Global emissions and temperature

To assess the climatic impact of the targets put forward by countries, we first construct a global emissions pathway to 2100. This global pathway is then used as input to a reduced-complexity carbon-cycle / climate model (MAGICC7) which is calibrated against data from complex general circulation models (GCMs), including climate sensitivity and carbon cycle information. The MAGICC emulations reflect the complex model response ranges for the assessed scenarios in the calibration datasets, in particular the Representative Concentration Pathways (RCPs). MAGICC7 is run multiple times in order to obtain a probability distribution of outcomes such as global mean temperature, CO₂ concentration, and total greenhouse gas concentration. These distributions are used for deriving the central median estimate of e.g. the global mean warming in 2100 and corresponding temperature exceedance likelihoods over the 21st century. The detailed methodology of the climate model is outlined in Meinshausen et al. 2009 & 2011 and Nicholls et at. In prep.

Based on the individual CAT country analyses, global pathways are generated for 4 different scenarios: current policy projections, short-term pledges (up to 2030), and long-term pledges (up to 2050 and later) and an optimistic scenario. For some of these scenarios, we also evaluate an upper and lower pathway, based on the uncertainties in the underlying country analyses, or the ranges of pledges.

To aggregate these scenarios to a global emissions pathway, several steps are required.

• First, we include emissions pathways for countries the CAT does not individually assess.
• Incomplete pathway information is extended to 2100 based on the AR6 database scenarios.
• Emissions from international shipping and aviation are added to the national emission profiles to complete the global emission pathway.
• Individual greenhouse-gas emission pathways are derived from the resulting total global GHG emissions pathways.
• Finally, CO₂ emissions from land use change are added to the global emission pathway to complete the global emission pathway. Each of these steps is described below.

Countries assessed by the Climate Action Tracker were responsible for 81% of global emissions in 2010 (excluding LULUCF). For countries that are not individually assessed, we either use NDC quantification by the mitiQ tool (Günther et al, 2021) or assume that the emissions of these countries will follow a ‘business-as-usual’ (BAU) pathway. The BAU pathways used in this analysis are from the PRIMAP baseline. The baseline construction is described in greater detail on the PRIMAP website.

Although the CAT doesn’t directly assess many countries that have submitted an NDC, these countries together are only responsible for a small share of emissions in 2030 under our BAU scenario. Any reduction from BAU for the non-CAT countries would only be a fraction of this amount which is small compared with total emissions. Therefore, we do not assume any deviation from BAU for these countries in the various CAT scenarios as the impact on global emissions and temperatures is expected to be small.

The Climate Action Tracker does not have sufficient resources to individually assess all NDCs. However, we continue to monitor the overall coverage and level of ambition for non-CAT countries. If a major deviation from the BAU scenario described above is found, an adjustment will be applied.

The CAT uses a range of emissions pathways from the IPCC AR6 scenario database to extend the short-term CAT analysis pathways out to 2100. The methodology was revised in October 2022 in order to incorporate the latest science into this key step.

The revised methodology is based on the idea that the level of mitigation effort corresponds to the relative position of an emissions pathway in a set of pathways. For each CAT case, we identify this relative level of effort and keep it constant until 2100. This methodology ensures that the long-term projection is as consistent as possible with the shorter-term action or pledges by accounting for the inertia of near-term actions.

For each CAT case the country information is aggregated to the level of the R5 regions (R5ASIA, R5MAF (Middle East and Africa), R5LAM (Latin America), R5OECD, and R5REF (reforming economies, i.e. Russia and former Soviet states)).

We take into account selected AR6 emission pathways from models that simulate all sectors and gases. The models that we use are AIM, COFFEE, GCAM, IMAGE, MESSAGE, REMIND, TIAM and WITCH (see references for details). Some AR6 pathways do not include information for all regions explicitly. Those pathways are not taken into account. We also exclude the scenarios with incomplete scenario data, or scenarios that include changing policy assumptions.

To identify the relative level of effort of the CAT scenarios, we calculate the quantiles in the AR6 DB pathway distribution which correspond to the CAT scenario for the last scenario years. We use a linear fit to determine the quantile in the first year after the scenario end which we then keep constant until 2100 to maintain the level of effort. Dependent on the scenario, this extension is done from 2030 or 2050 onwards.

The whole analysis is carried out on the level of the 5 regions, so the last step is to sum the regional pathways to a global pathway, which is then used to calculate the global temperature after emissions from bunkers and deforestation are also included.

Bunkers

The data for bunkers is drawn from our international shipping and international aviation assessments.

Deforestation

A business-as-usual pathway for global deforestation emissions is provided by the median of baseline scenarios of land-use emissions from the LIMITS project (Kriegler et al., 2013). This pathway is virtually equal to the median of the wide range of baseline scenarios assessed by Working Group III in IPCC's Fifth Assessment Report (AR5), and somewhat lower than RCP8.5 (Riahi et al. 2007), the high side of the range of new emission scenarios (see iiasa.ac.at).

This global pathway is consistent with global carbon budget modelling (making sure that historical observed changes in atmospheric CO₂ concentrations are reproduced) and is subsequently split into contributions by individual countries (Houghton 2009; van der Werf et al. 2009; Houghton et al., 2012).

Proposed reductions by individual countries are then applied to the business-as-usual pathways for these countries that are thus consistent with the historical global carbon budget.

Multi-gas pathways

The aggregate Kyoto gas pathway is transformed into a multi-gas pathway using the Quantile-rolling window (Lamboll et al. 2021). This pathway is used as input to the reduced-complexity climate model MAGICC7 (www.magicc.org). MAGICC7 is calibrated to emulate higher complexity global circulation models (AOGCMs) in a probabilistic manner.

• Günther, Annika, Gütschow, Johannes, & Jeffery, M. Louise. (2021). NDCmitiQ: a tool to quantify and analyse GHG mitigation targets (v1.0.2). Zenodo. https://doi.org/10.5281/zenodo.5113987
• Gütschow, J. (2013). CP2 Surplus Calculator
• Houghton RA, House JI, Pongratz J, van der Werf GR, DeFries RS, Hansen MC, Le Quere C, and Ramankutty N (2012) Carbon emissions from land use and land-cover change, Biogeosciences, 9, 5125-5142. https://doi.org/10.5194/bg-9-5125-2012
• Kriegler E, Tavoni M, Aboumahboub T, Luderer G, Calvin K, Demaere G, Krey V, Riahi K, Rösler H, Schaeffer M, and Van Vuuren DP (2013) What does the 2C target imply for a global climate agreement in 2020? The LIMITS study in Durpan Platform scenarios, Climate Change Economics, 4, 4
• Meinshausen M, Meinshausen N, Hare W, Raper S C B, Frieler K, Knutti R, Frame D J and Allen M R (2009) Greenhouse-gas emission targets for limiting global warming to 2 °C. Nature 458 1158–62
• Meinshausen, M. et al. Multi-gas Emissions Pathways to Meet Climate Targets. Clim. Change 75, 151–194 (2006).
• Meinshausen, M., S. C. B. Raper and T. M. L. Wigley (2011) "Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6: Part I – Model Description and Calibration", Atmospheric Chemistry and Physics 11 1417-1456
• Nicholls, Z. R. J., Meinshausen, M., Lewis, J., Meinshausen, N., Hirons, N. (in prep.). Capturing climate science assessments with probabilistic distributions for reduced complexity climate models. Geoscientific Model Development Discussions
• Lamboll, R. D., Nicholls, Z. R. J., Kikstra, J. S., Meinshausen, M., and Rogelj, J.: Silicone v1.0.0: an open-source Python package for inferring missing emissions data for climate change research, Geosci. Model Dev., 13, 5259–5275, https://doi.org/10.5194/gmd-13-5259-2020, 2020.
• PRIMAP4 baseline
• UNEP (2013) The Emissions Gap Report 2013. United Nations Environment Programme (UNEP), Nairobi

This is a list of models providing scenarios used by the Climate Action Tracker:

• GCAM: http://www.globalchange.umd.edu/models/gcam/
• MESSAGE: http://www.iiasa.ac.at/web/home/research/modelsData/MESSAGE/MESSAGE.en.html
• REMIND: https://www.pik-potsdam.de/research/sustainable-solutions/models/remind
• IMAGE: https://www.pbl.nl/en/image/home
• MERGE: http://web.stanford.edu/group/MERGE/
• WITCH: http://www.witchmodel.org/index.html

More information on the MAGICC model can be found here:

• http://www.magicc.org/
• http://wiki.magicc.org/index.php?title=Main_Page