Analysing uncertainties in climate change impact assessment across sectors and scenarios

Brown, Calum; Brown, Evan; Murray-Rust, Dave; Cojocaru, George; Savin, Cristina; Rounsevell, Mark

doi:10.1007/s10584-014-1133-0

Cited by 44 publications

(54 citation statements)

References 38 publications

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“…By contrast, the F1 and F3 cropland futures are very distinct from one another, with a higher level of confidence, indicated by their peaked distributions. The simulated discrepancy between total demand and production in F3 indicates that a focus on regional production with limited trade can risk food insecurity for countries with limited potential for domestic production, which agrees with Brown et al (2014). When considering F1 and F3, the F2, F4 and F5 range of cropland changes by 2100 (−20 to +17 %) increases to a total range of −41 to 58 % by 2100 compared with 2000.…”

Section: Discussionsupporting

confidence: 70%

“…Land use and land cover change (LULCC) is a fundamental aspect of global environmental change, but large uncertainties exist in estimating the effect of multiple drivers on LULCC in the future (Brown et al, 2014). A range of different models and scenarios have been used to project future cropland areas to 2100 with estimates in the range of 930 to 2670 Mha Prestele et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…In this case, the probability distribution of each model input parameter is conditional on the internal logic and assumptions within the contextualizing scenario. Hence, conditional probabilistic futures are useful in exploring parameter uncertainty within and across scenarios (Brown et al, 2014;van Vuuren et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework

et al. 2016

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Abstract. We present a modelling framework to simulate probabilistic futures of global cropland areas that are conditional on the SSP (shared socio-economic pathway) scenarios. Simulations are based on the Parsimonious Land Use Model (PLUM) linked with the global dynamic vegetation model LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator) using socio-economic data from the SSPs and climate data from the RCPs (representative concentration pathways). The simulated range of global cropland is 893-2380 Mha in 2100 (± 1 standard deviation), with the main uncertainties arising from differences in the socio-economic conditions prescribed by the SSP scenarios and the assumptions that underpin the translation of qualitative SSP storylines into quantitative model input parameters. Uncertainties in the assumptions for population growth, technological change and cropland degradation were found to be the most important for global cropland, while uncertainty in food consumption had less influence on the results. The uncertainties arising from climate variability and the differences between climate change scenarios do not strongly affect the range of global cropland futures. Some overlap occurred across all of the conditional probabilistic futures, except for those based on SSP3. We conclude that completely different socio-economic and climate change futures, although sharing low to medium population development, can result in very similar cropland areas on the aggregated global scale.

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Section: Discussionsupporting

confidence: 70%

Section: Introductionmentioning

confidence: 99%

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Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework

et al. 2016

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“…Each meta-model has been calibrated and validated against either historical observations or the outputs from the validated complex models -see citations within Supplementary Table 1. In addition, all of the meta-models have undergone comprehensive sensitivity analysis 19 and uncertainty analysis 20,21 and been reported within integrated cross-sectoral impact, adaptation and vulnerability assessments 4,14,17 .…”

Section: Methodsmentioning

confidence: 99%

“…The IAP is a spatiallyexplicit modelling platform that operates on a 10 x 10 minute grid for the countries of the European Union plus Norway and Switzerland. It has been thoroughly validated (Supplementary Table 1) and widely applied in climate change impact 2,4,6,14,15 , adaptation 16 and vulnerability 17 assessment, in robust policy analysis 18 , and has been tested extensively through model sensitivity 19 and uncertainty analysis 20,21 . It was applied with and without coupling of the individual sectoral models for a number of scenario experiments for the 2050s that included different SRES emissions scenarios 22 , climate change models 23 and the socio-economic storylines underlying the SRES scenarios 22 .…”

Section: States: "Little Information Is Available On Integrated and Cmentioning

confidence: 99%

Climate change impact modelling needs to include cross-sectoral interactions

et al. 2016

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Climate change impact assessments often apply models of individual sectors such as agriculture, forestry and water use without considering interactions between these sectors. This is likely to lead to misrepresentation of impacts, and consequently to poor decisions about climate adaptation. However, no published research assesses the differences between impacts simulated by single sector and integrated models. Here we compare 14 indicators derived from a set of impact models run within single sector and integrated frameworks across a range of climate and socio-economic scenarios in Europe. We show that single sector studies misrepresent the spatial pattern, direction and magnitude of most impacts because they omit the complex interdependencies within human and environmental systems. The discrepancies are particularly pronounced for indicators such as food production and water exploitation which are highly influenced by other sectors through changes in demand, land suitability and resource competition. Furthermore, the discrepancies are greater under different socio-economic scenarios than different climate scenarios, and at the sub-regional rather than Europe-wide scale.

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