Transient climate response in the DICE integrated assessment model of climate-economy

2016 ◽  
Author(s):  
Salman Hafeez ◽  
Steven R. Weller ◽  
Christopher M. Kellett
Keyword(s):  
Integrated Assessment ◽  
Integrated Assessment Model ◽  
Assessment Model ◽  
Climate Response ◽  
Transient Climate Response

scholarly journals The $10 trillion value of better information about the transient climate response

2015 ◽  
Vol 373 (2054) ◽  
pp. 20140429 ◽  
Author(s):  
Chris Hope
Keyword(s):  
Climate Change ◽  
Integrated Assessment ◽  
Net Present Value ◽  
Integrated Assessment Model ◽  
Assessment Model ◽  
Climate Response ◽  
Transient Climate Response ◽  
Uncertainty Range ◽  
Integrated Assessment Modelling ◽  
Impacts Of Climate Change

How much is better information about climate change worth? Here, I use PAGE09, a probabilistic integrated assessment model, to find the optimal paths of CO 2 emissions over time and to calculate the value of better information about one aspect of climate change, the transient climate response (TCR). Approximately halving the uncertainty range for TCR has a net present value of about $10.3 trillion (year 2005 US$) if accomplished in time for emissions to be adjusted in 2020, falling to $9.7 trillion if accomplished by 2030. Probabilistic integrated assessment modelling is the only method we have for making estimates like these for the value of better information about the science and impacts of climate change.

scholarly journals The role of biomaterials for the energy transition from the lens of a national integrated assessment model

2021 ◽  
Vol 167 (3-4) ◽  
Author(s):  
Camilla C. N. de Oliveira ◽  
Gerd Angelkorte ◽  
Pedro R. R. Rochedo ◽  
Alexandre Szklo
Keyword(s):  
Integrated Assessment ◽  
Energy Transition ◽  
Integrated Assessment Model ◽  
Assessment Model

scholarly journals Evaluation of Integrated Assessment Model hindcast experiments: A case study of the GCAM 3.0 land use module

2017 ◽  
Author(s):  
Abigail C. Snyder ◽  
Robert P. Link ◽  
Katherine V. Calvin
Keyword(s):  
Integrated Assessment ◽  
Structural Changes ◽  
Spatial Scales ◽  
Integrated Assessment Model ◽  
Assessment Model ◽  
Land Allocation ◽  
Time Period ◽  
Skill Scores ◽  
Performance Benchmarks

Abstract. Hindcasting experiments (conducting a model forecast for a time period in which observational data is available) are rarely undertaken in the Integrated Assessment Model (IAM) community. When they are undertaken, the results are often evaluated using global aggregates or otherwise highly aggregated skill scores that mask deficiencies. We select a set of deviation based measures that can be applied at different spatial scales (regional versus global) to make evaluating the large number of variable-region combinations in IAMs more tractable. We also identify performance benchmarks for these measures, based on the statistics of the observational dataset, that allow a model to be evaluated in absolute terms rather than relative to the performance of other models at similar tasks. This is key in the integrated assessment community, where there often are not multiple models conducting hindcast experiments to allow for model intercomparison. The performance benchmarks serve a second purpose, providing information about the reasons a model may perform poorly on a given measure and therefore identifying opportunities for improvement. As a case study, the measures are applied to the results of a past hindcast experiment focusing on land allocation in the Global Change Assessment Model (GCAM) version 3.0. We find quantitative evidence that global aggregates alone are not sufficient for evaluating IAMs, such as GCAM, that require global supply to equal global demand at each time period. Additionally, the deviation measures examined in this work successfully identity parametric and structural changes that may improve land allocation decisions in GCAM. Future work will involve implementing the suggested improvements to the GCAM land allocation system identified by the measures in this work, using the measures to quantify performance improvement due to these changes, and, ideally, applying these measures to other sectors of GCAM and other land allocation models.

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