• Adaptation – Adjustment in natural or human systems in response to actual or expected climatic stimuli or their effects, which moderates harm or exploit beneficial opportunities. Various types of adaptation can be distinguished, including anticipatory, autonomous and planned adaptation. (IPCC Fourth Assessment Report 2007) Adaptation can be understood to mean intentional human action to prepare for climate change, both to realize gains from opportunities and reduce the damages caused by climate change.

  • Bottom-up approaches – analysis or scenario methods that begin with analysis of the details of a system or decision that is of interest and then identifies general contextual trends or conditions that affect the system or decision.

  • Adaptive Management – A systematic approach for improving resource management by learning from management outcomes. (National Research Council, 2004)

  • Climate model ensemble – a group of climate model simulations that use the same assumptions. Large ensembles are used to generate information about natural climate variability and to characterize uncertainty from different sources, such as different initial conditions or model differences

  • Climate scenarios – plausible representations of future climate conditions (temperature, precipitation, and other factors) produced using a variety of techniques including scaling of observed climate, spatial and temporal analogues in which climates from other locations or periods are used as example future conditions, extrapolation and expert judgment, and mathematical climate and Earth system models. All of these techniques continue to play a useful role in development of scenarios, with the appropriate choice of method depending on the intended use of the scenario. (Moss et al. 2011)  

  • Climate Smart Conservation – describes actions which address climate change impacts together with other threats and promote nature‐based in order to reduce greenhouse gas emissions and enhance carbon sinks, reduce climate change impacts on wildlife and people, enhancing their ability to adapt, and sustain vibrant, diverse ecosystems. (Moore et al.  2013)

  • Confidence – The level of confidence in the correctness of a result. (Rowland et al., 2014)

  • Decision context – Clearly defines what question or problem is being addressed by the planning or decision process and establishes the scope and limits of the effort (Gregory et al. 2012)

  • Decision scaling – “a new approach to using climate information within a decision making framework that links bottom-up, stochastic vulnerability analysis with top down use of GCM projections “Decision-scaling begins with a bottom-up analysis to identify a climate condition that impacts a decision and then uses sources of climate information such as GCMs to identify how often such conditions occur under different climate scenario. (Brown et al. 2011)

  • Discontinuities – Events or consequences that cannot be extrapolated from prior actions or events and are unpredictably new

  • Drivers – Underlying causes of system change that are external from the system of analysis. They come from higher scales and are not affected by what happens within the system (Walker et al. 2012)

  • Ecosystem‐based adaptation –  Ecosystem‐based adaptation uses biodiversity and ecosystem services in an overall adaptation strategy. It includes the sustainable management, conservation and restoration of ecosystems to provide services that help people adapt to the adverse effects of climate change (CBD, 2009, p. 10).

  • Emissions scenarios – descriptions of potential future emissions to the atmosphere of greenhouse gases and other radiatively important gases and particles that are used to explore the implications of alternative energy and technology futures and provide inputs to climate models (Moss et al. 2011).

  • Environmental scenarios (Moss et al. 2011) – these “focus on changes in environmental conditions such as water availability and quality, sea level rise (incorporating geological and climate drivers), land cover and use, and air quality. Climate change can drive changes in these factors, or scenarios can represent independently caused variations. The potential impact of climate change and the effectiveness of adaptation options cannot be understood without examining interactions of changes in climate, environmental conditions, and human responses.”

  • Exploratory scenario – a scenario that is used to explore the implications of a possible future on predetermined goals and values (Holway et al. 2012).

  • Foresight – Set of methods to better understand the range of possible futures (Mietzner and Reger 2005); gathering anticipatory intelligence from a wide range of knowledge sources in a systematic way and linking it to today’s decision making to meet future challenges proactively. Scenario planning is one foresight approach.

  • GCM – General Circulation Models represent physical processes in the atmosphere, ocean, cryosphere and land surface and are currently the most advanced tools currently available for simulating the response of the global climate system to increasing greenhouse gas concentrations

  • Interactive and immersive visualization tools – consist of a range of visual and spatial media derived from modeling, data, scenarios, and descriptive narratives used to contextualize and communicate climate change information in two and three dimensions at the local or regional level (Sheppard et al. 2011).

  • IPCC – Intergovernmental Panel on Climate Change

  • Irreducible uncertainty – Uncertainty that cannot be reduced. Situations characterized by irreducible uncertainty can arise is different ways. Uncertainty may be irreducible due to the inherently unpredictable variation in natural systems over space and time (environmental) or in human systems (linked to behavior). Changes in driving forces external to the system (e.g. related to social, economic, and technological choices) are uncontrollable, often resulting in irreducible uncertainty. Finally imperfect knowledge can also contribute to irreducible uncertainty if unresolvable within a decision timeframe (Shearer 2005, Walker et al. 2004, Bengston et al. 2012).

  • Level of uncertainty – Where the uncertainty manifests itself along the gradient between deterministic knowledge and total ignorance (Walker et al. 2004)

  • Likelihood (Rowland et al. 2014) – The likelihood of an occurrence, an outcome, or a result, where this can be estimated probabilistically. The IPCC developed a standard for their reports:

    Terminology Likelihood of the occurrence / outcome
    Virtually certain  >99% probability of occurrence
    Very likely  >90% probability
    Likely  >>66% probability
    More likely than not >50% probability
    About as likely as not 33 to 66% probability
    Unlikely   <33% probability
    Very unlikely <10% probability
    Exceptionally unlikely  <1% probability
  • Mental model testing – making a group’s mental model of how things work based on their successes explicit so it can be discussed and compared to other scenarios.

  • Monitorable Indicators (for scenarios) – variables that can be tracked through time to determine the occurrence of regimes, triggers, cascading events, discontinuities, and wild cards.

  • Narrative – see “Storyline

  • Non-linear response – a system for which the effects or responses (outputs) are not proportional to their causes (inputs) and cannot be modeled with linear equations. (Rowland et al. 2014)

  • Normative scenario – a scenario used to help identify a desired future. (Holway et al. 2012)

  • Mixed-method approaches – methods for scenario development that use elements of both a scenario planning approach, in which participants determine the purpose, substantive focus, and character of a scenario development effort, and other planning methods or scientifically derived scenarios, which can be used at points in the process to identify broader socioeconomic, climate, or other conditions that could affect relevant aspects of the future.

  • Mitigation – Actions to slow or constrain climate change. (Leary, 2006, p. 155)   Intentional human action to reduce greenhouse gas emissions locally or globally.

  • Participatory process – “a purposefully designed set of activities structured around framing (including clarifying objectives and identifying participants), a set of participatory activities that can include workshops and engagement of participants through other means such as social media or technology such as decision theaters, and a set of outcomes that could be a decision, a community plan, a report, films/audios, or other forms of knowledge sharing or exchange.” (Moss et al. 2011)

  • Prediction/Forecast – A statement about what will happen in the future with some degree of certainty often associated with probability distribution; focus on one future, considered most likely.

  • Projection – A potential future evolution of a quantity or set of quantities, often computed with the aid of a model. Projections are distinguished from predictions in order to emphasize that projections involve assumptions concerning, for example, future socio-economic and technological developments, future socio-economic and that may or may not be realized, and are therefore subject to substantial uncertainty.

  • Reducible uncertainty – Sources that could, feasibly, be controlled or refined to reduce or eliminate the particular uncertainty, scientific understanding (epistemic) and linguistic. Epistemic includes measurement error, sampling error, systematic error or bias (from measurement, sample selection, etc.), model uncertainty (potentially reducible), and reliance on subjective judgment. Note that these sources may or may not be reducible within a given timeframe and may need to be treated as irreducible in some decision contexts.

  • Regimes – the persistent status of a system

  • Risk – The probability of an event occurring and magnitude of the consequences

  • Risk management – Deciding what to do (how to reduce risk) in light of imperfect knowledge

  • Scale – Description of the spatial extent of an area or temporal extent time period

  • Scenarios (for scenario planning) – Plausible futures of a system under different conditions; “scenario’” as a “hypothetical sequence of events constructed for the purpose of focusing attention on causal processes and decision points” (Kahn and Wiener, 1967, page 6).

  • Scenario dimensions – Uncertainties around which scenarios are constructed, represented as axes in some methods

  • Scenario logics - (Rowland et al. 2014) Methods for structuring the relationships between different drivers and assumptions in scenarios

  • Scenario planning – Comprehensive process for strategic planning that involves the development scenarios, consideration of their impacts, and implications for strategy and action choices

  • Stationarity – The assumption that natural systems fluctuate within an unchanging envelope of variability through time (Milly et al. 2008)

  • Storyline (Rowland et al. 2014) –  A narrative description of a scenario (or family of scenarios), highlighting the main scenario characteristics, relationships between key driving forces and the dynamics of their evolution

  • Scenarios – Plausible futures that facilitate one’s evaluation of the outcomes of potential decisions in the context of different sets of background conditions. Scenarios as they are chiefly discussed in this paper are composed of narratives created by considering the interactions of multiple critical uncertain drivers of management decisions. These are different from the scenarios used in the Global Climate Models (GCMs), which are entirely mathematical, representing probable outcomes from the accumulation of greenhouse gases in the atmosphere. The scenarios discussed in this guidance for use in scenario planning can incorporate improbable but plausible drivers of change, extremes, first and second‐order interactions, non‐climatic drivers, and other elements not explicitly factored into GCMs.

  • Socioeconomic scenarios – narrative and/or quantitative descriptions of plausible patterns or pathways of demographic change (fertility, mortality, migration, and other factors that affect the size and location of human populations), economic development (patterns of trade, employment, economic development, etc.), technology (for energy, agriculture, water resources, etc., considering factors such as efficiency, fuel sources, and others), and institutions (types and effectiveness of governance arrangements, patterns of association in civic organizations, etc.). These factors are important for understanding human contributions to climate change as well as the vulnerability or resilience of society. Historically, these scenarios have been developed to inform emissions scenarios.

  • Source or type of uncertainty – whether the uncertainty is due to the imperfection/lack of our knowledge or is due to the inherent variability (non-linear dynamics) of the phenomena being described (Walker et al. 2004)

  • Storyline and simulation – Combination of qualitative narrative development and quantitative modeling (scenario construction-sensu (Mahmoud et al. 2009, Wollenberg et al. 2000)

  • System –  Defined by (composed of) its state variables, and it is the relationships among them that are of central interest. The system changes as a consequence of both these internal relationships and the effects of external drivers. (Walker et al. 2012)

  • Thresholds – Conditions in time and space that produce notably different experiences in a system’s state or response

  • Top-down approaches – methods that analyze general trends or properties of a system (e.g., global socioeconomic trends that give rise to emissions, then climate scenarios) todepict the broad context of future conditions which impact specific places, entities, or how decisions play out.

  • Triggers – particular combination of conditions that lead to a change in a system’s regime

  • Uncertainty –  An expression of the degree to which a value or outcome is unknown. Uncertainty can result from lack of information or from disagreement about what is known or even knowable. It may have many sources, from quantifiable errors in the data or limited ability to characterize/model system, to ambiguously defined concepts or terminology, or uncertain projections of human behavior, environmental variation and stochasticity.

  • Uncertainty – a description of the extent to which something is unknown. Uncertainty can arise because of a lack of information and/or disagreement about how to interpret the available information. It can also arise from ambiguous definitions, lack of understanding of underlying processes, errors in observations, lack of model skill, and other sources. Uncertainty can be represented both qualitatively (e.g., terms used by experts to describe the state of knowledge) or quantitatively (ranges of future variables as well as other statistical properties).

  • Vulnerability – The degree to which a system is susceptible to and unable to cope with the adverse effects of climate change, including climate variability and extremes. Vulnerability is a function of the character, magnitude, and rate of climate change and variation to which a system is exposed, its sensitivity, and its adaptive capacity. (IPCC 2008) For the purposes of this paper, vulnerability can be understood to be a condition produced by exposure (i.e., location of the management target in regard to impact area), sensitivity (i.e., degree to which the impact can damage the management target), and the management target’s capacity to adapt to change, taking advantage of positive change and avoiding or minimizing the damage of negative change. Exposure  x  Sensitivity  x  Capacity to adapt  =  Vulnerability

  • Vulnerability assessment – A systematic evaluation of projected or observed exposure to negative impacts from an event or process, analyzing sensitivity and capacity to adapt, and on those bases creating a ranking of impacts to assist in planning.  

  • Wild cards – Major surprises caused by low probability events that have high impacts

  • Wind-tunneling – after building the event or endstate scenarios, the testing of alternative decisions for robustness. In this case, the scenarios are used for context. 

Sources used for glossary