Climate Sensitivity: Uncertainties & Learning Workshop on GHG - PowerPoint PPT Presentation

Climate Sensitivity: Uncertainties & Learning Workshop on GHG Stabilization Scenarios Tsukuba , Japan, 23 January 2004 Michael Schlesinger and Natasha Andronova Climate Research Group Department of Atmospheric Sciences University of Illinois at Urbana-Champaign

Introduction • Climate sensitivity, ∆ T 2x : The change in global-average near-surface temperature resulting from a doubling of the preindustrial carbon dioxide concentration. • If ∆ T 2x is small, then the problem of human-induced climate change may not be acute. If ∆ T 2x is large, then human-induced climate change may be one of the most severe problems of the 21st century.

Outline • Primer on Climate Sensitivity, ∆ T 2x • Estimates of Climate Sensitivity, ∆ T 2x • Uncertainty in ∆ T 2x due to uncertainty in the radiative forcing • Causes of temperature changes from 1856 to present • Learning ∆ T 2x over time

dH t = − Δ T t ( ) ( ) + F t ( ) dt λ • F(t): Radiative Forcing – The change in the net downward radiative flux at some level in the atmosphere, usually the tropopause, caused by some “external” factor, such as changed solar insolation or GHGs. • Instantaneous Radiative Forcing – Radiative forcing before any temperature changes. • Adjusted Radiative Forcing – Radiative forcing after temperatures above the tropopause change, with tropospheric temperatures held constant.

dH t = − Δ T t ( ) ( ) + F t ( ) dt λ • dH(t)/dt – The change in heat storage of the climate system; on earth, essentially the heat taken up or lost by the ocean. • λ – Climate Sensitivity • Equilibrium Climate Sensitivity , λ eq – F(t) = constant and sufficient time elapsed for dH/dt = 0. ∆ T = ∆ T eq . – λ = λ eq = ∆ T eq /F ; e.g., λ eq = ∆ T 2x /F 2x . F 2x = 3.71 W/m2. ∆ T 2x taken as a synonym for λ eq .

∆ T 2x Simulated By The UIUC Stratosphere/Troposphere General Circulation Model 2xCO2 – 1xCO2 surface air temperature (°C) 3.5 3.5 3 3 WO PCHEM 2.5 2.5 2 2 W PCHEM 1.5 1.5 1 1 0.5 0.5 y = m1*(1-exp(-0.5*M0/m1)) y = m1*(1-exp(-0.5*M0/m1)) Value Error Value Error m1 2.2227 0.01223 m1 2.3625 0.011291 0 0 Chisq 14.79 NA Chisq 23.68 NA 0.82407 NA R 2 0.76598 NA R 2 -0.5 -0.5 0 10 20 30 40 50 60 Time (years)

1 Cumulative distribution function 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Arrhenius Range -0.1 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 Climate sensitivity, ∆ T 2x (°C)

1 Cumulative distribution function 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 EBM Arrhenius Range -0.1 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 Climate sensitivity, ∆ T 2x (°C)

1 Cumulative distribution function 0.9 0.8 RCM 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 EBM Arrhenius Range -0.1 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 Climate sensitivity, ∆ T 2x (°C)

1 Cumulative distribution function 0.9 0.8 RCM 0.7 GCM 0.6 0.5 0.4 0.3 0.2 0.1 0 EBM Arrhenius Range -0.1 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 Climate sensitivity, ∆ T 2x (°C)

1 0.9 Cumulative distribution function Paleo 0.8 RCM 0.7 0.6 GCM 0.5 0.4 0.3 0.2 0.1 0 Arrhenius -0.1 EBM Range -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 Climate sensitivity, ∆ T 2x (°C)

• U.S. National Research Council study chaired by Jule Charney wrote: "We estimate the most probable global warming for a doubling of CO 2 to be near 3°C with a probable error of ±1.5°C". • The Intergovernmental Panel on Climate Change interpreted the findings of the Charney report to mean that 1.5°C ≤ ∆ T 2x ≤ 4.5°C. • We assert that this interpretation is incorrect and that the correct interpretation is that there is only a 50% likelihood that ∆ T 2x lies within 1.5° to 4.5°C.

1 Cumulative distribution function 0.9 Paleo 0.8 RCM 0.7 NRC 97 79 0.6 0.5 0.4 0.3 0.2 GCM 0.1 0 Range Arrhenius -0.1 EBM -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 Climate sensitivity, ∆ T 2x (°C)

1 Cumulative distribution function 0.9 Paleo 0.8 RCM 0.7 NRC 97 79 0.6 0.5 0.4 0.3 0.2 GCM 0.1 0 IPCC Range Arrhenius -0.1 EBM -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 Climate sensitivity, ∆ T 2x (°C)

1 0.9 Cumulative distribution function 0.8 0.7 "Expert" NRC 97 opinion 79 0.6 0.5 0.4 0.3 0.2 0.1 0 IPCC Arrhenius Range -0.1 EBM -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 Climate sensitivity, ∆ T 2x (°C)

1 Cumulative distribution function 0.9 Expert Forest et al 0.8 Expert Wigley & Raper 0.7 Tol & de Vos 0.6 0.5 0.4 "Expert" 0.3 opinion 0.2 0.1 0 IPCC NRC 97 79 Arrhenius Range -0.1 EBM -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 Climate sensitivity, ∆ T 2x (°C)

1 Cumulative distribution function 0.9 Andronova & Schlesinger 0.8 0.7 0.6 Uniform 0.5 Uniform Gregory et al Forest et al Forest et al Gregory et al 0.4 0.3 0.2 Knutti et al Knutti et al 0.1 0 IPCC NRC 79 Arrhenius -0.1 EBM Range -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 Climate sensitivity, ∆ T 2x (°C)

1 Cumulative distribution function 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 IPCC Arrhenius -0.1 EBM Range -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 Climate sensitivity, ∆ T 2x (°C) Arrhenius (1896) GCM Expert Gregory et al. Knutti et al. EBM Paleo Uniform Forrest et al. Andronova & Schlesinger Tol & de Vos RCM IPCC Expert Forrest et al. Expert Wigley & Raper NRC 79

Simple climate/ocean model Δ F oi F λ Δ λ Li k N Atmosphere Atmosphere β a over Ocean over Land k S λ Na,o λ Sa,o λ a ,L β o Mixed Layer Land Interior Ocean Bottom Ocean σ o , i σ L , i Δ F oN Δ F oS λ λ NH Atmosphere SH Atmosphere β a over Ocean over Ocean λ Sa,o λ Na,o NH Mixed Layer SH Mixed Layer W W κ κ β o NH Polar Ocean SH Polar Ocean NH Interior Ocean SH Interior Ocean κ κ W W β o W κ W κ NH Bottom β o SH Bottom Ocean Ocean σ o, N σ o, S

Radiative Forcing 2.5 GHG 2 Sulphate aerosol 1.5 Tropospheric ozone Total anthropogenic 1 0.5 0 -0.5 -1 (A) -1.5 1750 1800 1850 1900 1950 2000 1 0 -1 -2 -3 -4 -5 (B) -6 1750 1800 1850 1900 1950 2000 1369 1368 1367 1366 1365 1364 LN HS (C) 1363 1750 1800 1850 1900 1950 2000 Year

0.6 (A) (NH+SH)/2 o C 0.4 Temperature Departure, Temperature Changes Observed Surface Air Obs 0.2 0 -0.2 -0.4 -0.6 1850 1880 1910 1940 1970 2000 0.6 (B) (NH–SH) o C Temperature Departure, 0.4 Obs 0.2 0 -0.2 -0.4 1850 1880 1910 1940 1970 2000

Simulated Vs. Observed Surface Air Temperature Change 0.6 0.6 (B) (NH–SH) (A) (NH+SH)/2 o C o C 0.4 Temperature Departure, 0.4 Temperature Departure, Obs Obs 0.2 0.2 0 0 -0.2 -0.2 Sim(GTAS) -0.4 Sim(GTAS) -0.6 -0.4 1850 1880 1910 1940 1970 2000 1850 1880 1910 1940 1970 2000 0.4 0.3 (C) (NH+SH)/2 (D) NH–SH o C ) o C ) 0.3 0.2 Obs – sim temperature ( Obs – sim temperature ( 0.2 0.1 0.1 0 0 -0.1 -0.1 -0.2 Res = Obs – Sim(GTAS) -0.2 -0.3 Res = Obs – Sim(GTAS) -0.3 -0.4 1850 1880 1910 1940 1970 2000 1850 1880 1910 1940 1970 2000

Schlesinger & Ramankutty (1994) 0.08 100 IPCC 92 (1858-1992) Eigenvalues λ k / Total Variance x 100 (%) N = 135, M = 40 0.06 0.04 Mode Time Series (°C) ∧ 10 X 1 0.02 0 ∧ X 2 -0.02 1 -0.04 -0.06 A C -0.08 0 5 10 15 20 25 30 35 40 1850 1870 1890 1910 1930 1950 1970 1990 Mode Year 0.4 0.3 Detrended Temperature Anomaly (°C) 0.3 ∧ ∧ X 1 + X 2 0.2 0.2 o C) ρ 1 Eigenvectors (EOFs, 0.1 0.1 0 0.0 -0.1 -0.1 ρ 2 -0.2 -0.2 IPCC -0.3 Obs D B -0.4 -0.3 0 5 10 15 20 25 30 35 40 1850 1870 1890 1910 1930 1950 1970 1990 Year i (years)

Schlesinger & Ramankutty (1994) 180 120W 60W 0 60E 120E 180 90N 90N 60N 60N 1 2 3 5 5 30N 30N 4 10 8 0 0 7 11 6 30S 30S 9 9 60S 60S 90S 90S 180 120W 60W 0 60E 120E 180

∆ T 2x Versus Radiative Forcing 5 4 IPCC Range ∆ T 2x (°C) 3 2 1 0 GT GTA GTAV1 GTAS GTASV1 GTASV2 Radiative forcing model

Conclusion 1 • To reduce the uncertainty in climate sensitivity requires reducing the uncertainty in the radiative forcing, not only by aerosols, but also by the Sun and volcanoes.

Climate Sensitivity: Uncertainties & Learning Workshop on GHG - PowerPoint PPT Presentation

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