Imprecise probabilities of climate change: aggregation of fuzzy scenarios and model uncertainties

Fu, Guangtao; Lawry, Jonathan

doi:10.1007/s10584-006-9175-6

Cited by 49 publications

(44 citation statements)

References 58 publications

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“…This expression would be generalized to address uncertainty in the model, f, itself. (Duong 2003;Hall et al 2007). …”

Section: Possibility Theorymentioning

confidence: 99%

Characterizing Uncertainty for Regional Climate Change Mitigation and Adaptation Decisions

Unwin¹,

Moss²,

Rice³

et al. 2011

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SummaryThis white paper describes the results of new research to develop an uncertainty characterization (UC) process to help address the challenges of regional climate change mitigation and adaptation decisions. This research is being carried out as part of the integrated Regional Earth System Model (iRESM) initiative, a new scientific framework developed at Pacific Northwest National Laboratory to evaluate the interactions between human and environmental systems and mitigation and adaptation decisions at regional scales. The framework integrates a regional climate model; a regional energyeconomy model; and highly spatially-resolved models of crop productivity, building energy demands, electricity infrastructure operation and expansion, and water supply and management. The iRESM framework is intended to help regional stakeholders (scientists as well as decision makers) understand the consequences of climate change as well as the consequences of policies to mitigate or adapt to such change within regions.The initiative has developed the following four science questions to guide its research:• How do intrinsic regional characteristics shape, enhance, or constrain regional mitigation and adaptation opportunities?• How do projected changes in mean climate versus climate extremes affect the development of adaptation and mitigation strategies?• How might interactions between management decisions and natural processes contribute to rapid or nonlinear changes, and do they contribute to climate feedbacks?• How will adaptation and mitigation strategies interact in the next few decades in terms of achieving their respective goals?An important consideration for the iRESM initiative is that Earth system mechanisms and future changes are imperfectly understood and in some cases deeply uncertain-especially at the level of resolution required for regional analyses and decision making. The UC process developed for the initiative addresses uncertainty by first identifying through sensitivity analysis the key uncertainties in data inputs, individual model structures, and coupled models that are important for particular stakeholder questions and evaluation criteria. These key uncertainties are then characterized and propagated to determine the robustness of the framework's results for the particular questions, thereby providing insights for researchers and decision makers alike. The process differs from many traditional applications of uncertainty quantification (UQ) because of its focus on stakeholder needs and its allowance for qualitative and semi-quantitative methods for describing uncertainty. The process not only permits the dimensionality of the UQ problem to be reduced, it also allows research efforts to be targeted at the uncertainties that really matter for the question in hand.This decision-specific orientation for UC has multiple implications for the iRESM initiative that will continue to be explored, including: the importance of stakeholder interactions and the development of methods for communicating results; the dev...

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“…This expression would be generalized to address uncertainty in the model, f, itself. (Duong 2003;Hall et al 2007). …”

Section: Possibility Theorymentioning

confidence: 99%

Characterizing Uncertainty for Regional Climate Change Mitigation and Adaptation Decisions

Unwin¹,

Moss²,

Rice³

et al. 2011

View full text Add to dashboard Cite

show abstract

“…We take this approach because 1) there is not enough information in the PCMDI data set to fully specify the joint variation of precipitation and temperature temporally (Tebaldi and Sanso 2008), 2) the analysis shows the specific choice of the motif does not dominate the conclusion of the analysis (see section 4.3), and 3) other studies have also had to revert to selecting a motif to make the uncertainty analysis tractable (Hallegate 2006). By maintaining a self-consistent relationship among precipitation, temperature, frequency and intensity, we attempt to minimize the statistical concerns (or at least make them transparent) when sampling a single variable (precipitation) to reflect variability across multiple dimensions, in addition to propagating the uncertainty within simulation models (Hall 2007). …”

Section: Approachmentioning

confidence: 99%

Climate uncertainty and implications for U.S. state-level risk assessment through 2050.

Loose¹,

Lowry²,

Malczynski³

et al. 2009

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Decisions for climate policy will need to take place in advance of climate science resolving all relevant uncertainties. Further, if the concern of policy is to reduce risk, then the best-estimate of climate change impacts may not be so important as the currently understood uncertainty associated with realizable conditions having high consequence. This study focuses on one of the most uncertain aspects of future climate change -precipitation -to understand the implications of uncertainty on risk and the near-term justification for interventions to mitigate the course of climate change.We show that the mean risk of damage to the economy from climate change, at the national level, is on the order of one trillion dollars over the next 40 years, with employment impacts of nearly 7 million labor-years. At a 1% exceedance-probability, the impact is over twice the mean-risk value. Impacts at the level of individual U.S. states are then typically in the multiple tens of billions dollar range with employment losses exceeding hundreds of thousands of laboryears.We used results of the Intergovernmental Panel on Climate Change's (IPCC) Fourth Assessment Report 4 (AR4) climate-model ensemble as the referent for climate uncertainty over the next 40 years, mapped the simulated weather hydrologically to the county level for determining the physical consequence to economic activity at the state level, and then performed a detailed, 4 seventy-industry, analysis of economic impact among the interacting lower-48 states. We determined industry GDP and employment impacts at the state level, as well as interstate population migration, effect on personal income, and the consequences for the U.S. trade balance. 5 ACKNOWLEDGMENTS

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“…In many practical situations, e.g., in climate modeling, we have bounds on the probability density or, in the discrete case, bounds on the probabilities of individual values; see, e.g., [7], [8]. How can we process this uncertainty?…”

Section: Practical Situation: Bounds On Probabilitiesmentioning

confidence: 99%

Computing Statistical Characteristics When We Know Probabilities with Interval or Fuzzy Uncertainty: Computational Complexity

Xiang

2007

NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society

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In traditional statistics, we usually assume that we know the exact probability distributions. In practice, we often only know the probabilities with interval uncertainty.The main emphasis on taking this uncertainty into account has been on situations in which we know a cumulative distribution function (cdf) with interval uncertainty. However, in some cases, we know the probability density function (pdf) with interval uncertainty. We show that in this situations, the exact range of some statistical characteristics can be ef ciently computed. Surprisingly, for some other characteristics, similar statistical problems which are ef ciently solvable for interval-valued cdf become computationally dif cult (NP-hard) for interval-valued pdf.

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Imprecise probabilities of climate change: aggregation of fuzzy scenarios and model uncertainties

Cited by 49 publications

References 58 publications

Characterizing Uncertainty for Regional Climate Change Mitigation and Adaptation Decisions

Characterizing Uncertainty for Regional Climate Change Mitigation and Adaptation Decisions

Climate uncertainty and implications for U.S. state-level risk assessment through 2050.

Computing Statistical Characteristics When We Know Probabilities with Interval or Fuzzy Uncertainty: Computational Complexity

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