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

World Sustainable Development Forum, Mexico CityFebruary 1-2, 2018

Carlos Gay García1,2

cgay@unam.mxOscar C. Sánchez1

casimiro@atmosfera.unam.mx1Centro de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México

2Programa de Investigación en Cambio Climático, Universidad Nacional Autónoma de México

Gay Garcia, Carlos.; Sánchez, O. To reach the 1.5 °C of global temperature we will need much more then increased ambition . 2018. 2

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

Contents:

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1) Objectives

• Estimate global mean temperature incrementsassociated to cumulative emissions.

• Build a simple fuzzy model to relate the CO2 emissions(fossil+deforestation) around year 2030 with the CO2emissions for the year 2100 considering the thresholds1°C and 2 °C.

• Estimate the effect of delaying the taking of actions.

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2) Background

• In COP21 (Dec 2015, Paris) it was stated, the objective ofelaborating a “special report on the impacts of globalwarming of 1.5°C (SR15) above pre-industrial levels andrelated global greenhouse gas emission pathways, inthe context of strengthening the global response to thethreat of climate change, sustainable development, andefforts to eradicate poverty”

(http://www.ipcc.ch/meetings/session44/l2_adopted_outline_sr15.pdf).

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• Also, in the COP21: the Intended Nationally DeterminedContributions (INDCs).

The INDC´s reflect the “climate actions that largelydetermine whether the world achieves the long-term goalsof the Paris Agreement: to hold the increase in globalaverage temperature to well below 2°C, to pursue effortsto limit the increase to 1.5°C, and to achieve net zeroemissions in the second half of this century”

(http://www.wri.org/indc-definition).

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• Fuzzy models of type FIS (Fuzzy Inference System)has been used to establish, via linguistic rules IF-THEN, fuzzy sets associated to values of climatesensitivity and GHG emissions with the values of theglobal temperature provided as output variable fromAOGCM´s (Gay and Sánchez, 2013 and Gay et al., 2013).

• In this work we build a simple fuzzy model to relatethe CO2 emissions (fossil+deforestation) around year2030 with the CO2 emissions for the year 2100considering the thresholds 1 °C and 2 °C.

• Then, we show the effects of delaying decisions forreducing GHG emissions from 2030.

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• 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 °Cstarting from emissions reached in year 2030.

• The same kind of polygonal pathways are used to estimate

the effects of delaying the decision of reducing GHGemissions from 2020, 2030, 2040 and 2050.

3) Method

*Model for the Assessment of Greenhouse-gas Induced Climate Change/SCenario GENerator

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

-15

-10

-5

0

5

10

15

20

25

30

35

40

1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

A1FIB1-IMARCP8.5RCP6.0RCP4.5RCP3PD Observed emissions INDCs

CO2 Emission Scenarios for Linear PathwaysFossil+Deforestation (Gt C)

Data obtained with MAGIC V.5.3 and MAGICC V.6.0Observed emissions 1990-2010 from Global Carbon Project*

*Global carbon Project (https://www.co2.earth/global-co2-emissions)

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

Emission Trajectory

1 Gt C= 3.664 Gt CO2

GtCO2e Gt C

Baseline AR5 65 (60-70) 17.74

Current policy trajectory 60 (58-62) 16.38

Unconditional INDC 56 (54-59) 15.28

Conditional INDC 54 (52-57) 14.74

2°C pathways 42 (31-44) 11.46

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Figure 2: Global temperature increments for linear emission pathways (-2)CO2 to5CO2 (dotted lines), RCPs and some SRES, all calculated with MAGICCv5.3.(Adapted from Gay et al., 2013).

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

ObservedA1FIB1 IMARCP8.5RCP6.0RCP4.5

DT (°C) for sensibility 3.0 °C/W/m2(DT before 1990 = 0.421 °C, 0.34°C for Observed*)

Data obtained from MAGICC V.5.3 and MAGICC V.6.0

* Lowess smoothing data https://climate.nasa.gov/system/internal_resources/details/original/647_Global_Temperature_Data_File.txt

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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 SmoothingDTglobal at 1990 is 0.34 °C

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4) ResultsSimple Fuzzy Model for Emission Data for 2100

The figure 3 shows the relation between CO2 concentrationsand cumulative area for linear emissions from Gay et al., 2013.(the RCP and SRES emission pathways are shown forcomparison).Also we present the relation between DT global and cumulativeemissions (Fig. 4).

For an increment of 1 °C the cumulative emission must be-105.39 Pg C

and for 2 °C the corresponding value is390.39 Pg C

With these data we draw the polygonal emission pathwaysshown in figures 5 and 6.

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

y = -4E-09x3 + 4E-05x2 + 0.2412x + 300.78R² = 1

0

200

400

600

800

1000

1200

-500 0 500 1000 1500 2000 2500

Linear emissions

SRES markers

RCPs

RCP3PDRCP4.5

B1-IMA

RCP8.5

RCP6.0

A1FI-MI

A1T-MESB2-MES

A1B-AIM

A2-ASF

CO2 concentration (ppmv) vs Cumulative Emissions (Pg C) to 2100

5CO2

-2CO2

y = 4E-11x3 - 4E-07x2 + 0.0021x + 1.2258R² = 1

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

-500 0 500 1000 1500 2000 2500

DT (°C) vs Cumulative Emissions (Pg C*year)to 2100 for Linear Pathway emissions

(Data obtained using MAGICCv5.3)

RCP8.5

RCP6.0

RCP4.5

RCP3PD

A1FI-MI

A2-ASF

A1B-AIMB2-MES

A1T-MES

B1-IMA

Fig. 4: (After Gay García, C., O. Sánchez Meneses. 2017) Global mean temperatureincrements as function of Cumulative Emissions (area under the linear path ofemissions) 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). 14

390.39

133.94-105.39

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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).

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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).

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-20

-15

-10

-5

0

5

10

15

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1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100Observed 20165CO2 2030 21004CO2 2030 21003CO2 2030 21002CO2 2030 21001CO2 2030 2100INDCs

CO2 Polygonal Emission Pathways going towards DT = 2°C at year 2100

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A simple fuzzy (Mamdani) model can now be constructed, withthe input and output fuzzy sets shown in Tables 1 and 2, forboth 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 areshown in figures 9 and 10.

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

Membership value (m)0 1 0

Emission for 2030 fuzzy set InputLow 7.10 9.68 12.26Med 9.68 12.26 14.84High 12.26 14.84 17.42

Emission for 2100 fuzzy set Output

Less negative -26.33 -22.28 -18.22

Negative -30.39 -26.33 -22.28

Far negative -34.45 -30.39 -26.33

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

Membership value (m)0 1 0

Emission for 2030 fuzzy set InputLow 7.10 9.68 12.26Med 9.68 12.26 14.84High 12.26 14.84 17.42

Emission for 2100 fuzzy set Output

Less negative -12.17 -8.11 -4.06

Negative -16.22 -12.17 -8.11

Far negative -20.28 -16.22 -12.17

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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 ofthe emission for year 2100. The fuzzy sets were calculated with a free temporary license MatLab.

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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 ofthe emission for year 2100. The fuzzy sets were calculated with a free temporary license of MatLab.

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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.

5) Results (cont.)

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Figure 9: (After Gay García, C., O. Sánchez Meneses. 2017) Polygonal emissionpathways 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´sglobal total emissions for 2030 (data taken from Table 3.2 of UNEP, 2015).

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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).

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

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-1.0

0.0

1.0

2.0

3.0

4.0

5.0

1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Observed3CO2-2deg3CO2-1deg4CO2-2deg4CO2-1egRCP8.5

DT (°C) for sensibility 3.0 °C/W/m2(DT before 1990 = 0.421 °C, 0.35°C for Observed)

Data obtained from MAGICC V.5.3 and MAGICC V.6.0

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

Membership value (m)0 1 0

Date to take actions fuzzy set InputSoon 2020 2030 2040

Late 2030 2040 2050Emission for 2100 fuzzy set Output

Less negative -34.27 -30.39 -20.38

Far negative -45.38 -34.27 -30.29

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Table 5: (From Gay García, C., O. Sánchez Meneses. 2017) Fuzzy sets for2 °C simple model Date vs Emissions 2100 (in Pg C)

Membership value (m)0 1 0

Date to take actions fuzzy set InputSoon 2020 2030 2040Late 2030 2040 2050

Emission for 2100 fuzzy set Output

Less negative -17.75 -12.17 -7.99

Far Negative -25.55 -17.75 -12.17

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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.

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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.

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6) Discussion and Conclusions• The linear emission pathways provide a convenient

framework to estimate the uncertainties associated withconcentrations, forcings and temperature increments,because any other realistic emission pathway (RCP´s asexample) lies within the purposed limits 5CO2 and -2CO2(even more 1CO2).

• The concentrations for year 2100, from the linear emissionspathways, can be associated with the cumulative emissionsfor year 2100.

• DT global can be associated with the cumulative emissionstoo.

• Polygonal emission pathways can be used to estimate the netemissions of year 2100, throughout simple fuzzy models, if DTglobal thresholds are proposed.

• Polygonal emissions can be used to estimate theconsequences of delaying the taking of actions.

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• With the simple fuzzy models developed for calculateemission for year 2100, applied to the emissions related toINDCs, we obtain the values shown in the table 6.

• The value 17.74 corresponding to baseline scenario of TheEmissions 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 adequatevalues of emissions.

• For decision-making purposes, we have shown that theexpectations of reaching stabilisation global meanincremental temperatures of 1 °C and 2 °C (with respect to1990) for the year 2100 are not plausible.

• If the preindustrial levels are considered, it is even moredifficult.

• There is a need to strongly reduce the emissions, as soon aspossible, with the objective of reaching the expected 2 °C.

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Emission scenario cases

Projected emission

Fuzzy model for 1 °C

Fuzzy model for 2 °C

2°C pathways 11.46 -24.9 -10.8

Conditional INDC

14.74 -30.2 -16.0

Unconditional INDC

15.28 -30.4 -16.2

Current policy trajectory

16.37 -30.4 -16.2

Baseline 2010 17.74 Out of range Out of range

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)

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ACKNOWLEDGEMENTSThis work was supported by the

Programa de Investigación en Cambio Climático (PINCC, www.pincc.unam.mx)

of the Universidad Nacional Autónoma de Méxicoand

Centro de Ciencias de la Atmósferaof the Universidad Nacional Autónoma de México

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7) References-Gay García, C., O. Sánchez Meneses. 2017. A Simple Fuzzy Model to EstimateCarbon Emissions towards 2100 Consistent with Expected Temperature Increases.7th International Conference on Simulation and Modeling Methodologies,Technologies and Applications (SIMULTECH). Special Session on Applications ofModeling 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 inFuzzy Climate Models. 3rd International Conference on Simulation and ModelingMethodologies, Technologies and Applications (SIMULTECH). Special Session onApplications of Modeling and Simulation to Climatic Change and EnvironmentalSciences - MSCCEC 2013. July 29-31. Reykjavík, Iceland. Thomson Reuters ConferenceProceedings 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. FuzzyModels: Easier to Understand and an Easier Way to Handle Uncertainties in ClimateChange Research. In: Simulation and Modeling Methodologies, Technologies andApplications. Volume Editor(s): Pina, N., Kacprzyk, J. and Filipe, J. In the series"Advances in Intelligent and Soft Computing". Springer- Verlag GmbH BerlinHeidelberg.

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- Meinshausen, M., S. C. B. Raper and T. M. L. Wigley (2011-1). "Emulating coupledatmosphere-ocean and carbon cycle models with a simpler model, MAGICC6: Part IModel 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 andtheir 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 WorkingGroup III of the Intergovernmental Panel on Climate Change. Cambridge UniversityPress, Cambridge, 599 pp.- UNEP (2015). The Emissions Gap Report 2015. United Nations EnvironmentProgramme (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/)

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Thanks Contact information:

Dr. Carlos Gaycgay@unam.mx

Centro de Ciencias de la Atmósferawww.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