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TO REACH THE 1.5°C OF GLOBAL TEMPERATURE WE WILL NEED MUCH MORE THAN INCREASED AMBITION

This research paper discusses the need for more than increased ambition to reach the 1.5°C global temperature goal. It explores the estimation of temperature increments associated with cumulative emissions and presents a simple fuzzy model for relating CO2 emissions to temperature thresholds. The effects of delaying actions on reducing greenhouse gas emissions are also analyzed.

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TO REACH THE 1.5°C OF GLOBAL TEMPERATURE WE WILL NEED MUCH MORE THAN INCREASED AMBITION

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  1. TO REACH THE 1.5°C OF GLOBAL TEMPERATURE WE WILL NEED MUCH MORE THAN INCREASED AMBITION Carlos Gay García1,2 cgay@unam.mx Oscar C. Sánchez1 casimiro@atmosfera.unam.mx 1Centro de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México 2Programa de Investigación en Cambio Climático, Universidad Nacional Autónoma de México World Sustainable Development Forum, Mexico City February 1-2, 2018

  2. Contents: 1) Objectives 2) Background 3) Method 4) Simple Fuzzy Model for Emission Data for 2100 5) When Should We Begin to Take Actions? 6) Discussion and Conclusions 7) References Gay Garcia, Carlos.; Sánchez, O. To reach the 1.5 °C of global temperature we will need much more then increased ambition . 2018.

  3. 1) Objectives • Estimate global mean temperature increments associated to cumulative emissions. • Build a simple fuzzy model to relate the CO2 emissions (fossil+deforestation) around year 2030 with the CO2 emissions for the year 2100 considering the thresholds 1°C and 2 °C. • Estimate the effect of delaying the taking of actions.

  4. 2) Background • In COP21 (Dec 2015, Paris) it was stated, the objective of elaborating a “special report on the impacts of global warming of 1.5°C (SR15) above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty” (http://www.ipcc.ch/meetings/session44/l2_adopted_outline_sr15.pdf).

  5. Also, in the COP21: the Intended Nationally Determined Contributions (INDCs). • The INDC´s reflect the “climate actions that largely determine whether the world achieves the long-term goals of the Paris Agreement: to hold the increase in global average temperature to well below 2°C, to pursue efforts to limit the increase to 1.5°C, and to achieve net zero emissions in the second half of this century” • (http://www.wri.org/indc-definition).

  6. Fuzzy models of type FIS (Fuzzy Inference System) has been used to establish, via linguistic rules IF-THEN, fuzzy sets associated to values of climate sensitivity and GHG emissions with the values of the global temperature provided as output variable from AOGCM´s (Gay and Sánchez, 2013 and Gay et al., 2013). • In this work we build a simple fuzzy model to relate the CO2 emissions (fossil+deforestation) around year 2030 with the CO2 emissions for the year 2100 considering the thresholds 1 °C and 2 °C. • Then, we show the effects of delaying decisions for reducing GHG emissions from 2030.

  7. 3) Method • Gay et al. (2013) built linear emission paths (Fig. 1), and the subsequent concentrations, forcings and temperature increments (Fig. 2) were calculated using the free software Magicc/Scengen v5.3* (Wigley, 2008) up to the year 2100. • Concentrations and temperature increments can be associated with cumulative emissions (the area under any emission pathway) to year 2100. • Then, we construct polygonal emission pathways aimed over target temperature increments of 1 °C and 2 °C starting from emissions reached in year 2030. • The same kind of polygonal pathways are used to estimate the effects of delaying the decision of reducing GHG emissions from 2020, 2030, 2040 and 2050. *Model for the Assessment of Greenhouse-gas Induced Climate Change/SCenario GENerator

  8. Figure 1: Some emissions scenarios of CO2 (fossil + deforestation), compared to Linear Pathways (-2)CO2, (-1)CO2, …, 5CO2. For all the linear paths, the emissions of non CO2 greenhouse gasses are the same as A1FI (Nakicenovic, et al., 2000) for running with MAGICCv5.3. INDCs values are shown in Table 1. (Adapted from Gay et al., 2013).

  9. Table 1: The range of INDC´s global total emissions for 2030 (data taken from Table 3.2 of UNEP, 2015).

  10. Figure 2: Global temperature increments for linear emission pathways (-2)CO2 to 5CO2 (dotted lines), RCPs and some SRES, all calculated with MAGICCv5.3. (Adapted from Gay et al., 2013).

  11. Considering DTglobal = 0.0 °C, for 1765, the beginning of the historical time series, we have: With MAGICCv5.3 the DTglobal at 1990 is 0.421 °C With MAGICC 6.0 the DTglobal at 1990 is 0.490 °C With observed time series: (https://climate.nasa.gov/system/internal_resources/details/original/647_Global_Temperature_Data_File.txt Annual mean: DTglobal at 1990 is 0.44 °C Locally Weighted Scatterplot Smoothing DTglobal at 1990 is 0.34 °C

  12. 4) Results Simple Fuzzy Model for Emission Data for 2100 The figure 3 shows the relation between CO2 concentrations and cumulative area for linear emissions from Gay et al., 2013. (the RCP and SRES emission pathways are shown for comparison). Also we present the relation between DT global and cumulative emissions (Fig. 4). For an increment of 1 °C the cumulative emission must be -105.39 Pg C and for 2 °C the corresponding value is 390.39 Pg C With these data we draw the polygonal emission pathways shown in figures 5 and 6.

  13. -2CO2 Fig. 3:(After Gay García, C., O. Sánchez Meneses. 2017) CO2 concentration vs Cumulative Emissions for 2100. Linear emission pathways are shown as black bullets. RCP´s values are taken from MAGICC 6.0 (Meinshausen et al., 2011-2) and SRES scenarios from MAGICC v5.3 (Wigley, 2008).

  14. 390.39 -105.39 133.94 Fig. 4:(After Gay García, C., O. Sánchez Meneses. 2017) Global mean temperature increments as function of Cumulative Emissions (area under the linear path of emissions) for 2100. The bullets represent cumulative emissions from -2CO2 to 5CO2. Also included the corresponding temperature increments for RCPs (taken from Magicc 6.0) and SRES (taken from Magicc v5.3).

  15. Fig. 5:(After Gay García, C., O. Sánchez Meneses. 2017) Polygonal emission pathways that lead to global mean temperature increment of 1 °C. Observed data from Global Carbon Project (https://www.co2.earth/global-co2-emissions). The vertical thick bar represents the range of INDC´s global total emissions for 2030 shown in table 1 (data taken from Table 3.2 of UNEP, 2015).

  16. Fig. 6:(After Gay García, C., O. Sánchez Meneses. 2017) Polygonal emission pathways that lead to global mean temperature increment of 2 °C. Observed data from Global Carbon Project (https://www.co2.earth/global-co2-emissions). The vertical thick bar represents the range of INDC´s global total emissions for 2030 shown in table 1 (data taken from Table 3.2 of UNEP, 2015).

  17. A simple fuzzy (Mamdani) model can now be constructed, with the input and output fuzzy sets shown in Tables 1 and 2, for both thresholds, 1 °C and 2 °C. We use the fuzzy rules: • 1. If (Emission for 2030 is low) then (Emission for 2100 is Less negative) • 2. If (Emission for 2030 is Med) then (Emission for 2100 is Negative) • 3. If (Emission for 2030 is High) then (Emission for 2100 is Far negative). The same rules are valid for the case of 2 °C. The explicit rules are shown in tables 2 and 3. The results obtained from the fuzzy model for 1 °C and 2°C are shown in figures 9 and 10.

  18. Table 2:(After Gay García, C., O. Sánchez Meneses. 2017)Fuzzy sets for 1 °C simple model Emissions 2030 vs Emissions 2100 (in Pg C)

  19. Table 3:(After Gay García, C., O. Sánchez Meneses. 2017) Fuzzy sets for 2 °C simple model Emissions 2030 vs Emissions 2100 (in Pg C)

  20. Fig. 7:(After Gay García, C., O. Sánchez Meneses. 2017) Left panel: Fuzzy rules for 1 °C simple model (emissions in Pg C). The uncertainty in output is (-34.45, -26.33). Right panel: Corresponding graph of the output range of the values of the emission for year 2100. The fuzzy sets were calculated with a free temporary license MatLab.

  21. Fig. 8: (After Gay García, C., O. Sánchez Meneses. 2017)Left panel: Fuzzy rules for 2 °C simple model (emissions in Pg C). The uncertainty in output is (-20.28, -12.17). Right panel: Corresponding graph of the output range of the values of the emission for year 2100. The fuzzy sets were calculated with a free temporary license of MatLab.

  22. 5) Results (cont.) When Should We Begin to Take Actions? Other simple fuzzy model to estimate the effect of delaying the taking of actions can be constructed starting from the information obtained in our previous calculations. First, it can be noted that the actual CO2 emission path is near the linear emission path 3CO2 (see figures 9 and 10). Then, using 3CO2 as reference, it can be calculated the emissions for 2100, if actions are taken in 2020, 2030, 2040 or 2050, for both thresholds of temperature increments (see figures 11 and 12) with the linguistic fuzzy rules: 1. If (Date is Soon) then (Emission for 2100 is Less negative) 2. If (Date is Late) then (Emission for 2100 is Far negative) Table 4 and 5 shown the explicit fuzzy rules for 1 °C and 2 °C in this case, and figures 13 and 14 shown the results of this fuzzy model.

  23. Figure 9:(After Gay García, C., O. Sánchez Meneses. 2017) Polygonal emission pathways starting from 3CO2 for years 2020, 2030, 2040 and 2050 for 1 °C (emissions in Pg C). The vertical thick bar represents the range of INDC´s global total emissions for 2030 (data taken from Table 3.2 of UNEP, 2015).

  24. Figure 10: (After Gay García, C., O. Sánchez Meneses. 2017) Polygonal emission pathways starting from 3CO2 for years 2020, 2030, 2040 and 2050 for 2 °C (emissions in Pg C). The vertical thick bar represents the range of INDC´s global total emissions for 2030 (data taken from Table 3.2 of UNEP, 2015).

  25. Figure 11: Global temperature increments for polygonal emission pathways, for both experiments, also included RCPs and some SRES. (Adapted from Gay García, C., O. Sánchez Meneses. 2017).

  26. Table 4:(From Gay García, C., O. Sánchez Meneses. 2017) Fuzzy sets for 1 °C simple model Date vs Emissions 2100 (in Pg C)

  27. Table 5:(From Gay García, C., O. Sánchez Meneses. 2017)Fuzzy sets for 2 °C simple model Date vs Emissions 2100 (in Pg C)

  28. Figure 12:(After Gay García, C., O. Sánchez Meneses. 2017) Left panel: Fuzzy rules for 1 °C simple model (emissions in Pg C). The date in the input fuzzy set is 2030 and the uncertainty in output is (-34.27, -20.38). Right panel: Graph of the output range of the values of the emission for year 2100. The fuzzy sets were calculated with a free temporary license of MatLab.

  29. Figure 13:(From Gay García, C., O. Sánchez Meneses. 2017) Left panel: Fuzzy rules for 2 °C simple model (emissions in Pg C). The date in the input fuzzy set is 2030 and the uncertainty in output is (-17.75, -7.99). Right panel: Graph of the output range of the values of the emission for year 2100. The fuzzy sets were calculated with a free temporary license of MatLab.

  30. 6) Discussion and Conclusions • The linear emission pathways provide a convenient framework to estimate the uncertainties associated with concentrations, forcings and temperature increments, because any other realistic emission pathway (RCP´s as example) lies within the purposed limits 5CO2 and -2CO2 (even more 1CO2). • The concentrations for year 2100, from the linear emissions pathways, can be associated with the cumulative emissions for year 2100. • DTglobal can be associated with the cumulative emissions too. • Polygonal emission pathways can be used to estimate the net emissions of year 2100, throughout simple fuzzy models, if DT global thresholds are proposed. • Polygonal emissions can be used to estimate the consequences of delaying the taking of actions.

  31. With the simple fuzzy models developed for calculate emission for year 2100, applied to the emissions related to INDCs, we obtain the values shown in the table 6. • The value 17.74 corresponding to baseline scenario of The Emissions Gap Report 2015 is slightly out of the input range, but uncertainty interval is (-34.45, -26.33) for 1 °C and (-20.28, -12.17) for 2 °C. • For the remaining cases, the models calculate adequate values of emissions. • For decision-making purposes, we have shown that the expectations of reaching stabilisation global mean incremental temperatures of 1 °C and 2 °C (with respect to 1990) for the year 2100 are not plausible. • If the preindustrial levels are considered, it is even more difficult. • There is a need to strongly reduce the emissions, as soon as possible, with the objective of reaching the expected 2 °C.

  32. Table 6: (From Gay García, C., O. Sánchez Meneses. 2017) Emissions for year 2100 calculated with the emissions projected by INDCs until 2030 (Pg C)

  33. ACKNOWLEDGEMENTS This work was supported by the Programa de Investigación en Cambio Climático (PINCC, www.pincc.unam.mx) of the Universidad Nacional Autónoma de México and Centro de Ciencias de la Atmósfera of the Universidad Nacional Autónoma de México

  34. 7) References • Gay García, C., O. Sánchez Meneses. 2017. A Simple Fuzzy Model to Estimate Carbon Emissions towards 2100 Consistent with Expected Temperature Increases. 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH). Special Session on Applications of Modeling and Simulation to Climatic Change and Environmental Sciences - MSCCEC 2017. 26-28 julio. Madrid, Spain, Proceedings of SIMULTECH 2017. SCITEPRESS – Science and Technology Publications, Portugal. ISBN: 978-989-758-265-3, pp.466-473. Thomson Reuters Conference Proceedings Citation Index (ISI), INSPEC, DBLP and EI (Elsevier Index) Abstracts en: http://www.simultech.org/Abstracts/2017/MSCCES_2017_Abstracts.htm • Gay García, C., O. Sánchez Meneses. 2013. Natural Handling of Uncertainties in Fuzzy Climate Models. 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH). Special Session on Applications of Modeling and Simulation to Climatic Change and Environmental Sciences - MSCCEC 2013. July 29-31. Reykjavík, Iceland. Thomson Reuters Conference Proceedings Citation Index (ISI), INSPEC, DBLP and EI (Elsevier Index) Abstracts in: http://www.simultech.org/Abstracts/2013/MSCCEC_2013_Abstracts.htm • Gay, C., Sánchez, O., Martínez-López, B., Nébot, Á., Estrada, F. 2013. Fuzzy Models: Easier to Understand and an Easier Way to Handle Uncertainties in Climate Change Research. In: Simulation and Modeling Methodologies, Technologies and Applications. Volume Editor(s): Pina, N., Kacprzyk, J. and Filipe, J. In the series "Advances in Intelligent and Soft Computing". Springer- Verlag GmbH Berlin Heidelberg.

  35. - Meinshausen, M., S. C. B. Raper and T. M. L. Wigley (2011-1). "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. doi:10.5194/acp-11-1417-2011. - Meinshausen, M., Smith, S.J., Calvin, K., Daniel, J.S., Kainuma, M.L.T., Lamarque, J-F, Matsumoto, K., Montzka, S.A., Raper, S.C.B., Riahi, K., Thomson, A., Velders, G.J.M., van Vuuren, D.P.P. 2011 -2. "The RCP greenhouse gas concentrations and their extensions from 1765 to 2300", Climatic Change 109 (1-2): 213–241, doi:10.1007/s10584-011-0156-z - Nakicenovic, N., J. Alcamo, G. Davis, B. de Vries, J. Fenhann, S. Gaffin, K. Gregory, A. Grübler, T. Y. Jung, T. Kram, E. L. La Rovere, L. Michaelis, S. Mori, T. Morita, W. Pepper, H. Pitcher, L. Price, K. Riahi, A. Roehrl, H.-H. Rogner, A. Sankovski, M. Schlesinger, P. Shukla, S. Smith, R. Swart, S. van Rooijen, N. Victor, Z. Dadi, 2000. Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, 599 pp. - UNEP (2015). The Emissions Gap Report 2015. United Nations Environment Programme (UNEP), Nairobi. 97 pp. - Wigley T. M. L. 2008. MAGICC/SCENGEN V. 5.3: User Manual (version 2). NCAR, Boulder, CO. 80 pp. (On line: http://www.cgd.ucar.edu/cas/wigley/magicc/)

  36. Thanks Contact information: Dr. Carlos Gay cgay@unam.mx Centro de Ciencias de la Atmósfera www.atmosfera.unam.mx Programa de Investigación en Cambio Climático Universidad Nacional Autónoma de México www.pincc.unam.mx Phone number: 52 (55) 56 22 52 19

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