Estimation of climate sensitivity

Transcription

1 Estimation of climate sensitivity Magne Aldrin, Norwegian Computing Center and University of Oslo Smögen workshop 2014

2 References Aldrin, M., Holden, M., Guttorp, P., Skeie, R.B., Myhre, G. and Berntsen, T.K. (2012). Bayesian estimation of climate sensitivity based on a simple climate model fitted to observations of hemispheric temperatures an global ocean heat content. Environmetrics, vol. 23, p Skeie, R.B., Berntsen, T., Aldrin, M., Holden, M., Myhre, M. (2014). A lower and more constrained estimate of climate sensitivity using updated observations and detailed radiative forcing time series. Earth System Dynamics, vol. 5, p

3 Climate sensitivity S Definition: Climate sensitivity = S = The temperature increase due to a doubling of CO 2 concentrations compared to pre-industrial time (1750), when all else is constant Today: 40 % increase in CO 2 concentrations Estimate from IPCC AR4 (2007): 3 C, 90 % C.I. =( ) Estimate from IPCC AR5 (2013): 2.5 C, 90 % C.I. =( ) 2

4 Radiative forcing CO 2 is only one of several factors that affect the global temperature Radiative forcing = The change in net irradiance into the earth relative to 1750 Measured in Watts per square meter The global temperature depends on the radiative forcing The climate sensitivity measures the strength of this dependency 3

5 To estimate the climate sensitivity Aim of study by modelling the relationship between estimates of radiative forcing since 1750 and estimates of hemispheric temperature based on measurements since 1850 estimates of global ocean heat content based on measurements since about 1950 using a climate model based on physical laws 4

6 Climate model Could use an Atmospheric Ocean General Circulation Model, but complex and very computer intensive an approximation to an AOGCM, an emulator a simple climate model, our approach 5

7 The true global state of the earth in year t T NH t - Temperature at the northern hemisphere T SH t - Temperature at the southern hemisphere OHC t - Ocean heat content 6

8 Simple climate model Deterministic computer model (Schlesinger et al., 1992) based on energy balance X S upwelling diffusion ocean SH Atmosphere where the earth is divided into atmosphere and ocean θ M Mixed layer northern and southern hemisphere with radiative forcing into the system θ VHD θ UV θ OIHE energy mixing θ VHD θ UV θ OIHE between the atmosphere and the ocean within the ocean θ VHD θ UV θ OIHE SH Polar Ocean X N NH Atmosphere θ ASHE θ ASHE θ M Mixed layer θ UV θ UV θ UV θ VHD θ VHD θ VHD NH Polar Ocean θ P S 7

9 m t (x 1750:t, S, θ) Simple climate model cont. Yearly time resolution Output temperature northern hemisphere temperature southern hemisphere ocean heat content Input x 1750:t - yearly radiative forcing from 1750 until year t, separate for northern and southern hemisphere S - the climate sensitivity, the parameter of interest θ - 6 other physical parameters 8

10 Response data y t - 9-dimensional vector with yearly observed temperatures and ocean heat content Three pairs of series with temperature measurements for northern and southern hemisphere (HadCRUT3, Brohan et al.,2006) (GISS, Hansen et al. 2006) (NCDC, Smith and Reynolds 2005) Three series with ocean heat content measurements 0-700m (Levitus et al. 2009) (Domingues et al. 2008; Church et a. 2011) (Ishii and Kimoto 2009) 9

11 Observations Temperature [ C] (a) Observed temperatures, northern hemisphere NH1, HadCRUT3 NH2, GISS NH3, NCDC Temperature [ C] (b) Observed temperatures, southern hemisphere SH1, HadCRUT3 SH2, GISS SH3, NCDC Ocean heat content [10^22 J] (c) Observed global ocean heat content Levitus CSIRO Ishii and Kimoto

12 Radiative forcing We will specify our best knowledge about historical radiative forcing as prior distributions of 11 independent components, based on temperature-independent estimates of each component, including uncertainties long-lived greenhouse gases direct aerosols indirect aerosols solar radiation volcanoes land use... 11

13 Priors of components of radiative forcing Figure not updated Radiative forcing [W/m2] Radiative forcing [W/m2] Radiative forcing [W/m2] Radiative forcing [W/m2] LLGHG NH Sun NH Volcanos NH diraero NH Radiative forcing [W/m2] Radiative forcing [W/m2] Radiative forcing [W/m2] Radiative forcing [W/m2] LLGHG SH Sun SH Volcanos SH diraero SH

14 Prior of total radiative forcing Mean Radiative forcing [W m 2 ] % credible interval

15 Model for true global state of the earth g t = (T NH t, T SH t, OHC t ) T Combined deterministic + stochastic model g t = m t (x t:1750, S, θ) + n siv t + n liv t + n m t n siv t n liv t : short-term internal variation, related to El Ninõ episodes : long-term internal variation, estimated from an AOGCM n m t : model error, VAR(1) All terms have dimension 3 14

16 Model for observations y t = Ag t + n o t A: 9x3 matrix copying each data series 3 times, to compare model values with observations n o t: observational (measurement) error, dimension 9, VAR(1) 15

17 Estimation Bayesian approach (Kennedy and O Hagan 2001), using MCMC Vague prior for S Informative priors for x t:1750 and θ Vague priors for other parameters 16

18 Posterior of the climate sensitivity S Probability density Probability density a) Main analysis (CanESM 10) E(ECS) = % C.I. = (0.91,3.21) P(ECS>4.5) = Degrees Celcius b) NorESM 10 E(ECS) = % C.I. = (0.76,3.61) P(ECS>4.5) =

19 0.5 Dashed lines AR4 studies From the 5th Assessment Report of IPCC Transient Climate Response ( C) b) Aldrin etal. (2012) Bender et al. (2010) Lewis (2013) Lin etal. (2010) Lindzen &Choi (2011) Murphy et al. (2009) Olson etal. (2012) Otto et al. (2013) Schwartz (2012) Tomassini et al. (2007) Similar feedbacks Similar climate base state Instrumental Overall level of scientific understanding Uncertainties accounted for/known Close to equilibrium Probability / Relative Frequency ( C -1 ) Chylek &Lohmann (2008) Hargreaves et al. (2012) Holden et al. (2010) Köhler etal. (2010) Palaeosens (2012) Schmittner et al. (2012) Palaeoclimate Aldrin etal. (2012) Libardoni &Forest (2013) Olson etal. (2012) Combination Equilibrium Climate Sensitivity ( C) Figure (a) Examples of distributions of the transient climate response (TCR, top) and the equilibrium climate sensitivity (ECS, bottom) 18

20 Effect of 10 more years of data Main analysis R90 = 1.23 Data up to 2008 R90 = 1.39 Data up to 2006 R90 = 1.59 Data up to 2004 R90 = 1.93 Data up to 2002 R90 = 1.78 Data up to 2000 R90 = Equilibrium climate sensitivity [ C ] 19

21 Based on only one OHC series Validation 20

22 Re-estimation prediction Temperature [ C] Temperatures, northern hemisphere, NH1, HadCRUT3 Predicted Observed Fitted 95% credible interval Temperature [ C] Ocean heat content [10^22 J] Temperatures, southern hemisphere, SH1, HadCRUT3 Predicted Observed Fitted 95% credible interval Global ocean heat content Predicted Observed Fitted 95% credible interval

23 Validation on data from an AOGCM The reality is complex, but our model are simple Can we trust the posterior for the climate sensitivity? True S is unknown, can not validate on real data Validate on artificial data generated from an AOGCM 22

24 The CMIP3 experiment Coupled Model Intercomparison Project phase 3 CO 2 increased by 1 % per year until a doubling in 1920, then constant Corresponding RF increased from 0 to 3.7 W/m 2 (Deterministic) simulation of temperature and OHC Our validation experiment, based on the Canadian CGCM3.1 model True climate sensitivity = 3.4 C Training data: Temperatures , OHC

25 CMIP3 - Radiative forcing prior Radiative forcing [W/m2] Mean 95% credible interval

26 CMIP3 - Data and predictions Temperature [ C] Temperatures, northern hemisphere, NH1, HadCRUT3 Predicted True Observed Fitted 95% credible interval Temperature [ C] Temperatures, southern hemisphere, SH1, HadCRUT3 Predicted True Observed Fitted 95% credible interval Ocean heat content [10^22 J] Global ocean heat content Predicted True Observed Fitted % credible interval

27 CMIP3 - Posterior for climate sensitivity True climate sensitivity = 3.4 C Posterior mean 3.5, CI=( ) 26

28 Further work Work in progress Update model using data including 2013 Using updated RF prioirs from IPCC AR5 Including one more temperature series Including one more OHC (above 700 m) series Including data for OHC below 700 meters! Planned work Improve the simple climate model Using different simple climate models Including other data types (ice melting, sea level,...) 27

29 Thank you for your attention! 28