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The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500

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Meinshausen, Malte, Nicholls, Zebedee R. J., Lewis, Jared, Gidden, Matthew J., Vogel, Elisabeth, Freund, Mandy, Beyerle, Urs, Gessner, Claudia, Nauels, Alexander, Bauer, Nico, Canadell, Josep G., Daniel, John S., John, Andrew, Krummel, Paul B., Luderer, Gunnar, Meinshausen, Nicolai, Montzka, Stephen A., Rayner, Peter J., Reimann, Stefan, Smith, Steven J., van den Berg, Marten, Velders, Guus J. M., Vollmer, Martin K., Wang, Ray H. J.·2020·Geoscientific Model Development

DOI 10.5194/gmd-13-3571-2020

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Abstract

Anthropogenic increases in atmospheric greenhouse gas concentrations are the main driver of current and future climate change. The integrated assessment community has quantified anthropogenic emissions for the shared socio-economic pathway (SSP) scenarios, each of which represents a different future socio-economic projection and political environment. Here, we provide the greenhouse gas concentrations for these SSP scenarios – using the reduced-complexity climate–carbon-cycle model MAGICC7.0. We extend historical, observationally based concentration data with SSP concentration projections from 2015 to 2500 for 43 greenhouse gases with monthly and latitudinal resolution. CO 2 concentrations by 2100 range from 393 to 1135 ppm for the lowest (SSP1-1.9) and highest (SSP5-8.5) emission scenarios, respectively. We also provide the concentration extensions beyond 2100 based on assumptions regarding the trajectories of fossil fuels and land use change emissions, net negative emissions, and the fraction of non-CO 2 emissions. By 2150, CO 2 concentrations in the lowest emission scenario are approximately 350 ppm and approximately plateau at that level until 2500, whereas the highest fossil-fuel-driven scenario projects CO 2 concentrations of 1737 ppm and reaches concentrations beyond 2000 ppm by 2250. We estimate that the share of CO 2 in the total radiative forcing contribution of all considered 43 long-lived greenhouse gases increases from 66 % for the present day to roughly 68 % to 85 % by the time of maximum forcing in the 21st century. For this estimation, we updated simple radiative forcing parameterizations that reflect the Oslo Line-By-Line model results. In comparison to the representative concentration pathways (RCPs), the five main SSPs (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) are more evenly spaced and extend to lower 2100 radiative forcing and temperatures. Performing two pairs of six-member historical ensembles with CESM1.2.2, we estimate the effect on surface air temperatures of applying latitudinally and seasonally resolved GHG concentrations. We find that the ensemble differences in the March–April–May (MAM) season provide a regional warming in higher northern latitudes of up to 0.4 K over the historical period, latitudinally averaged of about 0.1 K, which we estimate to be comparable to the upper bound (∼5 % level) of natural variability. In comparison to the comparatively straight line of the last 2000 years, the greenhouse gas concentrations since the onset of the industrial period and this studies' projections over the next 100 to 500 years unequivocally depict a “hockey-stick” upwards shape. The SSP concentration time series derived in this study provide a harmonized set of input assumptions for long-term climate science analysis; they also provide an indication of the wide set of futures that societal developments and policy implementations can lead to – ranging from multiple degrees of future warming on the one side to approximately 1.5 ∘ C warming on the other.

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Characterization of the future northeast monsoon rainfall based on the clustered climate zone under CMIP6 in Peninsular Malaysia
Sa’adi, Z., Alias, N., Yusop, Z., Hamed, M. M., Shukla, P., Houmsi, M. R., Mohamad, N., Shiru, M. S., Sa'adi, N., Muhammad, M. K. I., et al. (2024) · Atmospheric research · doi:10.1016/j.atmosres.2024.107407 · Cited by 13
Past and future land use/land cover, and climate change impacts on environmental sustainability in Vea catchment, Ghana
Arfasa, G. F., Owusu-Sekyere, E., & Doke, D. (2023) · Geocarto International · doi:10.1080/10106049.2023.2289458 · Cited by 13
“The models projected climate variables and changes using a set of five new climate scenarios known as shared socio-economic pathways (SSPs) (Meinshausen et al. 2020).”
Method
Assessing carbon cycle projections from complex and simple models under SSP scenarios
Melnikova, I., Ciais, P., Boucher, O., & Tanaka, K. (2023) · Climatic Change · doi:10.1007/s10584-023-03639-5 · Cited by 16
“We used the CO 2 concentration by Meinshausen et al. (2020) to estimate G Atm for Eq.”
Influential
Sex-dependent phenological responses to climate vary across species’ ranges
Xie, Y., Thammavong, H. T., Berry, L., Huang, C. H., & Park, D. S. (2023) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.2306723120 · Cited by 13
“…of the road development with medium challenges to mitigation and adaptation, which may be the most realistic development trajectory, while SSP5 - 8.5 represents fossil - fueled development with high challenges to mitigation and low challenges to adaptation, as the low - effort baseline (68, 69).”
Influential
Projections of Temperature-Related Suicide under Climate Change Scenarios in Japan
Thawonmas, R., Hashizume, M., & Kim, Y. (2023) · Environmental Health Perspectives · doi:10.1289/ehp11246 · Cited by 16
“37,38 SSP5-8.”
Future distribution of the epiphytic leafless orchid (Dendrophylax lindenii), its pollinators and phorophytes evaluated using niche modelling and three different climate change projections
Kolanowska, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-42573-5 · Cited by 14
Future Drought Changes in China Projected by the CMIP6 Models: Contributions from Key Factors
Chen, Q., Zhao, T., Hua, L.-J., Yu, J.-H., Wang, Y., & Xu, C. (2023) · Journal of Meteorological Research · doi:10.1007/s13351-023-2169-8 · Cited by 13
Ecological niche modelling of Tecomella undulata (Sm.) Seem: an endangered (A2a) tree species from arid and semi-arid environment imparts multiple ecosystem services
Mathur, M., & Mathur, P. (2023) · Tropical Ecology · doi:10.1007/s42965-023-00311-y · Cited by 13
“0, and SSP5-85 (Sohel et al. 2016; Meinshausen et al. 2020).”
Predicting the potential impacts of climate change on the endangered endemic annonaceae species in east africa
Mkala, E. M., Mwanzia, V. M., Nzei, J. M., Oluoch, W. A., Ngarega, B. K., Wanga, V. O., Oulo, M. A., Mutie, F. M., Kilingo, F. M., Rono, P., et al. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17405 · Cited by 19
A comparison of the climate and carbon cycle effects of carbon removal by afforestation and an equivalent reduction in fossil fuel emissions
Jayakrishnan, K. U., & Bala, G. (2023) · Biogeosciences · doi:10.5194/bg-20-1863-2023 · Cited by 22
Estimating the Global Influence of Cover Crops on Ecosystem Service Indicators in Croplands With the LPJ‐GUESS Model
Ma, J., Anthoni, P. M., Olin, S., Rabin, S. S., Bayer, A. D., Xia, L., & Arneth, A. (2023) · Earth s Future · doi:10.1029/2022ef003142 · Cited by 23
High economic costs of reduced carbon sinks and declining biome stability in Central American forests
Baumbach, L., Hickler, T., Yousefpour, R., & Hanewinkel, M. (2023) · Nature Communications · doi:10.1038/s41467-023-37796-z · Cited by 13
“For nitrogen deposition we used data from Lamarque et al. 70 , whereas CO 2 data was taken from 71 .”
Desert Locust (Schistocerca gregaria) Invasion Risk and Vegetation Damage in a Key Upsurge Area
Mongare, R., Abdel-Rahman, E., Mudereri, B., Kimathi, E. K., Onywere, S., & Tonnang, H. (2023) · Earth · doi:10.3390/earth4020010 · Cited by 13
“5 W/m2, respectively, for the carbon dioxide (CO2) concentration for the period 2021–2040 averaged at 2030 [51].”
Background
Projected Effects of Climate Change on Species Range of Pantala flavescens, a Wandering Glider Dragonfly
Liao, J., Wu, Z., Wang, H., Xiao, S., Mo, P., & Cui, X.-F. (2023) · Biology · doi:10.3390/biology12020226 · Cited by 13
Projected changes in mean annual cycle of temperature and precipitation over the Czech Republic: Comparison of CMIP5 and CMIP6
Holtanová, E., Belda, M., & Halenka, T. (2022) · Frontiers in Earth Science · doi:10.3389/feart.2022.1018661 · Cited by 13
Atmospheric impacts of chlorinated very short-lived substances over the recent past – Part 1: Stratospheric chlorine budget and the role of transport
Bednarz, E., Hossaini, R., Chipperfield, M., Abraham, N., & Braesicke, P. (2022) · Atmospheric Chemistry and Physics · doi:10.5194/acp-22-10657-2022 · Cited by 13
“5 scenario (Meinshausen et al., 2020) from 2015 to 2019.”
Why the Effect of CO2 on Potential Evapotranspiration Estimation Should Be Considered in Future Climate
Zhou, J., Jiang, S., Su, B., Huang, J., Wang, Y.-J., Zhan, M.-J., Jing, C., & Jiang, T. (2022) · Water · doi:10.3390/w14060986 · Cited by 13
Climate change response in wintertime widespread fog conditions over the Indo-Gangetic Plains
Hingmire, D., Vellore, R. K., Krishnan, R., Singh, M., Metya, A., Gokul, T., & Ayantika, D. C. (2021) · Climate Dynamics · doi:10.1007/s00382-021-06030-1 · Cited by 17
“2007, 2009; Mohan and Payra 2008; Sharma et al.”
Strong Increase of Thawing of Subsea Permafrost in the 22nd Century Caused by Anthropogenic Climate Change
Wilkenskjeld, S., Miesner, F., Overduin, P., Puglini, M., & Brovkin, V. (2021) · doi:10.5194/tc-2021-231 · Cited by 13
Evaluating stratospheric ozone and water vapor changes in CMIP6 models from 1850–2100Preprint
Keeble, J., Hassler, B., Banerjee, A., Checa‐Garcia, R., Chiodo, G., Davis, S., Eyring, V., Griffiths, P. T., Morgenstern, O., Nowack, P., et al. (2020) · doi:10.5194/acp-2019-1202 · Cited by 13
Tracer-based Rapid Anthropogenic Carbon Estimation (TRACE)
Carter, B., Schwinger, J., Sonnerup, R., Fassbender, A., Sharp, J., Dias, L. M., & Sandborn, D. (2025) · Earth System Science Data · doi:10.5194/essd-17-3073-2025 · Cited by 12
“The remaining 8 options are SSPs: 1-1.9, 1-2.6, 2-4.5, 3-7.0, 3-7.0-lowNTCF, 4-3.4, 4-6 4-6.0, and 5-3.4-over, all as defined by Meinshausen et al. (2020).”
Influential
Tree methane exchange in a changing world
Gauci, V. (2025) · Nature Reviews Earth & Environment · doi:10.1038/s43017-025-00692-9 · Cited by 15
Future land-use pattern projections and their differences within the ISIMIP3b framework
Bacca, E. J. M., Stevanović, M., Bodirsky, B. L., Doelman, J., Chini, L. P., Volkholz, J., Frieler, K., Reyer, C., HURTT, G. C., Humpenöder, F., et al. (2025) · Earth System Dynamics · doi:10.5194/esd-16-753-2025 · Cited by 16
“…regional trends Speciﬁcally, we used the so-called SSP regions, which have been widely applied in studies involving the Shared Socioeconomic Pathways (SSPs) and climate change, e.g., in Popp et al. (2017), Bauer et al. (2017), Meinshausen et al. (2020), and Fu et al. (2021) (see Appendix Fig.”
Artificial Intelligence and IoT for Water Saving in Agriculture: A Systematic Review
Colizzi, L., Dimauro, G., Guerriero, E., & Lomonte, N. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101008 · Cited by 12
Understanding spatiotemporal variation of heatwave projections across US cities
Bhattarai, S., Bokati, L., Sharma, S., & Talchabhadel, R. (2025) · Scientific Reports · doi:10.1038/s41598-025-95097-5 · Cited by 15
“This pathway leads to around 4.5 °C of global warming by 2100 63 .”
Influential
Physics-informed neural networks for enhanced reference evapotranspiration estimation in Morocco: Balancing semi-physical models and deep learning
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Supply chain serviceability under climate change with application in the Arctic
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Quantifying the Impact of Climate Change and Forest Management on Swedish Forest Ecosystems Using the Dynamic Vegetation Model LPJ‐GUESS
Bergkvist, J., Lagergren, F., Islam, M. R., Wårlind, D., Miller, P. A., Linderson, M.-L. F., Lindeskog, M., & Jönsson, A. (2024) · Earth's Future · doi:10.1029/2024ef004662 · Cited by 12
“We used climate data simulated in the Coupled Model Intercomparison Project Phase 6 (CMIP6) at a spatial resolution of 0.5 ◦ × 0.5 ◦ during all transient simulations as model input (Figure 1 and Table B1) (Eyring et al., 2016).”
Influential
Global climate-driven sea surface temperature and chlorophyll dynamics
Venegas, R. M., Rivas, D., & Treml, E. A. (2024) · Marine Environmental Research · doi:10.1016/j.marenvres.2024.106856 · Cited by 18
Impact of methane and other precursor emission reductions on surface ozone in Europe: scenario analysis using the European Monitoring and Evaluation Programme (EMEP) Meteorological Synthesizing Centre – West (MSC-W) model
van Caspel, W. E., Klimont, Z., Heyes, C., & Fagerli, H. (2024) · Atmospheric chemistry and physics · doi:10.5194/acp-24-11545-2024 · Cited by 14
“(1) also for the projected CH 4 concentrations, an approach similar to that of Meinshausen et al. (2020) is followed, by effectively using EOF1 to extrapolate the latitudinal gradient into the future based on anthropogenic CH 4 emissions.”
Method
Latitudinal gradients of biodiversity and ecosystem services in protected and non-protected oak forest areas can inform climate smart conservation
Forsman, A., Sunde, J., Salis, R. K., & Franzén, M. (2024) · Geography and sustainability · doi:10.1016/j.geosus.2024.09.002 · Cited by 8
Interannual ENSO diversity, transitions, and projected changes in observations and climate models
Freund, M. B., Brown, J. R., Marshall, A. G., Tozer, C. R., Henley, B. J., Risbey, J. S., Ramesh, N., Lieber, R., & Sharmila, S. (2024) · Environmental Research Letters · doi:10.1088/1748-9326/ad78db · Cited by 16
“Thereafter, to the year 2100, we make use of the simulations forced by the high emissions representative concentration pathway (RCP) 8.5 scenario (for CMIP5) and the approximately equivalent Shared Socioeconomic Pathway (SSP) 5–8.5 (for CMIP6) [60, 61].”
InfluentialSelf-citation
Opportunities beyond net-zero CO2 for cost-effective greenhouse gas mitigation in China.
Kang, Y., Tian, P.-P., Feng, K., Li, J.-S., & Hubacek, K. (2024) · Science Bulletin · doi:10.1016/j.scib.2024.08.012 · Cited by 12
Machine-learning-constrained projection of bivariate hydrological drought magnitudes and socioeconomic risks over China
Liu, R., Yin, J., Slater, L. J., Kang, S., Yang, Y., Liu, P., Guo, J., Gu, X., Zhang, X., & Volchak, A. (2024) · Hydrology and earth system sciences · doi:10.5194/hess-28-3305-2024 · Cited by 18
Applying global warming levels of emergence to highlight the increasing population exposure to temperature and precipitation extremes
Gampe, D., Schwingshackl, C., Böhnisch, A., Mittermeier, M., Sandstad, M., & Wood, R. R. (2024) · Earth System Dynamics · doi:10.5194/esd-15-589-2024 · Cited by 21
A warming climate will make Australian soil a net emitter of atmospheric CO2
Rossel, R. V. V., Zhang, M., Behrens, T., & Webster, R. (2024) · npj Climate and Atmospheric Science · doi:10.1038/s41612-024-00619-z · Cited by 12
“We aim to assess whether Australia ’ s soil is a sink or a source of atmospheric CO 2 and use climate forcing from an ensemble of six Earth System Models (ESM) from the CMIP6 19,20 , site-speci ﬁ c C inputs, and three shared socioeconomic pathways (SSPs) 21,22”
The Role of the North Atlantic for Heat Wave Characteristics in Europe, an ECHAM6 Study
Bischof, S., Kedzierski, R. P., Hänsch, M., Wahl, S., & Matthes, K. (2023) · Geophysical Research Letters · doi:10.1029/2023gl105280 · Cited by 12
“5 scenario (Meinshausen et al., 2020).”
Illustrative Multi‐Centennial Projections of Global Mean Sea‐Level Rise and Their Application
Turner, F. E., Santos, V. M., Edwards, T. L., Slangen, A., Nicholls, R., Cozannet, G. L., O'Neill, J. F., & Adhikari, M. (2023) · Earth's Future · doi:10.1029/2023ef003550 · Cited by 12
“Projections beyond 2300 under the SSP extensions were also not provided (Meinshausen et al., 2020).”
Background
Selective photoelectrocatalytic CO2 reduction to ethanol using nanotubular oxides grown on metastable Ti-Cu alloy
de Almeida, J., Câmara, S. H., Bertazzoli, R., Rajeshwar, K., Silva, R. A. G., & Rodrigues, C. D. A. (2023) · Chemical Engineering Journal · doi:10.1016/j.cej.2023.147117 · Cited by 15
Quantifying the ecological consequences of climate change in coastal ecosystems
Schoeman, D. S., Bolin, J. A., & Cooley, S. R. (2023) · Cambridge Prisms Coastal Futures · doi:10.1017/cft.2023.27 · Cited by 21
“This is impractical with current CMIP6 model outputs because each scenario subsumes a shared socioeconomic pathway (Table 3), but the increasingly prominent use of climate-model emulators (Nicholls et al., 2020; IPCC, 2021) might provide a solution.”
BackgroundMethod
Feeding in mixoplankton enhances phototrophy increasing bloom-induced pH changes with ocean acidification
Flynn, K. J., & Mitra, A. (2023) · Journal of Plankton Research · doi:10.1093/plankt/fbad030 · Cited by 12
Current and Future Habitat Suitability Models for Four Ticks of Medical Concern in Illinois, USA
Kopsco, H., Gronemeyer, P., Mateus-Pinilla, N., & Smith, R. (2023) · Insects · doi:10.3390/insects14030213 · Cited by 12
“SSP 585 is a future climate scenario that describes the expected baseline high greenhouse gas impact resulting from a lack of carbon emission mitigation policies [52], i.”
Background
Spatiotemporal projections of extreme precipitation over Algeria based on CMIP6 global climate models
Sahabi-Abed, S., Ayugi, B., & Selmane, A. N.-E.-I. (2023) · Modeling Earth Systems and Environment · doi:10.1007/s40808-023-01716-3 · Cited by 12
Quantifying the Role of Internal Climate Variability and Its Translation from Climate Variables to Hydropower Production at Basin Scale in India
Upadhyay, D., Dixit, S., & Bhatia, U. (2022) · Journal of Hydrometeorology · doi:10.1175/jhm-d-22-0065.1 · Cited by 13
Absorbing Aerosol Choices Influences Precipitation Changes Across Future Scenarios
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Assessing the potential for crop albedo enhancement in reducing heatwave frequency, duration, and intensity under future climate change
Kala, J., Hirsch, A., Ziehn, T., Perkins-Kirkpatrick, S., Kauwe, M. D. D., & Pitman, A. (2022) · Weather and Climate Extremes · doi:10.1016/j.wace.2022.100415 · Cited by 12
Assessment of the Current and Projected Conditions of Water Availability in the Sevastopol Region for Grape Growing
Vyshkvarkova, E., Rybalko, E., Marchukova, O., & Baranova, N. V. (2021) · Agronomy · doi:10.3390/agronomy11081665 · Cited by 12
“As part of phase 6 of the CMIP project within the framework of the World Climate Research Programme (WCRP), a new set of scenarios has been developed that reflect various socio-economic changes (SSPs, or Shared Socioeconomic Pathways), as well as various ways of anthropogenic changes in the concentration of greenhouse…”
Modeling soil organic matter changes under crop diversification strategies and climate change scenarios in the Brazilian Cerrado
Locatelli, J. L., Grosso, S. J. D., Santos, R. S., Hong, M., Gurung, R., Stewart, C. E., Cherubin, M. R., Bayer, C., & Cerri, C. E. P. (2024) · Agriculture Ecosystems & Environment · doi:10.1016/j.agee.2024.109334 · Cited by 18
Challenges and advances in understanding the roadmap for direct hydrogenation of carbon dioxide to ethanol
Pamei, M., Sharma, S. K., Das, D., Vadivel, S., Paul, B., & Puzari, A. (2026) · Catalysis Reviews · doi:10.1080/01614940.2024.2400974 · Cited by 11
“[2] To address the challenges posed by climate change and limit the rise in global temperatures, there is a growing imperative to reduce CO 2 emissions and achieve net-zero CO 2 emissions.”
Review of climate simulation by Simple Climate Models
Romero-Prieto, A., Mathison, C., & Smith, C. (2026) · Geoscientific model development · doi:10.5194/gmd-19-115-2026 · Cited by 10
Indirect climate impacts of the Hunga eruption
Bednarz, E., Butler, A. H., Wang, X., Zhuo, Z., Yu, W., Stenchikov, G. L., Toohey, M., & Zhu, Y. (2026) · Atmospheric chemistry and physics · doi:10.5194/acp-26-197-2026 · Cited by 7
From short-term uncertainties to long-term certainties in the future evolution of the Antarctic Ice Sheet
Coulon, V., Klose, A., Edwards, T. L., Turner, F. E., Pattyn, F., & Winkelmann, R. (2025) · Nature Communications · doi:10.1038/s41467-025-66178-w · Cited by 11
“5 29 arebasedonasubsetofCMIP6GCMs (MRI-ESM2-0, IPSL-CM6A-LR, CESM2-WACCM,and UKESM1-0-LL; P1 in Table 2).”
Antarctic meltwater alters future projections of climate and sea level
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“5 38 .”
Using optimization tools to explore stratospheric aerosol injection strategies
Brody, E., Zhang, Y., MacMartin, D. G., Visioni, D., Kravitz, B., & Bednarz, E. (2025) · Earth System Dynamics · doi:10.5194/esd-16-1325-2025 · Cited by 7
“CC BY 4.0 License. evolution of other non-SAI concentrations from the Shared Socioeconomic Pathway (SSP) 2-4.5, (Meinshausen et al., 2020).”
Influential

Authors: Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., Canadell, J. G., Daniel, J. S., John, A., Krummel, P. B., Luderer, G., Meinshausen, N., Montzka, S. A., Rayner, P. J., Reimann, S., Smith, S. J., van den Berg, M., Velders, G. J. M., Vollmer, M. K., & Wang, R. H. J.
Affiliations: The University of Melbourne (AU)
International Institute for Applied Systems Analysis (AT)
Commonwealth Scientific and Industrial Research Organisation (AU)
ETH Zurich (CH)
Potsdam Institute for Climate Impact Research (DE)
CSIRO Oceans and Atmosphere (AU)
NOAA Chemical Sciences Laboratory (US)
National Oceanic and Atmospheric Administration (US)
Empa (CH)
Joint Global Change Research Institute (US)
Utrecht University (NL)
Georgia Institute of Technology (US)
Corresponding author: Meinshausen, Malte
Gidden, Matthew J.
Vogel, Elisabeth
Beyerle, Urs
Gessner, Claudia
Nauels, Alexander
Canadell, Josep G.
Krummel, Paul B.
Luderer, Gunnar
Montzka, Stephen A.
Rayner, Peter J.
Reimann, Stefan
Smith, Steven J.
Velders, Guus J. M.
Vollmer, Martin K.
Wang, Ray H. J.
ORCID: Meinshausen, Malte
Nicholls, Zebedee R. J.
Lewis, Jared
Gidden, Matthew J.
Vogel, Elisabeth
Freund, Mandy
Beyerle, Urs
Gessner, Claudia
Nauels, Alexander
Bauer, Nico
Canadell, Josep G.
Daniel, John S.
John, Andrew
Krummel, Paul B.
Luderer, Gunnar
Montzka, Stephen A.
Rayner, Peter J.
Reimann, Stefan
Smith, Steven J.
van den Berg, Marten
Velders, Guus J. M.
Vollmer, Martin K.
Wang, Ray H. J.
Venue: Geoscientific Model Development
Publisher: Copernicus GmbH
Published: 2020-08-13
Volume: 13
Issue: 8
Pages: 3571-3605
ISSN: 1991-959X (ISSN-L)
1991-9603
DOI: 10.5194/gmd-13-3571-2020
OpenAlex: W3048501727
Cited by: 1,815
References: 78
Citations by year: 2026: 288 · 2025: 412 · 2024: 396 · 2023: 336 · 2022: 241 · 2021: 107 · 2020: 33
FWCI: 66.93
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Geoscientific model development (publishedVersion, cc-by)
Minerva Access (University of Melbourne) (submittedVersion, cc-by-sa)
IIASA PURE (International Institute of Applied Systems Analysis) (submittedVersion, cc-by-sa)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
DORA Empa (Swiss Federal Laboratories for Materials Science and Technology (Empa)) (submittedVersion, cc-by)
Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
NOAA Institutional Repository (submittedVersion)
RENATE (submittedVersion, cc-by-nc-nd)
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
Publication Database PIK (Potsdam Institute for Climate Impact Research (PIK)) (submittedVersion, other-oa)
Rivm (National Institute for Public Health and the Environment) (submittedVersion)
Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
Rivm (National Institute for Public Health and the Environment) (submittedVersion, other-oa)
RENATE (cc-by)
ETH Zürich Research Collection (cc-by)
Funders: Australian Research Council (FT130100809)
Topics: Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Environmental Impact
Keywords: Shared Socioeconomic Pathways, greenhouse gas concentrations, long-lived greenhouse gases, radiative forcing, fossil fuel emissions, land use change emissions, net-negative emissions, non-CO2 emissions, surface air temperature, RCP scenarios

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The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500

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