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CESD SAGES Scottish Alliance for Geoscience, Environment & Society Constraining Climate Sensitivity using Top Of Atmosphere Radiation Measurements Simon.

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Presentation on theme: "CESD SAGES Scottish Alliance for Geoscience, Environment & Society Constraining Climate Sensitivity using Top Of Atmosphere Radiation Measurements Simon."— Presentation transcript:

1 CESD SAGES Scottish Alliance for Geoscience, Environment & Society Constraining Climate Sensitivity using Top Of Atmosphere Radiation Measurements Simon Tett 1, Mike Mineter 1, Coralia Cartis 2, Dan Rowlands 3 & Ping Liu 2 1: School of Geosciences, University of Edinburgh 2: School of Mathematics, University of Edinburgh 3: AOPP, Department of Physics, University of Oxford

2 CESD Aims Report on attempts to optimise atmospheric model parameters to observed global-mean radiation measurements. Use results from these simulations to give probabilistic estimate of climate sensitivity.

3 CESD Key results Successfully optimised HadAM3 to outgoing longwave (OLR) and Reflected Shortwave (RSR) observations. Many large scale simulated variables correlate strongly with OLR and RSR There is a relationship between equilibrium climate sensitivity and simulated outgoing radiation Uncertainty analysis on “plausible” HadAM3 models rules out ECS > 5.6K (and < 2.7K)

4 CESD Estimates of Climate Sensitivity using models and observations From IPCC CH9 (fig 9.2), after Hegerl et al., Nature Bars are 5-95% range

5 CESD Philosophy Model is a tool which encapsulates our (best) knowledge of relevant physics of the system. Future predictions are based on models A model is useful for specific purpose if it is consistent with relevant observations Uncertainty in future predictions arises because many models are consistent with observations but make different predictions.

6 CESD Observational Data Use the CERES (Clouds and the Earth's Radiant Energy System) record of Leob et al, CERES flying on TERA & AQUA satellites Two instruments: –0.3 to 5 μm (SW) –0.3 to 200 μm (Total) –Estimate LW from difference between Total & SW –Adjusted to be in net imbalance as estimated from ocean data. –For M arch 2000-Feb 2005 Also explore sensitivity to use of ERBE data

7 CESD Experimental Design HadAM3 (Atmospheric model – Pope et al, 2000) simulations forced by observed Sea Surface Temperatures & Sea Ice. Modify four parameters in model which are known to affect climate sensitivity –ENTCOEF -- rate at which convective plumes and environment mix –VF1 -- speed of falling precipitation – CT -- rate at which water vapour converted to precipitation –RHCRIT -- critical humidity at which clouds form. Simulations started Dec 1998 and ran though to April 2005 starting from same initial condition.

8 CESD Experimental Design (Cont.) Modified package of forcings as Tett et al, 2007 –Well mixed GHG, Ozone, Land-Surface, Aerosols, Volcanoes and Total Solar Irradiance Modifications: –Fix to long-standing bug in SW Rayleigh scattering –Use recent values of TSI (1361 W/m 2 from Kopp & Lean, 2011) –Slight changes to GHG – using observed values rather than A2. Compare simulated global-avg RSR and OLR with CERES observations for the average March 2000 to Feb First 14 months ignored allowing land-sfc to adjust to parameter change.

9 CESD Optimisation Algorithm applied to HadAM3 simulations Error= Root Mean Square difference of 5-year global average RSR and OLR from simulation vs target “Line search” –Compute finite derivatives d Error/d param for each one of four parameters. Need to perturb parameters enough to make reasonable estimate of the derivative. We perturbed by 5- 10% of the parameter range. –Use these to compute direction in which Error is most reduced.

10 CESD Optimisation Algorithm (Cont.) Parameters are scaled so they have similar magnitudes. Jacobian is under-determined so regularise by adding scaled identity matrix till it is invertible. This makes us prefer solutions where all parameters have similar magnitudes. Do trials at 30%, 90 % and the full vector. If these values are outside the range of permissible parameter values we move along the boundary.

11 CESD Optimisation Algorithm (Cont.) Use the smallest of the three “line search” values to update the parameters and start again. Terminate when error less than specified value (normally 1 W m -2 ) or no improvement in line search. Initial Deriv Line Search

12 CESD Optimisation Trials ERBE CERES Low Sens High Sens Convergence to “zero” error for all but CERES high sens case CERES case took 3 iterations ERBE cases took 8 & 9 iterations

13 CESD Optimisation Carried out 16 optimisation cases with initial values the extreme parameter choices with CERES target. Extreme parameter choice is set each one of 4 parameters to maximum or minimum value. Many simulations failed… (“blew up”) Redo at 75% of distance between reference case and max/min. Then carry out set of trials with various target values

14 CESD Optimisation Trials (Summary) Name TargetSuc.M. FailFail Con. Iter. CERES (100%) (99.5, 239.6) (4) CERES(75%) (99.5, 239.6) (4) ERBE (106.5, 234.4) (3) Nr ERBE (105.5, 234.4) (4) TGT#1 (97, 245) (5) TGT#2 (98, 237) (3) TGT#3 (101, 243) (4)

15 CESD Optimisation Summary Failure to converge as (un)common as model failure. Can adjust model parameters to produce models that are within 1 Wm 2 of observations. Takes about 3-4 iterations. Each iteration requires 7 simulations each of 6 years of atmospheric model. This is very efficient…

16 CESD What is responsible for changes in RSR/OLR? Little change in clear sky OLR due to compensation between RH and temperature. Changes in radn arise from cloud changes.

17 CESD Other variables Why did you not include more variables in RMS error and minimise the total? Answers: 1.Model needs to have good simulation of Energy balance as energy key to climate. 2. Interested in radiative processes as those key to climate sensitivity 3.Other variables are strongly related to radiation so including them would add no new information.

18 CESD Predicted from RSR & OLR vs Simulated 67% 70 % 90% 93% 38% 89 % Simulated Predicted Simulated Tech issue. Need to weight to cope with uneven sampling. “Voronoi” – see latter.

19 CESD Estimating Climate Sensitivity Our experimental design doesn’t give us an estimate of climate sensitivity But climateprediction.net done 14,000 doubled CO2 slab model experiments (HadSM3) each 20 years long. Can estimate equilibrium climate sensitivity (ECS) for many parameters from these simulations. We constructed an emulator of ECS for HadSM3. Give the emulator model parameters and the emulator provides an estimate of climate sensitivity. Essentially sophisticated regression/interpolation and so generates some additional uncertainty.

20 CESD Climate Sensitivity vs radn (optimisation simulations) Reflected SW Outgoing LW

21 CESD Sampling equally in CS and from the CPDN configurations

22 CESD Feedback vs total outgoing radn Summary There is a relationship between climate feedback/ sensitivity and outgoing radiation

23 CESD Probabilistic ECS estimate: Schematic Parameters Simulated Observed Likelihood Prior S Posterior Uncertainty Estimate 23

24 CESD Uncertainty estimate for OLR and RSR Need estimate of uncertainty to decide what is a “plausible” simulation of OLR and RSR? Make estimates for sources of uncertainty and sum them assuming everything Gaussian. For Audience.. –Anything major missing? –Anything too small? From “plausible” configurations can generate “plausible” range. Using (simple) Bayesian reasoning make probabilistic estimate of ECS.

25 CESD Sources of Uncertainty Consider uncertainties that don’t affect climate sensitivity but could affect the outgoing radiation. Assume all uncertainties are independent multi- variate Gaussian and combine them. Observational uncertainty Forcing uncertainty Modelling uncertainty SST uncertainty NOT including uncertainty in model structure –results are conditional on HadAM3 structure.

26 CESD Observational uncertainty –Reflected SW Radn (RSR): 1 Wm -2 –Outgoing LW Radiation (OLR): 1.4 Wm -2 –Then combined with uncertainty on total energy leaving the Earth (0.5 Wm -2 ) mainly arising from uncertainty in incoming solar radiation.

27 CESD Forcing and Aerosol Uncertainty Forcing Uncertainty (from IPCC AR4) –RSR: 1 Wm -2 (mainly aerosol uncertainty) –OLR: 0.25 Wm -2 (O 3 & GHG) Convert to TOA radiation using simulations with aerosols, human forcings, and GHG removed. Then scaling results based on comparison between forcing and TOA fluxes. Natural aerosol – RSR 1 Wm -2 (Carslaw et al, 2010 is about 0.5) while Penner et al, 2006 models are about 1 Wm -2

28 CESD Modelling Error Internal climate variability: 0.1 Wm -2 in both RSR and OLR and is negligible. Parameter uncertainty – what about parameters that don’t affect climate sensitivity but do affect radiation? –From climateprediction.net database find those model configurations that have a climate sensitivity from K (standard configuration is 3.3K). Gives 13 cases. Run the cases and compute covariance of RSR and OLR. Then treat as another source of uncertainty.

29 CESD SST Uncertainty

30 CESD SST Uncertainty Computed: –Difference between two successive Hadley climatologies is less than 0.2K over most of globe. –Being conservative estimate SST 1 sigma error as 0.2K. –Increase HadISST by 0.5K over non sea-ice points, run standard configuration and compute difference in RSR and OLR from reference case (-0.4 & 1.2 W/m 2 ). Then scale for 0.2K case. Replacement of HadISST dataset with Reynolds dataset (AVHRR + buoys) has negligible impact on OLR and RSR

31 CESD Sources of Uncertainty

32 CESD Which Simulations are consistent with Observations?

33 CESD Which HadAM3 climate sensitivities are consistent with observations? Find all configurations that have OLR and RSR consistent (95%) level with observations. What is the range of climate sensitivies? CERES: K ERBE: K

34 CESD Generating A PDF for Climate Sensitivity

35 CESD Generating a PDF for climate Sensitivity

36 CESD Calculating Likelihoods for each configuration f(r) Areas are the areas of the Voronoi polygons capped at π (gray polygons) 36

37 CESD Computing a probability distribution Individual model realisations not randomly generated though have 16 different initial conditions and 5 different targets Explore several different prior assumptions on the individual realisations. Uniform all configurations equally likely Radiation OLR and RSR equally likely Parameter Parameter values equally likely SECS values equally likely in range 1/SAll feedbacks equally likely in range

38 CESD Cumulative Distribution Functions 38

39 CESD Taking Account of Emulator Uncertainty Error varies Error varies with climate sensitivity. Compute its impact by assuming error is coherent over all samples and monte- carlo sampling. (modify the estimated sensitivity of each configuration)

40 CESD Taking account of Emulator Error

41 CESD Sensitivity Studies Use ERBE observations Amplify Observational co-variance by a factor of 20 – makes CERES and ERBE consistent… Amplify total covariance by two. And some more including using a single year rather than 5. Only the first three make any difference.

42 CESD Cumulative Distribution Functions ERBE: xSat: x Cov: ERBE

43 CESD Summary Found could automatically tune HadAM3 to fit TOA radiation observations. Variation largely due to changes in clouds with clear sky cancelling Get to within 1 W/m 2 RMS with median of 3-4 iterations. Other climatologies strongly related to RSR/OLR Made an estimate of uncertainty in model-data difference which is dominated by modelling uncertainty rather than observational uncertainty. Used this to make probabilistic estimate of climate sensitivity for HadAM3. This is K for CERES with little sensitivity to prior assumptions Amplifying observational error to make CERES and ERBE consistent gives range of K with considerable sensitivity to prior.

44 CESD Thoughts Straight forward to tune models to global SW & LW radiation Could probably do it with a series of short (18 months) simulations It matters for three reasons 1.Coupled models with poor radiation balance will drift (or need flux correction) 2.Related to feedbacks in HadAM3 (and probably other models) 3.Modifies the climate in SST forced – energy balance is probably physical reason.

45 CESD Next stages Couple tuned atmosphere models to ocean. –Do they behave as well as we expect? Explore methods to allow more parameters and observations –Would allow better constraints and explore which parameters and observations really matter. –Bringing observations and modelling together in coherent way. –Which observations are sufficiently independent? –Parameter importance from d error/d param

46 CESD Next Stages (cont.) Use optimisation to efficiently generate atmospheric model configurations that are appropriately sampled from model/obs uncertainty estimate –Allows generation of uncertainty in future model properties. Not just ECS. Would be helped by reasonable first guesses. Use optimisation to efficiently generate models of differing resolution consistent (on large scales) with observations and each other –Allows exploration of impact of resolution change without error in energy balance/basic climatology

47 CESD Thanks and Questions

48 CESD Failures

49 CESD Equi-finality Can different parameter choice results in models with similar climatologies? Do this by looking at final configurations of CERES and ERBE optimisation cases + cases within 1 W/m 2 of target.

50 CESD Precip vs Temp

51 CESD Parameter Values Compensation

52 CESD Land Zonal Means

53 CESD Difference from Standard Config for CERES warm cluster

54 CESD Difference from Standard Config for ERBE warm cluster ERBE Cluster #2 (Warm)


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