Thank you for your invitation and kind hospitality ! Isaac Held, Beijing, 2011 Thank you for your invitation and kind hospitality ! Seminars: Monday: Time Scales of Global Warming Tuesday: Simulating the climatology, interannual variability, and trends of tropical cyclone genesis Wednesday: The hydrological cycle and global warming Thursday: Shifting latitude of surface westerlies – a case study in utilizing a hierarchy of climate models (understanding climate by starting with comprehensive models and gradually removing layers of complexity) Friday: Problems in quasi-geostrophic dynamics (understanding climate by starting with very idealized models and gradually adding layers of complexity)

Time scales of climate responses, climate sensitivity, and the recalcitrant component of global warming Isaac Held Beijing, 2011 Importance of Ocean Heat Uptake Efficacy to Transient Climate Change Winton, Takahashi, Held, J. Clim, 2010 Probing the fast and slow components of global warming by returning abruptly to pre-industrial forcing Held, Winton, Takahashi, Delworth, Zeng, Vallis, J. Clim 2010

Uncertainty in climate sensitivity has not been reduced appreciably in past 30 years 2 well-known assessments reach similar conclusions : “Charney report” (1979)  IPCC/AR4 (2006) Equilibrium global mean surface temperature warming due to doubling of CO2 is most probably in the range 1.5-4.5 K

Assorted estimates of equilibrium sensitivity Knutti+Hegerl, 2008 23

Time scales of climate response Ultra-fast Fast Slow Ultra-slow Months (Atmosphere) a few years (mixed layer) Multiple centuries (deep ocean)

t Equilibrium climate sensitivity: Double the CO2 and wait for the system to equilibrate Transient climate response: Increase CO2 1%/yr and examine climate at the time of doubling Typical setup – increase till doubling – then hold constant CO2 forcing T response W/m2 t Heat uptake by deep ocean After CO2 stabilized, warming of near surface can be thought of as due to reduction in heat uptake 11

CMIP3/AR4 models 2.5 2 Transient response 1.5 1 2 3 4 5 Equilibrium sensitivity Not well correlated across models – equilibrium response brings into play feedbacks/dynamics (especially in subpolar oceans) that are suppressed in transient response 19

Histogram of TCR/TEQ for AR4 models Increase CO2 by 1%/yr ; global mean warming at the time of doubling = Transient Climate Response (TCR)

Response of global mean temperature in GFDL’s CM2.1 to instantaneous doubling of CO2 Equilibrium sensitivity 3.4K Transient response 1.5K Slow response evident only after 80 yrs Fast response 20

forcing Mixed layer Heat capacity Heat exchange between mixed layer and deep ocean Deep ocean heat capacity in equilibrium

Forcing varies on time scales longer than

“Intermediate regime” Forcing varies on time scales longer than and time scales shorter than “Intermediate regime”

Forcing computed from differencing TOA fluxes in two runs of a model (B-A) B = fixed SSTs with varying forcing agents; A fixed SSTs and fixed forcing agents total OLR SW down 51 SW up

Temperature change averaged over 5 realizations of coupled model 52

Fit with 53

Forcing (with no damping) fits the trend well, if you use transient climate sensitivity, which takes into account magnitude/efficacy of heat uptake Forcing with no damping 54

GFDL’s CM2.1 with well-mixed greenhouse gases only Global mean temperature change Observations (GISS) 46

GFDL’s CM2.1 with well-mixed greenhouse gases only Global mean temperature change Observations (GISS) “It is likely that increases in greenhouse gas concentrations alone would have caused more warming than observed because volcanic and anthropogenic aerosols have offset some warming that would otherwise have taken place.” (AR4 WG1 SPM). 46

A1B-CM2.1

Return instantaneously to pre-industrial forcing the “Recalcitrant” warming

Relaxation to recalcitrant warming 5 years 3 years

Normalized to unity over the globe

Normalized to unity over the globe Fast Slow “Recalcitrant”

Sea level response mostly recalcitrant Sea level response due to thermal expansion Control drift Sea level response mostly recalcitrant

The simplest linear model If correct, evolution should be along the diagonal N/F T/TEQ 15

Suppose you have two forcing agents C02 and B (something else) leading to radiative forcing FC02 and FB . But suppose the global mean temperature responses TC02 and TB are not proportional to the the radiative forcing Following Hansen, define efficacy eB (using CO2 as a standard)

Efficacy can orten be understood in terms of the spatial structure of the response , Coupling of surface with troposphere is weaker in high latitudes => harder to radiate away a perturbation => Radiative restoring strength is weaker for responses that are larger in higher latitudes => Forcings with stronger high latitude responses have larger efficacy

replace F = bT + H or bT = F + H with bT = F + eH H with eH > 1 Forcings with stronger high latitude responses have larger efficacy Think of heat uptake as a forcing – ie replace F = bT + H or bT = F + H with bT = F + eH H with eH > 1 Equivalently, T = TF + TH = F/b - H/bH With bH = b/eH

Efficiency Efficacy Heat uptake = gT ; g = efficiency of heat uptake Cooling due to heat uptake = egT ; e = efficacy of heat uptake Efficiency CM 2.1 CM 2.0 Efficacy

Assorted estimates of equilibrium sensitivity Knutti+Hegerl, 2008 23

Models can produce very good fits by including aerosol effects, (GFDL CM2.1 -- Includes estimates of volcanic and anthropogenic aerosols, as well as estimates of variations in solar irradiance) Models can produce very good fits by including aerosol effects, but models with stronger aerosol forcing and higher climate sensitivity are also viable (and vice-versa) 45

Internal Fluctuations Seasonal cycle etc Observational constraints 20th century warming 1000yr record Ice ages – LGM Deep time Volcanoes Solar cycle Internal Fluctuations Seasonal cycle etc 36

Observed total solar irradiance variations in 11yr solar cycle (~ 0 Observed total solar irradiance variations in 11yr solar cycle (~ 0.2% peak-to-peak) 42

(other studies yield ~0.1K) Seems to imply large sensitivity Camp and Tung, 2007 => 0.2K peak to peak (other studies yield ~0.1K) Seems to imply large sensitivity 4 yr damping time Only gives 0.05 peak to peak 1.8K (transient) sensitivity 43

Global mean cooling due to Pinatubo volcanic eruption Observations with El Nino removed Range of ~10 Model Simulations GFDL CM2.1 Courtesy of G Stenchikov 40 Relaxation time after abrupt cooling contains information on climate sensitivity

Low sensitivity model Pinatubo simulation High sensitivity model Yokohata, et al, 2005 41

Response to pulse of forcing (volcano), F(t): 2-box model:

Stenchikov, et al 2009

Near surface air temperature response (20 member ensemble) Courtesy of Stenchikov, et al

Integrated forcing and response Wm-2yr Response with exponential fit TOA flux Forcing

CM2.1 Pinatubo summary -- fast response --

2.8 5.0 2.2 CM2.1 Pinatubo summary -- fast response -- Radiative restoring (W/m2)yr 2.8 Forcing (W/m2)yr 5.0 Heat uptake (W/m2)yr 2.2 CM2.1 Pinatubo summary -- fast response --

Pinatubo => b ~ 1.0 (W/m2)/K g ~ 0.8 (W/m2)/K 1%/yr CO2 increase => b ~ 1.7 (W/m2)/K g ~ 0.7 (W/m2)/K

Can we use interannual variability to determine the strength of the radiative restoring? Model results (CM2.1) raise some roadblocks

bLW Longwave regression across ensemble Wm-2K-1 (collaboration with K. Swanson) All-forcing 20th century bLW Wm-2K-1 year 61 Following an idea of K. Swanson, take a set of realizations of the 20th century from one model, and correlate global mean TOA with surface temperature across the ensemble

bLW Longwave regression across ensemble, Wm-2K-1 collaboration with K. Swanson All-forcing 20th century A1B scenario bLW Wm-2K-1 62

bLW Longwave regression across ensemble, Wm-2K-1 collaboration with K. Swanson bLW Wm-2K-1 63 Estimate of noise in this statistic from 2000yr control run

bLW Longwave regression across ensemble, Wm-2K-1 collaboration with K. Swanson bLW Wm-2K-1 Well-mixed greenhouse gases only 64

bLW Longwave regression across ensemble, Wm-2K-1 collaboration with K. Swanson bLW Wm-2K-1 Independent set of 10 A1B runs 65

bLW Longwave regression across ensemble, collaboration with K. Swanson But we can fit the models 20th century simulations without time-dependence in OLR-temperature relationship! bLW Wm-2K-1 Independent set of 10 A1B runs 65 May be telling us that ENSO is changing, but with no obvious connection to global sensitivity

Thank you for listening I suspect that: Transient climate sensitivity can be constrained more tightly that it currently is, despite the uncertainty in aerosol forcing Volcanic responses may play a central role in tightening this constraint, along with the observed warming trend Less hopeful about use of interannual variability Solar cycle response has some mysteries Thank you for listening

21st century emissions commitment AR4/IPCC

Wm-2K-1 Shortwave regression across ensemble, following K. Swanson 2008 All-forcing 20th century Wm-2K-1 56 Following an idea of K. Swanson, take a set of realizations of the 20th century from one model, and correlate global mean TOA with surface temperature across the ensemble

Wm-2K-1 Shortwave regression across ensemble, following K. Swanson 2008 A1B scenario All-forcing 20th century Wm-2K-1 57 Is this a sign of non-linearity? What is this?

Wm-2K-1 Shortwave regression across ensemble, following K. Swanson 2008 A1B scenario All-forcing 20th century Wm-2K-1 90% 58 Estimate of noise in this statistic from 2000yr control run

Wm-2K-1 Shortwave regression across ensemble, following K. Swanson 2008 Well-mixed greenhouse gases only Wm-2K-1 59

Wm-2K-1 Shortwave regression across ensemble, following K. Swanson 2008 Independent set of 10 A1B runs Wm-2K-1 60