Reconciling Information From Climate-Economic Model Ensembles

13362 12 64764 2 175865 document 64764 2016BS01Sochi.pdf application/pdf d0c351ef2ef9dfdae8d5100e0660d6d2 MD5 114919 2016-07-18 06:11:33 https://pure.iiasa.ac.at/id/eprint/13362/1/2016BS01Sochi.pdf 13362 1 1 application/pdf text en public cc_by

2016BS01Sochi.pdf

accepted 64767 1 175880 document 64767 indexcodes.txt text/x-pascal 1f7a4b7ebac90ffb04ebda0f19032607 MD5 6341 2016-07-18 06:21:22 https://pure.iiasa.ac.at/id/eprint/13362/2/indexcodes.txt 13362 2 2 text/x-pascal other Generate index codes conversion from text to indexcodes en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/64764 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/64764 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/64764 archive 2 disk0/00/01/33/62 2016-07-18 06:14:37 2021-08-27 17:27:20 2016-07-18 06:14:37 article show Rovenskaya E. 8206 0000-0002-2761-3443 Shchiptsova A. 2049 Kovalevsky D. Reconciling Information From Climate-Economic Model Ensembles pub prog_asa INTERNATIONAL CONFERENCE “Data Intensive System Analysis for Geohazard Studies” (Session 1. Geophysical systems dynamics, data analysis and extreme events) 18–21 July 2016, Sochi region, Mountain cluster, Russia http://sochi2016.gcras.ru To date, no model building process can ensure full representation of the complex climate-economic processes – instead, multiple highly detailed models are put forward by individual research groups to capture some selected aspects. On the other hand, a number of the simplified integrated assessment models (IAMs) have been developed attempting to consider the full causal loop between accumulated emissions, economy and climate, and study associated uncertainty. Here we present a simplified system dynamics IAM based on the model from Kovalevsky and Hasselmann (2014) with stochastic climate sensitivity and a nonlinear climate damage function. We explore the structural sensitivity of the long-term projections (focusing on global temperature, global economy output, GHG emissions and atmospheric concentrations) to a probabilistic distribution describing the climate sensitivity. We investigate the model robustness under different assumptions on climate sensitivity distribution. For this purpose, we use the approach suggested by Kryazhimskiy (2016), which attempts to ‘integrate’ several independent distributions representing the same variable into one posterior distribution using a Bayesian approach based on the posterior event being the one when stochastic variables in all models have the same realization. The results show that model ‘integration’ leads to a higher mean global output, emissions and concentrations and lower mean global temperature than both prior means, coming along with lower uncertainty in the integrated scenario. The authors would like to acknowledge DG research for funding through the FP7-funded COMPLEX project #308601, www.complex.ac.uk. 1. D.V. Kovalevsky, K. Hasselmann (2014): Assessing the transition to a low-carbon economy using actor-based system-dynamic models, Proceedings of the 7th International Congress on Environmental Modelling and Software (iEMSs), 15-19 June 2014, San Diego, California, 4, 1865-1872 2. A.V. Kryazhimskiy (2016): Posteriori integration of probabilities. Elementary theory, Theory Probab. Appl., 60:1, 62–87 2016 Geophysical Center RAS 10.2205/2016BS01Sochi 260 280 public paper Geoinformatics Research Papers 4 BS4002 Data Intensive System Analysis for Geohazard Studies Sochi region, Mountain cluster, Russia 18-21 July 2016 conference TRUE 2308-5983 Knowledge Based Climate Mitigation Systems for a Low Carbon Economy (COMPLEX, FP7 308601) FALSE yes info:eu-repo/grantAgreement/EC/FP7/308601/EU/Knowledge Based Climate Mitigation Systems for a Low Carbon Economy/COMPLEX info:eu-repo/semantics/article info:eu-repo/semantics/openAccess