The Physics of Climate Change

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1 The Physics of Climate Change Michael E. Mann Departments of Meteorology and Geosciences & Earth and Environmental Systems Institute Director, Earth System Science Center Penn State University C dt e /dt = (1- α)s/4-σt e 4 Physics Department University of Virginia Charlottesville, VA Feb 5, 2016

2 The Physics of Climate Change Basic principles Theoretical Climate Models The Zero-Dimensional EBM Applications

3 The Physics of Climate Change Basic principles Theoretical Climate Models The Zero-Dimensional EBM Applications

4 Discovery of the Greenhouse Effect Joseph Fourier (1827) Recognized that gases in the atmosphere might trap the heat received from the Sun. James Tyndall (1859) Careful laboratory experiments demonstrated that several gases could trap infrared radiation. The most important was simple water vapor. Also effective was carbon dioxide, although in the atmosphere the gas is only a few parts in ten thousand. Svante Arrhenius (1896) Performed numerical calculations that suggested that doubling the amount of carbon dioxide in the atmosphere could raise global mean surface temperatures by 5-6 C. Guy Callendar (1939) Argued that rising levels of carbon dioxide were responsible for measurable increases in Earth surface temperatures. Estimated that doubling the amount of CO2 in the atmosphere could raise global mean surface temperatures by 2 C.

5 GREENHOUSE EFFECT? X

6

7 Feedbacks

8 Feedbacks

9 Water Vapor Feedback

10 warming Decreased snow and ice; less reflectivity More solar radiation absorbed at surface Ice-Albedo Feedback

11 Initial Change Climate warming Reduced Warming Uncertain Increased clouds Greater reflected radiation Cloud Radiative Feedbacks

12 FEEDBACKS INVOLVED IN GLOBAL WARMING

13 FEEDBACKS INVOLVED IN GLOBAL WARMING

14 FEEDBACKS INVOLVED IN GLOBAL WARMING

15 OBSERVATIONS

16 Atmospheric Carbon Dioxide Measured at Mauna Loa, Hawaii Carbon Dioxide Concentration (ppmv)

17 Temperature Anomaly ( C) Surface Temperature Changes

18 Temperature Anomaly ( C) Surface Temperature Changes

19 Surface Temperature Changes

20 The Physics of Climate Change Basic principles Theoretical Climate Models The Zero-Dimensional EBM Applications

21 Climate Models

22 General Circulation Models (GCMs) take into account the full three-dimensional structure of the atmosphere & ocean

23 Hansen s 1988 Predictions Annual Mean Global Temperature Change ( C) Model Simulation of Past Model Predictions of Future (in 1988) SCENARIO A SCENARIO B SCENARIO C OBSERVED WEATHER STATION DATA

24 Hansen s 1988 Predictions Annual Mean Global Temperature Change ( C) Model Simulation of Past Model Predictions of Future (in 1988) SCENARIO B OBSERVED WEATHER STATION DATA

25 Projected Future Warming

26 The Physics of Climate Change Basic principles Theoretical Climate Models The Zero-Dimensional EBM Applications

27 The Physics of Climate Change Basic principles Theoretical Climate Models The Zero-Dimensional EBM Applications

28 ZERO-DIMENSIONAL EBM α=0.3 S=1370 W/m 2 T e = 255K! σt e 4 = (1- α)s/4 A Climate Modeling Primer, A. Henderson-Sellers and K. McGuffie, Wiley (1987).

29 ZERO-DIMENSIONAL EBM (1- α)s/4 (1- α)s/4 = λa+ (1-λ)G (1-λ/2)σT s 4 = (1- α)s/4 G = (1- α)s/[4(1-λ/2)] G = σt s 4 (1- α)s/4+λa = G

30 ZERO-DIMENSIONAL EBM λ = 0.77 (1-λ/2)σT 4 4 e = s (1- = α)s/4 (1- α)s/4 T e = 288K! A Climate Modeling Primer, A. Henderson-Sellers and K. McGuffie, Wiley (1987).

31 ZERO-DIMENSIONAL EBM (Equilibrium) (1-λ/2)σT s 4 = (1- α)s/4 What about non-equilibrium? CdT s /dt = (1- α)s/4 - (1-λ/2)σT 4 s Account for stochastic weather forcing, CdT s /dt = (1- α)s/4 - (1-λ/2)σT 4 s + w(t) linearize the quartic term, σt 4 s = a+bt CdT/dt = F BT + w(t) F (1- α)s/4 - a(1-λ/2) B (1-λ/2)b

32 ZERO-DIMENSIONAL EBM Take: C=2.08 x 10 8 J K -1 m -2 (effective heat capacity associated with 70 m depth mixed-layer ocean covering 70% of Earth surface. In equilibrium, we have:δf = BΔT ΔT/ΔF = 1/B ΔF 2xCO2 =3.74 Wm -2 Equilibrium climate sensitivity (ECS) ΔT 2xCO2 For B=1.25 Wm -2 K -1, ΔT 2xCO2 =3.0 o C CdT/dt = F BT + w(t) F (1- α)s/4 - a(1-λ/2) B (1-λ/2)b

33 The Physics of Climate Change Basic principles Theoretical Climate Models The Zero-Dimensional EBM Applications

34

35 Volcanic Forcing Forcing (Wm -2 ) Greenhouse Gases Solar Forcing CdT/dt = F BT F (1- α)s/4 - a(1-λ/2) B (1-λ/2)b Sulphate Aerosols

36 CdT/dt = F BT F (1- α)s/4 - a(1-λ/2) B (1-λ/2)b

37 Refining ECS estimates with paleodata Hegerl G.C. et al, Climate sensitivity constrained by temperature reconstructions over the past seven centuries. Nature 440, (2006). Climate Sensitivity underestimated?

38 Refining ECS estimates with paleodata Climate Sensitivity underestimated? Simulated tree-ring composite Model Simulation D Arrigo et al tree-ring composite D Arrigo et al tree-ring based NH reconstruction (blue) along with the climate model (NCAR CSM 1.4) simulated NH mean temperatures (red) and the simulated tree-ring NH temperature series based on driving the biological growth model with the climate model simulated temperatures (green). The two insets focus on the response to the AD 1258 and AD volcanic eruption sequences.

39 Refining ECS estimates with paleodata PDF of ECS using decadally smoothed data between) AD (red = simulated actual temperature series; green = synthetic tree ring temperature series). Shown by dashed vertical lines are mean of the ESC distribution for simulated temperature series (red), mean of ECS distribution for synthetic tree ring temperature series (green), ECS estimate using MFR12 simulated tree ring temperature series where chronological error accumulation due to inferred missing rings is taken into account (cyan), and sensitivity estimate for D06 tree ring temperature reconstruction (blue). True value of ESC is 3.0 in both cases.

40 Climate Signal Detection The AMO

41 Climate Signal Detection

42 Climate Signal Detection (in review) (in revision) CMIP5-All ensemble mean of Northern Hemisphere SST+SAT, North Atlantic SST, and North Pacific SST (black curves) shown with individual model means (colored curves). Thin black line depicts the 95% confidence limits of the model mean determined via bootstrap resampling. Blue line depicts observed temperatures

43 Climate Signal Detection Temp. anomaly ( C) CMIP5 All AMO CMIP5 All AMO Detrended Global SST regress Regional regress Regional difference Regional difference - rescaled (in review) (in revision) A CMIP5 All PMO D CMIP5 All PMO Temp. anomaly ( C) Temp. anomaly ( C) B C CMIP5 All NMO 1930 Year (AD) E F CMIP5 All NMO 1930 Year (AD) (A-C) CMIP5-all mean (black lines) and 24 individual realizations (colored lines) of AMO, PMO and NMO determined using target region regression. Predicted 1 sigma limits for mean series are shown by two horizontal dashed lines. (D-F) Mean (solid lines) and 1 sigma limits determined using detrending (blue) global SST regression (red) target region regression (black), target region differencing (green), and rescaled target region differencing (purple).

44 Climate Signal Detection (in review) (in revision) Semi-empirical estimate of AMO (blue), PMO (green), and NMO (black) based on target region regression using (A) CMIP5-GISS, (B) CMIP5-AIE, and (C) CMIP-All historical climate model realizations. Bivariate regression-based approximation of NMO (red) strongly correlates (R2=0.86/0.88/0.91 for CMIP5-All/CMIP5-GISS, CMIP5-AIE, respectively) with semi-empirical NMO estimate (black). 95% confidence limits of the AMO, PMO, and NMO CMIP5-All means (determined using bootstrap resampling) are shown as colored shading.

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46

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49 Conclusions The 0d EBM is useful for exploring a broad range of climate change issues. Uncertainty in ECS unlikely to buy significant time in avoiding 2C warming under business-as-usual carbon emissions. Very low-end sensitivity (~2.0C) in some paleoclimate studies likely an artifact of biases in estimated volcanic cooling. Recent temperature records very unlikely to have happened in absence of human-caused climate change.

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