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Uncertainty guidance topic 1

The purpose of this section is to provide a basic understanding of the main sources of uncertainty which should be considered in making decisions using climate information. It highlights the importance of understanding uncertainty in order to be able to a) reduce it and/or b) ensure it is reflected in effective decision-making.

This section starts by considering uncertainty in its widest sense (not just the climate science), and focuses on uncertainty in climate information as an important subsection.

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What are sources of uncertainty in adaptation planning?

A wide range of information sources and data can be used to support adaptation planning. Climate data is one type of information that is used in support of adaptation planning. Other information derives from impact assessment models, and from decision-making itself. As with any data and information, particularly when it has been derived from models, there are associated uncertainties that those using the data and information should be aware of. The uncertainties result from a number of sources. Some of these uncertainties have to do with imperfect knowledge while others relate to the intrinsic variability in the climate, economic, social and environmental systems. There will always be an element of uncertainty to adaptation planning and decision-making.

Adaptation to climate change presents a complex methodological challenge. It calls for individuals to make decisions with potentially long term consequences on the basis of incomplete knowledge and uncertain information. There are numerous sources of uncertainty that need to be considered, such as social, economic and technical trends as well as potential changes in the legal, fiscal and regulatory system. There are also uncertainties associated with the assessment of current vulnerabilities to the impacts of climate variability and identifying and evaluating adaptive responses.

Adaptation decisions need to be made now, particularly those with long-term implications, and therefore decisions need to be made with imperfect information. Adding to the uncertainty associated with adaptation planning is the fact that  we will not know for some time whether the right decisions and choices have been made – the real test will only come some time in the future. Given the multiple uncertainties, and the fact that many of these cannot be adequately quantified, guidance on decision-making in the face of uncertainty is needed.  

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Why is there uncertainty in climate information?

Uncertainty in climate information stems the natural variability inherent in the climate system and from limitations in our ability to model the climate system and in our understanding of how future greenhouse gas emissions will change. Our understanding and modelling of climate change has advanced significantly in recent decades and increased the confidence we can place in the projected changes that are likely for key climate variables such as temperature, sea-level rise, snow cover, and the risk of heat waves and drought. There is also an improving understanding of projected patterns of precipitation which suggest that patterns observed in recent trends are likely to continue.

In general, we can have greater confidence in projections for larger regions than for specific locations, in temperature projections than those for precipitation, and for gradual changes in average conditions than we can have for extreme weather events such as storms. These characteristics of the projections present challenges to adaptation planning but they do not mean that adaptation is impossible or cannot be addressed. Instead, adaptation planners need to understand the information that is available, including the associated uncertainties at different temporal and spatial scales and consider what that uncertainty means for decision-making. They also need to ensure that the uncertainties and implications for the resulting decisions are clearly communicated, particularly in the context of supporting, evaluating and updating adaptation actions and plans.

Uncertainties in climate change projections arise from three primary sources:

  • Natural climate variability resulting from natural processes within the climate system which cause changes in climate over relatively short time scales;
  • Future emissions of greenhouse gases arising from uncertainty over the scale of future global emissions of greenhouse gases by human society, and thus the scale of future radiative forcing; this becomes a dominant source of uncertainty on time scales of 50 years or more.
  • Modelling uncertainty arising from incomplete understanding of Earth system processes and incomplete representation of these processes in climate models. 

More information on each of these sources follows here.

  1. Natural variability: Climate can and does vary naturally, regardless of any human influence. Natural climate variability arises as a result of two causes: natural internal forcing and natural external forcing, such as volcanic eruptions and variations in solar activity. Natural internal climate variability is one of the three main sources of uncertainty in estimating future climate change, and is often addressed by running multiple simulations of climate models.
  2. Future emissions of greenhouse gases: The starting point for projecting future climate change is the development of scenarios of future emissions of the greenhouse gases and other pollutants that affect climate (e.g. sulphur dioxide). Such scenarios extend data on past emissions with estimates of how emissions may change with future changes in technology, demography, economic development, etc. All these factors, and hence future emissions of greenhouse gases, are a source of uncertainty about future climate change. The most comprehensive attempt so far to characterise global emissions is the IPCC Special Report on Emissions Scenarios (SRES). It should be noted that the consequence of uncertainty in emissions for climate projections is much less for the near future climate (2020s) than for the distant future (2080s). Climate projections based on four of the commonly used SRES scenarios do not start to diverge significantly until just before mid-century. Near-term (next 15-20 years) climate is dominated by historic emissions of greenhouse gases, and natural climate variability.  Uncertainty about future emissions, which in turn depend on political decisions as well as uncertain economic and technological development, is an important source of uncertainty in climate projections of more than 50 years.
  3. Modelling uncertainty: Uncertainty about the functioning of the climate system, and the responses of biological and social systems to changes in climate, is another source of uncertainty for adaptation planning. Continued scientific research may help to resolve some of this uncertainty but it may also uncover additional uncertainty. Because different climate models represent these processes in different ways, their outcomes (for the same emissions scenarios) will be different. Methods for quantifying the uncertainties that are associated with different climate models have therefore been developed and are described in Topic 3.

One dimension of uncertainty in climate projections that is related to modelling uncertainty arises from downscaling. Regional climate models or statistical downscaling techniques are often used in order to provide climate change information at a scale smaller than that of global models give (typically 300km). Regional climate models can better take account of regional geography and topography (e.g. mountains and oceans), and are therefore better at representing local variations in climate. Statistical downscaling applies statistical relationships between observed small-scale (often station level) variables and larger (global model) scale variables to derive climate projections at a more detailed spatial resolution. It is important to note that both regional climate models and statistical downscaling techniques inherit errors from the global models that drive them.

Resources for further reading:

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What emission scenarios are the basis of most climate projections?

Most climate projections use the storylines and the associated emissions scenarios published by the Intergovernmental Panel on Climate Change (IPCC) in 2000 in the Special Report on Emissions Scenarios (SRES). These scenarios, often called the SRES scenarios, represent the outcome of different assumptions about the future course of economic development, demography and technological change. The SRES scenarios are "baseline" (or "reference") scenarios, which means that they do not take into account any current or future measures to limit greenhouse gas (GHG) emissions (e.g., the Kyoto Protocol to the United Nations Framework Convention on Climate Change). The SRES emission scenarios are organized into families, which contain scenarios that are based on similar assumptions regarding demographic, economic and technological development. The six families of emissions scenarios discussed in the IPCC's Third Assessment Report (TAR) and Fourth Assessment Report (AR4) are A1FI ("fossil intensive"), A1B ("base"), A1T ("technology"), A2, B1, and B2.

The next generation of scenarios to support climate change research and assessments are called Representative Concentration Pathways.  These scenarios prescribe trajectories for the concentration (rather than the emissions) and therefore are not simply updates of the SRES emission scenarios.  Unlike SRES in which no mitigation policies are implied, the RCPs cover  the full range of stabilisation, mitigation and baseline emission scenarios available in the scientific literatureThe RCPS provide a consistent set of greenhouse concentration trajectories that are intended to serve as input for climate modelling, pattern scaling and atmospheric chemistry modelling. They are named according to their 2100 radiative forcing level and have been chosen to represent the full range of radiative forcing scenarios and thus facilitate the mapping of all plausible climate evolutions.

Resources for further reading:

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