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, ∆T2x: The change in global-average near-surface temperature resulting from a doubling of the preindustrial carbon dioxide concentration.

• If ∆T2x is small, then the problem of human-induced climate change may not be acute. If ∆T2x is large, then human-induced climate change may be one of the most severe problems of the 21st century.

Outline

• Primer on Climate Sensitivity, ∆T2x

• Estimates of Climate Sensitivity, ∆T2x

• Uncertainty in ∆T2x due to uncertainty in the radiative forcing • Causes of temperature changes from 1856 to present

• Learning ∆T2x over time

dH ()t ΔT() 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 ΔT() 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 , λeq – F(t) = constant and sufﬁcient time elapsed for dH/dt = 0. ∆T = ∆Teq. – λ = λeq = ∆Teq/F ; e.g., λeq = ∆T2x/F2x. 2 F2x = 3.71 W/m . ∆T2x taken as a synonym for λeq. ∆T2x Simulated By The UIUC Stratosphere/Troposphere General Circulation Model 3.5 3.5

3 3 WO PCHEM 2.5 2.5

2 2

1.5 W PCHEM 1.5

1 1

0.5 y = m1*(1-exp(-0.5*M0/m1)) y = m1*(1-exp(-0.5*M0/m1)) 0.5 Value Error Value Error m1 2.2227 0.01223 m1 2.3625 0.011291 0 Chisq 14.79 NA Chisq 23.68 NA 0 R2 0.82407 NA R2 0.76598 NA -0.5 -0.5 2xCO2 – 1xCO2 surface air temperature (°C) 0 10 20 30 40 50 60 Time (years) Outline

• Primer on Climate Sensitivity, ∆T2x

• Estimates of Climate Sensitivity, ∆T2x

• Uncertainty in ∆T2x due to uncertainty in the radiative forcing • Causes of temperature changes from 1856 to present

• Learning ∆T2x over time

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Climate sensitivity, ∆T2x (°C) • U.S. National Research Council study chaired by Jule Charney wrote: "We estimate the most probable global warming for a doubling of CO2 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 ≤ ∆T2x ≤ 4.5°C. • We assert that this interpretation is incorrect and that the correct interpretation is that there is only a 50% likelihood that ∆T2x lies within 1.5° to 4.5°C. 1

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EBM Paleo Uniform Forrest et al. Andronova & Schlesinger Tol & de Vos

RCM IPCC Expert Forrest et al. Expert Wigley & Raper NRC 79 Outline

• Primer on Climate Sensitivity, ∆T2x

• Estimates of Climate Sensitivity, ∆T2x

• Uncertainty in ∆T2x due to uncertainty in the radiative forcing • Causes of temperature changes from 1856 to present

• Learning ∆T2x over time

Simple climate/ocean model λ ΔF ΔF oi Li λ

Atmosphere k N Atmosphere β a

. over Ocean over Land k S λ Na,o λ Sa,o λa , L

βo Mixed Layer

Interior Ocean Land

Bottom Ocean

σo , i σL , i

λ ΔF ΔF oN oS λ

NH Atmosphere βa SH Atmosphere over Ocean over Ocean λ Na,o λ Sa,o

NH Mixed Layer SH Mixed Layer

κ κ W βo W SH Polar Ocean

NH Interior Ocean SH Interior Ocean κ κ

W W NH Polar Ocean βo

W κ κ W

NH Bottom βo SH Bottom Ocean Ocean

σo, N σo, S 2) Radiative Forcing 2.5 2 GHG Sulphate aerosol 1.5 Tropospheric ozone 1 Total anthropogenic Anthropogenic forcing0.5 (W/m 0 -0.5 -1 -1.5 (A) 1750 1800 1850 1900 1950 2000 2) 1 0 -1 -2 Volcanic forcing-3 (W/m -4 -5 -6 (B) 1750 1800 1850 1900 1950 2000 2) 1369 1368 1367

Solar irradiance 1366 (W/m 1365 1364 LN (C) HS 1363 1750 1800 1850 1900 1950 2000 Year 0.6 (A) (NH+SH)/2 C o 0.4 Obs 0.2

Air 0

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-0.4 1850 1880 1910 1940 1970 2000 Simulated Vs. Observed Surface Air Temperature Change

0.6 0.6 (A) (NH+SH)/2 (B) (NH–SH) C C o 0.4 o 0.4 Obs Obs 0.2 0.2 0 0 -0.2

-0.2 -0.4 Sim(GTAS) Temperature Departure,

Sim(GTAS) Temperature Departure,

-0.6 -0.4 1850 1880 1910 1940 1970 2000 1850 1880 1910 1940 1970 2000 0.4 (C) (NH+SH)/2 0.3 (D) NH–SH C) 0.3 C) o o 0.2

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0 -0.1

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Mode Time Series (°C) -0.04

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A -0.08 C Eigenvalues 0 5 10 15 20 25 30 35 40 1850 1870 1890 1910 1930 1950 1970 1990 Mode Year 0.3 0.4

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B Detrended Temperature Anomaly (°C) D -0.3 -0.4 0 5 10 15 20 25 30 35 40 1850 1870 1890 1910 1930 1950 1970 1990 i (years) Year

Schlesinger & Ramankutty (1994)

180 120W 60W 0 60E 120E 180 90N 90N

60N 60N 1 5 2 3 5 30N 30N 4 0 8 10 0 7 6 11 30S 30S 9 9

60S 60S

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∆T2x Versus Radiative Forcing 5

3 (°C) 2x IPCC Range ∆ T 2

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.

Outline

• Primer on Climate Sensitivity, ∆T2x

• Estimates of Climate Sensitivity, ∆T2x

• Uncertainty in ∆T2x due to uncertainty in the radiative forcing • Causes of temperature changes from 1856 to present

• Learning ∆T2x over time

Contributions to the Observed Temperature Changes

150 150 (A) 1856–1990 (B) 1904–1944

100 100 ∆ T (%) ∆ T (%)

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0 0 Contribution to Contribution to

-50 -50 Anthro Volcano Sun Residual Anthro Volcano Sun Residual

150 80 anthro anthro+HS 60 100 anthro+LN ∆ T (%) anthro+volc ∆ T (%) 40 anthro+volc+HS 50 anthro+volc+LN 20

0 0 Contribution to Contribution to -20 (C) 1944–1976 (D) 1976–1990 -50 -40 Anthro Volcano Sun Residual Anthro Volcano Sun Residual Conclusions 2 • The observed warming during 1904-1944 and cooling during 1944-1976 were not human induced. • The observed warming during 1976-1990 was equally due to humans and the residual.

Conclusions 2 • The observed warming during 1856-1990 was mostly human induced.

Outline

• Primer on Climate Sensitivity, ∆T2x

• Estimates of Climate Sensitivity, ∆T2x

• Uncertainty in ∆T2x due to uncertainty in the radiative forcing • Causes of temperature changes from 1856 to present

• Learning ∆T2x over time

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Temperature departure, ( -0.4

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Climate sensitivity, ( 5

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Temperature departure, ( -0.4

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Climate sensitivity, ( 5

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Temperature departure, ( -0.4

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Climate sensitivity, ( 5

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Temperature departure, ( -0.4

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Climate sensitivity, ( 5

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Climate sensitivity, ( 5

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Climate sensitivity, ( 5

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Temperature departure, ( -0.4

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Climate sensitivity, ( 5

0 1940 1950 1960 1970 1980 1990 2000

Fig.1 Conclusion 3 • The uncertainty in climate sensitivity due to climate noise can be reduced by learning over time, that is, by performing future estimations using longer observational records.

Conclusions 4 • It is quite likely that the formulation and negotiation of policies to abate human-induced climate change will, for the foreseeable future, continue to be made against a backdrop of deep uncertainty.

Recommendation • Focus not only on where we end, but also on how we can begin. • Thus, need to develop near-term hedging strategy(ies) that will get buy-in by the US and Australia.

dH ()t ΔT() t = − + F() t dt λ • Effective Climate Sensitivity, λe(t) – F(t) need not be constant and/or equilibrium achieved. – λ(t) = λe(t) = ∆T(t)/[F(t) – dH(t)/dt] . – If F = constant, then λe(t) –> λeq as t –>inﬁnity; for F > 0, from above, λe(t) ≥ λeq . • Transient Climate Response – Change in global-mean surface air temperature for a transient climate forcing at the time of

doubling of the CO2 concentration. ∆T2x Simulated By Some General Circulation Models

Transient Climate Response

Outline

• Primer on Climate Sensitivity, ∆T2x

• Estimates of Climate Sensitivity, ∆T2x

• Uncertainty in ∆T2x due to uncertainty in the radiative forcing • Causes of temperature changes from 1856 to present

• Uncertainty in ∆T2x due to uncertainty in the temperature data

• Learning ∆T2x over time Estimates of ∆T2x and ∆FSO4 from three datasets for GA

Dataset/Quantity ΔT °C −2 2x () Δ FSO4 (Wm ) Jones et al. [1999] 4.8564 –0.2183 Jones and Moberg 4.2437 –0.2068 [2003] Folland et al. 1.2771 +0.0095 [2001a]

0.6 Northern Hemisphere

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∆ T ( -0.2

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1850 1870 1890 1910 1930 1950 1970 1990

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-0.6 1850 1870 1890 1910 1930 1950 1970 1990 Conclusion 3 • The uncertainty in the southern hemisphere temperatures must be reduced.

Conclusions 5 • Such policy formulation can be aided by the tool of robust adaptive decision analysis. But, that is a topic for a future lecture.