Represen'ng Model Uncertainty in Earth-System Modelling:
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1 Represen'ng Model Uncertainty in Earth-System Modelling: Stochas'c and Perturbed Parameter Approaches Hannah Christensen, Fenwick Cooper, Andrew Dawson, Stephan Juricke, Dave MacLeod, Aneesh Subramanian, Peter Watson, Antje Weisheimer Visit of Andy Majda, December 1 st 2015
2 Andrew Dawson Peter Antje Hannah Aneesh Watson Weisheimer Christensen Subramanian Dave MacLeod Stephan Juricke Fenwick Cooper
3 Andrew Dawson 1. Consider new approaches we are working on at Oxford 2. Consider performance of SPPT scheme in seasonal and climate forecasts 3. Constraining stochas'c schemes Peter Antje Hannah Aneesh Watson Weisheimer Christensen Subramanian Dave MacLeod Stephan Juricke Fenwick Cooper
4 Andrew Dawson 1. Consider new approaches we are working on at Oxford 2. Consider performance of SPPT scheme in seasonal and climate forecasts 3. Constraining stochas'c schemes Peter Antje Hannah Aneesh Watson Weisheimer Christensen Subramanian Dave MacLeod Stephan Juricke Fenwick Cooper
5 Stochas'c schemes used/developed at Oxford Stochas'cally Perturbed Parametrisa'on Tendencies (SPPT) Represents errors due to the parametrisa'on schemes Mul'plica've noise perturbs the parametrised tendencies (Palmer et al., 2009) Stochas'cally varying parameter schemes Represents uncertainty in poorly constrained physical parameters T = D + (1 + e) 5 i= 1 T Total tendency D Dynamics tendency P Physics tendency convecoon, clouds, radiaoon, turbulence, gravity waves P i
6 Perturbing Parameters in IFS Convec'on Scheme Hannah Christensen Es'mate uncertainty using Bayesian parameter es'ma'on approach posterior probability densi'es of closure parameter Pirkka Ollinaho (FMI) & Peter Bechtold (ECMWF) Joint uncertainty in four parameters: ENTRORG, ENTSHALP, DETRPEN, RPRCON. Fixed & stochas'cally varying parameter perturba'ons Perturbed parameter scheme significantly increases spread over SPPT Fixed PP scheme improves forecast skill Christensen et al, 2015, JAS RMS No MU SPPT Fixed P.P. Vary P.P U lead time / hrs
7 Independent SPPT in IFS Hannah Christensen SPPTi T = D + 5 i= 1 (1 + e i ) P i Perturb IFS physics schemes with independent random fields Assumes errors from different schemes are uncorrelated No tuning performed same σ, φ as opera'onal SPPT Christensen, Moroz, Palmer, in prep
8 Why does independent SPPT have such a big impact? e.g. Warming from convec'on associated with cooling from clouds If you apply a different padern to each tendency, the large convec'on tendency correctly has a large associated uncertainty, which will then dominate the spread Christensen, Moroz, Palmer, in prep
9 Stochas'c NEMO Ocean Model Stephan Juricke 1. Mul'plica've perturba'on within the GM parametrisa'on scheme T t = (T(U +U G ))+ D T + F T Where, e.g. u G = z (AS) SPPT: u G = ((1+ r)as) z U 3D velocity U G GM eddy velocity R Resolved tendency D T parametrised diffusion/mixing F T Forcing A eddy induced velocity coefficient S slope isoneutral surfaces r zero mean random number
10 Stochas'c NEMO Ocean Model Stephan Juricke 2. Perturbing parameters within eddy kine'c energy calcula'on eddy kine'c energy sets ver'cal mixing Mul'plica've perturba'on to ver'cal eddy diffusivity and ver'cal eddy viscosity coefficients impacts shear and buoyancy terms in eddy kine'c energy calcula'on Interannual standard deviaoon of eddy induced MOC over 60 years: colour = overturning (Sv) Reference StochasOc
11 Generalised stochas'c parametrisa'on for eddies Fenwick Cooper Linear stochasoc system for sub-grid scale turbulent eddy-eddy interacoons Tests with an idealised ocean model. Stochas'c forcing term represents unresolved eddies Sea surface height (m) u t Removes bias Low resolution Parametrised High resolution = Advec'on + Other forces + Viscosity + ξ(x,y,z,t) La'tude (degrees) Eddy kine'c energy (Joules) 2.5 x Corrects variability Low resolution Parametrised High resolution La'tude (degrees) Low resolu'on Parameterised High resolu'on
12 Perturba'on of Sea Ice Strength Parameter Stephan Juricke P *(i, j) = 1+ x(i, j) ( )P*(ref ) Small P* means weak ice and higher veloci'es under convergence X(i,j) spa'ally and temporally correlated noise See significant increase in ensemble spread in ice thickness 1 to 10 days after to 30 days after to 90 days after 01.01
13 Uncertainty in HTESSEL Land Surface Schemes Dave MacLeod and Antje Weisheimer Perturbed parameter experiment (PP): sta'c perturba'on of saturated hydraulic conduc'vity and van Genuchten alpha Compare to mul'plica've perturba'on of soil T and moisture tendencies Impact on seasonal forecasts of 2003 European summer heatwave Reliability: upper quin'le soil moisture CTRL PP MacLeod et al, 2015, QJRMetS
14 Andrew Dawson 1. Consider new approaches we are working on at Oxford 2. Consider performance of SPPT scheme in seasonal and climate forecasts 3. Constraining stochas'c schemes Peter Antje Hannah Aneesh Watson Weisheimer Christensen Subramanian Dave MacLeod Stephan Juricke Fenwick Cooper
15 Impact of SPPT in System 4 Antje Weisheimer Stochas'c physics reduces the overly ac've tropical convec'on (OLR plots) Reduced biases also observed for cloud cover, precipita'on and winds, especially over the Western tropical Pacific (crucial for ENSO) Stochas'c physics impacts on MJO sta's'cs with more and stronger MJO events under SPPT (but model s'll underes'mates MJOs) Seasonal forecast quality of tropical Pacific SSTs is improved Pacific-North American (PNA) weather regime sta's'cs improve OLR bias DJF stochphysoff reanalysis S4 reanalysis S4 stochphysoff Weisheimer et al, 2014, Phil Trans
16 Impact of SPPT in Climate Models: Regimes in IFS Andrew Dawson Compare ERA40 North Atlan'c Regimes (blocking, NAO,...) with those from IFS at different resolu'ons (prescribed SST) Stochas'c physics improves the regime structure of a T511 run to make it comparable to a T1279 run Dawson and Palmer, 2015, Clim Dyn 4(7)
17 Impact of SPPT in Climate Models: ENSO in CAM Hannah Christensen Test SPPT in coupled runs of CAM4 (1 o atmosphere and ocean), Large improvement in representa'on of ENSO (e.g. Nino 3.4 variability) OBS CTRL SPPT Christensen, Berner, Coleman and Palmer, in prep
18 Impact of SPPT in Climate Models: ENSO in CAM Hannah Christensen Test SPPT in coupled runs of CAM4 (1 o atmosphere and ocean), Large improvement in representa'on of ENSO (e.g. Nino 3.4 variability) OBS CTRL SPPT Christensen, Berner, Coleman and Palmer, in prep
19 SPPT is in the atmosphere: how do we impact SSTs? Hannah Christensen Variable Variability improved in AMIP? Variability improved in coupled? Wind U 850, U surface Y Y Precipita'on N Y ω 500 convec'on N Y Cloud cover N Y SST n/a Y Due to combina'on of asymmetry in impact of stochas'c perturba'ons on convec've hea'ng in La Nina phase Improved sta's'cs of WWBs N.b. Also see reduc'on in ENSO amplitude in both simple DO model and CZ model when mul'plica've noise is applied
20 Consider impact of SPPT on MJO in CAM AMIP runs Hannah Christensen OBS CNTL SPPT Lag correla'on of precipita'on (colours) and winds (lines) correlated against precipita'on at reference point in Indian Ocean, Nov-April
21 Consider impact of SPPT on MJO: Compare CAM and EC-Earth Hannah Christensen and Aneesh Subramanian OBS CNTL SPPT é CAM AMIP runs: CAM: 1deg. (110km) EC-E: T255 (80km) EC-Earth è With Susanna Cor' and Jost von Hardenberg
22 Consider impact of SPPT on MJO: Compare CAM and EC-Earth Hannah Christensen and Aneesh Subramanian OBS CNTL SPPT é CAM EC-Earth è Winter Power spectra for U
23 Impact of SPPT on Tropical Variability Peter Watson SPPT in IFS AMIP runs increases variability at frequencies more than about 0.1/day, reducing the bias in this model. Ra'o of power in symmetric modes in OLR data: ra'o between power in model without SPPT and NOAA satellite data (ler), between power in model with SPPT and NOAA satellite data (centre) and between models with and without SPPT (right).
24 Andrew Dawson 1. Consider new approaches we are working on at Oxford 2. Consider performance of SPPT scheme in seasonal and climate forecasts 3. Constraining stochasoc schemes Peter Antje Hannah Aneesh Watson Weisheimer Christensen Subramanian Dave MacLeod Stephan Juricke Fenwick Cooper
25 Consistency of Stochas'c Schemes with Observa'ons Peter Watson and Hannah Christensen IFS forecasts have similar sta's'cs to observa'ons (Watson et al. 2015). Contrary to some expecta'ons (Davies et al., Peters et al. 2013), use of mul'plica've noise is consistent with obs. Results are similar with and without stochas'c physics can observa'ons be found to test different ways of adding stochas'city?
26 New: Coarse graining experiments to constrain SPPT Hannah Christensen and Andrew Dawson AIM: To use high resoluoon model data sets as truth for coarse graining experiments? > CASCADE warm pool dataset (4km) Q: Is it possible to use this data to measure characterisocs of the stochasoc noise term? > spa'al and temporal correla'ons? > correla'on of errors between tendencies? > standard devia'on? 1. Coarse grain to T639 model grid to provide ICs 2. Evolve in 'me 2. Evolve in 'me 3. Compare coarse grained CRM to forecast
27 Thank you for listening any ques'ons?
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