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How are uncertainties communicated?

Describing and quantifying uncertainty can play a valuable role in informing decision-making. Quantification cannot eliminate uncertainty, but it can help to understand the levels of uncertainty we are dealing with. Probabilistic information can be a useful way of explaining the likelihood of possible futures. Statistical methods and models play a key role in the interpretation and synthesis of observed climate data and of projections from numerical climate models.

However, probabilistic information is not always available. In this case, clear descriptions of future changes, even if qualitatively in nature, can provide valuable insights into what to expect and how to decide based on that information. Approaches such as the use of scenarios and pathways can be used when probabilities are not available.

The type and time horizon of the adaptation decision context will determine the most appropriate information (probabilistic or not) to use.

How are uncertainties quantified and described?

Treatment of uncertainty in IPCC

The IPCC has developed a common approach and a calibrated language to evaluating and communicating the degree of certainty in its findings. This approach has been put forward in the IPCC Guidance Note on Consistent Treatment of Uncertainties (Mastrandrea et al., 2010) and applied in the IPCC Fifth Assessment Report (IPCC AR5, 2013-2014) and the recent Special Report on Global Warming of 1.5 °C (IPCC SR1.5, 2018).

The approach relies on two metrics (confidence and likelihood) for communicating the degree of certainty in key findings, based on the IPCC author teams’ evaluations of the underlying scientific understanding:

Confidence: Five qualifiers are used to express levels of confidence in key findings, ranging from very low, through low, medium, high, to very high. The level of confidence synthesizes the judgements about the validity of findings as determined through evaluation of the available evidence (type, quality, amount or internal consistency) and the degree of scientific agreement between different lines of evidence (see figure 1).


Figure 1 - The basis for the confidence level is given as a combination of evidence (limited, medium, robust) and agreement (low, medium and high). Confidence increases towards the top right corner. Generally, evidence is most robust when there are multiple, consistent independent lines of high quality (Mastrandrea et al., 2010).


Likelihood: Quantified measures of uncertainty in a finding expressed probabilistically (based on statistical analysis of observations or model results, or expert judgement). If uncertainties can be quantified probabilistically, a finding can be characterized using the following terms (table 1):


Table 1 - Likelihood terms associated with outcomes used in the IPCC AR5 and SR1.5

Note: Additional terms that may also be used when appropriate include extremely likely (95–100% probability), more likely than not (>50–100% probability), more unlikely than likely (0– < 50 %) and extremely unlikely (0–5% probability).


Because the IPCC calibrated language was developed in English, precaution should be used with the translation of this approach to other languages as it may lead to a loss of precision.

Scenarios and pathways

In the absence of probabilistic evidence or as a means to support climate change impact and vulnerability assessments, scenarios and other qualitative descriptions of future changes are often used. Care should be taken since scenarios, pathways and other terms are sometimes used interchangeably, with a wide range of overlapping definitions (Rosenbloom, 2017). Some useful definitions are provided by the IPCC AR5 (2014) and IPCC SR1.5 (2018):

Scenarios as plausible descriptions of how the future may develop based on a coherent and internally consistent set of assumptions about key driving forces (e.g., rate of technological change, prices) and relationships. Note that scenarios are neither predictions nor forecasts, but are useful to provide a view of the implications of developments and actions.

Pathways describe the temporal evolution of natural and/or human systems towards a future state. The pathway concepts range from sets of quantitative and qualitative scenarios (or narratives) of potential futures to solution oriented decision-making processes targeting desirable societal goals. Pathway approaches typically focus on biophysical, techno-economic, and/or socio-behavioural trajectories and involve various dynamics, goals, and actors across different scales.

Different types of scenarios and pathways of future conditions that are useful for adaptation decision-making are available at global and in some cases, national to local scales. These typically include:

Emission scenarios: Plausible representations of the future development of emissions of greenhouse gases and aerosols based on a coherent and internally consistent set of assumptions about driving forces (such as demographic and socioeconomic development, technological change) and their key relationships. Concentration scenarios, derived from emission scenarios, are used as input to climate models to compute climate projections at multiple scales.


Representative Concentration Pathways (RCPs) are a new set of scenarios that were developed for, but independently of the IPCC AR5 (2014). They describe four different 21st century pathways of greenhouse gas (GHG) emissions and atmospheric concentrations, air pollutant emissions and land use (Moss et al., 2008).

The RCPs have been developed using Integrated Assessment Models (IAMs) as input to a wide range of climate model simulations to project their consequences for the climate system. These climate projections, in turn, are used for impacts and adaptation assessment (IPCC AR5, 2014).

The word representative signifies that each RCP provides only one of many possible scenarios that would lead to the specific radiative forcing characteristics. These are referred to as pathways in order to emphasize that they are not definitive scenarios, but rather internally consistent sets of (time-dependent) forcing projections that could potentially be realized with more than one underlying socioeconomic scenario. The number after the acronym RCP identifies the approximate value of radiative forcing (in W m–2) expected to be reached at 2100 (IPCC AR5, 2013).

Four RCPs were selected and used as a basis for the climate predictions and projections in the IPCC AR5: RCP2.6 (stringent mitigation); RCP4.5 and RCP6.0 (intermediate stabilization scenarios); and RCP8.5 (very high GHG emissions).


Socioeconomic scenarios: Scenarios that describe a possible future in terms of population, gross domestic product, and other socioeconomic factors relevant to understanding the implications of climate change at the national to local level.


Shared socio-economic pathways (SSPs) were developed to complement the RCPs with varying socio-economic challenges to adaptation and mitigation (O’Neill et al., 2014). Based on five narratives, the SSPs describe alternative socio-economic futures in the absence of climate policy intervention, comprising sustainable development (SSP1), regional rivalry (SSP3), inequality (SSP4), fossil–fueled development (SSP5), and a middle-of-the-road development (SSP2) (O’Neill, 2000; O’Neill et al., 2017; Riahi et al., 2017).

The combination of SSP-based socio-economic scenarios and Representative Concentration Pathway (RCP)-based climate projections provides an integrative frame for climate impact and policy analysis.


Climate projections (and climate impact projections): Simulated response of the climate system (or a climate-sensitive system) to a scenario of future emission or concentration of greenhouse gases and aerosols generally derived using climate models (or climate impact models). Climate projections often serve as the raw material for constructing climate (change) scenarios, but these usually require additional information such as the observed current climate.

For applications informing important policy decisions or major investment decisions, it is recommended that decision-makers should make use of the full range of available climate change (and impacts) scenarios and model information.


Other main topics:

1. What is meant by uncertainty?

3. How to factor in uncertainty?