webs and fishery questions, population dynamic models 

coupled with bioenergetics models, sea ice distribution 

models, and prey distribution models—all with the characteristic scales discussed above—is needed to move the 

science forward. This composite modeling program would 

allow a detailed investigation of how key organisms and 

ecological processes would respond to climate changes as 

well as anthropogenic forcing, such as catch pressures, as 

new fishing grounds are opened. A key aspect would be 

to develop realistic trajectories and identify thresholds of 

climatically driven ecological change. 

Although work has been ongoing, there remain significant challenges. Issues include identification of the most 

critical biological processes that must be measured through 

field process studies and measurement campaigns, and in 

particular, how to scale point and patch measurements up 

to the larger marine ecosystem domain. 

What Is Required for Progress

• Foster baseline studies of key biotic processes, 

including better quantification of the growth, reproductive, and survival rates of key marine organisms.

Such small-scale measurements are needed to adequately 

scale up to population size estimates (medium scale) and 

regional and pan-Arctic ecosystem models (large scale), 

under ambient as well as climate-modified physical 

changes anticipated for the future. A digital encyclopedia 

of such information would constitute an important data 

resource for Arctic marine ecosystem modelers for both 

model calibration and validation.

• Initiate biological and chemical time-series studies, 

as an important repository of information for understanding the temporal dynamics of marine ecosystems, 

and assessing the importance of trends, episodic 

events, and response times to change. This precursor 

is necessary for developing procedures to scale up to the 

temporal domains necessary to analyze regional and 

pan-Arctic ecosystem dynamics. Multivariable time 

series can be analyzed using statistical techniques to 

uncover potential links that can then be tested in deterministic models. The sensitivity of Arctic marine ecosystems to imposed change, and their capacity to adaptively 

reconfigure themselves and/or to remain resilient are 

today critical unknowns.

• Design optimal Arctic system biological and chemical 

sampling strategies, with scales matched to the 

physical and chemical dynamics to which ecosystems 

are most closely linked. The tight coupling of physical 

and biogeochemical processes imparts control over the 
29

biological features of marine ecosystems, which vary 

from the environment of the individual up to whole 

communities and populations of important marine 

organisms. Nutrient concentration data collected in the 

late winter and early spring would fill a major gap in 

understanding the chemical dynamics in the plankton.

• Unite physical, chemical, and biological simulations 

at the inherent scales of control on key processes, 

both under contemporary and future climate scenario 

conditions. Anticipated patterns of high-latitude 

changes to marine ecosystems can cascade and potentially become amplified through nonlinear responses and 

thresholds. Concerns regarding irreversible system states 

and positive feedbacks, thus far articulated most cogently 

for the sea ice loss question, are also relevant in terms of 

reconfigured Arctic ocean ecosystems. 

2.3.2. Terrestrial Ecosystems 

Including Freshwater

Recommendations

•	 Develop research programs to advance understanding 

of mechanistic controls on processes operating over 

contrasting spatial and temporal scales.

•	 Create observational networks that can support multiscale analysis of the space and time characteristics of 

key terrestrial and aquatic ecosystem processes. 

•	 Foster research that focuses on representing fine-scale 

processes in models that are used as tools to address 

questions at coarser scales, including those appropriate to the domain of the pan-Arctic. 

Science Context

Arctic terrestrial and freshwater ecosystems provide many 

direct and indirect benefits to society. These benefits, generally referred to as ecosystem services, are derived from 

ecosystem processes, which provide food, fuel, and fiber; 

regulate floods, disease, and climate; and support aesthetic 

values, spiritual traditions, recreation, and education. 

To ensure the long-term sustainability of these services, 

through proper stewardship of the ecosystems that convey 

these services, requires a multiscale understanding of the 

Arctic—from the scale of small Arctic terrains that span 

hillslopes and small domains (where human management is focused) to whole regions—a capability that is 

not currently available.

In addition, terrestrial ecosystems play a central role in 

climate regulation, in terms of both vegetation character 

and soil carbon storage and their links to water, energy, 

carbon dioxide, and trace gas (e.g., methane and nitrous 

oxide) exchange with the overlying atmosphere. Vegetation 

distribution is a sensitive indicator of climate change. The 

paleo record, for example, shows enormous northward 

shifts of boreal forests during warm periods within the last 

10,000 years. Vegetation changes alter the character of the 

Arctic land mass’ thermal signature, including its reflectivity (reduced with more green biomass, increasing solar 

radiation absorption), ability to capture windblown snow 

(creating an important water resource for the plants during 

snowmelt), and capacity to dry soils (as evapotranspiration 

increases with more vegetative biomass). Arctic ecosystem 

soils are a globally important repository of land-based 

carbon and there are great concerns about how a warmer 

growing season, degrading permafrost, and exposing soil 

to oxidizing conditions will regulate their collective contribution to global CO2

 gas fluxes. Further, these systems 

are increasingly becoming vulnerable to drying as well as 

insect infestations with resultant changes in fire frequency 

and severity, once again bearing impacts on carbon balance 

but also on residents of the North. Many of the statements made above were derived from site-specific, and 

indeed plot-specific, experiments, yet the implications are 

borne out over a much broader spatial domain, including 

the full pan-Arctic.

The Scaling Issue

Understanding ecosystem structure and function is largely 

derived from a tradition based on measurements taken over 

a spatial scale of but a few square meters and over temporal 

scales of minutes to a few years. To address today’s pressing 

issues of Arctic environmental and climate-related change, 

it is essential to translate fine-scale knowledge to larger 

regions and to longer time periods. 

There have been rapid advances in global satellite surveillance systems, geospatial models and data sets, airborne 

atmospheric monitoring, and flux towers—among many 

other technologies—capable of depicting the state of 
30

ecosystems over spatially large extents and at a relatively 

coarse grain. These developments represent a formidable 

information resource but at the same time a substantial 

technical challenge rests in harmonizing results obtained 

from fine-scale studies into this larger context. 

Ecosystem processes have traditionally been depicted 

at well-defined spatial and temporal scales (Figure 2.14). 

Some processes have been addressed at a relatively fine scale 

(e.g., leaf photosynthesis), while other processes have been 

depicted, by their very nature, at a regional scale (e.g., wildfires). The controls on photosynthesis, decomposition, and 

nutrient cycling in the Arctic have all been measured and 

modeled at the scale of individual tussocks, stream riffles, 

and small patches of lake sediments. 

One approach to assessing responses at the pan-Arctic 

scale would be to model these processes for every plot 

(approximately the meter-squared scale) across the extent 

of terrestrial ecosystems in the Arctic. However, the 

computational burden and limits in knowledge on how to 

explicitly parameterize models that depict these processes 

at this scale is clearly prohibitive for even an average-sized 

watershed (hillslope and catchment scales in the figure), 

and requires the development of strategies for projecting 

how fine-scale processes are manifested at coarser scales. 

In fact, there has been progress in representing at coarser 

scales the operation of some fine-scale ecosystem processes 

relevant to the climate system (Box 2.3), for example, 

primary productivity in Arctic land ecosystems. In the 

early 1990s, regional carbon cycle models only considered 

fine-scale ecosystem processes such as photosynthesis 

and decomposition in estimating carbon exchange. More 

recently, these models are considering the dynamics of fire 

at regional scales and are now better able to partition effects 

of climate change and disturbance on simulated changes in 

carbon storage at the regional scale. Although research to 

date has illustrated how to scale some component processes, 

the challenge that now faces the scientific community is in 

scaling all the relevant fine-scale processes to coarser scales. 

Specifically, the challenge lies in identifying and then representing those processes that propagate to the scale of the 

whole Arctic in models that operate across the full domain 

of the Arctic system.

Observational systems employing both fine- and coarsescale measurements are required for evaluating whether 

scaling approaches have worked. For example, carbon 

flux studies can use technologies that employ chambers 

(relevant to one square meter), short towers (relevant to 

one hectare), and tall towers (relevant to hundreds of 

square kilometers). The mechanistic 

understanding of carbon exchange 

is generally provided by the chambers, but this information needs 

to be scaled up to the region to 

determine why the region is losing 

or sequestering carbon. Remote 

sensing is an important tool for 

scaling fine-scale measurements to 

the regional scale, particularly when 

observations at the regional scale 

can be used to evaluate the scaling 

application. Measurements at the 

tall-tower scale provide the regional 

data to evaluate whether scaling to 

the region has worked. Analogous to 

the spatial considerations of scaling, 

long-term observations are needed 

Hour Day Week Month Season

TEMPORAL SCALE

SPATIAL SCALE 

Year Decade Century Millennium

Treeline shifts,

species invasions,

permafrost thaw

Land use, shrubication, wildre,

caribou migration, decomposition,

carbon storage

Population dynamics,

sh migration

Community dynamics,

denitrication,

secondary production

Transport of nutrients,

hydrology

Photosynthesis,

nitrication,

microbrial growth

Pan-Arctic

Region

River Basin

Catchment

Hillslope

Plot

Figure 2.14. Dominant processes operating at different temporal and spatial 

scales in terrestrial ecosystems of the Arctic. 
31

Box 2.3. Scaling of an Ecosystem Process

Box Figure 3. Predicted gross 

primary production in the 

Kuparak River basin, AK, 

from the sum of individual 

days in 1995. The resolution 

is 1 km2

 and the units are 

106 g C km-2 yr-1. (From Williams 

et al., 2001, copyright by the 

Ecological Society of America).

Box Figure 1. Predictions of hourly GPP using measured hourly PAR data 

(squares). (From Williams et al., 2001, copyright by the Ecological Society 

of America)

Box Figure 2. The simple ACM model predicts daily GPP forced with 

measured 30-min PAR (lines). It compares well with the more complex 

SPA model (squares). (From Williams et al., 2001, copyright by the 

Ecological Society of America)

Site-specific information is important 

to developing an understanding of key 

biogeophysical properties of Arctic watersheds. However, to gain broader relevance 

and to explore the implications of these 

properties at the regional scale, scaling 

strategies are critical. For example, a group 

of scientists used gross primary production (GPP) measurements on plots in the 

Kuparuk River Basin, Alaska, along with 

meteorological data, to develop a scaling 

protocol to enable hourly gas exchange 

data to be used for daily estimates of GPP. 

Box Figure 1 shows hourly GPP predictions using measured photosynthetically 

active radiation (PAR) and a detailed, 

ten-canopy layer, half-hourly time step 

model (Soil-Plant-Atmosphere [SPA]). In 

Box Figure 2, SPA predictions of GPP 

have been scaled to develop a simpler 

model (Aggregated Canopy Model [ACM]) 

that applies to whole canopies on a daily 

time step. This simpler model required 

many fewer and more readily available 

parameters and input data. The authors 

also estimated the GPP for the entire 

9200 km2

 river basin (Box Figure 3) 

through the use of the simple ACM 

model of daily GPP and vegetation and 

meteorological constraints on production 

of individual square kilometers derived 

from field surveys, distributed climate 

stations, a land cover data base, and satellite data (NDVI). The river basin estimate 

is a projection based on scaling protocols, 

which moves understanding from hourly 

to daily estimates (see also the Primer on 

Scaling in Appendix 1).
32

to evaluate whether the scaling of short-term observations 

to longer time scales works. 

Scaling not only requires representation of fine-scale 

processes at a coarser scale, but also the representation of 

interactions that are taking place at these coarser scales. For 

example, caribou grazing might not be a critical consideration when modeling plant production at the plot scale. 

However, when modeling production at a catchment or 

river-basin scale, caribou grazing could be vitally important. Herbivory can also influence the pattern of vegetation 

changes at the river-basin scale. For example, moose prefer 

willows over alders on newly vegetated floodplain silt bars 

along rivers in Alaska. Thus, preferred herbivory on willow 

accelerates the transition of floodplain willow sites to alder 

sites. Because alder is a nitrogen fixer, the transition to 

alder is important for the nitrogen economy as alder sites 

transition into forest sites. The regional scale brings in other 

process such as the seasonal migration of caribou, land use, 

and fire. Finally, large changes in treeline and permafrost 

integrity may influence ecosystem function and structure 

over the pan-Arctic at century to millennial time scales.

The decadal scale is particularly critical for policy decisions, and scaling up to decadal scales with models is a 

major challenge because appropriate data at decadal scales 

are often lacking. While the enhancement of observational 

systems to make continuous measurements over decadal 

to century time scales holds promise for providing these 

data, the scientific community cannot wait for decades to 

conduct this scaling. Manipulative experiments represent 

a great resource for testing decadal-scale model behavior 

than are generally captured by shorter duration studies. 

Also, links to paleo studies to test models of vegetation dynamics holds promise for evaluating decadal to 

millennial-scale behaviors of models. 

An important issue with respect to temporal scaling 

concerns the use observational time-series data to constrain 

the behavior of models at various temporal scales. Data 

assimilation is an important technique that uses time series 

data to optimize parameters in a process model. As parameters in a process model are associated with dynamics that 

operate at different time scales, for example, fast photosynthesis responses or slow decomposition processes, the 

assimilation of data to constrain parameters is a powerful 

means to combine information, across a range of time 

scales, in a modeling analysis. Data assimilation techniques 

have great utility for temporal scaling as continuous longterm time-series data are collected by observational systems 

in the Arctic (e.g., AON, SAON).

What Is Required for Progress

•	 Develop research programs to advance understanding 

of mechanistic controls on processes operating over 

contrasting spatial and temporal scales. Research to 

develop coarse-scale models must build upon the mechanistic understanding gained from studies conducted at 

fine scales. Ecosystems represent highly linked physical, 

chemical, and biotic domains through which process 

dynamics reverberate. A search for the key scales and 

conditions under which component processes are amplified or dampened and signals carried through each of the 

three domains will constitute a key step forward.

•	 Create observational networks that can support multiscale analysis of the space and time characteristics 

of key terrestrial and aquatic ecosystem processes. 

Information gathered from a new generation of largescale and long-term observation networks, linked to 

smaller-scale and long-term observatories (e.g., NSF 

Long-Term Ecological Research sites) should yield new 

insights into the coherency of ecosystem response to 

climate and other forcings across scales.

•	 Foster research that focuses on representing fine-scale 

processes in models that are used as tools to address 

questions at coarser scales including those appropriate 

to the domain of the pan-Arctic. Although the goal 

is to improve predictive capabilities using models that 

provide coherent results across a spectrum of temporal 

and spatial scales, such models will be difficult if not 

impossible to construct without process-level studies 

and analysis of observational records. Models must 

be part of a unified experimental and observational 

networking strategy.
33

2.4. Human Systems

2.4.1. Arctic Communities

Recommendations

•	 Replicate case studies and surveys to generate 

cross-site comparisons, essential for securing largescale understanding. 

•	 Systematically improve completeness, access to, and 

integration of human-dimensions data with biogeophysical information. 

•	 Provide Arctic residents and crisis managers environmental information at local scales and closer 

to real time.

Science Context

Although uncertainty has always been a part of life in the 

North, unprecedented and decidedly nonlinear changes 

to high-latitude ecosystems are confounding livelihoods 

in both urban and rural settings. Changing climate and 

weather patterns, with related changes in fire and hydrological regimes, interact in complex ways with industrial 

and resource development, socioeconomic changes, as 

well as internal and external geopolitical conflicts. Some 

biophysical system changes are sudden and catastrophic, 

for example, storm-, wind-, and wave-driven coastal and 

river erosion. Others are much slower, and cumulative, in 

their onset and impact. In addition, colonial, cultural, and 

historical legacies each influence livelihoods today. Climate, 

social, and ecological models for these changes therefore 

abound, accompanied by stark uncertainties regarding how 

they will affect human activities at household, community, 

and institutional policy/regulatory scales. 

Climate change is prompting new research into human 

vulnerabilities and risk. Although recent scholarship has 

provided a variety of standardized Integrated Assessment 

(IA) frameworks for facilitating local-scale assessment, they 

remain limited by their case-study approach, which tells 

us little about spatial- and temporal-scale interactions. In 

addition, there has been almost no formal collaboration 

between Arctic researchers and researchers working elsewhere in the world to explore the benefits of cross-border 

and cross-regional synthesis. The intent here is to advance 

a general and comparative framework for understanding, 

for facilitating a better collaborative environment, and 

for working through genuine stakeholder input to design 

more effective and culturally appropriate assessments that 

link science to policy, that in turn will help design adaptive 

responses to ongoing Arctic change.

The Scaling Issue

Individual people comprise the atomic units for most 

research on Arctic communities. People gather into larger 

units of many overlapping and loosely defined kinds such as 

households, families, social groups, and organizations. Any 

of these larger-scale units could be important for understanding a particular community, and how it compares 

with other places. Well-developed social-science methods 

exist for study of various units, although the same methods 

do not necessarily work well across different scales.

Who is vulnerable, and to what? Who is resilient, and 

to what? Our ability to evaluate the wellbeing, vulnerability, and resilience of communities and the ecosystems 

they rely on, and implement policy and regulations that 

address these needs, depends largely upon our ability to 

translate what we already know about “large-scale” drivers 

of change to scales that matter to people and livelihoods. 

In this context, day-to-day weather, precipitation trends, 

shifts in seasonality, and wildlife phenology matter greatly. 

In addition, there is a significant challenge in matching 

policy implementation, traditionally carried out using its 

own set of spatial and temporal scales, with both large-scale 

changes and small-scale needs (Box 2.4). No single conceptual, theoretical, or modeling framework has emerged that 

is capable of distilling the interactions of large-scale drivers 

into high-quality, locally relevant information.

An Example of Scale Approaches in Arctic 

Demographic Research

Demography, or the study of population, focuses on some of 

the most fundamental and best-measured social dynamics. 

Analytical steps, from individuals to communities, regions, 

and states, are comparatively straightforward in demographic data, providing a best-case path for exploring some 

key issues associated with scaling in social research. For 

example, the population of one community is basically the 
34

Residents of the Arctic live in a complex milieu of social 

and also biogeophysical conditions, all highly dynamic 

and arguably more extreme than what is typically 

encountered at the lower latitudes. How might humandimensions data such those plotted in Figures 2.15–2.17

be integrated with climate or other physical science data, 

on an Arctic-system (as opposed to local-community) 

scale? Social and economic data in general describe 

socially defined units such as town, borough, or state, 

and the typical points of entry into the social sciences are 

multiple, as indicated by the tables in this box (right). In 

contrast, natural sciences data are displayed using different 

kinds of spatial units such as watersheds or grid cells. One 

approach to data integration is to map the social units in 

terms of grid cells, ready for combination with geospatial 

Earth system science data. The box figure (below) shows 

an example involving 23 administrative subdivisions of 

Alaska, depicted as collections of the 25 km2 equal-area 

grid cells. Combining these with watershed delineations, 

among many other possibilities such as biomes or climate 

regimes, allow important re-samplings of the social 

science data sets to be made with biogeophysical information. These combinations permit new issues of consequence to the inhabitants to be addressed. In this case, we 

could ask questions such as: What are the distributions 

of landscapes above and below particular administrative 

units, how are they changing, and how do they regulate 

rural water supply? How much rain or snowmelt is available 

as a water resource at particular times of the year and how 

is its variability changing in response to climate change? 

Should coping strategies for Arctic communities best be 

applied at local or administrative or watershed-specific 

scales? The classification systems depicted in this box 

are both valid. Combining different accounting systems 

highlights the need for a common nomenclature; that is, 

if system-level understanding is the ultimate goal of the 

community, standardization is required.

Box 2.4. Human Dimensions Research: Scaling Complexities Born of Other Disciplines

Box Figure 1. Administrative subdivisions of pan-Arctic Alaska, mapped in terms of 

EASE grid cells. Dillingham Census Area at lower left.

Box Figure 2. Drainage basin boundaries in Alaska, 

highlighting the organization of the landscape from 

an important biogeophysical perspective, namely 

drainage of the landmass. The map highlights many 

scales of interest, from the largest Yukon River 

basin down to the many small coastal watersheds 

of the region and the disparity with the human 

administrative divisions across the same region. 

Many biogeophysical data sets are already gridded 

and frameworks for their use are well-establisheduniting these with human dimensions data sets as 

in Box Figure 1 will enable new lines of inquiry 

to be pursued.
35

Scales of Policy & Regulation

Temporal

• Historical orthodoxy (26 Game 

Management Units, or GMUs, were 

established in 1956; few subdivisions 

and no reconfigurations made since)

• Decisions must be made years to 

months in advance

• Legislative cycles

• Emergency orders

Spatial

• Institutional and political borders 

(e.g., nations and sub-national units, 

communities, individuals GMUs, state 

and federal protected lands)

Scales of Ecological Change

Temporal

• Succession (fire regime, invasive species, 

successional change)

• Seasonality (breakup, freezeup, 

growing season)

• Animal life history (e.g., rut, migration)

Spatial

• Landscape, topography

• Watershed

Scales of Household Experience

Temporal

• Season

• Animal life history (e.g., rut, migration)

• Wage season (e.g., fire jumping)

• Daily, weekly (can adjust tactics to weather 

and environmental cues)

Spatial

• Working landscape, “foodshed” 

(e.g., known places, culturally important 

harvest areas)

sum of individuals living there. Population changes over 

time with the number of births and deaths (their difference 

being “natural increase”) plus the number of in-migrants 

and out-migrants (their difference being “net migration”). 

Figure 2.15 gives a time series of population changes from 

1990–2006 in the southwestern Alaska fishing town of 

Dillingham. During the first decade of this period, the town 

grew by 20%, but after 2000, population leveled off and 

then declined slightly. Births substantially exceeded deaths 

throughout the whole period, as shown by light and dark 

bars in the lower part of the figure—so natural increase 

steadily favored growth. Net out-migration since 2000 

caused the leveling and then decline of total population.

Figure 2.15 takes information about a few thousand 

people and scales it up to the level of the community they 

form. Thus, at the community level, we see a pattern of 

change partly driven by demographic, socioeconomic, 

and health conditions affecting birth and death rates, 

but also partly by changing conditions in the fishery that 

provides livelihoods.

A further step up in scale allows comparisons across 

communities. Figure 2.16 presents similar plots of population changes in four other predominantly Alaska Native 

(Yupik) communities that are also within the Dillingham 

Census Area. Like Dillingham itself, each place experienced 

periods of growth, stability, and decline. Some of the placeto-place variations could be random, but they also challenge researchers to identify systematic causes—Why did 

the population of New Stuyahok and Togiak, for example, 

grow steadily through the first decade of this period, much 

like Dillingham, but then level off and decline? The other two 

villages in Figure 2.16 each followed a somewhat different 

pattern, in which net migration played a more dominant 

role. Such fluctuations reflect interactions between external 

forces and the qualities or internal dynamics of different 

communities. Studying them can provide important clues 

on what to expect as conditions change elsewhere in the 

future. The highly varied sensitivity of individuals and 

communities to external forces remains a key issue in 

Arctic social science research.

Figure 2.17 steps back further (i.e., upscales), combining 

all Dillingham Census Area communities in one plot at 

center left. At this scale, we can compare a number of 

other areas such as the North Slope Borough or Northwest 
36

Arctic Borough. Some areas exhibit patterns of steady 

growth, while others show recent declines. Figure 2.17

lacks the details of Figures 2.15 and 2.16. Communitylevel time plots are based on much smaller numbers, and 

consequently show erratic year-to-year variations that 

obscure underlying trends. Confidence bands at local scales 

would be wide, encompassing the possibility of increasing, 

declining or stable population. Many other social and 

health indicators share this property of being highly 

variable at small scales and over small numbers of individuals. Figure 2.17 brings a new possibility that becomes 

increasingly valuable as scale increases: a quantitative 

model of change. In this case, a multilevel quadratic model 

can be used to roughly summarize population changes that 

overlay or combine large-scale with local-scale trends. As 

human-dimensions research moves from local case studies 

to larger-scale comparative analysis, formal models become 

increasingly useful and commonplace.

Our discussion thus far concerned 

mainly issues of spatial scaling, as they 

are applied to human geography. In principle, thousands of indicator variables 

(e.g., any information recorded for U.S. 

counties) could be added to the pan-Arctic 

spatial framework described in Box 2.4. 

From a practical standpoint, time series 

can be difficult to obtain for social scales 

smaller than sub-national. Nevertheless, 

it is still important to briefly mention 

the key temporal scaling issues for those 

data sets that are today being collected or 

anticipated to be collected. Information at 

community and larger scales commonly 

is recorded by year, which often makes a 

convenient metric for integration. But for 

some purposes, such as oil-spill response, 

the relevant time scales are much shorter, 

on the order of hours to days rather than 

years (although long-term consequences 

might become visible over years or even 

decades). Political cycles of two to four 

years affect many policy decisions, so 

that consequences that might unfold 

more slowly sometimes get lower weight. 

Uniting these different time perspectives 

requires additional new research.

Human Dimension Data 

Requirements and Challenges

Basic demographic, economic, and 

health statistics are commonly collected 

by governmental agencies, which have 

access to resources that dwarf those of 

Figure 2.16. Components of population change in four Dillingham Census 

Area villages, 1990–2006.

Births and deaths Births and deaths 

Population Population

0 10

180 200 220 240

1990 1994 1998 2002 2006

Aleknagik

0

380

20

400 420 440

1990 1994 1998 2002 2006

Manokotak

0

380

30

420 460 500

1990 1994 1998 2002 2006

New Stuyahok

-10

600

30

700 800 900

1990 1994 1998 2002 2006

Togiak

Year Year

Vertical lines show

estimated net

migration eects

0

50

Births and deaths 

2000 2100 2200 2300

Population

2400 2500

1990 1992 1994 1996 1998 2000 2002 2004 2006

Year

Dillingham, Alaska

Figure 2.15. Components of population change in the town of Dillingham, 

Alaska, 1990–2006.
37

most research projects. Researchers often benefit from 

such data, but information does not necessarily exist (or it 

is not openly accessible) across the range of scales—from 

specific communities to regional or pan-Arctic—needed by 

researchers. Social science data often also lack continuity 

over time, making it difficult to track change. Moreover, the 

particular variables needed to address research questions 

may never be collected. Dedicated surveys and case studies 

can supplement basic population data, but these tend to 

be scale-specific, dealing with a few selected communities or, conversely, region-wide with little 

geographic resolution. 

Research aimed at meeting the needs 

of the environmental policy and management communities confronts a number of 

significant, scale-related challenges. Given 

the complex causalities of social systems, 

scientists need substantial volumes of data to 

detect and understand signals amidst much 

noise. Geographically specific time series of 

health and social indicators, and replicated 

case studies or surveys of northern residents 

are currently rare, but much needed for practical research. Without geographical specificity and time series, it is difficult to secure 

indicator data at the community scales, or 

precisely where it is most needed. Conversely, 

without replication, it will be difficult to 

establish the generality and utility of research 

results for better management, because 

conclusions will be based on stand-alone case 

studies or surveys.

Local-scale and real-time information 

become particularly critical with respect to 

fast-moving phenomena such as oil spills or 

extreme weather. Impacts of climate shifts 

will also have costs, at local levels and over 

shorter time scales, as they affect particular 

resources, transportation, infrastructure, and 

activities (see Section 4.4).

New data compendia should be developed through: targeted surveys; enhanced 

monitoring, recording, and publication of 

indicators by government agencies; and more 

sharing of existing records (such as logs of transportation or other weather-dependent activities) maintained 

by the private sector (e.g., energy companies). Alongside a 

broader effort at producing and releasing data, starting at 

community-level or smaller scales, we need significantly 

more work on integration strategies. The place/year integration approach shown by examples in this section is one 

possible route, but both this approach and others need 

0

5

10

15

20

0

5

10

15

20

0

5

10

15

20

1970 1980 1990 2000 1970 1980 1990 2000 1970

Year

1980 1990 2000 1970 1980 1990 2000

Aleutians East Aleutians West Bethel Census Area Bristol Bay

Dillingham Lake & Peninsula Nome Census Area North Slope

Northwest Arctic Wade Hampton Wrangell-Petersburg Yukon-Koyukuk Population in 1,000s

Figure 2.17. Observed and modeled population trends in 12 Alaska administrative subdivisions, 1970–2003. Dillingham Census Area at center left.

 subsistence activities 

environmental crisis

 population size, composition, migration 

human health

ecosystem integrity

 economic development 

and infrastructure

political

election cycles

climate change

local regional system

SPATIAL SCALE

days years decades

TEMPORAL SCALE

Figure 2.18. Schematic representation of some overlapping temporal and 

spatial scales involving human communities in the Arctic.
38

development. Modeling and other analytical methods that 

work with such integrated data sets are still in their early 

stages of development.

What Is Required for Progress

•	 Replicate case studies and surveys to generate crosssite comparisons, essential for securing large-scale 

understanding. Although data collected by the government is a mainstay of social science research, information gathered by scientists to support question-driven 

research is also an essential knowledge commodity. 

In this context, the value of local-scale information 

collected in the best traditions of human dimensions 

research, will find new value in addressing new globalchange-driven issues, like Arctic climate change. A 

sufficient number of case studies will be required for 

comparative studies to position a particular site within 

the context of larger pan-Arctic patterns, and indeed to 

identify such patterns. 

•	 Systematically improve completeness, access to, and 

integration of human-dimensions data with biogeophysical information. New approaches in social research 

are needed to improve and fully utilize the limited data 

resources. Multivariable statistical methods (e.g., regression-type modeling, including time series and multilevel 

analysis) could aid research particularly as we move up 

to larger scales in the pan-Arctic system. A focus on the 

Arctic climate change question, which is of sufficient 

interdisciplinary breadth, could help catalyze the wider 

application of statistical and other modeling approaches, 

along with the development of new synthesis techniques.

•	 Provide Arctic residents and crisis managers environmental information at local scales and closer to real 

time. Climate change, while inherently long term and 

pan-Arctic in nature is a part of a larger environmental 

change issue, which also has shorter temporal and spatial 

scales. Chief among these are the influence of natural 

events, such as storms and coastal erosion, and humanmade events, such as oil spills. The social science data and 

analytical methods needed to provide warning, assessment of damage, and response are in their infancy but 

ripe for new development. 
39

3. The Role of Scaling In 

Interdisciplinary Arctic 

and Earth System Science

3.1. Introduction

3.2. Tipping Points in the Arctic System

It is no longer news that the Arctic is changing—broadscale increases in air temperature, rapid gains/losses in lake 

area, major “greening” and geographic shifts in vegetation, 

historic reduction in seasonal sea ice, glacier melt, permafrost thaw, increased river flows, longer ice-free periods, 

and diminished snow cover. Changes are in many cases 

coordinated and systematic, and some observed rates of 

change are substantially faster than state-of-the-art models 

today can predict. A grand challenge thus faces the scientific community—and indeed society at large—to interpret, 

understand, forecast, and respond to reverberations of 

Arctic system change to other parts of the Earth system. 

Answering this call will require a strategy to integrate 

emerging knowledge of complex Arctic-centered dynamics 

with assessments of the larger Earth system. Because of 

the highly coupled character of human and biogeophysical 

systems in the Arctic, a major step forward will be to 

Recommendations

•	 Support research to identify thresholds.

•	 Better collate and integrate information over key 

system components and at different scales.

•	 Develop scenarios of the consequences of alternative 

management options.

•	 Match scientific research to the needs of decisionmakers at multiple scales.

develop an improved understanding of how the critical 

linkages are “wired” together and how individual disciplinary notions of scale can be unified.

The Arctic research community has substantial experience in synthesizing complex interdisciplinary perspectives. 

We seek now to address the urgent challenge of understanding how Arctic system dynamics are teleconnected to 

and affect other parts of the globe. This report presents a 

brief discussion of interdisciplinary scaling challenges across 

two strategic domains: Arctic-centered tipping points and 

the role of Arctic terrestrial ecosystems in the broader Earth 

system. Each example still requires substantial improvement 

in our understanding and in reducing uncertainties about 

how Arctic change affects the twenty-first century Earth 

system. In both cases, the focus is on climate change, as an 

overriding backdrop of Arctic system change and as one of 

its principal forcings.

Science Context

A threshold, which is also often referred to as a tipping 

point, is the state beyond which there is an abrupt change 

in the system quality, property, or phenomenon being 

considered, or where small changes in one or more external 

conditions produce large and persistent responses in the 

system. Thresholds occur when external factors, positive 

feedbacks, or nonlinear instabilities in a system cause 
40

changes to propagate along potentially irreversible pathways. Once a threshold is crossed, the system in question 

may be unable to return to its previous state. For example, it 

has been proposed that the Arctic may be near a threshold 

between increased solar absorption in the ocean during 

summer and its ability to regrow first-year sea ice during 

the following winter. 

The Scaling Issue

Although thresholds are fundamentally an issue of 

temporal scale, the factors that lead to system-wide thresholds may be manifested across a spectrum of temporal 

and spatial scales. The example of sea ice loss discussed 

earlier (Section 2.2.4) illustrates several issues of temporal 

and spatial scale. Sea ice loss involves warming of the 

Arctic, heat transport from the tropics to the Arctic via 

the atmosphere and ocean, and melt in the Arctic. Thus, 

understanding processes at multiple temporal and spatial 

scales—extending beyond the pan-Arctic—is essential to 

understanding when system thresholds may occur. 

Thresholds may occur in physical, ecological, and human 

components of the Arctic, and may involve feedbacks and 

impacts in physical, ecological, and human systems, both 

within and outside the Arctic. For example, although 

summer sea ice loss can be characterized as a physical 

process, its link to climate change involves human systems 

(i.e., the burning of fossil fuels) and ecological systems 

(the ability of marine and terrestrial biota to take up emissions of carbon dioxide from the burning of fossil fuels). 

Furthermore, the impacts of the loss of summer sea ice have 

substantial implications for ecological and human systems. 

For example, the warmer waters and deeper light penetration of an ice-free summer have the potential to influence 

ecological systems in the Arctic Ocean and the increased 

transportation and resource access is almost certain 

to influence economic and political systems globally. 

Furthermore, the potential for sudden, unanticipated shifts 

in system dynamics makes planning, preparation, management, and policy very difficult. These sudden changes to 

system dynamics are not well understood, but they are 

extremely important if managers and policymakers are to 

succeed in developing mitigation and adaptation strategies 

in a changing world.

If climate change is pushing the Arctic system toward 

thresholds, what can be done by decision-makers and others 

to better cope with the threat of transformative change? 

Although the science of thresholds is still in its infancy, 

there are actions that, taken together or separately, can 

improve the understanding of thresholds and increase 

the likelihood of success in developing management and 

adaptation strategies in a changing climate, before, during, 

and after thresholds are crossed. The emphasis should be on 

research that identifies thresholds and tipping points, better 

integrates requisite data sets across scales and disciplines, 

and assesses alternate futures for adaptive management. 

The broad set of time-space plots shown in Section 2 of 

this report is a testament to the challenge ahead, and it will 

become ever more important for traditional disciplines to 

develop a shared nomenclature, develop methodologies for 

cross-component “hand-shakes,” and assess the relevancy 

of particular scales that determine full-system behaviors. 

These obstacles to progress will require new mathematical 

and statistical techniques to help handle the many orders 

of magnitude in scale. This constitutes a difficult numerical 

stability challenge for modeling and data interpretation.

What is Required for Progress

•	 Support research to identify thresholds. Better integration of existing monitoring information across a 

range of spatial scales will be needed to detect potential 

thresholds, and research will need to focus on systems 

undergoing a threshold shift to better understand the 

underlying processes. In a world being altered by climate 

change, managers and decision-makers may also have 

to be increasingly nimble, and adjust their goals for 

desired states of the system away from static, historic 

benchmarks and focus on increased resilience and adaptive capacity as measures of success. Reliable identification of thresholds in the Arctic system across different 

subsystems should be a priority given their potential to 

reverberate throughout the system.

•	 Better collate and integrate information over key 

system components and at different scales. Once the 

key factors controlling adaptive capacity and resilience 

of different subsystems are known, monitoring strategies should explicitly develop the means to track these 

factors. Of particular concern is whether thresholds 
41

crossed at one subsystem or scale will manifest themselves in a regime shift within another subsystem or 

scale. Consideration should be given to monitoring indicators of overall system stress as these changes may be 

indicative of regime shift. Because agencies and institutions have different management mandates, there can be 

a focus on particular resources, subsystems, and scales of 

interest to the exclusion of others. To overcome their sole 

foci, better information sharing and integration can help 

to improve the understanding of thresholds, identify the 

spatial and temporal scales at which they might occur, 

and detect whether threshold changes at one scale are 

manifested in system reorganization at another scale. 

•	 Develop scenarios of the consequences of alternative management options. In some cases, the kinds of 

external factors that can precipitate threshold changes 

are well known, and furthermore are known in advance 

(e.g., storms, wildfire, or invasive species). In these cases, 

scenario analysis is a powerful tool for predicting and 

understanding the potential consequences of specific 

management actions. Scenarios should be cast at the 

native resolutions that make a difference to managers. 

•	 Match scientific research to the needs of decisionmakers at multiple scales. Much of the recent information on climate change impacts suggests that changes are 

occurring more quickly than forecast only a few years 

ago. It is also apparent that many changes are causing 

secondary, or cascading, domino-like, changes in other 

parts of the system. Management policies that were 

developed during relatively stable climate conditions may 

be inadequate for a variable world with more surprises. 

A shift toward multiple scales of information integration and subsequent decision-making can enhance and 

leverage existing management resources and approaches.

3.3. Changing Arctic Terrestrial Ecosystems and 

Their Impacts on the Earth System

Recommendations

•	 Conduct research that focuses on understanding the 

mechanistic controls over processes that operate at 

different spatial and temporal scales.

•	 Develop observational networks designed to address 

the challenges of scaling in space and in time.

•	 Facilitate research that focuses on representing 

processes that operate at finer spatial scales into 

models that are used as tools to address questions at 

coarser scales.

Science Context

High-latitude terrestrial ecosystems occupy approximately 

one-fourth of Earth’s vegetated surface. Substantial climatic 

warming has occurred across the high-latitude landmass 

during the latter half of the twentieth century, and evidence 

continues to mount that this warming has been affecting 

the structure and function of terrestrial ecosystems in this 

region. It is important to understand these changes because 

they may have consequences for climate system functioning, particularly in the way that (a) greenhouse gases 

are exchanged with the atmosphere, (b) water and energy 

are exchanged with the atmosphere, and (c) freshwater 

is delivered to the Arctic Ocean. The exchange of greenhouse gases and the delivery of freshwater to the Arctic 

Ocean are processes that could directly influence climate 

at the global scale, while the exchange of water and energy 

has implications for regional climate that may in turn 

influence global climate.

To understand how responses of terrestrial ecosystems 

will influence the climate system of the future requires 

integrated understanding of how terrestrial ecosystem 

processes will control feedback pathways that influence the 

climate system. Arctic terrestrial ecosystems influence the 

climate system through both direct physical feedbacks of 

changes in snow, ice, and hydrology and through ecosystem 

processes that are characterized by active biotic control 

(Figure 3.1). Despite the diversity of feedback loops and 
42

processes within terrestrial ecosystems, only a few terrestrial features determine the coupling of Arctic ecosystems 

with the climate system: (1) albedo, (2) energy partitioning 

(i.e., the degree to which evaporative cooling or permafrost 

dynamics influence the fractionation of ground heat flux or 

transfer of heat to the atmosphere as latent or sensible heat 

flux), and (3) the emissions of the greenhouse gases CO2

and CH4. Although it is clear that changes in high-latitude 

regions have consequences for the climate system as a whole 

through numerous possible pathways, we do not completely 

understand whether the net effect of changes to ecosystems 

will enhance or mitigate warming. 

The Scaling Issue

The exchange of water, energy, and trace gases involves 

high-latitude ecosystems, the atmosphere, and the ocean, 

which are all closely coupled. Thus, the representation of 

these processes requires integration of understanding from 

numerous disciplines, including climate science, hydrology, 

ecosystem physiology, and disturbance ecology. Forecasting 

the response of high-latitude ecosystems to global change 

will therefore rely on an integrated understanding of how 

the linkages will change across a spectrum of spatial and 

temporal scales. The fundamental scales were elucidated 

earlier for individual subsystems (Section 2), but quite 

possibly may involve new combinations of spatial and 

temporal scales as we analyze jointly all of the relevant 

components that comprise the pan-Arctic system.

Figure 3.1. Terrestrial responses to warming in the Arctic that influence the climate system. Note the strong interconnectivity, which implies that 

change in one component has the potential to reverberate throughout the system. Responses of permafrost on the left are coupled with functional 

(physiological) and structural biotic responses on the right either directly (arrows B and D) or through mediating processes of disturbance and 

land use (arrows C and E). Functional and structural biotic responses are also coupled more directly (arrow A). Response pathways are identified 

at three time scales (seconds to months, months to years, and years to decades). Physical responses will generally result in positive feedbacks. In 

general, functional responses of terrestrial ecosystems act as either positive or negative feedbacks to the climate system. In contrast, most of the 

structural responses to warming are ambiguous as they result in both positive and negative feedbacks to the climate system. Each of these coupled 

subsystems have unique scaling challenges as discussed throughout Section 2. (From McGuire et al., 2006)

Physiology Climate

warming Structure

Land-use

composition,

vegetation shifts

Disturbance

CO2, SH

Permafrost warming, melting

(8) shrub expansion: A

(9) treeline advance: A , CO2

(10) forest degradation: A but CO2, SH

(11) light to dark taiga: A but CO2, SH

(12) more deciduous forest : A , SH

(13) fire/treeline retreat: A

Physical

feedbacks

Biotic control

(1) higher decomposition: CO2

(2) reduced transpiration: SH

(3) drought stress: CO2

(4) PF melting: CH4

(5) longer production period: CO2

(6) NPP response to N min: CO2

(7) NPP response to T: CO2

Mediating

processes

(14) reduced heat sink : SH

(15) watershed drainage: SH

(16) earlier snowmelt:A

±

Snow

cover

1, 2, 3, 4

5, 6, 7

8, 9

10, 11

12, 13

A

B

C

14, 15

16

enzymes, stomates

fire, insects

logging, drainage,

reindeer herding

Physiological feedbacks

Structural feedbacks

Physical feedbacks

D

E

fast (seconds to months)

intermediate (months to years)

slow (years to decades )

Response time

Mechanisms:

A: albedo

SH: sensible heat flux

CO2, CH4: atmospheric concentration
43

The carbon cycle of the Arctic provides an example of 

the scaling challenges faced by the scientific community. 

The global land-ocean carbon sink has been responsible for 

taking up approximately 50% of fossil fuel emissions, and 

therefore any weakening of this sink has the potential to 

compromise policy actions aimed at mitigating the increase 

in the carbon dioxide concentration of the atmosphere. 

From an atmospheric perspective, the Arctic in recent 

decades has been responsible for 0 to 25% of the net landocean sink of carbon dioxide. These are very large-scale 

estimates based on changes in atmospheric concentrations 

integrated to scales of the Arctic Ocean, northern Asia, 

and northern North America. Analysis of changes in forest 

biomass in the Arctic based on numerous hectare-scale plots 

indicate that increases in vegetation carbon of the Arctic 

are responsible for 10–20% of the global land-ocean sink. 

However, the land-based studies really have no reliable 

information on how soil carbon in the Arctic has changed 

in recent decades. To complicate matters, remote-sensing 

studies indicate that photosynthesis in Arctic tundra is 

increasing but that photosynthesis in Arctic boreal forests 

is decreasing. There is a clear need to reconcile the information from these different approaches and scales to reduce 

the uncertainty about the role that the Arctic is playing the 

global land-atmosphere carbon sink. 

There are similar needs to reconcile the approaches and 

scales of atmospheric, surface-based, and remote-sensing 

estimates of methane and water/energy exchanges between 

the surface and the atmosphere. Strategically enhanced 

observational systems that are designed to reduce uncertainties, measure the effects of mechanisms not effectively 

considered, and enhance an understanding of the scales of 

variability will improve the data for scaling and the ability 

to reconcile analyses conducted with different approaches 

and at different scales. 

In some cases, emerging technologies may need to be 

employed to bridge scales. For example, the current atmospheric analysis of carbon dioxide exchange could be greatly 

enhanced by mesoscale atmospheric simulation supported 

by tall tower and continental-scale airborne campaigns. But 

such campaigns need to be coupled with information from 

other approaches to understand the mechanisms responsible 

for observed patterns of change.

What is Required for Progress

•	 Conduct research that focuses on understanding the 

mechanistic controls over processes that operate 

at different spatial and temporal scales. Whether 

the response of terrestrial ecosystems of the Arctic 

will enhance or mitigate climate warming is highly 

uncertain. This uncertainty can potentially be reduced 

through integrated studies that (1) link observations of 

terrestrial dynamics related to climate feedbacks to the 

processes that are likely to influence those dynamics, 

and (2) incorporate the understanding gained from 

integrated studies into both uncoupled and fully coupled 

climate modeling efforts.

•	 Develop observational networks designed to address 

the challenges of scaling in space and time. Scaling is 

a key challenge to designing integrated studies that link 

observations and processes of terrestrial climate feedbacks, which are traditionally conducted at relatively fine 

spatial and temporal scales. This must be executed in a 

way that the understanding can be transferred to models 

that operate at coarse spatial and/or temporal scales. 

Design work on the Arctic Observing Network currently 

underway (AON Design and Implementation Task Force) 

is apropos in this context. 

•	 Facilitate research that focuses on representing 

processes that operate at finer spatial scales into 

models that are used as tools to address questions at 

coarser scales. This upscaling re-asserts itself and must 

find its way into the dialogue on coupled modeling. 

The approach must be guided by similar principles 

as treated earlier in Section 2.2.1 on weather and 

climate prediction, including the application of effective 

parameters, the use of synoptic-scale remote sensing in 

conjunction with modeling, the specification of reliable 

initial conditions for ecosystem models and associated 

uncertainties, and extrapolations of site-specific information. This argues for coincident interdisciplinary 

measurement campaigns.
44

4. the Role of Scaling in

Societal Applications

4.1. Introduction

The large number of examples in Sections 2 and 3 paints 

the picture of an Arctic experiencing rapid, arguably 

unprecedented, and ongoing environmental change. Such 

change, from a biogeophysical perspective, cascades into 

many domains—land and coastline permafrost degradation, sea ice retreat, winds and waves leading to coastal 

erosion, changes in vegetation, longer ice-free seasons, 

“greening” of the Arctic, waterlogging of soils, more variable weather, including extremes of cold or warm, icing, 

flooding, droughts, and increased incidence of fire. These 

linked changes in the Arctic environment are fundamentally interdisciplinary in nature and require system-level, 

synthetic, and multiscale thinking.

But the change also reverberates into many societally 

relevant domains: damage to and loss of civil infrastructure 

due to permafrost degradation, reduction in ice-dependent 

transportation routes over land, coastal infrastructure 

battered by waves, northward migration of pathogens and 

vectors affecting human health, sea ice retreat leading 

to coastal erosion, fires and smoke affecting navigation 

and infrastructure, pest outbreaks, and loss of species, 

including traditionally hunted/fished species. Although 

there will be many, clearly positive, effects, including access 

to ocean shipping, resource extraction, and new fisheries, 

preliminary assessment indicates substantial negative 

impacts from climate change alone—of billions if not tens 

of billions of dollars for the State of Alaska to year 2030. 

Such estimates are generated by extrapolating site-specific 

damage assessments through spatial extrapolation to the 

domain of the entire state—fundamentally a scaling issue, 

but one with substantial uncertainty surrounding it, as will 

be discussed below. 

It is thus important to identify the readiness of the 

research and assessment community across a wide spectrum of applications. Identifying and filling key gaps in 

science and technology readiness today helps to forestall 

delays in acquiring policy-actionable knowledge upon 

which future climate change adaptation can be based. Focus 

again will be on climate change, as an overriding backdrop 

of Arctic system change and as a principal forcing that will 

challenge policymakers and managers. Seven societal applications are presented: Arctic human health, climate change 

mitigation and adaptation, infrastructure at risk, subsistence fisheries, non-renewable resource extractions, sea ice 

navigation, and oil spill response and restoration.
45

4.2. Arctic human health research issues in Alaska 

Recommendations

•	 Develop a common set of core health status metrics 

among Arctic communities that allow comparability 

of populations.

•	 Develop common monitoring protocols for human 

tissue contaminant and zoonotic disease prevalence 

across contiguous international regions.

•	 Develop a common statistical approach to the scaling 

challenges of small populations with multiple exposures and multiple confounders.

•	 Mitigate scaling issues by creating a combined international indigenous population cohort in the Arctic in a 

longitudinal prospective study. 

Science Context 

The United States, Canadian, and Greenlandic circumpolar 

regions are sparsely populated, with few large cities, and 

a large number of indigenous residents who are predominantly located in very small, isolated, remote communities. 

These remote rural residents have unique dietary exposures, 

ranging from the dietary benefits of a traditional northern 

marine subsistence diet, to an industrially produced food 

supply with anthropogenic food-chain contaminants, 

which result in substantial health status disparities. In addition, the circumpolar regions of the Western Hemisphere 

have experienced the most rapid increase in mean temperature in North America. Alaska’s uniquely Arctic health 

issues center around its rural inhabitants, who are mostly 

Alaska Natives, and around the 40-year warming trend, seen 

as a 0.5–1.0°C per decade warming trend in average winter 

temperature. This has greatly altered lifestyles, including 

food acquisition from Arctic ecosystems. These many 

factors combine to create a unique need, and opportunity, 

for health research conducted at all scales, but particularly 

at large scales, over an observational time horizon long 

enough to detect distinct trends.

In human health research, large-scale data analysis 

provides the basis for the majority of individual, smallscale health guidance recommendations, and also drives 

small-scale individual patient research. Ultra-small-scale 

(molecular) research is most often used to develop hypotheses that drive large-scale, population-based research 

intervention trials.

Small remote populations are challenging to study and 

often require regional and sometimes international cooperation to achieve adequate sample size. Yet, one advantage 

to studying these populations is that in the United States, 

Canada, and Greenland, indigenous residents have federally financed health care systems, with unitary medical 

record systems and centralized data systems, which simplifies long-term follow-up and large-scale analysis of trends 

in health status.

Alaskan Arctic health research falls into two general categories: that associated with the changing climate, and that 

associated with the isolated, subsistence-dependent Alaska 

Native residents of rural Alaska, with some overlap between 

the two groups. These two groups will be briefly discussed 

separately in the sections that follow.

Alaska Winter Climate Warming

Emerging zoonotic diseases are potentially a major threat, 

due to the ability of infecting organisms to survive in 

warmer temperatures, as well as in-migration of new 

species with endemic diseases. Large-scale regional detection, surveillance, and adaptation strategies are required for 

these emerging threats, while the emerging infections are 

still confined to wildlife.

Unique Indigenous Health Problems

For unknown reasons, the incidence of colorectal, breast, 

and prostate cancer are rapidly rising in Alaska Native 

populations. This broad-scale population health disparity 

may have environmental and genetic etiologies, and needs 

population (large-scale) down to molecular (fine-scale) 

research. Exposure to certain compounds at critical stages 

of pregnancy can result in developmental abnormalities in 

the infant, which may only appear later in life. In very early 

pregnancy, certain exposures can “silence,” or activate, the 

expression of certain genes, without changing the base-pair 

structure of the gene. These changes in gene activity result 

in a different phenotypic expression in the infant, and the 

change in phenotype is transmittable to subsequent generations, without a change in the basic structure of the gene. 

This effect is called an epigenetic effect, and may be one of 

the most important of any sort of prenatal dietary exposure. 
46

The rising incidence of chronic diseases offers an opportunity for population-based, large-scale, multi-community 

research. The northern marine subsistence diet, which 

is high in omega-3 fatty acids, appears to reduce risk 

for certain chronic diseases such as heart disease, while 

organochlorine contaminants in certain subsistence species 

may raise risk for others such as diabetes. 

Alaska Native infants have six to ten times the risk 

compared to U.S. all-races infants for requiring hospitalization in the first 12 months of life for severe lung infection. As a group, 85% of Alaska Natives have acquired 

an infection with Helicobacter pylori by the age of 

10 years. This bacterium is associated with gastric ulcers 

and gastric cancer.

The Scaling Issue

There are two basic scaling issues that need to be addressed 

in Arctic human health research.

The most commonly encountered scaling exercise is 

that of attempting to statistically associate chronic lowlevel exposure to a mixture of toxic substances from the 

subsistence diet to a particular population health outcome. 

The usual variables in this scaling exercise are sample size 

(numbers of humans), and the duration of the exposure. 

The following problems are typically encountered:

• The sample size of the exposed population is marginal to 

achieve sufficient statistical power to detect significance. 

This issue can be addressed by combining similar populations from other regions. 

• The outcome examined, for instance, subtle childhood 

neurodevelopmental abnormalities, may have other etiologies, called confounders, which may be present in the 

population, such as prenatal alcohol or tobacco exposure.

• The addition of other small populations to a small sample 

may introduce the effect of genetic differences that can 

affect the sensitivity to toxins, as well as differences in 

amounts and types of toxins in a different region, so 

that populations may not represent useful additions 

to a small sample.

The second type of scaling exercise considers Arctic 

health outcomes with time and space as the variables, as 

applied to various types of disease-causing exposures. 

Useful spatial intervals in this construct involve human 

population space requirements, varying from the individual, through the family, community (from very small 

to a small city), to an eco-region, which tends to be the 

defining space for the wildlife and subsistence species that 

provides the benefits, and exposures, for the population 

occupying the space. Key temporal intervals chosen reflect 

the rate of disease development, depending on the cause, or 

the time required to see the benefit of interventions. Many 

negative impacts have both an acute and chronic phase, as 

do benefits from micronutrients. Pregnancy represents a 

specific interval, and both negative and positive impacts 

can take a lifetime to develop. As described earlier, epigenetic effects can persist over generations, without a genetic 

change. These scaling issues are represented in Figure 4.1.

1

day

7

days

1 mo. 9 mo.

(pregnancy)

20 yrs.

(one generation)

40+ yrs.

(multiple generations) 

Family

(200 m2

)

Individual

(1 m2

)

Community

(10– > 500 km2)

Eco Region

(106–106

 km2)

1. Acute high toxicity contaminant exposure

2. Chronic low-dose contaminant exposure in subsistence foods

3. Chronic exposure to micronutrients in subsistence foods

4. Infectious disease (acute and chronic)

5. Epigenetic eects of prenatal diet

SCALE ISSUES IN FIVE COMMON ARCTIC HEALTH PROBLEMS

1

4

3 2 5

Figure 4.1. Key space-time domains in Arctic health research.

In summary, Arctic health 

research in Alaska is needed on all 

scales, especially initial large-scale 

population research (and would 

be productive) given the unique 

risk factors (climate, diet/contaminant exposure), unique disparities (certain cancers, infectious 

disease), and unique risk reduction 

factors (diet). These topics could be 

pursued to advantage with Canada 

and Greenland (Denmark) and 

need to be carried out prospectively. 
47

4.3. Climate change mitigation 

Recommendations

• Develop Earth system models that effectively deal 

with the scaling issues involving the important 

processes that affect carbon dynamics in the Arctic.

• Develop integrated assessment models that can merge 

the understanding from fully coupled Earth system 

models with Arctic-initiated climate change mitigation 

actions at local to global scales.

Science Context

Observational evidence from all continents and most 

oceans shows that many natural systems are being 

affected by regional manifestations of global climate 

change. Climate change and its impacts are apparent now 

throughout the United States, including most prominently 

Alaska. It is clear that the recent warming in the Arctic has 

been affecting a broad spectrum of physical, ecological, 

and human/cultural systems. In Alaska, climate change 

has been linked to increases in the frequency of insect 

outbreaks and wildfires, declines in lake area, thawing 

permafrost, increases in the risk of coastal storms to 

villages and fishing fleets, and the displacement of marine 

species and associated effects on important commercial 

fisheries. Arctic responses to climate change may not be 

restricted to adaptation alone, and Arctic societies could be 

at the forefront of efforts to mitigate climate change. 

Climate change mitigation has traditionally been defined 

as the implementation of policies to reduce greenhouse 

gas emissions and enhance sinks for greenhouse gases. 

Effective climate change mitigation is important for 

avoiding changes to climate that impact human society. 

Climate change mitigation concerns itself with the 

following five questions:

1. What can be done to reduce or avoid the threats of 

climate change?

2. What are the costs of mitigation actions and how do they 

relate to the costs of inaction?

What Is Required for Progress?

• Develop a common set of core health status metrics 

among Arctic communities that allow comparability of 

populations. Common metrics would make health status 

comparison among Arctic countries feasible, and would 

greatly facilitate organizing large-scale international 

research on the impact of contaminants and micronutrients on health. Also, standardizing the definition 

of certain types of diseases would make international 

research on interventions easier and more meaningful.

• Develop common monitoring protocols for human 

tissue contaminant and zoonotic disease prevalence 

across contiguous international regions. Common 

elements in community monitoring protocols will ensure 

that data can be used to look at gradients of change in 

key parameters across the Arctic. Communities will, in 

addition, develop monitoring elements that represent 

local features or threats that are not shared across the 

network of monitoring sites.

• Develop a common statistical approach to the 

scaling challenges of small populations with multiple 

exposures and multiple confounders. Development 

of a set of common statistical methods for dealing with 

small, sparse data sets, with exposure elements that 

co-vary, would enable observations and epidemiologic 

analysis to proceed in comparable ways in different 

exposure eco-regions.

• Mitigate scaling issues by creating a combined international indigenous population cohort in the Arctic in 

a longitudinal prospective study. Minimizing scaling 

issues by creating multi-cohort prospective population 

studies, with common data points, will permit better 

information on the risks and benefits of traditional and 

modern diets over the life span to be obtained.
48

3. How much time is available to realize the reductions 

needed to stabilize greenhouse gas concentrations in 

the atmosphere?

4. What are the policy actions that can overcome barriers 

to implementation?

5. How can climate mitigation policy be aligned with 

sustainable development policies?

In the context of greenhouse gases, mitigation in the 

Arctic is primarily concerned with the exchange of the 

greenhouse gases carbon dioxide and methane. Northern 

high-latitude terrestrial ecosystems are generally considered 

to have been sinks for carbon dioxide through their storage 

of large quantities of carbon in unglaciated regions prior 

to the last glacial maximum and in regions that have since 

been deglaciated since the last glacial maximum. In general, 

this accumulation is considered to have been promoted by 

cold and wet soils that inhibit decomposition of dead plant 

tissue that enters the soil organic matter pool. In contrast, 

the wetlands of northern high-latitude terrestrial ecosystems are substantial sources of methane to the atmosphere. 

A key question emerges: Will the response of northern 

high-latitude regions to climate change be the release to the 

atmosphere of large volumes of carbon stored over millennia 

as either carbon dioxide or methane? Enhanced releases of 

carbon dioxide and methane could occur through a number 

of responses in Arctic terrestrial and marine systems 

(Figure 4.2; Figure 3.1). The answer to this question is very 

relevant to global mitigation efforts to stabilize greenhouse 

gas concentrations of carbon dioxide and methane, as it 

affects question number 3 above: How much time is available to realize reductions needed to stabilize greenhouse gas 

concentrations in the atmosphere?

Besides the effects of climate change on the emissions of 

carbon dioxide and methane in the Arctic, it is important 

to understand how responses will also influence water 

and energy exchange feedbacks. For example, it is possible 

that the northward migration of the treeline into existing 

tundra regions might promote carbon storage, which would 

be a response that would tend to mitigate climate change. 

However, the lower albedo of forest ecosystems results in 

more absorption of solar energy than tundra, which will 

tend to increase surface warming more than it is mitigated 

by the storage of carbon. Thus, any analysis of the responses 

of carbon dioxide and methane dynamics also needs to 

consider how functional and structural ecosystem changes 

associated with these dynamics will influence the full suite 

of feedback pathways involving the climate system. 

Increased

Terrestrial

Freshwater Inputs

Reduced Sea Ice 

and Glacier Cover

Climate Warming

Thawing Sea Bed

Permafrost

Carbon Dynamics

6, 7

1

+ - 

+ 

Physical Responses Biotic Control

2

3 - 4 + 

5 - 

Release of organic C

Increased C and nutrient export

Increased open water and erosion of organic C

Figure 4.2. Marine carbon responses to warming in the Arctic that influence the climate 

system. Responses of sea ice, glaciers, and sea bed permafrost (on the left) are coupled 

with biotic responses (on the right) through several mechanisms affecting carbon 

dynamics The scales discussed throughout Sections 2 and 3 become relevant to the 

understanding of Arctic climate dynamics and links to the carbon cycle. (Modified from 

McGuire et al., 2006)
49

The Scaling Issue

As indicated earlier, it is not clear if the response of highlatitude Arctic ecosystems will enhance or mitigate climate 

warming. We reiterate that scaling is the key challenge to 

designing integrated studies that link observations and 

process experiments of terrestrial and marine climate feedbacks in a way that the understanding can be transferred 

to models that operate at coarse spatial and/or temporal 

scales (see recommendations in Sections 3.2 and 3.3). 

Assessment of terrestrial carbon dynamics (Figure 3.1) 

requires simultaneous consideration of the scaling 

challenges in the atmosphere (Figure 2.1), permafrost 

and snow (Figure 2.4), and terrestrial and freshwater 

ecosystems (Figure 2.14). Assessment of marine carbon 

dynamics (Figure 4.2) requires simultaneous consideration of the scaling challenges in the atmosphere, glaciers, 

and ice sheets (Figure 2.3), sea ice (Figure 2.10), Arctic 

oceanography (Figure 2.11), and Arctic marine ecosystems (Figure 2.12). These scaling challenges are further 

complicated by the need to consider land-ocean exchange 

of water, carbon, and other biogeochemical constituents 

as well human actions at local, regional, national, and 

global scales. Assessment of the Arctic’s full land-ocean 

carbon cycle requires progress in the development of fully 

coupled Earth system models that effectively deal with the 

scaling issues involving the important processes that affect 

carbon dynamics in the Arctic. Assessment of the role of 

humans requires integrated assessment models that can use 

understanding from fully coupled Earth system models to 

address the five questions presented above, particularly as 

these questions relate to human communities in the Arctic.

What Is Required for Progress?

• Develop Earth system models that effectively deal 

with the scaling issues involving the important 

processes that affect carbon dynamics in the Arctic. 

As indicated in Section 3.3, to better understand 

the sensitivity of the Arctic’s carbon cycle requires an 

integrated approach to research that focuses on incorporating the understanding gained from observational and 

process studies across a spectrum of spatial and temporal 

scales into uncoupled models of the carbon cycle of the 

Arctic. The incorporation of this information requires 

the implementation and testing of scaling approaches, 

which is generally most efficiently accomplished with 

uncoupled models. Such comparisons between scaling 

approaches and the information from large-scale and 

long-term hierarchical observational and process-study 

networks may yield new insights into the scales at which 

processes operate, and identify new processes that need 

to be considered at particular scales. Once this insight is 

achieved in regional applications of uncoupled models, 

it can be more efficiently incorporated into fully coupled 

carbon-climate models of the Earth system, which can 

more confidently explore the consequences of how 

responses of the Arctic’s carbon cycle may influence the 

global climate system. 

• Develop integrated assessment models that can merge 

the understanding from fully coupled Earth system 

models with Arctic-initiated climate change mitigation 

actions at local to global scales. Integrated assessment 

models have been developed to examine the potential of 

human activities to mitigate climate change through a 

variety of possible actions, including energy efficiency, 

use of biofuels, and carbon sequestration activities, and 

to explore the implications of such actions for the five 

questions presented above. Most of these analyses to date 

have considered human activities outside the Arctic as 

being important to carbon mitigation and have explored 

the five questions in relation to human communities 

across more populated and industrialized parts of the 

world. As human communities in the Arctic are likely 

to be the first to experience the effects of climate change, 

it is important to understand the adaptive capacity of 

these communities. As adaptive capacity is pushed to the 

limit, we expect that human communities in the Arctic 

will exert some political pressure that may influence 

mitigation efforts by human societies at local up to global 

scales. It is therefore important to develop the capability 

to consider the role of Arctic-initiated mitigation actions 

in integrated assessment modeling efforts based on more 

fully coupled Earth system models that effectively incorporate Arctic processes.
50

Recommendations

• Assemble and maintain an Arctic infrastructure 

database.

• Research and model structural depreciation from 

climate change.

• Design risk-based scenarios estimating infrastructure 

life-cycle costs.

• Promote research into local flooding and changes 

to sea level.

Science Context

Public infrastructure represents all of the man-made structures necessary to keep the Arctic functioning, including 

roads, bridges, airports, harbors, schools, military bases, 

post offices, fire stations, sanitation systems, and the power 

grid. Privately owned infrastructure includes residential 

homes and all structures associated with both small- and 

large-scale business activity. 

All types of infrastructure are vitally important to people 

living in the Arctic and beyond. For example, airports 

allow critical goods and services (e.g., emergency medical 

services) to be delivered to rural areas not serviced by roads 

(see Figure 4.3). Private housing provides the basic human 

necessity of shelter, and a complex network of pipelines 

deliver in-demand commodities like oil and gas to world 

markets. Without infrastructure, communities would 

cease to exist in the Arctic and important internationally traded commodities could not be exported to satisfy 

market demand. Therefore, it is important to protect critical 

infrastructure in the Arctic from the risks associated with 

rapidly changing environmental conditions. 

The most immediate climate-related impacts to Arctic 

infrastructure include permafrost instability, additional 

wildfire activity, and increased impacts from flooding and 

erosion. A preliminary analysis of this issue within Alaska 

found that climate change could add $3.6 to $6.1 billion—

representing +10% to + 20% above normal wear and 

tear—to future costs for public infrastructure from now 

to 2030. These estimates took into account different 

possible levels of climate change and assume government 

agencies partially offset the level of risk by strategically 

adapting infrastructure to changing conditions. However, 

subsequent analyses by some of the principal researchers 

involved in this study found that a number of factors may 

have contributed to a systematic underestimate of both the 

dollar amount of infrastructure at risk and the statistical 

uncertainty of their original results. Additional risk to 

Alaska (and Arctic) private infrastructure is also evident, 

but there has been no effort to date to systematically quantify this vulnerability. 

Assessing climate-related impacts to Arctic 

infrastructure requires collaboration between 

the empirical elements of a number of disciplines, including, but not limited to: economics, 

engineering, statistics, climatology, hydrology, 

and ecology. The issue thus entrains virtually 

all of the scaling issues treated in Section 2. 

Field research by civil engineers and experts in 

permafrost dynamics is needed to understand 

how the rate of structural depreciation changes 

with and without the effect of changing environmental conditions. Climatologists are needed 

to provide the latest information on the region’s 

projected climate along with measures of the 

statistical confidence of the models. In some 

places, hydrologists are needed to understand 

4.4. Public and private infrastructure vulnerabilities

Figure 4.3. Transportation Infrastructure in Alaska. (Larsen et al., 2008) 
51

There is also an important time component that greatly 

influences how infrastructure responds to changing environmental conditions. The fundamental issues involve 

shifts in seasonality, freeze-thaw dynamics, weather 

extremes, and events like wildfires. Studies to estimate 

structural depreciation rates from wildfire activity, 

increased erosion, and thawing permafrost in the North 

remain in their infancy. Figure 4.4 depicts hypothetical 

responses to one aspect of climate change and captures 

what is meant by financially quantifying structural damage. 

The rate of climate change over many decades influences 

the amount of funding needed today to adapt infrastructure 

the dynamics of coastal and river erosion. Urban and rural 

planners are needed to take an inventory of the region’s 

infrastructure by location, site characteristics, and current 

replacement (or maintenance) cost estimates. Economists, 

cost engineers, and actuaries could use the information 

supplied by the other disciplines to estimate the future 

degree of financial risk for all types of infrastructure, 

applying a type of economic analysis known as integrated 

assessment modeling (IAM). Environmental and civil engineers, materials manufacturers, and community planners

could then prioritize Arctic adaptation projects based on 

the amount of financial (and environmental) risk to each 

community. Coastal and terrestrial ecologists will 

be needed to assess both changes to wildlife habitats and subsistence food chains. 

The Scaling Issue

Both spatial and temporal dimensions must be 

considered when assessing the amount of Arctic 

public and private infrastructure at risk from 

rapid climate change. 

Spatially, the proximity of infrastructure 

to climate-related effects like wildfire activity 

(see Section 2.3.2), accelerated shoreline erosion, 

and thawing permafrost highlight the challenge 

of having to simultaneously consider large-scale 

forcings in the climate domain, while at the 

same time analyzing depreciation and damage 

at highly local scales. It is clear that the more 

environmental factors affect depreciation of 

the structure, the greater the amount of financial and physical risk from premature failure. 

Effective long-run planning to reduce vulnerability should be based on location-specific and 

representative engineering case studies across 

the Arctic. For example, a preliminary infrastructure damage assessment estimated costs at 

the Alaska state level, but the authors noted that 

their overly generalized quantitative methods to 

depreciate infrastructure were not realistic and 

that modeling structural depreciation using sitespecific information would greatly improve the 

accuracy of this integrated assessment model. 

Structural 

Component 

Structure 

Community 

State 

Country 

Day Week Month Season Year Decade 

Physical damage 

Structural failure 

Lost goods and essential services

Lost tax revenue; increased 

emergency funding 

Lost tax revenue; 

increased fuel and 

national security costs

SPATIAL SCALE 

TEMPORAL SCALE

$3

$ $* 4

27°F 0°C/32°F

Annual Maintenance Cost ($) or 

Depreciation Rate (∆ Lifespan)

Annual Average Temperature

$1

$2

Figure 4.4. Hypothetical damage functions for infrastructure on thawing 

permafrost. The range of responses arise from a complex interplay of biogeophysical factors—and their rates of change—together with regional socioeconomics, and engineering, playing out at different scales. Broad-scale climate 

shifts must be understood in terms of their local-scale manifestations, before 

infrastructure vulnerabilities can be fully analyzed.

Figure 4.5. Schematic representation of some overlapping temporal and spatial 

scales involving infrastructure at risk in the Arctic. 
52

to future environmental conditions. For example, a bridge 

that has a useful lifespan of 50–100 years may have a fundamentally different degree of climate-related risk than a road 

with an average lifespan of 15 years that will be replaced 

more frequently regardless of the changing climate. Again, 

estimating the financial and physical vulnerabilities of 

infrastructure builds on research from many different fields 

of study so that many of the temporal and spatial scale 

perspectives from the other disciplines, including uncertainties, are compounded at this level of analysis. 

What Is Required for Progress?

• Assemble and maintain an Arctic infrastructure database. There is incomplete information on the location, 

age, and current value of Arctic infrastructure. The 

advent of Internet-based mapping tools and data sets 

should facilitate the much-needed merger of high-resolution socio-economic and biogeophysical data sets.

• Research and model structural depreciation from 

climate change. There is a general shortfall in applied 

engineering research that quantifies how specific types of 

infrastructure built near at-risk locations will physically 

(and financially) respond to significant future changes in 

the Arctic climate. 

• Design risk-based scenarios estimating infrastructure 

life-cycle costs. Plausible scenarios are needed that can 

explain the full range of future infrastructure depreciation outcomes that may occur as the Arctic climate 

continues to rapidly change. These scenarios must 

incorporate a full spectrum of biogeophysical, economic, 

and engineering indicators, including future discount 

rates, structure replacement costs, depreciation rates, 

community-level adaptive capacity, and IPCC-sanctioned 

climate model emissions scenarios. The idea is to 

acknowledge and communicate the uncertainty inherent 

in modeling the futures, while encouraging decisionmakers to carry out strategic planning activities now to 

offset any and all potential risk to community infrastructure. It is clear that information is already needed in some 

Arctic communities to assess the compounded likelihood 

of uncertainty coming from many disciplines of science. 

• Promote research into local flooding and changes to 

sea level. There has been little or no applied research that 

details which Arctic communities may be more at risk 

to increased flooding and which ones will not. Localized 

projections for changes in sea level and river runoff vary 

widely based on community elevation and the effect will 

also depend on conditions such as, in Alaska, tectonic 

upheaval and local atmospheric pressure. 

4.5. Subsistence harvest and commercial fisheries

Recommendations

• Support habitat fragmentation studies to better understand the linkages between Arctic system change 

across spatial and temporal scales and habitat integrity 

and availability. 

•	 Develop new inventory techniques that allow better 

tracking and monitoring of individual species across 

the spatial domain of ecosystems, including indigenous and scientific knowledge bases.

•	 Conduct research on the best means and methods 

for managing species that may be utilized locally but 

are distributed or migrate across different regions—a 

mobile scaling issue.

Science Context

A changing climate may produce a number of significant 

changes to habitat integrity and migration patterns of both 

terrestrial and marine subsistence, recreational, and harvest 

species. On a local scale, specific habitats may be modified 

beyond a threshold such that a species may no longer be 

found there. The necessary conditions that support basic 

food and shelter requirements may be lost, or other species 

may also find it suitable and force the existing species out. 

At a larger scale, some migratory species may no longer 

be able to access suitable habitat because conditions along 

the travel route have changed too radically. Either case 

may present problems to both subsistence and commercial 
53

along the coast and up major rivers to fish camps to catch 

and dry fish during the summer months; in the winter, 

some inhabitants will use snowmobiles to travel across the 

tundra for caribou or to trap small fur animals. In many 

cases, however, it is the resource that is more mobile, traveling across great distances to a traditional location to build 

up energy reserves for the coming winter, to reproduce, or 

to molt (as in geese). 

harvesters because of a dwindling number or absence of 

desired species, and the amount of time and effort it takes 

to find and return with those species to a home base.

Two primary issues that deal with subsistence and 

commercial harvest include how best to inventory the 

species involved, and then deciding best management 

practices that would permit continued use of those species. 

Also at hand is the issue of how much energy one is willing 

to expend to harvest those species, 

given how often in space and time those 

species are available. Finding the proper 

balance between these issues requires 

input and expertise from lay and scientific audiences alike. Local inhabitants 

can provide much insight and knowledge about past and current conditions 

(habitat) and populations (numbers, 

locations, species). Individuals with more 

structured scientific background (in 

social, biological, and political sciences) 

can provide scientific and managerial 

knowledge that would lead to informed 

decisions on harvest times, locations, 

and bag limits.

The Scaling Issue

Management of subsistence and 

commercial harvest of living resources 

engages—and in many cases transcends—several scales (Figure 4.7) and 

scale-related challenges (Figure 4.8). The 

vast majority of humans living in arctic 

and boreal regions depend, at least in 

part, on a subsistence life style, whose 

primary and secondary food sources 

consist of fish (e.g., salmon, pike, white 

fish, char/trout), birds (e.g., ducks, geese, 

swan), and mammals (e.g., walrus, 

moose, caribou). Although most resource 

use occurs at a local scale (e.g., 1–10 km), 

some consumers travel much farther 

(> 20 km) depending on the resource and 

the season. For example, on the north 

slope of Alaska, local villagers will travel 

Figure 4.6. Inter-dimensional relationships of space, time, and location for 

subsistence and commercial use of wildlife species.

Figure 4.7. Challenges in scale occur at all levels, although each has an appropriate means to address issues using different tools. 

CLIMATE: Local to regional

SPECIES: Individual species to groups of 

same or similar species

INVENTORY/MANAGEMENT: Physical counts

(ground, aerial), remote sensing (aerial

photo, satellite)

CLIMATE: Local to regional

SPECIES: Individual large species (e.g., whales, caribou) to 

groups of species (e.g., flocks of birds)

INVENTORY/MANAGEMENT: Physical counts (ground, aerial), 

remote sensing (aerial photo, satellite), summary data

CLIMATE: Regional to continental to global

SPECIES: Species summaries (also for some large individuals such as whales)

INVENTORY/MANAGEMENT: Summary data from finer scales

Local

(site)

Regional 

(stand–landscape)

Continental

Single

Species

Single Events/

 Influences

Multiple Events/

Influences

Day/Month Season Multiple Seasons
54

Commercial harvest activities occur much like subsistence activities, albeit at much larger scales (space, time, 

duration) and normally involve one species (e.g., salmon). 

Commercial resource consumers (in the sense of harvest for 

resale) normally travel great distances to harvest a resource, 

even if that resource is encountered locally. For example, 

salmon fishing boats are normally moored at sea ports, 

but travel to distant bays and river outlets for the catch. 

For terrestrial mammal species, reindeer are currently the 

only commercially harvested species; in this case, more 

husbandry types of management and catch occur, with the 

animals being “moved” across large landscapes as food or 

habitat conditions change. 

Cause and effect of different forces on subsistence 

and commercial harvest stocks and their inventory and 

management occurs at a number of different spatial and 

temporal scales, but not necessarily along a continuum, nor 

are they equal in any dimension or activity (Figure 4.7). 

This is due in part to the habitat that any individual species 

occupies and whether that species is found singularly or in 

a group. The manner by which a type of animal is harvested 

depends on the species, such as an individual caribou, 

or large numbers of fish. In addition, harvests are often 

targeted at explicit areas, such as a specific bay (for salmon 

or herring) or in the case of subsistence harvests, traditional 

hunting or fishing areas. 

There are a number of issues that constantly need to be 

addressed at all spatial and temporal scales that influence 

the use, inventory, and ultimately the management of a 

given species. These issues may be different for each species 

or group of species. For some species, local influences 

directly control the numbers available for harvest and may 

have the greatest social, economic, and political impact, 

especially if the local populace is highly dependent on them 

for food. Inventory of these species, however, is often the 

easiest and least costly because they are normally harvested 

during a specific time period (e.g., when they are present) 

and are within a short distance from the point of harvest. 

Most Arctic species, however, are migratory and move 

between locations that have totally different management 

philosophies and modes of political governance. Inventory 

and management of migratory species can sometimes be 

easier (as in aerial surveys of herds) or more difficult (as in 

solitary large mammals). Because of the remote and migratory nature of these animals, overall management options 

may be less specific. On continental scales, although 

species use is conducted on a local scale (e.g., a bay for 

fish or specific habitat for moose), the consequences for 

management may cross numerous national and/or international boundaries, necessitating collegial and treaty 

endorsement for the sharing of population information 

and regulatory actions.

What Is Required for Progress?

• Support habitat fragmentation studies to better understand the linkages between Arctic system change 

across spatial and temporal scales and habitat integrity 

and availability. Such understanding can be fostered 

by research that increases understanding of minimum 

ecosystem function to define habitat (e.g., plant species, 

physical conditions, and short- and long-term climatic 

parameters) that allow existing subsistence or commercial species to maintain a steady state. Over the terrestrial 

domain, subtle local-scale changes may occur through 

time and space that could be linked to physics, biology, 

and chemistry (Section 2.2.3 and 2.3.2; Appendix 1), 

thus requiring an understanding of large- and smallscale dynamics. 

•	 Develop new inventory techniques that allow better 

tracking and monitoring of individual species across 

the spatial domain of ecosystems, including indigenous and scientific knowledge bases. Research needs 

to be conducted on incorporating traditional environmental knowledge (TEK) about past species occurrence 

(e.g., numbers and spatial location), timing of arrival and 

departure, use of specific habitats by those species, and 

human consumptive use (e.g., harvests).

•	 Conduct research on the best means and methods 

for managing species that may be utilized locally but 

are distributed or migrate across different regions—a 

mobile scaling issue. In addition to its direct physical 

and chemical consequences, a changing climate also 

will alter vegetation patterns and hence wildlife habitat 

in ways that can introduce new species, force existing 

species to go elsewhere, or change the population of 

current species. The manner in which these potential 

changes will be distributed over space and time scales 

(e.g., contiguous or fragmented, disrupted regionally or 

only locally) is currently unknown.
55

in the future. Oil developments on Alaska’s North Slope, 

as well as those in Russia, Norway, and Canada, support 

fully regional-scale economies, and feed the world 

demand for energy. 

Before oil, however, minerals and coal extracted from 

the Arctic found their way to ports around the world. The 

Red Dog Mine, for example, is one of the world’s largest 

producers of zinc concentrate (Figure 4.8). Red Dog 

operations contribute to Alaska’s economy, creating jobs, 

investment, and revenue with significant economic and 

community benefits to Alaska’s Native population. The 

total Red Dog operations payroll in 2007 of $48.9 million, 

for example, provided 475 full-time jobs for the local and 

regional economy. In a remote area of northwestern Alaska, 

this economic boost allows residents to remain in their 

region while maintaining a job with good pay.

The potential for summer sea travel in an Arctic with a 

reduced sea ice environment has spurned a “gold rush” for 

the oil, gas, methane hydrates, coal, and minerals that lie 

in the coastal shelf and on land. In this context, there are 

important challenges to resource exploration and development, many operating over contrasting scales. Although 

the intent might be to secure and transport non-renewable 

resources as effectively as possible from the site of extraction to delivery point, the lessons of the Exxon Valdez oil 

spill or current BP Gulf Horizon disaster teach us that these 

resources can be highly mobile and increase the scale of 

interest through one unfortunate accident. 

Due to its harsh, remote, and often inaccessible nature, 

some of the most basic information needed for the exploration, exploitation, and recovery of non-renewables are 

inadequate and outdated. Baseline maps of the State of 

Alaska are out of date and for some areas have errors in 

the range of kilometers. In addition, significant data sets 

already exist that relate to resources in the Arctic. This 

information would be helpful to future development of the 

same resource for which it was originally collected or for 

a different one. For example, the State of Alaska is in the 

midst of considering gas pipeline corridors. A gas pipeline 

was seriously considered at least twice in the past. A consortium of companies collected data along potential corridors 

(across state and federal land). However, no provision was 

4.6. Non-renewable resource extraction 

Recommendations

• Support the collection, integration, and sharing 

of multiscale data sets describing the inventories of current and potential stocks of various 

non-renewable resources.

• Integrate with resource inventory data the broad 

spectrum of environmental information that collectively represent not only the proven or potential 

resources, but also the spatial and temporal challenges of resource extraction in a complex and 

changing environment.

• The challenge of successfully extracting non-renewable 

resources economically and securing environmental 

protection provides an excellent opportunity to 

forward interdisciplinary and multiscale research.

Science Context

Non-renewable resources are generally thought of as those 

that are mined with a focus on fossil fuels and minerals. 

Non-renewable resources could, however, also refer to the 

non-sustainable harvest of what might otherwise be considered a renewable resource, such as freshwater, forests, and 

fisheries. For the purposes of this section, non-renewable 

resources refer directly to the extraction of minerals 

and fossil fuels.

Of nearly all human activities in the modern Arctic, 

non-renewable resource extraction has one of the longest 

histories, plays an extremely important role in the economies of Arctic people, and is likely to grow in importance 

Figure 4.8. Red Dog Mine. (Photo credit: J. Farrell)
56

ever made that these data would eventually be made public. 

As such, they are not available and the information is 

destined to be collected once again.

Major limits on exploration and development of nonrenewable resources are related to: (1) uncertainty associated with legal access to develop the resource, (2) uncertainty associated with the economic operating regime, 

including incentives and long-term tax structures, and 

(3) uncertainty associated with the engineering of extraction and transportation of the resource. 

The easiest of the grand challenges to solve for a resource 

development company is uncertainty associated with the 

identification, extraction, and transportation of the resource 

itself. Although addressing technical issues will require a 

great deal of investment and data collection, this is fundamentally an issue of technical feasibility. 

Certainly, some issues related to development 

of a non-renewable resource are well known, 

while climate uncertainty adds a new twist. 

Except for future climate scenarios, where 

data do not exist, the technology exists to 

collect such data, at least to characterize 

contemporary climate conditions.

A slightly more difficult problem for 

resource extraction is understanding the 

future economic operating regime. Although 

this problem cannot easily be solved by 

collecting data alone, models that require data 

input are valuable. The most difficult major 

challenge is forecasting legal access to the resource. If a 

resource is identified and quantified, it may not be developed 

for reasons of legal access. This would most often result from 

court challenges related to potential environmental disturbance. For example, future oil and gas development in the 

Cook Inlet area of Alaska will be predicated on the listing of 

the Cook Inlet beluga whale as an endangered subspecies. 

Likewise, development on Alaska’s North Slope is predicated 

on the potential listing of the polar bear. At this point, there 

is simply not the environmental data for the resource industries to be able to see where challenges will arise, where they 

are refutable, and how long challenges may last. Ecological 

studies are often needed before a project is started to gain a 

better understanding of the potential environmental issues. 

Environmental studies are then needed throughout the 

project to assess the actual and potential impacts. Often, 

environmental studies are needed long after a project to 

ensure compliance with the original operating permits.

The Scaling Issue

The distribution of non-renewable natural assets encompasses a wide range of scales and domains (Figure 4.9).

It is therefore critical that scaling issues related to the 

exploration for, extraction and transport of, and trading in 

minerals and fossil fuels be addressed. Companies around 

the world are investing money to extract minerals and 

fossil fuels from the Arctic. Private and public funding is 

dedicated to understanding the spatial distribution of the 

resource, as well as the spatial and temporal features of the 

environment in which resource industries operate. 

SPATIAL SCALE (k

m2)

BROAD RELEVANCY OF DATA

Local (102) System (106) Regional (104)

Local P Regional an-Arctic

Subsurface geophysics

Ice-structure interaction

Ecological studies

Properties of materials

in the cold

Core analysis of

mineral abundance

Near-surface soil data

Climatological data

Figure 4.9. The distribution of non-renewable assets encompasses a 

wide range of scales and domains.

Figure 4.10. North Slope stratigraphy. (Courtesy of Dave Houseknecht, 

U.S. Geological Survey)
57

For most resource extraction, the exploratory process 

includes a broad spectrum of spatial scales, exemplified by 

the mapping of the subsurface that extends kilometers deep 

and tens-to-hundreds-to thousands of square kilometers 

in areal extent (Figures 4.9 and 4.10). At the opposite 

end of the spectrum, proving of the resource, microscopic 

gold veins in a host matrix is the spatial scale at which the 

highest scrutiny occurs.

From the operations standpoint, offshore oil structures 

and transport must be able to withstand sea ice. This 

requires an understanding of the regional movement 

of sea ice as well as its thickness, strength, and age (see 

Sections 2.2.4 and 4.7). Slightly more refined are icerelated processes such as gouging of the seafloor, and strudel 

scour that would reduce a pipeline’s integrity. At a smaller 

scale, the forces of ice on structures, loading criteria, and 

even the microscopic contaminants in steel that make it 

brittle in the cold must be understood and addressed in the 

platform, ship, or pipeline design (Figure 4.11).

Resource extraction operators understand the value 

of the environment they operate in from an ecological 

sustainability standpoint. Oil and mining companies spend 

millions of dollars understanding the local and regional 

ecology and biodiversity, with the aim of ensuring that their 

permits to operate are fulfilled and operations do not cause 

harm to the environment. In this context, the many scales 

relevant to climate and weather (to provide for operations), 

ecosystems or wildlife on land and in the ocean (to ensure 

legal protections of the environment), all become relevant 

to the single activity of non-renewable resource extraction. 

These issues are reviewed throughout Section 2.

What Is Required for Progress?

• Support the collection, integration, and sharing of 

multiscale data sets describing the inventories of 

current and potential stocks of various non-renewable 

resources. Challenges associated with the Arctic being 

a remote, harsh, and highly seasonal system in many 

instances restricts access to its exploration and resource 

development. As a region, it is relatively data poor. In 

this context, protocols for sharing proprietary and public 

data sets become ever more important. Uniting fragmentary databases sets the stage for new research that 

can secure insights from the conjunction of otherwise 

disparate data resources. A policy that promotes more 

transparent access to otherwise proprietary data sets, 

particularly when describing conditions on state or 

federal lands, is in the public interest, at a minimum to 

avoid duplication of effort.

• Integrate with resource inventory data the broad 

spectrum of environmental information that collectively represent not only the proven or potential 

resources, but also the spatial and temporal challenges 

of resource extraction in a complex and changing 

environment. Resource exploration and development 

in the Arctic poses important challenges that are absent 

from more temperate settings. Strong environmental 

gradients, including seasonal phase changes from ice 

to water that limit land and marine transportation, 

unique ecosystem processes, and climate change argue 

for the integration of up-to-date weather, climate, and 

other environmental data sets (as for the distribution of 

migrating species), which currently do not exist. 

• The challenge of successfully extracting non-renewable 

resources economically and securing environmental 

protection provides an excellent opportunity to 

forward interdisciplinary and multiscale research.

Integrating data sets is one important objective, but falls 

short new integrative research. Federal and state permitting requirements that include environmental protection 

provisions for non-renewable resource activities represent 

a custom-made interdisciplinary and multiscale research 

challenge. How to best manage and protect other natural 

assets—at all scales, from borehole or mine, to watershed, 

to coastal shelf domains—requires knowledge across 

many disciplines. It also represents an opportunity to 

forward private-public-academic partnerships. 

Figure 4.11. Alpine Oil Facility (ConocoPhillips Alaska) in Arctic 

Alaska. (Photo credit: D. White)
58

4.7. Ice navigation in a changing Arctic

Recommendations

• Support basic sea ice research to increase the fidelity 

of estimates of the presence and character of Arctic 

marine ice, on temporal and spatial scales necessary 

for shorter-term tactical and strategic operations as 

well as longer-term operational planning.

• Promote a major structural transformation in sea ice 

operations through synthesis and integration of the 

broad suite of observational and modeling capabilities, 

supporting multiscale and multisensor data assimilation and simulation, from shipboard up to pan-Artic 

satellite surveillance domains.

• Improve training on advanced ice information systems 

that enhance human and environmental safety and 

promote more-efficient ship operations in the Arctic. 

• Renew commitments to maintain U.S. polar-orbiting 

satellites and to usher in a new generation of radar 

capabilities that are in the national interest with 

respect to the Arctic.

Science Context

One of the emblematic changes associated with global 

warming is the loss of seasonal sea ice across the Arctic 

ocean, and associated changes in its basic character—its 

longevity, shifting spatial distributions, abundance, thickness, reflectivity—over space and time. The complexity 

of characterizing contemporary ocean dynamics and the 

close correspondence of sea ice, ocean dynamics, and the 

atmosphere make this a particularly important challenge 

to Arctic system science (see Section 2.2.4). Aside from 

the important long-term role of Arctic sea ice in the Earth 

system, the remarkable changes observed even today in 

Arctic sea ice, especially the historic decreases in extent 

and thickness, have important implications for marine 

access and the duration of navigation seasons throughout 

the Arctic Ocean. 

One of the many challenges facing ice-capable ships is 

their probable encounter with a more dynamic and mobile 

Arctic sea ice cover (Figure 4.12). Modeling and scenario 

assessments conclude that a changing Arctic climate is 

likely to lead to more complicated sea ice topographies, for 

example, elongated ridges of sea ice that can render ship 

transits much more difficult and slow, and at times make 

passage impossible. Effective monitoring of short-term 

sea ice changes (within hours) and longer-term changes 

in the ice cover (days to weeks) is essential for the safety, 

reliability, and effectiveness of all future Arctic marine 

transportation systems. The changing nature of Arctic 

environmental conditions will require more timely and 

effective observations through a broad range of spatial and 

temporal scales. To this end, remotely sensed imagery of 

Arctic sea ice from satellites, aircraft, and ships will play an 

increasingly critical role in the safe and efficient movement 

of ships through ice-covered waters. An understanding of 

the range and mix of spatial-temporal scales for sea ice can 

be a valuable asset for mariners, ice center forecasters, ship 

designers, transportation planners, and others.

The Scaling Issue

Ship navigation through ice generally requires three basic 

levels of information aboard ship, which we can consider to 

represent issues essentially of scale. We first have tactical

information required for immediate navigation, such as 

course alteration and maneuvering. Next, strategic information is required for daily track planning and course 

decision-making. A third information class linked to scale 

is operational planning, which requires knowledge on 

both short and long time scales. This type information is 

Figure 4.12. Image from a helicopter of icebreakers (125-m length) 

operating in the central Arctic Ocean through 2-m-thick, multiyear 

sea ice. Note the linear features (leads), melt ponds on the ice surface, 

and pressure ridge in the lower left extending to the top of the image. 

(Photo credit: L.W. Brigham, 1994)
59

typically developed ashore in ice or weather centers that 

provide Arctic mariners with ice forecasts and generate 

predictions for ice conditions (days, weeks, and seasons 

ahead of transits), weather conditions, transit difficulty, and 

even optimized routes. There can be considerable overlap 

across the spatial scales, which reflects concepts developed 

further in Section 2.2.4. Using this nomenclature, the key 

sea ice and climatological parameters that are critical to 

ice navigation include:

• Topography (ridges, rafts, leads)—tactical, strategic

• Thickness—tactical, strategic, operational planning

• Concentration—tactical, strategic

• Floe size and small-scale features—tactical

• Snow cover—tactical

• Strength (ice age, first and multiyear ice)—tactical, 

strategic, operational planning

• Ice drift—strategic, operational planning

• Regional climatology (especially winds)— 

operational planning

Each of these variables operates over a range of spatial 

and temporal scales. The challenge for the ice navigators 

and national ice centers is to develop an integrated strategy 

of observations, across multiple spatial and temporal scales, 

with ice forecasts based on sea ice models.

Many national ice and weather centers provide largescale and regional-scale sea ice charts and forecasting products for the Arctic Ocean. Most of the products are based 

on (low-resolution) satellite-derived images of the Arctic as 

well as information from sea ice model simulations. Passive 

microwave, infrared, and optical (visible) satellite sensors 

provide a host of imagery used to produce sea ice charts 

that are of strategic and operational planning use. However, 

ice thickness information and high-resolution information on ridging and ice floe size are not generally available. 

Thus, the charts are of very limited tactical use for ships 

navigating through ice. Discriminating between first year 

and multiyear sea ice has also been challenging using these 

sensors (although passive microwave data have been used to 

identify winter multiyear ice). Table 4.1 includes selected 

examples of observing systems that can provide sea ice 

information across broad spatial and temporal scales.

The expanded use of imagery from space-borne synthetic 

aperture radar (SAR) sensors will provide robust sea 

ice information of tactical use for ice navigation in the 

twenty-first century. The latest SAR sensors—all active 

microwave (radar) sensors that produce their own energy 

that is reflected back to the sensor—operate in a range of 

frequencies and swath widths, providing information at 

several spatial resolutions. The SAR sensors operate in all 

weather and light conditions and can provide images with 

information on ice floes, deformed ice, ridges, thin ice, ice 

types, and times for ice freezeup and melt onset. One of 

the key challenges of using SAR information has been the 

development of effective and efficient methods or schemes 

of automatic sea ice classification. Also today, passive 

microwave, infrared, and visible images from satellites 

are increasingly used to assist in the interpretation of ice 

features in SAR imagery. 

There is an ongoing fusion of data in the sea ice interpretation process, which will continue in the years ahead. 

Greater and more effective data fusion will lead to new 

sea ice products and ice charts that could include route 

optimization and ship transit difficulty information. One 

of the key challenges will be the integration of enhanced 

information on sea ice thickness potentially derived from 

improved satellite and airborne sensors. A key issue will 

continue to be the speed at which this enhanced information can be transferred from the ice centers to the 

ships navigating through ice.

Figure 4.13. Passive microwave satellite image received directly 

aboard the U.S. Coast Guard icebreaker Polar Sea in near-real time 

on August 17, 1994. An algorithm converts the microwave brightness 

temperatures to sea ice concentrations shown in the false color scale. 

Note the swath (width) of the satellite pass, the ship’s track in yellow, 

and Alaska in the lower right. The resolution of the pixels in the image 

is 12.5 kilometers, useful for strategic, but not tactical, navigation. 

(Image from the U.S. Defense Meteorological Satellite Program)
60

What Is Required for Progress?

• Support basic sea ice research to increase the fidelity 

of estimates of the presence and character of Arctic 

marine ice, on temporal and spatial scales necessary 

for shorter-term tactical and strategic operations as 

well as longer-term operational planning. Ice charts 

are the mainstay of Arctic navigation and the need for 

enhanced daily and weekly Arctic sea ice charts that 

include transit difficulty and route optimization information on a regional scale is of obvious importance in 

a rapidly changing Arctic sea ice domain. Verification 

of general sea ice charting accuracy is needed through 

closely coincident in situ (surface) measurements and 

satellite sensor observations. Indications of in situ observations on satellite-derived charts representing smallscale features not observed by satellites should be a part 

of this data-merging effort.

• Promote a major structural transformation in sea ice 

operations through synthesis and integration of the 

broad suite of observational and modeling capabilities, 

supporting multiscale and multisensor data assimilation and simulation, from shipboard up to pan-Artic 

satellite surveillance domains. Continued research into 

the derivation of ice thickness estimates by integrating 

multiple satellite data sources (data fusion) and assimilating in situ ice observations is needed. Enhancing the 

use of ship-based marine radar for real-time, tactical ice 

information (at the local scale) by requiring add-on radar 

processing and high-resolution display systems is also 

recommended. Increased repeat satellite coverage and 

filling spatial-temporal gaps using data fusion is needed 

to provide higher-frequency ice information. Continued 

research into airborne electromagnetic measurements 

of sea ice thickness for regional-scale information is an 

essential part of this strategy.

• Improve training on advanced ice information systems 

that enhance human and environmental safety and 

promote more-efficient ship operations in the Arctic. 

This recommendation includes the expanded use of 

enhanced interpretation techniques for multiple-scale 

sea ice data as well as the use of expert systems and other 

artificial intelligence technologies to improve identification of ice features.

• Renew commitments to maintain U.S. polar-orbiting 

satellites and to usher in a new generation of radar 

capabilities that are in the national interest with 

respect to the Arctic. With the disappearance of 

seasonal sea ice comes intensifying pressures to open the 

Arctic to resource extraction and navigation. Accurate 

detection of sea ice is part of the larger challenge of environmental surveillance to ensure territorial integrity and 

free shipping lanes. In particular, the essential requirement for high-resolution SAR information for navigation 

in ice-dominated regions remains clear. Absent commitments by the United States to invest in such systems, 

the nation will remain dependent on foreign sources to 

provide this critical information.

Table 4.1. Selected Observing Systems for Ice Navigation and Their Scale Capabilities

Observing System Coverage/Image Resolution Scale/Delivery Issues

Satellite systems: Low resolution

Passive microwave, scatterometers

Large scale: 10–50 km

Sea ice edge, concentration, ice drift

Large-scale information

Daily delivery, real-time delivery aboard 

ship, no thickness data

Satellite systems: High resolution

Synthetic aperture radar (SAR), 

optical and infrared (IR)

Regional to local scale: 

50 km down to 10 m

Ice floes, surface roughness, ridges

Small-scale information

Near real-time delivery from ice and 

weather centers, ice thickness information limited

Airborne systems: High resolution

SAR, LIDAR, electromagnetic induction

Higher resolution

Ice types, ice floes, ice roughness, ridges 

(height, free-board, thickness from LIDAR)

Small-scale information along flight 

paths

Near real-time delivery

On-ice measurement systems

Drill holes, buoys, electromagnetic 

induction

Local coverage near ship and regional 

coverage (buoys)

Ice thickness, ice drift, ice temperature

Small-scale information

Real-time (e.g., thickness data) from 

drilling, real-time from buoy information
61

4.8. Oil spill preparedness, response, and restoration

Recommendations

•	 Improve prediction of winds and currents, from 

small-scale coastal features (e.g., Langmuir cells in the 

surface mixed layer) and terrain-steering winds in areas 

of rugged topography to large-scale coastal processes, 

which can move over long distances. 

•	 Provide better detection of oil in the Arctic environment, with solutions for both summer (light) and 

winter (dark) conditions.

•	 Conduct sensitivity tests on Arctic food webs that 

include experiments evaluating lethal and sublethal 

effects of exposure to dispersed oil, including bioaccumulation and behavioral observations.

•	 Develop more refined and verified algorithms 

for oil fate and transport from physical processes 

through biological systems at the individual and 

population levels.

Science Context

Environmentally conscious oil and gas development has 

three temporal domains: (1) planning for development 

and potential accidents, (2) responding to spills, and 

(3) recovery and restoration of the environment after a spill, 

including natural resource damage assessment (NRDA). 

Planning must consider development in the context of the 

lifetimes of structural investments and climate-scale variations and other environmental conditions. In the Arctic, 

oil hazard research should focus on detection of spilled oil, 

spill trajectory predictive capability, response techniques, 

and oil recovery methods. Success in oil spill response in ice 

is contingent on finding and re-finding the oil. Detection of 

oil in, on, and under ice in the darkness of the Arctic winter 

is arguably a major operational challenge. International 

cooperation is required as international borders mean little 

as oil spills move with the winds and currents.

Climate change creates a moving target for planning 

and environmental development strategies. Monitoring 

rapid changes in the Arctic is key to environmentally sound 

oil and gas development. As the Arctic changes from a 

benthic (bottom) to a more pelagic (open water) dominated 

ecosystem under climate change, we need to continually 

refresh our understanding of changing ecosystems to 

predict the effects and outcomes of spills.

Understanding spills requires detailed interdisciplinary 

knowledge. During oil spill response, prediction is the 

focus; after the spill, hindcasting the event is required to 

identify the domain (i.e., scale) of impact. Predicting the 

physical conditions involves integration of atmospheric, 

cryospheric, and oceanographic information. Layering and 

integrating this information with oil chemistry, toxicity, 

and biological resources adds enormous complexity in 

appropriately informing decision-makers, and encompasses 

the scale challenges discussed throughout Section 2. Some 

of the complexity results from having both oceanic and 

terrestrial dimensions at work—oil spills directly into open 

waters (from drilling rigs and ships), onto land (oil production and transport facilities), or sometimes a combination 

of the two (ocean spills making landfall). Thus, the domains 

of impact are unique and, in some cases, are highly 

mobile, with the relevant scales expanding and contracting 

rapidly and extensively.

Data collection starts with pre-assessment information 

(background levels of contaminants) to differentiate effects 

related to oil and gas development from other anthropogenic effects in the Arctic. High-latitude surveys (over all 

seasons and covering large spatial extent) provide the most 

comprehensive information, but are difficult to finance and 

stage. Pre-assessment studies of baseline biological population levels provide critical input data during a spill.

Figure 4.14. Prevention and Emergency Response Program (PERP) 

response to Kuparuk 2U Pad crude oil spill, January 5–6, 2008. Photo 

is of cleanup operations on January 6, 2008. (Photo credit: J. Ebel, 

Alaska Department of Environmental Conservation)
62

NRDA involves quantifying injuries using observations 

of the physical environment, oil distribution, and impacts 

on biological resources. Modeling is used to fill information gaps, for example, based on the number of oiled 

birds, fish, and mammals collected, which is less than the 

actual number killed. Biological effects modeling requires 

(1) quantification of oil fates (processes like weathering, 

emulsification, evaporation, dissolution) under Arctic 

environmental conditions; (2) evaluation of exposure and 

dose to arctic wildlife and aquatic biota at various life 

stages; (3) estimation of individual effects levels and physiological response; and (4) assessment of population and 

ecosystem-level impacts. 

Understanding water-column effects is important, 

particularly trophic levels below the headline species. 

Behavior and toxicity information is needed for biological 

resources in order to estimate how they could encounter 

oil, and how oil toxicity affects them. Within surface 

waters, behavior of resident invertebrates and larval fish is 

largely unknown. Their “encounter rate” with floating oil 

and dispersed oil droplets greatly influences exposure and 

determines the impacts from individuals to populations. 

Normally, toxicity is reported as median lethal concentrations after 96-hour exposures. However, this does not 

mimic a spill’s initial spike in concentrations and often 

rapid decay thereafter. To address this issue, the Chemical 

Response to Oil Spills: Ecological Research Forum 

(CROSERF) has recommended refinements in toxicity test 

methods for Arctic conditions. Modeling algorithms need 

verification under arctic conditions, as the cold temperatures slow biological uptake. Toxicity effects should vary 

greatly with temperature and duration of exposure. 

The Scaling Issue

Oil spills in the coastal environment are highly mobile and 

affect natural resources. The smallest scales considered 

are the net movement of droplets or tarballs, which affect 

individual animals. The largest scales are ocean currents 

and coastal winds that can move persistent oils hundreds 

of kilometers, causing ecological effects along the way. Oil 

spills on the water surface are unusual among pollutants 

because spills broken into small tarballs sometimes move 

into coastal convergence zones and re-coalesce into slicks. 

This process was seen in the T/V Exxon Valdez accident, 

where the greatest damage to bird populations occurred far 

from the location of the wreck.

The modern view of an oil spill slick is a continuum 

of droplets from tens of microns to several millimeters 

within an individual slick that is meters to kilometers in 

size. Biological interaction with a slick can occur from the 

smallest scale to the largest. Dissolution of toxics is faster 

from the smaller droplets because of the higher surfaceto-volume ratio. Oil becomes entrained into food webs. 

Marine copepods, for example, ingest oil droplets that fall 

within the larger size range of their preferred food. Large 

slicks can contact a variety of marine animals and wildlife. Combined knowledge from these varieties of scales 

is necessary to accurately inform response decisions and 

evaluate tradeoffs.

Accurate prediction of coastal circulation is required 

over the spectrum of scales discussed in Section 2.2.4, 

including synoptic-scale (major fronts and weather events), 

small-scale (e.g., sub-kilometer) models of winds and 

currents, and hydrological inputs from Arctic rivers. Long, 

immeasurably thin convergence lines, such as the edge of 

freshwater outflow over seawater, can play a critical role 

in controlling the ultimate fate of the oil spill. Oil can be 

moved long distances along a convergence line, can be held 

off from beaching a shoreline, or collect and reform into 

larger slicks. Predictions and observations at all these scales 

are essential for successful response interventions.

Figure 4.15. Cleanup 

crews at work in Skan 

Bay, Alaska. Photo 

taken January 8, 2005.

(Official Unified 

Command photo by 

U.S. Coast Guard Petty 

Officer T. McKenzie)
63

In the realm of event detection 

and response, remote sensing is 

critical for detection of oil on, in, 

and under ice. Ground-penetrating 

radar is an emerging technology for 

the detection of oil in and under ice, 

but more work needs to be done to 

bring this technology into the field 

(see MMS report http://www.mms.

gov/tarprojects/588.htm). Another 

technique for detecting oil under ice 

that is undergoing preliminary evaluation is nuclear magnetic resonance. 

Optical satellite imagery provides 

information on ice cover; however, 

satellite images arrive infrequently 

compared to the swift wind-forced 

changes in ice concentrations. Radarbased imaging has the potential to 

provide near-real-time ice concentration in the field, in particular from airborne platforms when 

the capricious Arctic weather permits.

What is Required For Progress?

•	 Improve prediction of winds and currents, from 

small-scale coastal features (e.g., Langmuir cells in the 

surface mixed layer) and terrain-steering winds in areas 

of rugged topography to large-scale coastal processes, 

which can move oil over long distances. Numerical 

model results allow decision-makers to have a broader 

view of the spill beyond surface observations. Predicting 

where oil could contact the shoreline is important for 

staging response equipment and resources.

•	 Provide better detection of oil in the Arctic environment, with solutions for both summer (light) and 

winter (dark) conditions. Timely and comprehensive 

observational networks need to be set up to achieve 

this goal, relying on remote sensing of spills and ice 

distributions and temperatures. Systems are needed to 

unite optical and microwave remote sensing, together 

with data assimilation for nowcasting and forecasting 

ocean/sea dynamics. 

•	 Conduct sensitivity tests on Arctic food webs that 

include experiments evaluating lethal and sublethal 

effects of exposure to dispersed oil, including bioaccumulation and behavioral observations. Laboratory 

and field sensitivity studies of representative life forms, 

like the important copepod Calanus glacialis, and other 

key species, are needed. Another sensitivity is associated 

with dispersed oil adhering to marine mammal skin 

and baleen. Determining background levels of food web 

contamination is needed in order to provide baseline 

levels for comparison during a spill. 

•	 Develop more refined and verified algorithms for 

predicting oil fate and transport from physical 

processes through biological systems at the individual 

and population levels. Models at scales from oil droplets 

to surface slicks need to be integrated with chemical 

fate, and toxicological and biological models, that treat 

multiple biological scales—from bacteria that degrade oil 

to zooplankton that ingest oil droplets to higher trophic 

levels, including the capacity to assess and forecast 

impacts on wildlife behavior. In this way, prevention of 

oil contact with wildlife, and the capture and rehabilitation of oiled wildlife, could be substantially improved. 

Individual slicks: 100s of m by

a few nautical miles; need to 

define from the air and then

direct response equipment

accordingly (e.g., skimmers)

Oil sheens: not recoverable

but can be seen by people

Droplets: dynamics important on 

scale of movement, detection, 

dissolution of toxics, weathering, and fate

Wavelengths

of light thick

cm to

microns

1

km

SPATIAL SCALE

Days Months

TEMPORAL SCALE

Heavy refined products

10s

of k

m

Offshore sources of oil (leaking tanker, 

well blowout): well blowouts can persist for 

months; tarballs persist for months to years 

but lose toxicity and surface stickiness

100s

of k

m

100s

of m

Hours Years

Lighter refined products

(diesel, jet fuel, gasoline): 

more toxic than heavier products

Figure 4.16. 
64

5. Synthesis of 

Key Findings

Synthesis Finding 1. Scaling issues and even the definitions of scale are so 

varied across individual disciplines that they hinder interdisciplinary research.

The discipline-specific approaches to scaling have led to 

a situation in which it has been difficult for different disciplines to effectively integrate. One way forward is to cast 

grand challenge research questions built around transdisciplinarity that embody multiscale perspectives to understand the current and future states of the fully coupled 

Arctic system, with all key natural and human components.

Chief Recommendations

•	 Support further studies into the manner in which 

individual disciplines define and attack scale-related 

questions, using this knowledge to identify the 

key temporal and spatial scales that will provide 

opportunities to engage multiple disciplines in 

cross-disciplinary research.

•	 Promote interdisciplinary research that attempts 

explicitly to cross the boundaries of discipline-specific 

scale perspectives by organizing the work around 

transdisciplinary “grand challenge” questions that 

recognize the Arctic as a coupled physical-biologicalchemical-human system.

The organization and numerical values of the spacetime scale plots presented for each of the subsections of 

Section 2 amply demonstrate the great variety in scales of 

interest across the biogeophysical and human dimensions 

research communities. This diversity arises in part because 

the nomenclature regarding scales differs so vastly across 

the disciplines. The term microscale to a microbial ecologist 

means something radically different to an Arctic ocean sea 

ice modeler. As another example, fluxes of trace gases over 

a several hour period over a small patch of landscape (1 m2) 

would constitute a macrolevel system to a microbial ecologist, yet falls off the lower spectrum of spatial scales that an 

atmospheric modeler would likely ever consider. 

This review of scaling approaches revealed that different 

scientific disciplines have traditionally approached the issue 

of scale in very different ways. This is not surprising, given 

that approaches to scaling in a particular discipline have 

largely been molded by the domains at which measurement 

technologies have been applied to observe the phenomena 

of interest, the degree of variability of these phenomena 

across temporal and spatial scales, and the types of models 

available within the discipline, for example, contrasts 

between census-based human population projections and 

atmospheric dynamic models. 
65

Although there have been different approaches to scaling 

within disciplines, most generally focus on measurements 

that have been made at discipline-specific finer scales, 

which are important for understanding the inherent 

dynamics of processes. Single disciplines generally have 

turned to coarser-scale models to achieve understanding 

over broader domains, but typically based on fine-scale 

study of the underlying processes. Thus, while there has 

been substantial progress in studying Arctic systems over 

small (logistically-feasible field studies) and large domains 

(global climate models treating a strategic policy issue), 

intermediate spatial and temporal scales have received 

relatively less attention. However, it is precisely along the 

interface of intermediate scales that systems are critically defined, for example, through boundary layer fluxes 

linking the highly heterogeneous Arctic land surface to 

a well-mixed overlying atmosphere. Viewed in this way, 

traditional plot-level experiments of gaseous carbon 

exchange will fail to be relevant in the global carbon cycle 

research agenda unless they can be scaled-up to more 

regionally meaningful domains, at which point they can be 

engaged by the weather and climate modeling community. 

Conversely, such intermediate or mesoscales provide an 

important perspective through which coarse-scale climate 

models become useful in setting the bounds of climate 

change impacts on terrestrial landscapes, which respond to 

the climate over much finer scales. 

The problem also presents itself through inherent scale 

incongruities that actually occur in nature. Difficult numerical stability problems thus challenge models in simulating 

component process, such as when atmospheric winds must 

interface with slow-moving ocean currents that have radically different time and space characteristics. Bridging these 

technical limitations and ensuring stable numerical “handshakes” across these contrasting time/length scales sets 

the stage for robust Arctic systems models that can then be 

useful for informing policy decisions.

Chief Recommendation

•	 Confirm the contention that intermediate scales 

over space and time are under-represented and 

encourage research that bridges the divide separating existing work executed at more traditional fine 

and coarse scales.

Synthesis Finding 3. Thresholds are scale-sensitive and 

important, yet prove difficult to detect, study, and/or predict. 

A key to identification of thresholds is proper representation of interactions of processes across a spectrum of scales. 

Again, the intermediate scale may be critical but more 

work needs to be focused within this space-time domain. 

Nested models, as described in the weather and atmosphere section, may be useful for exploring the modeling 

issues associated with intermediate scales and their role in 

threshold responses. 

Chief Recommendation

•	 Fund process, observational, and modeling studies to 

explore the mechanisms by which thresholds develop 

and test if cross-scale issues play a prominent role. 

Threshold responses occur at the point where there is an 

abrupt change in a system quality, property, or phenomenon, or where small changes in a driving variable of the 

system produces large, persistent, and potentially irreversible responses. Thresholds essentially represent tipping 

points, and involve time and space “edge effects.” Thus, to 

understand the future of sea ice, researchers must factor in 

the time dynamics of long-term climate warming, but also 

year-to-year water exchanges with the Pacific and Atlantic, 

polynya-open water couplings, river inputs of water and 

heat, and winds on much shorter time scales. All of these 

interact over many orders of magnitude in space and time, 

in which relatively fine-scale changes in driving variables 

can result in coarse-scale, system-level changes that bear 

long-term legacy effects. 

Synthesis Finding 2. Scale incongruities among components of the 

 Arctic system give rise to opportunities to study intermediate scales. 
66

Synthesis Finding 4. Scales of human perception are much 

 different than those associated with the study of natural systems. 

international levels. In an age of globalization, macrolevel 

decisions on the Arctic can easily fail to establish links 

to the biogeophysical processes operating in the Arctic 

itself and of relevance to people and livelihoods. Thus, 

improved access to Arctic resources are driven primarily 

by considerations of global economic development, with 

often little regard to implications at subsidiary scales. A 

prime example is the elevated levels of vulnerability from 

oil spills and catastrophic events on coastlines and open 

waters associated with increases in Arctic marine shipping. At the same time, the remoteness of the region can be 

viewed as an opportunity to catalyze technologies that can 

give Arctic residents near-real-time access to information 

on the changing nature of their environment, from panArctic down to the very local scales where they can respond 

to immediate threats yet also respond to longer-term 

environmental change. 

Chief Recommendation

•	 Document further the perceptions of space-time 

domains and Arctic system change by traditional and 

modern Arctic communities to better understand 

readiness to adapt to this evolving environment. 

Arctic human systems are complex and multi-faceted, 

encompassing both indigenous and industrial societies that 

vary greatly in their domains of perception and human 

footprints. Arctic system dynamics are conditioned around 

several temporal scales and give rise to a broad spectrum of 

perceptions, concerns, and planning/response horizons—

Pacific Decadal Oscillations versus election cycles versus

emergency response. Issues of vulnerability are linked to 

temporal and spatial scales. 

Traditional societies have evolved the capacity to detect 

and understand the implications of Arctic system change 

and have adapted using strategies through which they 

can cope with local shortages in renewable resources, 

for example, engaging a much larger spatial domain for 

hunting and gathering that encourages a higher level of 

mobility. Viewed as a scaling issue, native populations 

have developed strategies to effectively reduce the impact 

of high-frequency “noise” in the landscape, by integrating 

their interactions over a wider domain, which tends to 

dampen such variations. Decision-making in industrial 

societies also spans many spatial scales. For example, 

modern-day institutional and legal frameworks can be 

found at individual village, provincial, national, and 
67

Synthesis Finding 5. Information has not been 

 well structured to facilitate cross-scale studies. 

(e.g., administrative units versus watersheds) further 

impeding a unified system-level picture. Jointly developing 

models and integrated data compendia with a broad range 

of thematic data sets that are spatially and temporally 

harmonized will allow cross-disciplinary research to be 

more easily executed.

Chief Recommendations

•	 Support development of integrated data banks and 

models, which represent the collective knowledge 

drawn from several disciplines, spatially and temporally harmonized, and armed with data discovery 

tools to foster interoperability and thus improve 

transdisciplinary research.

The space-time domains treated in the discussion on 

individual disciplinary perspectives (Section 2) not only 

differ across the disciplines but even within a single one, 

often depicting broad and overlapping scales. In addition, there are seemingly counter-intuitive approaches to 

similar problems. For example, atmospheric GCMs are 

characterized by typically long length scales (on the order 

of hundreds of kilometers) yet short time scales (tens of 

seconds to minutes), whereas forest biological studies documenting the impact of climate change might be focused 

on small stands or individual trees but inventoried over 

seasons to multiple years. Coherent information systems 

are not yet in place to reconcile or at least deal with these 

incongruities. Furthermore, social and natural scientists 

organize information over very different accounting units 

Synthesis Finding 6. Science conclusions and uncertainties 

 require better translation into information for policymakers. 

Chief Recommendations

•	 Facilitate an open dialogue through which the 

decision-making community clearly articulates the 

space and time domains over which they need policyactionable scientific information and through which 

the science community can assess their readiness to 

provide this knowledge.

•	 Create research opportunities across the mathematical, natural, and social sciences to catalyze methods 

for assessing the nature of the Arctic and its degree 

of change, through which scientists and assessment 

experts can jointly characterize the system. 

•	 Create forums for an interchange among scientists, 

policymakers, and managers on the issue of uncertainty and how to interpret and use these estimates in 

a proactive and positive manner.

The complexity and capricious nature of the Arctic system 

conveys a substantial challenge to document the true degree 

of recent change, to place such change into longer historical 

and paleo context, and to design mitigation and adaptation 

strategies that minimize the anticipated negative consequences. This will not prove to be an easy task, as change 

manifests itself at several scales, over many domains, and 

affects several stakeholder communities, all unequally 

distributed. It is in this context that decision-makers and 

managers must operate and recognize that a particular 

action that targets one scale may not be wholly adequate 

—and in some cases detrimental—at another. An important responsibility of the scientific community is to convey 

not only its reasoned estimates regarding the level of Arctic 

change but also to assign a degree of fidelity, or conversely, 

uncertainty, to its findings. This, together with a suitable 

outreach effort to educate Arctic information users, will 

permit uncertainties to be adopted into the policy formulation process. Uncertainties in our knowledge base can be 

compounded when moving across scales, yet this has rarely 

been quantified or conveyed to decision-makers.
68

Appendix 1. A Primer on Scaling

Over the last few centuries, science has amassed an 

immense store of knowledge about the workings of the 

natural world. In terrestrial ecosystem research, this 

knowledge was derived from short-term studies (less than 

a few decades) made on small plots (less than a few thousand square meters). Because of the relatively fine-scale 

nature of this knowledge, it is difficult to apply it directly 

to address questions about processes and feedbacks acting 

over large regions or the entire Arctic. However, this vast 

store of accumulated knowledge might be brought to 

bear to address these coarse-scale questions if they can 

be appropriately scaled. The purpose of this primer is to 

introduce some of the fundamental concepts related to the 

process of scaling.

In a scientific context, “scale” refers to the characteristic 

distance or time over which a process acts. For example, 

plant photosynthesis acts on a spatial scale of a few meters 

over a time scale of a growing season. Migration of caribou 

acts on a spatial scale of hundreds of kilometers over a time 

scale of a few weeks. “Scaling” refers to taking a relationship or model derived from knowledge at one scale and 

transforming it so it can be applied at some other scale. This 

process of scaling has several hidden problems.

In the loose usage, “scaling” has three components: 

(1) projection, which involves a change in extent 

(the area or time period over which a model is applied), 

(2) extrapolation, which involves a change in domain 

(the range of data over which a model is corroborated), 

and (3) scaling proper, which involves a change in grain 

(the area or time period to which a single number predicted 

by a model applies). The conceptual distinction among 

these three components is vital to the assessment of the 

scaling problem because each component propagates errors 

differently and therefore has different effects on the resolution of the final scaled product. 

Projection

Spatial projections are based on a spatial model with a submodel that is applied pixel-by-pixel (or polygon-by-polygon) 

to develop an output map. The sub-model should have been 

developed and tested at the scale of the individual pixels. 

The extent of the sub-model is therefore the individual pixel 

and by repeated pixel-by-pixel application, this extent is 

projected to the whole map. For dynamic models, there is 

also a temporal projection involving the time step-by-time 

step application of the model. 

In many spatial-model applications, there is also an 

implicit projection involved if the sub-model is developed 

and tested at a smaller scale than the pixel, but applied to 

the whole pixel anyway. For example, models developed 

based on data from experimental plots of a few hundred 

square meters are often applied to pixels that are several 

thousand square kilometers. Thus, there is an implicit 

projection from the 100 m2 plot scale to the 1000 km2 pixel 

scale. This implicit projection requires the assumption 

that the pixel is spatially uniform in all characteristics that 

are important inputs to the model and that there are no 

plot-to-plot spatial interactions within the pixel that would 

influence the model output. Violation of these assumptions 

is probably the largest source of error in pure projections. 

To avoid these errors, either (1) the projection should 

start from a finer scale so that the sub-model is applied at 

the scale for which it was developed or (2) the sub-model 

should be scaled to the size of the pixels being used for the 

projection (see below).

In some cases, projection does not involve any added 

propagation of error beyond that inherent in the submodel and the assumptions associated with its application 

at the pixel scale. The exceptions are (1) dynamic models 

where the outcome of the previous time step is the starting 

point for the next time step and (2) spatially interactive 

models where the output from the sub-model applied 

to one pixel is used as input to the sub-model applied to 

other pixels. In the first case, errors propagate and can 

amplify through time. In the second case, errors generated 
69

at one location on the map are propagated spatially to 

other locations and can be amplified. Assessment of this 

propagation of error is difficult, but can be addressed using 

Monte-Carlo approaches. 

Extrapolation

Extrapolation involves the application of a model beyond 

the domain for which it was developed and tested. For 

example, an ecosystem model might be developed based 

on data collected under current climate conditions, but 

applied to predict ecosystem behavior under some future, 

drastically altered climate. Extrapolation should, of course, 

be avoided if at all possible, but it is rare that a model of a 

system as complex as an ecosystem can be fully tested for all 

the conditions under which it is applied. Simulations of the 

long-term future are particularly problematic because it is 

simply impossible to run the experiments necessary to fully 

test an ecosystem model under all the potential combinations of future climate and CO2

 concentration, for example. 

The need for extrapolation is perhaps the strongest 

argument for mechanistic rather than empirical modeling 

approaches. In a mechanistic model, equations 

describing individual processes are linked together 

to mimic the interactions among processes in the 

real system. The real system might be too large and 

respond too slowly to allow a full test of the full 

model at reasonable expense and in a reasonable time 

span. However, the individual processes respond 

much more quickly and can therefore be tested more 

fully for a broad range of conditions at reasonable 

expense and in a reasonable time span. In addition, a 

mechanistic approach can make use of the enormous 

amount of information already compiled in past 

studies of these processes. 

Scaling

Scaling involves a change in grain. For example, a 

model developed to predict the minute-to-minute 

photosynthetic rate of individual leaves would have 

to be scaled before it could be applied to predict the 

daily photosynthesis of a whole canopy. The most 

commonly used example of the need for scaling is 

the so-called fallacy of the averages (Figure A1); for 

any nonlinear model, the average of the predictions 

of the model applied over a range of conditions is not equal 

to the prediction of the model applied to the average of 

the conditions. Thus, in the example above, the predicted 

average photosynthesis of all the leaves in the canopy would 

not equal the leaf photosynthesis predicted for the average 

condition within the canopy. 

Predictions made ignoring the fallacy of the averages 

will have an aggregation bias except under very special 

conditions. No aggregation bias will arise if the fine-scale 

components function independently of one another and 

(1) the relationship between input data and predicted output 

is linear or (2) the input data is uniform across all the finescale components. If the fine-scale components function 

independently but the model is nonlinear and the input is 

not uniform, then a straightforward statistical scaling can 

be applied. The resulting scaled model will be a function of 

the statistical properties of the input data (e.g., mean, variance of the input). 

The fallacy of the averages is only one of the sources of 

scaling errors. For example, it does not deal with spatial 

interactions among the fine-scale components or with 

Figure A1: Fallacy of the averages. The bold black line depicts the predictions of a model relating canopy photosynthesis to the leaf area of small 

patches of uniform vegetation. The model had been inappropriately applied 

at a landscape scale. The depicted landscape is half tundra, with leaf area 

of 1, and half forest, with leaf area of 5. The predicted average photosynthesis 

for the landscape is about 16% higher than the actual photosynthesis. In the 

general case, a coarse-scale relationship without the bias can be derived 

statistically through the Expectation operator:

F = f(x)ρ(x)dx ∫

∞

∞

where F is the coarse-scale relationship, f is the fine-scale relationship, r is a 

probability density function describing how x is distributed. F will be a function of the statistical properties (mean, variance, etc) of how x is distributed, 

not a function of x itself.
70

legacies passed on through time. One approach to overcoming this problem is to use Monte Carlo simulations, 

with a fine-scale model used to generate pseudo-data that 

are averaged to the appropriate coarse scale (Figure A2). 

These data are then used in a nonlinear regression analysis 

to infer a coarse-scale model, but with the form of the 

nonlinear equations being guided by knowledge of the 

underlying processes. 

Key Terms Used in Scaling

There is no standard terminology associated with scaling; 

different terms are applied to the same concept and the same 

term to different concepts. This lack of a standard has led to 

needless confusion. Below we define several scaling concepts 

and point out some inconsistencies in terminology.

Scale: ratio of map distance to actual distance or 

a measure of the characteristic length over which a 

model operates. Cartographers define scale as the ratio 

of the distance on the map to the real-world distance 

depicted. Thus, maps representing large areas will be a 

small-scale maps (e.g., 1:39,370,000 = 2.54 × 10-8) and 

maps representing small areas will be a large-scale maps 

(e.g., 1:1000 = 1 × 10-3). The common meaning of scale is 

just the opposite; “large scale” refers to something large and 

“small scale” refers to something small. To avoid confusion, 

“fine scale” can be used to refer to detailed representations 

of small objects and “coarse scale” to refer to coarse representations of large objects. For a dynamic model, scale can 

also be in reference to time; a daily time step is a finer time 

scale than a monthly time step. Scale has two components, 

grain and extent (defined below).

Pixel: uniformly sized squares (or occasionally regular 

hexagons) arranged on a grid to completely cover the 

area mapped (called a “raster-based” map). Each pixel is 

assigned a single value of the characteristic being mapped 

(e.g., for a vegetation map, each pixel is assigned one 

vegetation type). 

Polygon: areas of irregular size and shape arranged 

to completely cover the area mapped (called a “vectorbased” map). Each polygon is also assigned a single value 

of the characteristic being mapped. 

Grain or spatial resolution: the size of the 

pixels or polygons. For example, maps with 30 m spacing 

between pixel centers would have a 30 m grain. The grain of 

a polygonal map relates to the size of the smallest polygons 

used to tile the map. Maps can have spatial information 

down to a particular grain and have additional information 

finer than that grain, but not in a spatially explicit form. 

For example, a map might have information on the relative 

amounts of different vegetation types within each pixel, 

but not on their spatial distribution within a pixel. Spatial 

models also have an associated grain. The simulation time 

step defines the temporal grain of a dynamic model. 

Figure A2: A general strategy for scaling. The need for scaling 

typically arises when a model (f) relating fine-scale process rates 

to fine-scale inputs is well known or easily studied, but when it is 

prohibitively expensive obtaining those fine-scale inputs over a large 

area or long time period to run the model at such a fine scale. It is 

then useful to scale f to derive a new model (F) that relates readily 

available, coarse-scale inputs to the coarse-scale process rates. In the 

scaling strategy depicted here, a large set of artificial coarse-scale 

data are randomly generated that covers all the conditions likely 

to be encountered when the scaled model is eventually applied. 

These data are disaggregated so they can be used in the fine-scale 

model f to generate the corresponding fine-scale output data. These 

fine-scale outputs are then aggregated back up to the coarse scale 

by averaging or summing over the appropriate area and time period. 

Finally, the coarse-scale model F is derived through a nonlinear 

regression of the coarse-scale outputs generated through the aggregation of the fine scale model simulations on the coarse-scale inputs 

generated to cover the desired domain of the coarse scale model. 

Selection of the nonlinear equations in the derived coarse-scale 

model is guided by knowledge of the underlying processes. 
71

Extent or domain: the total area covered by a map 

or the total area and time span over which a model is 

applied (e.g., all of the North Slope of Alaska between the 

years 1900 and 2100). Although there is usually a correlation between grain and extent among maps or models 

(small extent associated with fine grained and vice versa), 

there is no requirement for any relation between the 

two, except, of course, that the extent cannot be smaller 

than the grain. 

Resolution (see alternate definition above): the 

precision of the data represented by the map or model.

For example, a map of average annual temperatures in 

10°C increments on a grid with 10 km spacing has a resolution of ± 5°C and a grain of 10 km. 

Domain (see alternate definition above): the range of 

conditions for which a model is corroborated. A model 

designed and tested for wet-sedge tundra and applied to 

the North Slope of Alaska would have wet-sedge tundra as 

its domain and the North Slope as its extent. The domain 

might also refer to the range of input data for which 

the model is corroborated. For example, a soil respiration model might only be valid for tussock soils between 

freezing and 25°C; the domain would be tussock soils 

within that temperature range. For maps, the areas outside 

the domain are often masked out. 

Spatially explicit model: a model that can accept 

spatially mapped data as an input and generates spatially 

mapped data as output. Typically, spatially explicit 

models are run at the grain of the finest-grain input map. 

Spatially interactive model: a spatially explicit 

model in which the sub-models applied to individual 

pixels or polygons interact with one another. For 

example, a hydrological model might calculate a water 

balance for individual pixels on a hillslope. Each pixel then 

requires an estimate of runoff from pixels upslope and 

provides runoff estimates for pixels downslope. 

Fractal property: a property that is invariant across 

scales. Fractal properties do not need to be scaled because 

the equations describing those properties apply to all scales.

Emergent property: a property manifested at a 

particular scale that was unpredicted from fine-scale 

behaviors because of a high sensitivity to small perturbations at the fine scale and the inability to measure the 

fine-scale components precisely enough to discern these 

fine scale perturbations. 

Coherent property: a property of particular importance to the functioning of a system that serves to define 

a particular spatio-temporal scale.
72

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E. Fahrbach, I.E. Frolov, R.Gerdes, E. Hansen, J. Holfort, V.V. Ivanov, 

and others. 2005. One more step toward a warmer Arctic. Geophysical 

Research Letters 32, L17605, doi:10.1029/2005GL023740.

Rampal, P., J. Weiss, D. Marsan, R. Lindsay, and H. Stern. 2008. Scaling 

properties of sea ice deformation from buoy dispersion analysis. Journal 

of Geophysical Research 113, C03002, doi:10.1029/2007JC004143.

Rennermalm, A.K, E.F. Wood, A.J. Weaver, M. Eby, and S.J. Dery. 2007. 

Relative sensitivity of the Atlantic meridional overturning circulation 

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Geophysical Research 112, G04S48, doi:10.1029/2006JG000330.

Richter-Menge, J.A., S.L. McNutt, J.E. Overland, and R. Kwok. 2002. 

Relating arctic pack ice stress and deformation under winter conditions. 

Journal of Geophysical Research 107, 8040, doi:10.1029/2000JC000477.

Schauer, U., B. Rudels, E.P. Jones, L.G. Anderson, R.D. Muench, G. Björk, 

J.H. Swift, V. Ivanov, and A.-M. Larsson. 2002. Confluence and redistribution of Atlantic water in the Nansen, Amundsen and Makarov 

basins. Annales Geophysicae 20:257–273.

Serreze, M.C., A.P. Barrett, A.G. Slater, R.A. Woodgate, K. Aagaard, 

R.B. Lammers, M. Steele, R. Moritz, M. Meredith, and C.M. Lee. 2006. 

The large-scale freshwater cycle of the Arctic. Journal of Geophysical 

Research 111, C11010, doi:10.1029/2005JC003424.

Skyllingstad, E.D., and D.W. Denbo. 2001. Turbulence beneath sea ice 

and leads: A coupled sea ice/large-eddy simulation study. Journal of 

Geophysical Research 106:2,477–2,497.

Smith, D.C., and J.H. Morison. 1998. Nonhydrostatic haline convection 

under leads in sea ice. Journal of Geophysical Research 103:3,233–3,247.

Steele, M., G.L. Mellor, and M.G. McPhee. 1989. Role of the molecular 

sublayer in the melting or freezing of sea ice. Journal of Physical 

Oceanography 19:139–147.

Timmermans, M.L., and C. Garrett. 2006. Evolution of the deep water 

in the Canadian basin in the Arctic Ocean. Journal of Physical 

Oceanography 36:866–874.

Timmermans, M.L., P. Winsor, and J.A. Whitehead. 2005. Deep-water 

flow over the Lomonosov Ridge in the Arctic Ocean. Journal of Physical 

Oceanography 35:1,489–1,493.

Walsh, D., and E. Carmack. 2003. The nested structure of Arctic thermohaline intrusions. Ocean Modelling 5(3):267–289.

Weiss, J., E.M. Schulson, and H.L. Stern. 2007. Sea ice rheology from in 

situ, satellite and laboratory observations: Fracture and friction. Earth 

and Planetary Science Letters 255:1–8.

Woodgate, R.A., K. Aagaard, J.H. Swift, W.M. Smethie Jr., and 

K.K. Falkner. 2007. Atlantic water circulation over the Mendelev Ridge 

and Chukchi Borderland from thermohaline intrusions and water mass 

properties. Journal of Geophysical Research 112.

Zhang, J.L., and M. Steele. 2007. Effect of vertical mixing on the Atlantic 

Water layer circulation in the Arctic Ocean. Journal of Geophysical 

Research 112, C04S04, doi:10.1029/2006JC003732.

Zhang, J.L., M. Steele, D.A. Rothrock, and R.W. Lindsay. 2004. Increasing 

exchanges at Greenland-Scotland Ridge and their links with the North 

Atlantic Oscillation and Arctic sea ice. Geophysical Research Letters

31(9):L09307.1–L09307.5.

2.3. Ecosystems and Biology

2.3.1. Marine Ecosystems

Arrigo, K.R., G. van Dijken, and S. Pabi. 2008. Impact of a shrinking 

Arctic ice cover on marine primary production. Geophysical Research 

Letters 35, L19603, doi:10.1029/2008GL035028.

Grebmeier, J.M, S.E. Moore, J.E. Overland, K.E. Frey, and R. Gradinger. 

2010. Biological response to recent Pacific Arctic sea ice retreats. Eos, 

Transactions, American Geophysical Union 91(18):161–162.

Grebmeier, J.M., J.E. Overland, S.E. Moore, E.V. Farley, E.C. Carmack, 

L.W. Cooper, K.E. Frey, J.H. Helle, F.A. McLaughlin, and S.L. McNutt. 

2006. A major ecosystem shift in the northern Bering Sea. 

Science 311(5766):1,461–1,464, doi:10.1126/science.1121365.

Loeng, H. 2005. Marine systems. Pp. 453–538 in Arctic Climate Impact 

Assessment. Cambridge University Press, 1,042 pp.

Olson, E., V. Hill, L. Codispoti, V. Kelly, B. Light, M. Steele, W. Wermold, 

R. Zimmerman, and P. Matrai. 2009. A synthesis of Arctic Ocean 

primary production. Poster presented at the ASLO Aquatic Sciences 

Meeting, January 25–30, 2009, Nice, France. Abstract available at: http://www.sgmeet.com/aslo/nice2009/viewabstract2.

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2.3.2. Terrestrial Ecosystems Including Freshwater

Callaghan, T.V. 2005. Arctic tundra and polar desert ecosystems. 

Pp. 245–352 in Arctic Climate Impact Assessment. Cambridge 

University Press, 1,042 pp.

Chapin, F.S. III, M. Berman, T.V. Callaghan, P. Convey A.-S. Crépin, 

K. Danell, H. Ducklow, B. Forbes, G. Kofinas, A.D. McGuire, and 

others. 2005. Polar systems. Pp 717–743 in Ecosystems and Human 

Well-Being, Volume 1: Current State and Trends: Findings of the 

Condition and Trends Working Group. Millennium Ecosystem 

Assessment, Island Press, 966 pp.

McGuire, A.D., F.S. Chapin III, J.E. Walsh, and C. Wirth. 2006. 

Integrated regional changes in arctic climate feedbacks: Implications 

for the global climate system. Annual Review of Environment and 

Resources 31:61–91.

Williams, M., E.B. Rastetter, G.R. Shaver, J.E. Hobbie, and E. Carpino, 

and B.L. Kwiatkowski. 2001. Primary production of an arctic 

watershed: An uncertainty analysis. Ecological Applications

11(6):1,800–1,816.

Wrona, F.J., T.D. Prowse, J.D. Reist. 2005. Freshwater ecosystems and 

fisheries. Pp. 353–452 in Arctic Climate Impact Assessment. Cambridge 

University Press, 1,042 pp.
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2.4. Human Systems

2.4.1. Arctic Communities

Department for International Development (DFID). 1999. Sustainable 

Livelihoods Guidance Sheets: Overview, Department for International 

Development. Available online at: http://www.nssd.net/references/

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Gerlach, S.C., A.M. Turner, P. Loring, and L. Henry. In press. Coming 

to terms with rural Alaskan foodways. In Circumpolar Environmental 

Science: Current Issues in Resources, Health and Policy. L.K. Duffy and 

K. Erickson, eds, University of Alaska Press.

Hamilton, L.C. 2010. Northern places: Circumpolar human-dimensions 

data framework. Available online at: http://carseyinstitute.unh.edu/

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Hamilton, L.C., and A.M. Mitiguy. 2008. Population dynamics in Arctic 

Alaska: Graphic views of community change. Available online at: 

http://www.carseyinstitute.unh.edu/alaska-indicators.htm (accessed 

February 16, 2010).

Hamilton, L.C., and A.M. Mitiguy. 2009. Visualizing population 

dynamics of Alaska’s Arctic communities. Arctic 42(4):393–398.

Huntington, H.P., M. Boyle, G.E. Flowers, J.W. Weatherly, L.C. Hamilton, 

L. Hinzman, C. Gerlach, R. Zulueta, C. Nicolson, and J. Overpeck. 

2007. The influence of human activity in the Arctic on climate and 

climate impacts. Climatic Change 82(1–2):77–92, doi:10.1007/s10584-

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Loring, P.A. 2007. Coming out of the Foodshed: Change and Innovation in 

Rural Alaskan Food Systems. Ph.D. Dissertation, University of Alaska 

Fairbanks, Fairbanks, AK.

Loring, P.A., and S.C. Gerlach. In review. Linking food security and 

social-ecological health: Environmental change, policy, and other issues 

of foodshed viability in Alaska. Environmental Science and Policy.

Loring, P.A., F.S. Chapin III, and S.G. Gerlach. 2008. Ecosystem 

services and the services-oriented architecture: Computational 

thinking for the diagnosis of change in social ecological systems. 

Ecosystems 11(3):478–489, doi:10.1007/s10021-008-9136-1.

Murray, M.S. 2008. Zooarchaeology and arctic marine mammal 

biogeography, conservation, and management. Ecological Applications 

18(2):S41–S55.

Ostrom, E. 2007. A diagnostic approach for going beyond panaceas. 

Proceedings of the National Academy of Sciences of the United States of 

America 104(39):15,181–15,187.

Reid, W.V., H.A. Mooney, A. Cropper, D. Capistrano, S.R. Carpenter, 

K. Chopra, P. Dasgupta, T. Dietz, A.K. Duraiappah, R. Hassan, and 

others. 2005. Ecosystems and Human Well-being: Synthesis. Millennium 

Ecosystem Assessment, World Resources Institute, Washington D.C.

Sommerkorn, M., and N. Hamilton, eds. 2008. Arctic Climate Impact 

Science: An Update Since ACIA. WWF International Arctic Programme, 

Oslo, Norway, 114 pp. Available online at: http:// www.worldwildlife.

org/climate/WWFBinaryitem8706.pdf (accessed June 19, 2010).

Turner, B.L., R.E. Kasperon, P.A. Matson, J.J. McCarthy, R.W. Corell, 

L. Christensen, N. Eckley, J.X. Kasperson, A. Luers, M.L. Martello, and 

others. 2003. A framework for vulnerability analysis in sustainability 

science. Proceedings of the National Academy of Sciences of the United 

States of America 100(14):8,074–8,079.

White, D., S.C. Gerlach, P. Loring, A.C. Tidwell, and M.C. Chambers. 

2007. Food and water security in a changing Arctic climate. 

Environmental Research Letters 2(4):4.

3. The Role of Scaling in 

Interdisciplinary Arctic and 

Earth System Science

3.2. Tipping Points in the Arctic System

Fagre, D.B., C.W. Charles, C.D. Allen, C. Birkeland, F.S. Chapin III, 

P.M. Groffman, G.R. Gunternspergen, A.K. Knapp, A.D. McGuire, 

P.J. Mulholland, and others. 2009. Thresholds of Climate Change in 

Ecosystems. A report by the U.S. Climate Change Science Program and 

the Subcommittee on Global Change Research. U.S. Geological Survey, 

Reston, VA, 156 pp. Available online at: http://www.globalchange.

gov/publications/reports/scientific-assessments/saps/303 (accessed 

June 25, 2010). 

Lindsay, R.W., and J. Zhang. 2005. The thinning of arctic sea 

ice, 1988–2003: Have we passed a tipping point? Journal of 

Climate 18:4,879–4,894.

Overpeck, J.T., M. Sturm, J.A. Francis, D.K. Perovich, M.C. Serreze, 

R. Benner, E.C. Carmack, S. Chapin III, S.G. Gerlach, L.C. Hamilton, 

and others. 2005. Arctic system on trajectory to new, seasonally icefree state. Eos, Transactions, American Geophysical Union 86:309–316, 

doi:10.1029/2005EO340001.

3.3. Changing Arctic Terrestrial ecosystems

and Their Impacts on the Earth System

Chapin, F.S. III., A.D. McGuire, J. Randerson, R. Pielke Sr., D. Baldocchi, 

S.E. Hobbie, N. Roulet, W. Eugster, E. Kasischke, E.B. Rastetter, 

S.A. Zimov, W.C. Oechel, and S.W. Running. 2000. Feedbacks 

from arctic and boreal ecosystems to climate. Global Change 

Biology 6:S211–S223.

Chapin, F.S. III, J.T. Randerson, A.D. McGuire, J.A. Foley, and C.B. Field. 

2008. Changing feedbacks in the climate-biosphere system. Frontiers in 

Ecology and the Environment 6(6):313–320, doi:10.1890/080005.

Euskirchen, S.E., A.D. McGuire, and F.S. Chapin III. 2007. Energy feedbacks of northern high-latitude ecosystems to the climate system due 

to reduced snow cover during 20th century warming. Global Change 

Biology 13:2,425–2,438.

McGuire, A.D., F.S. Chapin III, J.E. Walsh, and C. Wirth. 2006. 

Integrated regional changes in Arctic climate feedbacks: Implications 

for the global climate system. Annual Review of Environment and 

Resources 31:61–91.

McGuire, A.D., F.S. Chapin III, C. Wirth, M. Apps, J. Bhatti, 

T. Callaghan, T.R. Christensen, J.S. Clein, M. Fukuda, T. Maximov, 

and others. 2007. Responses of high-latitude ecosystems to global 

change: Potential consequences for the climate system. Pp. 297–310 

in Terrestrial Ecosystems in a Changing World, J.G. Canadell, 

D.E. Pataki, and L.F. Pitelka, eds, The IGBP Series, Springer-Verlag, 

Berlin Heidelberg.

4. The Role of Scaling in 

Societal Applications

4.2. Arctic Human Health Research 

Issues in Alaska

CCSP (Climate Change Science Program) and the Subcommittee on 

Global Change Research. 2008. Analyses of the Effects of Global 

Change on Human Health and Welfare and Human Systems. Final 

Report, Synthesis and Assessment Product 4.6, J.L. Gamble, ed., 

U.S. Environmental Protection Agency, Washington, DC. Available 

online at: http//:www.climate science.gov/library/sap (accessed 

June 29, 2010).
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Haines, A., R.S. Kovats, D. Campbell-Landrum, C. Corvalan. 

2006. Climate change and human health: Impacts, vulnerability, and 

mitigation. The Lancet 367:2,101–2,109.

Hild, C., M. Fair, and S. Smith, eds. 2000. Arctic Telemedicine Project 

Final Report. Presented to the Sustainable Development Working 

Group of the Arctic Council, University of Alaska Institute for 

Circumpolar Health, 97 pp. Available online at: http://www.ichs.uaa.

alaska.edu/research/archives_telemedicine_arctic.htm (accessed 

March 5, 2010).

Levintova, M., W. Zapol, and N. Engmann, eds. 2010. Behavioral and 

Mental Health Research in the Arctic: Strategy Setting Meeting. A 

contribution to the International Polar Year, International Association 

of Circumpolar Health Publishers, Oulu, Finland, 64 pp. Available on 

line at: http: www.arctic.gov/publications/CHS%20(5)%20netti.pdf 

(accessed March 5, 2010).

Patz, J., C.-L. Diarmid, T. Holloway, J.A. Foley. 2005. Impact of regional 

climate change on human health. Nature 438:310–317. 

Wigle, D., A. Gilman, K. McAllister, and T. Gibbons. 2005. Analysis of 

Arctic Children and Youth Health Indicators. Produced for the Arctic 

Council Sustainable Development Working Group, Future of Children 

and Youth of the Arctic Initiative, Report of the Health Programme, 

111 pp. Available online at: http://en.mipi.nanoq.gl/Emner/

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4.3. Climate Change Mitigation

Chapin, F.S. III, J.T. Randerson, A.D. McGuire, J.A. Foley, and C.B. Field. 

2008. Changing feedbacks in the climate-biosphere system. Frontiers in 

Ecology and the Environment 6(6):313–320, doi:10.1890/080005.

Euskirchen, S.E., A.D. McGuire, and F.S. Chapin III. 2007. Energy feedbacks of northern high-latitude ecosystems to the climate system due 

to reduced snow cover during 20th Century warming. Global Change 

Biology 13:2,425–2,438.

Euskirchen, E.S., A.D. McGuire, F.S. Chapin III, S. Yi, and 

C.C. Thompson. 2009. Changes in vegetation in northern Alaska under 

scenarios of climate change 2003–2100: Implications for climate feedbacks. Ecological Applications 19(4):1,022–1,043.

Harden, J.W., E.T. Sundquist, R.F. Stallard, and R.K. Mark. 1992. 

Dynamics of soil carbon during deglaciation of the Laurentide ice 

sheet. Science 258:1,921–1,924.

IPCC. 2007. Polar regions (Arctic and Antarctic). Chapter 15 in Working 

Group II Report: Impacts, Adaptation and Vulnerability. Available 

online at: http://www.ipcc-wg2.gov/publications/AR4/index.html 

(accessed February 16, 2010)

IPCC. 2007. Summary for policymakers. Pp. 7–22 in Working Group II 

Report: Impacts, Adaptation and Vulnerability. Available online at: 

http://www.ipcc-wg2.gov/publications/AR4/index.html (accessed 

February 16, 2010).

McGuire, A.D., F.S. Chapin III, J.E. Walsh, and C. Wirth. 2006. 

Integrated regional changes in arctic climate feedbacks: Implications 

for the global climate system. Annual Review of Environment and 

Resources 31:61–91.

McGuire, A.D., F.S. Chapin III, C. Wirth, M. Apps, J. Bhatti, 

T. Callaghan, T.R. Christensen, J.S. Clein, M. Fukuda, T. Maximov, 

and others. 2007. Responses of high-latitude ecosystems to global 

change: Potential consequences for the climate system. Pp. 297–310 

in Terrestrial Ecosystems in a Changing World. J.G. Canadell, 

D.E. Pataki, and L.F. Pitelka, eds, The IGBP Series, Springer-Verlag, 

Berlin Heidelberg.

Metz, B., O.R. Davidson, P.R. Bosch, R. Dave, and L.A. Meyer, eds. 

2007. Contribution of Working Group III to the Fourth Assessment 

Report of the Intergovernmental Panel on Climate Change. Cambridge 

University Press, Cambridge, UK. Available online at: http://www.

ipcc.ch/publications_and_data/ar4/wg3/en/contents.html (accessed 

February 16, 2010).

U.S. Climate Change Science Program. 2008. Global Climate Change in 

the United States. Public Review Draft of the Unified Synthesis Product, 

National Oceanic and Atmospheric Administration, Department 

of Commerce, Washington, D.C. Available online at: http://www.

climatescience.gov/Library/sap/usp/public-review-draft (accessed 

February 16, 2010).

Zhuang, Q., J.M. Melillo, D.W. Kicklighter, R.G. Prinn, A.D. McGuire, 

P.A. Steudler, B.S. Felzer, and S. Hu. 2004. Methane fluxes between 

terrestrial ecosystems and the atmosphere at northern high latitudes 

during the past century: A retrospective analysis with a process-based 

biogeochemistry model. Global Biogeochemical Cycles 18, GB3010, 

doi:10.1029/2004GB002239.

Zimov, S.A., S.P. Davydov, G.M. Zimova, A.I. Davydova, E.A.G. Schuur, 

K. Dutta, and F.S. Chapin III. 2006b. Geophysical Research Letters 33, 

L20502, doi:10.1029/2006GL027484.

Zimov, S.A., E.A.G. Schuur, and F.S. Chapin III. 2006a. Permafrost and 

the global carbon budget. Science 312:1,612–1,613.

4.4. Public and Private Infrastructure 

Vulnerabilities

Cole, H., V. Colonell, and D. Esch. 1999. The economic impact and 

consequences of global climate change on Alaska’s infrastructure. 

Pp. 43–56 in Assessing the Consequences of Climate Change for Alaska 

and the Bering Sea Region. Workshop proceedings summarized for the 

U.S. Global Change Research Program, University of Alaska Fairbanks, 

Fairbanks, AK.

Douglas, B., and W.R. Peltier. 2002. The puzzle of global sea-level rise. 

Physics Today 55(3):35–41.

GAO (United States General Accounting Office). 2004. Alaska native 

villages: Villages affected by flooding and erosion have difficulty 

qualifying for federal assistance. Testimony before the Committee 

on Appropriations, U.S. Senate, Statement of Robert A. Robinson, 

Managing Director Natural Resources and Environment. Available 

online at: http://www.gao.gov/new.items/d04895t.pdf (accessed 

February 16, 2010).

Hitz, S., and J. Smith. 2004. Estimating global impacts from climate 

change. Global Environmental Change Part A 14(3):201–218.

Instanes, A., O. Anisimov, L. Brigham, D. Goering, L. Khrustalev, 

B. Ladanyi, and J. Larsen. 2005. Infrastructure: Buildings, support 

systems, and industrial facilities. Chapter 16 in Arctic Climate Impact 

Assessment. Cambridge University Press, Cambridge, UK.

Lalla, D., B. Krenzelok, J. Strawn, L. Sires, J. Hughes, J. Dygas, and 

M. Wrabetz. 2001. A Look at Alyeska Pipeline Service Company’s 

Operation of the Trans-Alaska Pipeline System 1999/2000. 

Comprehensive Monitoring Program Report, Alyeska Pipeline Service 

Company. Joint Pipeline Office, 38 pp. Available online at: http://

www.jpo.doi.gov/Publications/CMPs/CMP8/CMP_8.pdf (accessed 

February 16, 2010).

Larsen, P., S. Goldsmith, O. Smith, M. Wilson, K. Strzepek, P. Chinowsky, 

and B. Saylor. 2008. Estimating the future costs of Alaska public 

infrastructure at risk from climate change. Global Environmental 

Change 18(3):442–457, doi:10.1016/j.gloenvcha.2008.03.005.

Lonergan, S., R. Difrancesco, and M. Woo. 1993. Climate change and 

transportation in northern Canada: An integrated impact assessment. 

Climatic Change 24:331–351.

McBeath, J. 2003. Institutional responses to climate change: The case of 

the Alaska transportation system. Mitigation and Adaptation Strategies 

for Global Change 8:3–28.

Osterkamp, T.E., D.C. Esch, and V.E. Romanovsky. 1998. Permafrost. 

Pp. 115–127 in Implications of Global Change in Alaska and the Bering 

Sea Region. G. Weller and P.A. Anderson, eds., Proceedings of a workshop, Center for Global Change and Arctic System Research, University 

of Alaska Fairbanks, Fairbanks, AK.

USACE (United States Army Corps of Engineers). 2006. An Examination 

of Erosion Issues in the Communities of Bethel, Dillingham, Kaktovik, 

Kivalina, Newtok, Shishmaref, and Unalakleet. Alaska Village Erosion 

Technical Assistance Program, 44 pp. Available online at: http://

www.housemajority.org/coms/cli/AVETA_Report.pdf (accessed 

February 16, 2010).
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USARC (United States Arctic Research Commission). 2003. Climate 

Change, Permafrost, and Impacts on Civil Infrastructure. Permafrost 

Task Force Report, Special Report 01-03, United States Arctic Research 

Commission, Arlington, VA, 72 pp. Available online at: http://www.

arctic.gov/publications/permafrost.pdf (accessed February 16, 2010).

4.5. Subsistence harvest and 

Commercial Fisheries

Barron, M.G., and L. Ka’ahihue. 2003. Critical evaluation of CROSERF 

test methods for oil dispersant toxicity testing under subarctic conditions. Marine Pollution Bulletin 46(9):1,191–1,199.

French McCay, D.P., 2002. Development and application of an oil 

toxicity and exposure model, OilToxEx. Environmental Toxicology and 

Chemistry 21(10):2,080–2,094.

4.6. Non-Renewable Resource Extraction

Arctic Civil Infrastructure Workshop: https://sites.google.com/a/alaska.

edu/arctic-civil-infrastructure-workshop/

Coal: http://www.netl.doe.gov/technologies/coalpower/index.html

Energy: http://eia.gov/USGS Assessment updates (Oil and Gas): http://

energy.cr.usgs.gov/oilgas/noga/assessment_updates.html

Methane Hydrates: http://www.netl.doe.gov/technologies/oil-gas/

FutureSupply/MethaneHydrates/maincontent.htm

4.7. Ice Navigation in a Changing Arctic

Alexandrov, V., S. Sandven, O. Jahannessen, L.H. Pettersson, and 

Ø. Dalen. 2000. Winter navigation in the northern sea route using 

RADARSAT data. Polar Record 36:335–344.

AMSA. 2009. Arctic Marine Shipping Assessment 2009 Report. Arctic 

Council, April 2009, 2nd printing, 187 pp. Available online at: http://

www.arctic.gov/publications/AMSA_2009_Report_2nd_print.pdf 

(accessed March 5, 2010).

Falkingham, J. 1991. Operational remote sensing of sea ice. Arctic

44(Suppl.1):29–33. Available online at: http://pubs.aina.ucalgary.ca/

arctic/Arctic44-S-29.pdf (accessed June 20, 2010).

Hutchings, J., and C. Bitz. 2005. Sea Ice Mass Balance of the Arctic 

(SIMBA) Workshop: Bridging Regional to Global Scales. University of 

Alaska Fairbanks, 80 pp.

Johannessen, O., V. Alexandrov, I. Frolov, S. Sandven, L. Pettersson, 

L. Bobylev, K. Kloster, V. Smirnov, Y. Mironov, and N. Babich. 2007. 

Remote Sensing of Sea Ice in the Northern Sea Route: Studies and 

Applications. Springer-Praxis, Chichester, UK, 472 pp.

Wadhams, P., and G. Amanatidis, eds. 2005. International Workshop 

on Arctic Sea Ice Thickness: Past, Present and Future. European 

Commission, Brussels, 293 pp.

4.8. Oil Spill Preparedness, Response, 

and Restoration

Beegle-Krause, C.J., A Allen, F.L. Bub, J.D. Christensen, E. Howlett, 

S. Glen, J. Kohut, O Schofield, E.J. Terrill, J. Thomas, J. Tintoré. 

2009. Observations as Assets in Decision Support. Presentation at 

OceanObs’09 Conference, September 21–25, 2009, Venice, Italy. 

NOAA Office of Response and Restoration Web Site: 

http://response.restoration.noaa.gov

ORR Incident News: http://www.incidentnews.gov

Oil in the Sea III: http://www.nap.edu/openbook.php?record_id=10388

Oil Spill dispersants: Fates and Effects: http://www.nap.edu/

catalog.php?record_id=11283#toc

The Aleutian Islands Risk Assessment: 

http://www.aleutiansriskassessment.com/

White Paper. 2010. U.S. Arctic Research Commission Recommends Steps 

to Expanded U.S. Funding for Arctic/Subarctic Oil Spill Research. 

Final draft available online at: http://www.arctic.gov/publications/

usarc_oilspill_5-26-10.pdf (accessed June 29, 2010).

Appendix 1

Rastetter, E.B., J.D. Aber, D.P.C. Peters, D.S. Ojima, and I. Burke. 2003. 

Using mechanistic models to scale ecological processes across space 

and time. BioScience 53:68–76.

Rastetter, E.B., A.W. King, B.J. Cosby, G.M. Hornberger, R.V. O’Neill, 

and J.E. Hobbie. 1992. Aggregating fine-scale ecological knowledge to model coarser-scale attributes of ecosystems. Ecological 

Applications 2:55–70.

Williams, M., E.B. Rastetter, D.N. Fernandes, M.L. Goulden, G.R. Shaver, 

and L.C. Johnson. 1997. Predicting gross primary productivity in 

terrestrial ecosystems. Ecological Applications 7:882–894.
Acronyms

ACM.................Aggregated Canopy Model

AON ................Arctic Observing Network

AW...................Atlantic Water

CROSERF .........Chemical Response to Oil Spills: Ecological Research Forum

DEM.................Digital Elevation Model

GCM ................General Circulation Model

GIS...................Geographic Information System

GPP..................Gross Primary Production

GRACE.............Gravity Recovery and Climate Experiment (NASA satellite system)

IA.....................Integrated Assessment

IARPC ..............Interagency Arctic Research Policy Committee 

IPCC.................Intergovernmental Panel on Climate Change

LIDAR...............Light Detection and Ranging

MODIS.............Moderate Resolution Imaging Spectroradiometer (NASA satellite system)

MOS.................Model Output Statistics 

NDVI................Normalized Difference Vegetation Index (from remote sensing)

NRDA ..............Natural Resource Damage Assessment

PAH..................Polycyclic Aromatic Hydrocarbons

PAR ..................Photosynthetically Active Radiation

PW...................Pacific Water 

SAON...............Sustained Arctic Observing Network

SAR ..................Synthetic Aperture Radar

SPA...................Soil-Plant-Atmosphere complex

TEK...................Traditional Environmental Knowledge
www.arctic.gov

June 2010