The Interaction between Climate Sensitivity, the Carbon Cycle, and Mitigation Effort: Supplementary Material
Katherine Calvin, Ben Bond-Lamberty, James Edmonds, MohamadHejazi, Stephanie Waldhoff, Marshall Wise, Yuyu Zhou
1. Calibrating GCAM to EPPA
Population, as an exogenous input, was harmonized with the results provided by the EPPA model. To calibrate GDP, we held labor participation rates constant at their 2005 values and adjusted the labor productivity growth rate to match the growth in GDP per capita provided by the EPPA model.
Panel A: Global/ Panel B: United States
Figure 1: Population and Gross Domestic Product, Globally and in the United States, for All Scenarios
2. Water Supply, Water Demand, and WaterStress
Figure 2 shows water supply (runoff) and water demand globally and in the United States in the REF scenario with a climate sensitivity of 3°C.
Figure 2: Total water supply (runoff) and demand in the REF
To better quantify water stress conditions under the REF Scenario, and the effects of climate sensitivity and climate policy, we calculate the proportion of population living various levels of stress conditions in all individual water basins globally. In this study, we follow the definition of Raskin et al. (1997) andWada et al. (2011) for water stress, which defines the water scarcity index (WSI) as the ratio of total water demand to the total amount of runoff in each basin. Figure 1 shows the distributions of global populations facing each of the four levels of water scarcity conditions: severe stress (WSI ≥ 0.4), moderate stress (0.2 ≤ WSI < 0.4), low stress (0.1 ≤ WSI < 0.2), and no stress (WSI < 0.1) under various scenarios; WSI values are computed at the basin scale and then the shares of populations are aggregated to the global scale. Under the REF Scenario, the global population living under severe water stress conditions increases from 36% in 2005 to 46% in 2095, peaking at 55% in 2030. A similar behavior is observed under different climate sensitivities and the two climate policies; but the climate sensitivity of 6oC scenario slightly alleviates water stress (due to increasing runoff) while the climate policy scenarios tend to exacerbate water stress conditions (due to decreasing runoff and increasing demands).
Figure 2: Distributions of global populations facing each of the four levels of water scarcity conditions: severe stress (WSI ≥ 0.4), moderate stress (0.2 ≤ WSI < 0.4), low stress (0.1 ≤ WSI < 0.2), and no stress (WSI < 0.1) under various scenarios; WSI values are computed at the basin scale and then the shares of populations are aggregated to the global scale
3. Extra Experiments Conducted with MAGICC
3.1. Methodology
The analysis conducted in this paper relies heavily on the climate module within GCAM, MAGICC. MAGICC’s default parameters are calibrated to the mean climate model from the IPCC 4th Assessment Report. In the main text of the paper, we only adjusted climate sensitivity within MAGICC, leaving all other parameters at their default value.[1] However, other parameters within MAGICC are correlated with the climate sensitivity and may need adjustment. Typically, the values of these parameters are chosen so that historic temperature is constrained to match observations (see (Meinshausen et al., 2009)). To test the validity of our conclusions, we have conducted some additional experiments with alternative parameterizations of MAGICC. Specifically, we adjusted parameters to replicate seven different complex models (see Table S1). These models, and parameters, are provided as options within MAGICC. A more complete discussion of the calibration method is found in (Randall et al., 2007; Raper et al., 2001; Wigley and Raper, 2005). With each of these parameterizations, we calculated the carbon tax path needed to stabilize radiative forcing at 4.5 W/m2, following the methodology described in the main text.
3.2. Results
Figure S1a plots the cumulative carbon uptake of the terrestrial system, the oceans, and the remaining carbon in the atmosphere as a function of climate sensitivity, when radiative forcing is stabilized at 4.5 W/m2. From this figure, we see a clear negative correlation between climate sensitivity and terrestrial uptake. That is, as the climate sensitivity rises, temperature rises and the carbon stored in the terrestrial system declines. As a result, we also find a positive correlation between climate sensitivity and carbon price (Figure S1b). This relationship is consistent with the relationship found in the main text of the paper, where only climate sensitivity was varied within MAGICC.
Figure S2 plots transient temperature rise across the scenarios. Here, we observe a large range of temperature estimates, both in 2100 and in 2005, across the various parameterizations of MAGICC. Each scenario stabilizes radiative forcing at 4.5 W/m2. Thus, differences in temperature in 2100 largely reflect the uncertainty in the climate sensitivity. Differences in temperature rise in 2005 reflect uncertainty in the observed temperature and differences in calibration of the models.
Table 1: Parameters used to Calibrate MAGICC
Description / Unit / 0 / 1 / 2 / 3 / 4 / 5 / 6 / 7Name / Model MAGICC was calibrated against / DEFAULT / GFDL / CSIRO / HadCM3 / HadCM2 / ECH4/OPYC / CSM / PCM
CO2DELQ / Change in radiative forcing for a doubling of CO2 concentration / W/m2 / 5.35 / 5.352 / 4.977 / 5.396 / 5.006 / 5.482 / 5.194 / 5.194
DT2XUSER / Climate Sensitivity / °C / 3.0 / 4.2 / 3.7 / 3.0 / 2.5 / 2.6 / 1.9 / 1.7
TW0NH / Temperature at which decline in WNH is zero / °C / 8 / 8 / 5 / 25 / 12 / 20 / 1000 / 14
YK / Ocean diffusivity / cm2/s / 2.3 / 2.3 / 1.6 / 1.9 / 1.7 / 9 / 2.3 / 2.3
RLO / Ratio of land to ocean equilibrium temperature change / - / 1.3 / 1.2 / 1.2 / 1.4 / 1.4 / 1.4 / 1.4 / 1.4
XKLO / Land/ocean exchange coefficient / W/m2/°C / 1 / 1 / 1 / 0.5 / 0.5 / 0.5 / 0.5 / 0.5
T1990 / Temperature rise to 1990 for Ice Melt model / °C / 0.635 / 0.593 / 0.562 / 0.603 / 0.78 / 0.567 / 0.51
G1990 / Sea level rise in 1990 from GSIC / cm / 2.14 / 1.5 / 2.2 / 2.1 / 2.7 / 2.7 / 2.1 / 1.7
SEN / Sensitivity of sea level rise - GSIC / cm/yr-°C / 0.0625 / 0.0576 / 0.0733 / 0.0622 / 0.0613 / 0.0637 / 0.0608 / 0.0587
SENG / Sensitivity of sea level rise - Greenland / cm/yr-°C / 0.0110 / 0.0121 / 0.0157 / 0.0085 / 0.0096 / 0.0029 / 0.0146 / 0.0136
SENA / Sensitivity of sea level rise - Antarctica / cm/yr-°C / -0.0341 / -0.0177 / -0.0373 / -0.0354 / -0.0214 / -0.0478 / -0.0305 / -0.0484
ERRG / Greenland / 1.896 / 1.879 / 2.042 / 1.443 / 1.441 / 1.153 / 3.147 / 2.165
ERRA / Antarctica / 1.242 / 0.799 / 1.12 / 1.288 / 1.239 / 1.484 / 1.143 / 1.618
Panel A: Cumulative Carbon Uptake
/ Panel B: Average Discounted CO2 Price
Figure S2: Cumulative Carbon Uptake and Average Discounted CO2 Price in the POL4.5
Figure S3: Temperature Rise in the POL4.5 Scenarios
3. References
Meinshausen M, Meinshausen N, Hare W, Raper S, Frieler K, Knutti R, Frame D, Allen M (2009) Greenhouse-gas emission targets for limiting global warming to 2℃. Nature 458:1158-1162.
Randall DA, Wood RA, Bony S, Colman R, Fichefet T, Fyfe J, Kattsov V, Pitman A, Shukla J, Srinivasan J, Stouffer RJ, Sumi A, Taylor KE (2007) Climate Models and their Evaluation. in Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds.) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Raper S, Gregory JM, Osborn TJ (2001) Use of an upwelling-diffusion energy balance climate model to simulate and diagnose A/OGCM results. Climate Dynamics 17:601-613.
Wigley T, Raper S (2005) Extended scenarios for glacier melt due to anthropogenic forcing. Geophysical Research Letters 32.
[1]Note that while we only changed a single parameter (climate sensitivity), variables that are endogenous within MAGICC (e.g., ocean heat uptake, carbon fluxes from land and ocean, sea level rise, etc.) will respond to this change in an internally consistent manner.