By Matthias Jonas, IIASA, and  Gregg Marland, Appalachian State University

Greenhouse gas emissions are seldom measured directly. They must be estimated from data such as on energy use and changes in land use. That means that estimates of greenhouse gas emissions from human sources are inherently uncertain.

This image of Central and Eastern Europe at night was taken from the NASA, International Space Station in 2011. Image Credit: NASA

Uncertainty around emissions may mean that reaching temperature targets would require greater cuts than previously thought. Central and Eastern Europe at night, taken from the NASA, International Space Station in 2011. Image Credit: NASA

In a new study with colleagues at IIASA and the Polish Academy of Sciences, we asked how uncertainty over time will affect short-term GHG emission commitments and long-term efforts to meet global temperature targets for 2050 and beyond. The new study addresses a fundamental problem: how to combine uncertainty about current and historic emissions (diagnostic uncertainty) with uncertainty about projected future emissions (prognostic uncertainty).

The paper introduces a concept we call the Emissions, Temperature, Uncertainty (ETU) framework.The ETU framework allows any country to understand its national and near-term mitigation and adaptation efforts in a more realistic context, where uncertainty is taken into account.

The ETU assumes that cumulative emissions can be constrained over time by international agreements that are binding, but that emissions can be estimated only imprecisely, and whether or not they will achieve an agreed temperature target  is also uncertain. The ETU framework allows policymakers to understand diagnostic and prognostic uncertainty so that they can make more educated (precautionary) decisions for reducing emissions given an agreed future temperature target.

Diagnostic uncertainty refers to the uncertainty contained in current inventoried emission estimates and relates to the risk that true greenhouse gas emissions are greater than inventoried emission estimates. Prognostic uncertainty refers to cumulative emissions between a start year and a future target year and the global average temperature increase they would generate. It relates to the risk that an agreed temperature target is exceeded. In a nutshell, the ETU framework can be used to monitor a country’s performance – that is, past achievements as well as projected achievements – in complying with a future warming target in a quantified uncertainty-risk context.

While our study addresses whether or not the future increase in global temperature can be kept below 2, 3, or 4ºC targets, its primary aim is to use those targets to demonstrate the relevance of both diagnostic and prognostic uncertainty.   The paper shows:

  • Uncertainty is important in emissions: Both diagnostic and prognostic uncertainty need to be considered to facilitate better decisions on reducing emissions, given an agreed future temperature target.
  • What these risks mean for emissions targets: We find, for example, that to nullify the diagnostic uncertainty-related risk, and to maintain a similar level of risk for exceeding a 2o target, the universally valid per-capita emissions target for 2050 resulting from the underlying cumulative emissions constraint needs to be shifted downward by nearly 10%.
  • Risk and uncertainty are interdependent: This interdependence poses a challenge for decision-makers because they have to deal with uncertainty and risk simultaneously.
  • Including land-use change is tricky: Determining cumulative emissions from land use and land-use change in this emission-temperature setting is difficult, because an achievable future state of sustainability for the terrestrial biosphere has not yet been defined.

Matthias Jonas, Gregg Marland, Volker Krey, Fabian Wagner, Zbigniew Nahorski (2014).  Uncertainty in an emissions-constrained world. Climatic Change. April 2014.

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.

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