Joint Frequency Distributions for Future European Climate Change

1. Joint Frequency Distributions for Future European Climate Change Glen Harris, Ben Booth, Kate Brown, Mat Collins, James Murphy, David Sexton, Mark Webb Quantifying Uncertainty in Model Predictions (QUMP) Research Theme, Hadley Centre for Climate Prediction and Research, Met Office, Exeter, UK. Jonty Rougier, Durham University. Ensembles Work Package 6.2 Meeting, Helsinki, 26-27 April 2007

2. Gulf of Finland joint frequency distribution  Joint frequency distributions for annual temperature and annual precipitation anomalies, with respect to 1961-90 baseline climate.  A1B forcing,2080-2100 mean anomaly.  129 time-scaled versions of HadSM3 equilibrium response (blue points).  Sample distribution of scaling error, including internal variability (black points).  Medians: T=5.1K, P=12%

3. HadCM3 European Land Grid-points Finnmark Western_Tver Hungary North_Cape Moscow_North North_West_Romania Varangerfjord Denmark North_East_Romania Westfjord West_Lithuania Moldova Swedish_Lapland East_Lithuania Lower_Dniepr North_Bothnia Vitebsk Donetsk Finnish_Lapland Smolensk South_West_France Russian_Lapland Moscow_South South_East_France Murmansk Holland French_Italian_Alps Kola_Peninsula North_Germany Po_Dolomites Central_Norrland Berlin Slovenia_Croatia West_Bothnia North_Poland Bosnia East_Bothnia Warsaw South_West_Romania North_West_Karelia Pripet South_East_Romania North_East_Karelia South_East_Belarus Pyrenees White_Sea Briansk Tuscany Sognefjord Kursk Albania_Montenegro Trondheim Ireland Central_Balkans South_Norrland Channel Eastern_Bulgaria Western_Finland Belgium_NE_France Galicia Eastern_Finland Rhine Northern_Spain North_Ladoga South_East_Germany Eastern_Spain Onega Czech_Republic Greece South_West_Archangel Slovakia_South_Poland West_Marmara Telemark South_East_Poland Bosphorus Oslo Western_Ukraine Ankara Svealand Kiev Black_Sea_Turkey Gulf_of_Finland Sumi Northern_Portugal Saint_Petersburg Kharkov Central_Spain East_Ladoga Western_France South_West_Turkey West_Vologda Burgundy Taurus_Mountains Gotaland Switzerland Turkish_Euphrates Latvia Austrian_Alps Southern_Portugal Pskov Eastern_Austria Andalucia  Exclude 4 UK points (avoid potential conflicts with UKCIP08 project).  Eastward to Moscow only.  Rather coarse resolution (3.752.5 deg).  102 points in this set.

4. Where are the uncertainties? Natural unforced variability Unknown future forcing Modelling of Earth system processes QUMP: focus on modelling uncertainties

5. QUMP approach Predictions are uncertain so… • Run an ensemble of simulations with a climate model in which perturbations are made to the uncertain inputs and processes. • Compare each model simulation with observations and assign a relative score to each. • Produce a weighted distribution of the forecast variable of interest. i.e.: Posterior = Prior  Likelihood  QUMP project pragmatically uses a Bayesian framework.

6. Parameter Perturbations – 31 quantities perturbed Large Scale Cloud • Ice fall speed. • Critical relative humidity for formation. • Cloud droplet to rain: conversion rate and threshold. • Cloud fraction calculation. Dynamics • Diffusion: order and e-folding time. • Gravity wave drag: surface and trapped lee wave constants. • Gravity wave drag start level. Boundary layer • Turbulent mixing coefficients: stability-dependence, neutral mixing length. • Roughness length over sea: Charnock constant, free convective value. Convection • Entrainment rate. • Intensity of mass flux . • Shape of cloud (anvils). • Cloud water seen by radiation. Radiation • Ice particle size/shape. • Cloud overlap assumptions. • Water vapour continuum absorption. Land Surface Processes • Root depths. • Forest roughness lengths. • Surface-canopy coupling. • CO2 dependence of stomatal conductance. Sea Ice • Albedo dependence on temperature. • Ocean-ice heat transfer.

7. Some issues for ensemble climate prediction  Limited computational resources.  use HadSM3/HadCM3 models, not expensive flagship HadGEM model  mainly use mixed-layer (slab) ocean models.  predict pdfs for equilibrium climate response.  Large number of uncertain climate model parameters.  to obtain robust predictions independent of sampling, emulators are required to predict response for parts of parameter space unsampled by GCM simulation.  Sample prior distributions of uncertain model parameters.  use expert ranges, prior distribution shape (triangular, uniform,…)  test sensitivity to sampling assumptions.  Likelihood weighting.  want to choose as many observational constraints as possible to down-weight unrealistic model variants.  Scale equilibrium response, to create “pseudo-transient” ensemble  validate scaling with GCM ensemble  Physics perturbations upset radiative balance, potential for climate drift.  flux-correct transient GCM simulations.

8. “Perturbed-Physics” Atmosphere-Slab Equilibrium Ensemble Simulations Typical slab member • Additional simulations underway to explore interesting regions of parameter space (currently ~300 members). • Distribution differences due to different sampling strategies and parameter choices.  Murphy et al, 2004. Stainforth et al, 2005. Webb et al, 2006.

9. Simple example for climate sensitivity “emulated” prior predictive distribution posterior predictive distribution histogram of “perturbed physics” ensemble likelihood weighting via comparison with real world Murphy et al., 2004, Nature, 430, 768-772

10. Probabilistic Predictions - Framework • Perform a limited ensemble of GCM experiments with perturbed input parameters. • Build an emulator which can estimate the GCM output at untried parameter values. • Sample emulator to produce model prior predictive distributions of climate variables. • Use observations to produce a likelihood function and posterior (observationally-constrained) predictive distributions. • Sample weighted posterior distribution and time-scale with Simple Climate Model (SCM) to predict pdfs for transient regional future climate change, at GCM resolution. • Run ensemble of 25km Regional Climate Model (HadRM3) variants driven by equivalent GCM transient runs, and downscale responses to predict regional pdfs.

11. Emulation for any perturbed-parameter value. Emulator: statistical model designed to predict the outputs of a climate model which one could in principle run. Emulators predict not only the mean response, but also the error in the predicted response. Built from a sample of runs.  Multiple linear regression; entertain many possible functional relationships for explanatory variables.  Emulator error used to select interesting parameter combinations to create additional members, and improve emulator.  Emulator uncertainty is propagated through to the final PDFs. Joint prior equilibrium pdf for Eng-Wales summer temperature and precipitation response, for CO2 doubling. Rougier, Sexton et al, J.Clim (submitted)

12. Compare models with observations (likelihood weighting) Each “ensemble member” gets a weight w, something like: simulated variable observed variable Sum over all observables variance of “discrepancy” variance of emulator error variance of observations (including natural variability, obs. error etc.) More precisely, model skill is likelihood of model data given some observations: Sexton et al, J.Clim (in prep)

13. Discrepancy  Following Murphy et al (Nature, 2004), began collaboration with statisticians (Rougier and Goldstein, Durham Univ.) to improve robustness of predictions.  Introduce “discrepancy”: Measure of uncertainty associated with model imperfection: “distance” between unknown true future climate and “best” possible choice of the uncertain model input parameters.  Unknown, but we assume this distance similar to that between other climate models and our best perturbed-physics emulation of the future predictions from these same models.  Discrepancy therefore also a quantification of structural modelling error.

14. Compare model prior pdf with observationally-constrained pdf  Equilibrium warming for England-Wales for a doubling of CO2.  Observational-constraints: narrow the spread in pdf, and can also move it (e.g., less than 2C warming unlikely).  Discrepancy: flattens likelihood, and broadens spread in observationally-constrained posterior.  Need discrepancy to avoid over-confidence, spiky posterior distributions. observationally-constrained posterior pdf (no discrepancy) model prior pdf posterior pdf, with discrepancy D.Sexton, J.Rougier

15. Transient Ensembles  Need coupled model experiments to capture time-dependent climate change.  Run 17 of the perturbed atmosphere HadSM3 versions coupled instead to dynamic ocean, i.e. HadCM3 setup.  Transient ensembles smaller because of spin-up, additional ocean model, and longer runtime required.  Flux adjustments used to prevent model drift, and reduce SST biases.  HadCRUT observed series. Historical + A1B forcing Observations

16. Compare perturbed physics ensemble with multi-model ensemble  Increase CO2 by 1% per annum.  Spread in transient response comparable in the two ensembles. Collins et al., Clim. Dyn.

17. Scaling the equilibrium response Problem: Can only afford relatively few simulations in transient GCM ensemble (17 here). Aim: Want to predict the transient response for the 129 slab-ocean experiments (or indeed any emulated equilibrium response), if they were coupled instead to a dynamic ocean (HadCM3). Solution: Scale anomaly patterns for each slab member by global mean surface temperature anomaly ΔT(t) predicted by a Simple Climate Model (SCM) Proposed in 1990 by Santer, Wigley, Schlesinger & Mitchell as way of predicting transient regional response from slab equilibria, before fully-coupled AOGCM’s had been developed.  F in principle any climate surface variable, e.g. mean temperature, seasonal precipitation, soil moisture, percentiles of daily Tmax

18. Time-Scaling to Produce Pseudo-Transient Ensembles   129 SCM projections for global surface temperature anomaly, using diagnosed equilibrium feedbacks (1% p.a. CO2 inc). Typical response pattern for annual surface temperature to a doubling in CO2 concentration. Frequency distributions for Northern Europe annual temperature (including scaling error).

19. Scaling Assumptions 1. 20 year mean for equilibrium response sufficient to give good signal (compared to internal variability). • 2. Slab equilibrium response patterns represent transient patterns. 3. Climate anomalies linear in global temperature anomaly ΔT(t). 4. ΔT(t) can be predicted by a Simple Climate Model (SCM), driven by emulated equilibrium climate feedbacks λ. 5. Assume equilibrium climate feedbacks represent transient feedbacks. Justification and Validation Compare pattern-scaling with the 17 fully-coupled simulations to give scaling error, and include this in predicted transient distributions. Any partial failure in assumptions quantified by validation: errors in scaling  bigger uncertainty.

20. Scaling – validation with 17 member GCM ensemble SCM-GCM error GCM anomaly SCM scaled prediction Global (ghg only) . Mediterranean Basin (all forcing)

21. Frequency distribution for Transient Climate Response (TCR)  Assume distribution of error in scaled response to be Gaussian (no evidence to contrary). Estimate variance and bias from validation with 17 member GCM ensemble.  For each region and time, sum 129 t distributions (red curve) to obtain frequency distribution (blue curve).  Parameter uncertainty more important than scaling uncertainty.  Distribution shape here mainly reflects sample design, not model prior distribution. (TCR: surface temperature response for years 60-80 during 1% per annum CO2 increase).

22. Time-scaling equilibrium patterns of change Example: djf precipitation, 1% CO2 pa increase Transient regional frequency distributions, using 129 perturbed atmosphere models. Plumes of evolving uncertainty (median, 80, 90, 95% confidence ranges) Harris et al., 2006, Clim.Dyn. 27, p357.

23. Pattern scaling A1B scenario • SCM uses forcing diagnosed from GCM runs. • compare here internal variability for one GCM run (green), with parameter and scaling uncertainty (red). Improvement of scaling to reduce error Using the A1B and A1B-GHG GCM ensembles, we can calculate - additional patterns for the normalised aerosol response saero - correction patterns to represent differences between the slab and dynamic ocean response cgcm

24. Production of interim data - summary 1. Scale 129 equilibrium responses, to predict transient joint temperature-precipitation response if we were to run with dynamic ocean and A1B forcing. 2. For each equilibrium member, sample (40 times for this test) the scaling error distribution (red curve), with variance and bias obtained from validation.  Still a lot more to do…

25. Gulf of Finland future annual temperature/precipitation 80%, 90% and 95% confidence ranges. 17 GCM anomalies 2080-2100 anomalies with respect to 1961-90 baseline.

26. European pdfs – still to do Will do - Instead of annual data, process seasonal means and produce frequency distributions, based once again on 129 member ensemble. - Data now all back so can be done. Possible (time/resource constraints) - Build emulators for selected European GCM grid-points, and at same time obtain weights to observationally-constrain model variants. - Then resample weighted equilibrium distributions and time-scale to produce observationally constrained pdfs for future European climate change (HadCM3 resolution). Unlikely at moment - Redo UKCIP08 but for other parts of European domain, down-scaling to 25km resolution.

27. Down-scaling to the UK (and Europe?): UKCIP08 • Also running a 17-member 25km resolution HadRM3 (regional model) ensemble . • Driven by boundary forcing from the HadCM3 A1B ensemble (1950-2100). • Runs will finish in July. • We will construct regression relationships between the 17 GCM and 17 RCM simulations of future climate. • Then sample predicted GCM transient pdfs and use these regression models to deliver regional response pdfs at 25km scales (this will introduce further uncertainty). R.Clark, D.Sexton, K.Brown, G.Harris, many others…

28. Additional perturbed physics ensembles (PPE) Atmosphere PPE. Also done two other forcing scenarios: A1B-GHG, and B1. Will also do A1FI. RCM ensemble 4 additional transient ensembles Murphy et al (to appear in Phil. Trans. special issue, 2007)

29. Acknowledgments QUMP Team: David Sexton, Mat Collins, Ben Booth, James Murphy, Mark Webb, Kate Brown Also: Robin Clark, Penny Boorman, Gareth Jones, B. Bhaskaran, Jonty Rougier And: Hadley Centre, Met Office, DEFRA (Department for the Environment, Food and Rural Affairs) UK Govt, ENSEMBLES, ClimatePrediction.net. Thank You.