ORCID Profile
0000-0001-9405-1228
Current Organisations
Imperial College London
,
International Institute for Applied Systems Analysis
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Publisher: Copernicus GmbH
Date: 28-05-2020
DOI: 10.5194/GMD-2020-138
Abstract: Abstract. Integrated assessment models (IAMs) project future anthropogenic emissions for input into climate models. However, the full list of climate-relevant emissions is lengthy and most IAMs do not model all of them. Here we present silicone, an open-source Python package which infers anthropogenic emissions of missing species based on other known emissions. For ex le, it can infer nitrous oxide emissions in one scenario based on carbon dioxide emissions from that scenario plus the relationship between nitrous oxide and carbon dioxide emissions in other scenarios. This broadens the range of IAMs available for exploring projections of future climate change. Silicone forms part of the open-source pipeline for assessments of the climate implications of IAMs by the IAM consortium (IAMC). A variety of infilling options are outlined and their suitability for different cases are discussed. The code and notebooks explaining details of the package and how to use it are available from the GitHub repository, github.com/GranthamImperial/silicone. There is an additional repository showing uses of the code to complement existing research at github.com/GranthamImperial/silicone_ex les.
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-20564
Abstract: & & The assessment of long-term greenhouse gas emissions scenarios and societal transformation pathways is a key component of the IPCC Working Group 3 (WG3) on the Mitigation of Climate Change. A large scientific community, typically using integrated assessment models and econometric frameworks, supports this assessment in understanding both near-term actions and long-term policy responses and goals related to mitigating global warming. WG3 must systematically assess hundreds of scenarios from the literature to gain an in-depth understanding of long-term emissions pathways, across all sectors, leading to various levels of global warming. Systematic assessment and understanding the climate outcomes of each emissions scenario, requires coordinated processes which have developed over consecutive IPCC assessments. Here, we give an overview of the processes involved in the systematic assessment of long-term mitigation pathways as used in recent IPCC Assessments& sup& & /sup& and being further developed for the IPCC 6& sup& th& /sup& Assessment Report (AR6). The presentation will explain how modelling teams can submit scenarios to AR6 and invite feedback to the process.& & & & Following discussions amongst IPCC Lead Authors to define the scope of scenarios desired and variables requested, a call for scenarios to support AR6 was launched in September 2019. Modelling teams have registered and submitted scenarios through Autumn 2019 using a new and secure online submission portal, from which authorised Lead Authors can interrogate the scenarios interactively.& & & & This analysis is underpinned by the open-source software pyam, a Python package specifically designed for analysis and visualisation of integrated assessment scenarios& sup& & /sup& . Submitted scenarios are automatically checked for errors and processed using a new climate assessment pipeline. The climate assessment involves infilling and harmonization& sup& & /sup& of emissions data, then the scenarios are processed through Simple Climate Models, using the OpenSCM framework& sup& & /sup& , to give probabilistic climate implications for each scenario & #8211 atmospheric concentrations, radiative forcing and global mean temperature. The climate assessment accounts for updated climate sensitivity estimates from CMIP6 and WG1,s scenarios are categorized according to climate outcomes and distinguish between timing and levels of net-negative emissions, emissions peak and temperature overshoot. Scenarios are also categorized by other indicators, for consistent use across WG3 chapters, such as: population and GDP Primary and Final energy use and shares of renewables, bioenergy and fossil fuels.& & & & The automated framework also facilitates bolt-on analyses, such as estimating the population impacted by biophysical climate impacts& sup& & /sup& , and estimates of avoided damages with the social cost of carbon& sup& & /sup& .& & & & Upon publication of the WG3 AR6 report, all scenario data used in the WG3 Assessment will be publicly available on a Scenario Explorer, an online tool for interrogating and visualizing the data that supports the report. In combination, this framework brings new levels of consistency, transparency and reproducibility to the assessment of scenarios in IPCC WG3 and will be a key resource for the climate community in understanding the main drivers of different transformation pathways.& & & ol& & li& Huppmman et al 2018, Nature Climate Change& /li& & li& Gidden and Huppmann, 2019, Journal of Open Source Software& /li& & li& Gidden et al 2018 Environ. Model. Softw& /li& & li& Nicholls et al 2020& /li& & li& Byers et al 2018 Environmental Research Letters& /li& & li& Ricke et al 2018 Nature Climate Change& /li& & /ol&
Publisher: Informa UK Limited
Date: 30-08-2023
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-18182
Abstract: & & Consistent and comparable climate assessments of scenarios are critical within the context of IPCC assessment reports. Given the number of scenarios assessed by WG3, the assessment & #8220 ipeline& #8221 must be almost completely automated. Here, we present the application of a new assessment pipeline which combines state-of-the-art components into a single workflow in order to derive climate outcomes for integrated assessment model (IAM) scenarios assessed by WG3 of the IPCC. A consistent analysis ensures that WG3& #8217 s conclusions about the socioeconomic transformations required to maintain a safe climate are based on the best understanding of our planetary boundaries from WG1. For ex le, if WG1 determines that climate sensitivity is higher than previously considered, then WG3 could incorporate this insight by e.g. considering much smaller remaining carbon budgets for any given temperature target.& & & & & & & & & The scenario-climate assessment pipeline is comprised of three primary components. First, a consistent harmonization algorithm which maintains critical model characteristics between harmonized and unharmonized scenarios [1] is employed to harmonize emissions trajectories to a common and consistent historical dataset as used in CMIP6 [2]. Next, a scenario& #8217 s reported emissions trajectories are analyzed as to the completeness of its species and sectoral coverage. A consistent set of 14 emissions species are expected, aligning with published work within ScenarioMIP and CMIP6 (see ref [2], Table 2). Should any component of this full set of emissions trajectories be absent for a given scenario, an algorithm (e.g., generalised quantile walk [3]) is employed in order to & #8220 back-fill& #8221 missing species at the native model regional resolution. Finally, full emissions scenarios are analyzed by an Earth System Model emulator, e.g., MAGICC [4].& & & & & & & & & In this presentation, we explore differences in climate assessments and estimated remaining carbon budgets across various components of the pipeline for available scenarios in the literature. We consider the impact of alternative choices, especially those made in prior assessments by the IPCC (AR5, SR15), including, for ex le, the historical emissions database used, the effect of harmonization and back-filling, as well as the version and setup of MAGICC used.& & & & & & & & & & References& & & & & & & & & [1] Gidden, M.J., Fujimori, S., van den Berg, M., Klein, D., Smith, S.J., van Vuuren, D.P. and Riahi, K., 2018. A methodology and implementation of automated emissions harmonization for use in Integrated Assessment Models. Environmental Modelling & Software, 105, pp.187-200.& & & & & & & & & [2] Gidden, M. J., Riahi, K., Smith, S. J., Fujimori, S., Luderer, G., Kriegler, E., van Vuuren, D. P., van den Berg, M., Feng, L., Klein, D., Calvin, K., Doelman, J. C., Frank, S., Fricko, O., Harmsen, M., Hasegawa, T., Havlik, P., Hilaire, J., Hoesly, R., Horing, J., Popp, A., Stehfest, E., and Takahashi, K.: Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century, Geosci. Model Dev., 12, 1443-1475, 0.5194/gmd-12-1443-2019, 2019.& & & & & & & & & [3] Teske, S. et al., Achieving the Paris Climate Agreement Goals. Springer, 2019.& & & & & & & & & [4] Meinshausen, M., Raper, S.C. and Wigley, T.M., 2011. Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6& #8211 Part 1: Model description and calibration. Atmospheric Chemistry and Physics, 11(4), pp.1417-1456.& &
Publisher: Research Square Platform LLC
Date: 02-09-2022
DOI: 10.21203/RS.3.RS-1934427/V1
Abstract: The remaining carbon budget (RCB), the net amount of carbon dioxide humans can still emit without exceeding a chosen global warming limit, is often used to evaluate political action against the goals of the Paris Agreement. RCB estimates for 1.5C are small, and minor changes in their calculation can therefore result in large relative shifts. Here we evaluate recent RCB assessments by the IPCC and explain differences between them. We present calculation refinements together with robustness checks that increase confidence in RCB estimates. We conclude that the RCB for a 50% chance of keeping warming to 1.5C is around 300 GtCO2 as of January 2022, less than 8 years of current emissions. This estimate changes to 530 and 110 GtCO2 for a 33% and 66% chance, respectively. Key uncertainties affecting RCB estimates are the contribution of non-CO2 emissions, which depends on socioeconomic projections as much as on geophysical uncertainty, and the potential warming after net zero is reached.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-7143
Abstract: The remaining carbon budget (RCB), the net amount of carbon dioxide humans can still emit without exceeding a chosen global warming limit, is often used to evaluate political action against the goals of the Paris Agreement. RCB estimates for 1.5C are small, and minor changes in their calculation can therefore result in large relative shifts. Here we evaluate recent RCB assessments by the IPCC and explain differences between them. We present calculation refinements together with robustness checks that increase confidence in RCB estimates. We conclude that the RCB for a 50% chance of keeping warming to 1.5C is around 250 GtCO2 as of January 2023, around 6 years of current CO2 emissions. This estimate changes to 480 and 60 GtCO2 for a 33% and 66% chance, respectively. Key uncertainties affecting RCB estimates are the contribution of non-CO2 emissions, which depends on socioeconomic projections as much as on geophysical uncertainty, and potential warming after net zero is reached.&
Publisher: Copernicus GmbH
Date: 20-12-2022
Abstract: Abstract. While the Intergovernmental Panel on Climate Change (IPCC) physical science reports usually assess a handful of future scenarios, the Working Group III contribution on climate mitigation to the IPCC's Sixth Assessment Report (AR6 WGIII) assesses hundreds to thousands of future emissions scenarios. A key task in WGIII is to assess the global mean temperature outcomes of these scenarios in a consistent manner, given the challenge that the emissions scenarios from different integrated assessment models (IAMs) come with different sectoral and gas-to-gas coverage and cannot all be assessed consistently by complex Earth system models. In this work, we describe the “climate-assessment” workflow and its methods, including infilling of missing emissions and emissions harmonisation as applied to 1202 mitigation scenarios in AR6 WGIII. We evaluate the global mean temperature projections and effective radiative forcing (ERF) characteristics of climate emulators FaIRv1.6.2 and MAGICCv7.5.3 and use the CICERO simple climate model (CICERO-SCM) for sensitivity analysis. We discuss the implied overshoot severity of the mitigation pathways using overshoot degree years and look at emissions and temperature characteristics of scenarios compatible with one possible interpretation of the Paris Agreement. We find that the lowest class of emissions scenarios that limit global warming to “1.5 ∘C (with a probability of greater than 50 %) with no or limited overshoot” includes 97 scenarios for MAGICCv7.5.3 and 203 for FaIRv1.6.2. For the MAGICCv7.5.3 results, “limited overshoot” typically implies exceedance of median temperature projections of up to about 0.1 ∘C for up to a few decades before returning to below 1.5 ∘C by or before the year 2100. For more than half of the scenarios in this category that comply with three criteria for being “Paris-compatible”, including net-zero or net-negative greenhouse gas (GHG) emissions, median temperatures decline by about 0.3–0.4 ∘C after peaking at 1.5–1.6 ∘C in 2035–2055. We compare the methods applied in AR6 with the methods used for SR1.5 and discuss their implications. This article also introduces a “climate-assessment” Python package which allows for fully reproducing the IPCC AR6 WGIII temperature assessment. This work provides a community tool for assessing the temperature outcomes of emissions pathways and provides a basis for further work such as extending the workflow to include downscaling of climate characteristics to a regional level and calculating impacts.
Publisher: Copernicus GmbH
Date: 06-09-2023
DOI: 10.5194/GMD-2023-176
Publisher: Copernicus GmbH
Date: 28-06-2022
DOI: 10.5194/EGUSPHERE-2022-471
Abstract: Abstract. While the IPCC’s physical science report usually assesses a handful of future scenarios, the IPCC Sixth Assessment Working Group III report (AR6 WGIII) on climate mitigation assesses hundreds to thousands of future emissions scenarios. A key task is to assess the global-mean temperature outcomes of these scenarios in a consistent manner, given the challenge that the emission scenarios from different integrated assessment models come with different sectoral and gas-to-gas coverage and cannot all be assessed consistently by complex Earth System Models. In this work, we describe the “climate assessment” workflow and its methods, including infilling of missing emissions and emissions harmonisation as applied to 1,202 mitigation scenarios in AR6 WGIII. We evaluate the global-mean temperature projections and effective radiative forcing characteristics (ERF) of climate emulators FaIRv1.6.2, MAGICCv7.5.3, and CICERO-SCM, discuss overshoot severity of the mitigation pathways using overshoot degree years, and look at an interpretation of compatibility with the Paris Agreement. We find that the lowest class of emission scenarios that limit global warming to “1.5 °C (with a probability of greater than 50 %) with no or limited overshoot” includes 90 scenarios for MAGICCv7.5.3, and 196 for FaIRv1.6.2. For the MAGICCv7.5.3 results, “limited overshoot” typically implies exceedance of median temperature projections of up to about 0.1 °C for up to a few decades, before returning to below 1.5 °C by or before the year 2100. For more than half of the scenarios of this category that comply with three criteria for being “Paris-compatible”, including net-zero or net-negative greenhouse gas (GHG) emissions, are projected to see median temperatures decline by about 0.3–0.4 °C after peaking at 1.5–1.6 °C in 2035–2055. We compare the methods applied in AR6 with the methods used for SR1.5 and discuss the implications. This article also introduces a ‘climate-assessment’ Python package which allows for fully reproducing the IPCC AR6 WGIII temperature assessment. This work can be the start of a community tool for assessing the temperature outcomes related to emissions pathways, and potential further work extending the workflow from emissions to global climate by downscaling climate characteristics to a regional level and calculating impacts.
Publisher: Springer Science and Business Media LLC
Date: 30-10-2023
Publisher: F1000 Research Ltd
Date: 28-06-2021
DOI: 10.12688/OPENRESEUROPE.13633.1
Abstract: The open-source Python package pyam provides a suite of features and methods for the analysis, validation and visualization of reference data and scenario results generated by integrated assessment models, macro-energy tools and other frameworks in the domain of energy transition, climate change mitigation and sustainable development. It bridges the gap between scenario processing and visualisation solutions that are "hard-wired" to specific modelling frameworks and generic data analysis or plotting packages. The package aims to facilitate reproducibility and reliability of scenario processing, validation and analysis by providing well-tested and documented methods for timeseries aggregation, downscaling and unit conversion. It supports various data formats, including sub-annual resolution using continuous time representation and "representative timeslices". The code base is implemented following best practices of collaborative scientific-software development. This manuscript describes the design principles of the package and the types of data which can be handled. The usefulness of pyam is illustrated by highlighting several recent applications.
Publisher: Copernicus GmbH
Date: 28-06-2022
Publisher: Copernicus GmbH
Date: 04-11-2020
Abstract: Abstract. Integrated assessment models (IAMs) project future anthropogenic emissions which can be used as input for climate models. However, the full list of climate-relevant emissions is lengthy and most IAMs do not model all of them. Here we present Silicone, an open-source Python package which infers anthropogenic emissions of unmodelled species based on other reported emissions projections. For ex le, it can infer nitrous oxide emissions in one scenario based on carbon dioxide emissions from that scenario plus the relationship between nitrous oxide and carbon dioxide emissions found in other scenarios. Infilling broadens the range of IAMs available for exploring projections of future climate change, and hence Silicone forms part of the open-source pipeline for assessments of the climate implications of IAM scenarios, led by the Integrated Assessment Modelling Consortium (IAMC). This paper presents a variety of infilling options and outlines their suitability for different cases. We recommend certain infilling techniques as good defaults but emphasise that considering the specifics of the model being infilled will produce better results. We demonstrate the package's utility with three ex les: infilling all required gases for a pathway with data for only one emission species, splitting up a Kyoto emissions total into separate gases, and complementing a set of idealised emissions curves to provide a complete, consistent emissions portfolio. The code and notebooks explaining details of the package and how to use it are available on GitHub (github.com/GranthamImperial/silicone, last access: 2 November 2020). The repository with this paper's ex les and uses of the code to complement existing research is available at github.com/GranthamImperial/silicone_ex les (last access: 2 November 2020).
Publisher: F1000 Research Ltd
Date: 09-2021
DOI: 10.12688/OPENRESEUROPE.13633.2
Abstract: The open-source Python package pyam provides a suite of features and methods for the analysis, validation and visualization of reference data and scenario results generated by integrated assessment models, macro-energy tools and other frameworks in the domain of energy transition, climate change mitigation and sustainable development. It bridges the gap between scenario processing and visualisation solutions that are "hard-wired" to specific modelling frameworks and generic data analysis or plotting packages. The package aims to facilitate reproducibility and reliability of scenario processing, validation and analysis by providing well-tested and documented methods for working with timeseries data in the context of climate policy and energy systems. It supports various data formats, including sub-annual resolution using continuous time representation and "representative timeslices". The pyam package can be useful for modelers generating scenario results using their own tools as well as researchers and analysts working with existing scenario ensembles such as those supporting the IPCC reports or produced in research projects. It is structured in a way that it can be applied irrespective of a user's domain expertise or level of Python knowledge, supporting experts as well as novice users. The code base is implemented following best practices of collaborative scientific-software development. This manuscript describes the design principles of the package and the types of data which can be handled. The usefulness of pyam is illustrated by highlighting several recent applications.
Location: United Kingdom of Great Britain and Northern Ireland
Location: Austria
No related grants have been discovered for Jarmo Kikstra.