Managing uncertainty in climate science and meeting sustainability constraints

Greenhouse gas (GHG) emissions are a particular challenge for sustainability. Decisions need to be made now to avoid unsustainable futures, but the basis for these decisions, in terms of both data and models, encompasses vast inherent uncertainties. Proper accounting of uncertainties when verifying mitigation efforts and analyzing scenarios of future emissions is the focus of several Advanced Systems Analysis (ASA) Program studies.

© Bertold Werkmann | Dreamstime

© Bertold Werkmann | Dreamstime

Uncertainty is a pervasive characteristic of sustainability research [1]. Science should aim to describe uncertainties as comprehensively and well as possible, both quantitatively and qualitatively, and to develop methods that can lead to improved decision-making under uncertainty. A basic research strategy aimed at elucidating uncertainties in parameters as well as in alternative model representations, and developing improved models for robust decision-making is needed [1].

A new ASA study has put forward a method to reconcile structural (model) uncertainty, which produces an “integrated” probabilistic distribution based on several prior distributions using the principle of their mutual compatibility [2]. This method can be used to integrate alternative models from the multi-model ensembles, and has been applied to alternative models of forests’ net primary production of carbon in Russia. The integration methodology was found to reduce the output uncertainty of distributions for all considered climatic zones [3][4].

ASA research has enhanced our understanding of the uncertainty in estimating greenhouse gas (GHG) emissions and dealing with related challenges, including monitoring emissions; adhering to emissions commitments; ensuring functioning emissions trading markets; and meeting low-carbon or low-GHG futures in the long term [5][6][7][8][9][10]. The approaches to address uncertainty attempt to improve national inventories, and from a wider, systems analytical perspective, seek to increase the value of such inventories for use in a compliance and global monitoring and reporting framework. Other ASA climate change research has covered policy advice for improving energy efficiency in industry and enhancing renewables to mitigate Turkish GHG emissions [11][12].

References

[1] Grubler A, Ermoliev Y & Kryazhimskiy A (2015). Coping with uncertainties-examples of modeling approaches at IIASA. Technological Forecasting and Social Change 98:213-222.

[2] Kryazhimskiy AV (2015). Posterior integration of probabilities. Elementary theory. Probability Theory and Applications 60(1): 45-79 [In Russian].

[3] Kryazhimskiy A, Rovenskaya E, Veshchinskaya V, Gusti M, Shchepashchenko D & Shvidenko A (2015). Towards harmonizing competing models: Russian forests' net primary production case study. IIASA Interim Report IR-15-003.

[4] Kryazhimskiy A, Rovenskaya E, Shvidenko A, Gusti M, Shchepashchenko D & Veshchinskaya V (2015). Towards harmonizing competing models: Russian forests' net primary production case study. Technological Forecasting & Social Change 98:245-254.

[5] Ometto JP, Bun R, Jonas M & Nahorski Z eds. (2015a). Uncertainties in Greenhouse Gas Inventories. Expanding Our Perspectives. Cham, Switzerland: Springer International Publishing AG.

[6] Jonas M, Ometto JP, Bun R & Nahorski Z (2015b). Preface. In: Greenhouse Gas Inventories: Expanding Our Uncertainty Perspective, eds. Ometto JP, Bun R, Jonas M and Nahorski Z, pp. v–xii. Dordrecht, Netherlands: Springer.

[7] Ometto JP, Bun R, Jonas M, Nahorski Z, Gusti M (2015b). Uncertainties in greenhouse gases inventories – Expanding our perspective. In: Greenhouse Gas Inventories: Expanding Our Uncertainty Perspective eds. Ometto JP, Bun R, Jonas M and Nahorski Z, pp. 1–8. Dordrecht, Netherlands: Springer.

[8] Jonas M, Marland G, Krey V, Wagner F & Nahorski Z (2015a). Uncertainty in an emissions-constrained world. In: Greenhouse Gas Inventories: Expanding Our Uncertainty Perspective, eds. Ometto JP, Bun R, Jonas M and Nahorski Z, pp. 9–26. Dordrecht, Netherlands: Springer.

[9] Lesiv M, Bun A, Jonas M (2015). Analysis of change in relative uncertainty in GHG emissions from stationary sources for the EU15. In: Greenhouse Gas Inventories: Expanding Our Uncertainty Perspective, eds. Ometto JP, Bun R, Jonas M and Nahorski Z, pp. 55–68. Dordrecht, Netherlands: Springer.

[10] Hryniewicz O, Nahorski Z, Verstraete J, Horabik J & Jonas M (2015). Compliance for uncertain inventories via probabilistic/fuzzy comparison of alternatives. In: Greenhouse Gas Inventories: Expanding Our Uncertainty Perspective, eds. Ometto JP, Bun R, Jonas M and Nahorski Z, pp. 69–84 Dordrecht, Netherlands: Springer.

[11] Ates SA (2015). Energy efficiency and CO2 mitigation potential of the Turkish iron and steel industry using the LEAP (long-range energy alternatives planning) system. Energy 90(1):417-428.

[12] Ates SA (2015). Life cycle cost analysis: An evaluation of renewable heating systems in Turkey. Energy Exploration and Exploitation 33(4):621-638.

Collaborators

Transition to New Technologies Program (TNT), IIASA

Ecosystem Services and Management Program (ESM), IIASA

Energy Program (ENE), IIASA

Systems Research Institute, Polish Academy of Sciences, Poland 

Lviv Polytechnic National University, Ukraine 

Austrian Institute for Economic Research, Austria 

Environment Agency Austria, Austria 

Wegener Center for Climate and Global Change, University of Graz, Austria 


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Last edited: 15 March 2016

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