1. LINDSEY NOLAN WILLIAM COLLINS PETA-APPS TEAM MEETING OCTOBER 1, 2009 Stochastic Physics Update: Simulating the Climate Systems Accounting for Key Uncertainties in Atmospheric Convection

2. Overview Formulation of problem Parameter Perturbations  Convective Momentum Transport  Entrainment/Dilution Stochastic aspect Modeling  CMMAP data from CRMs  Interactive Ensembles Future plans Incorporation into PetaApps

3. Global models and cloud-scale physics Unresolved sub- grid processes affect large-scale  Mass  Energy  Momentum 100 km http://visibleearth.nasa.gov/view_rec.php?id=2710 http://collaboration.cmc.ec.gc.ca/science/rpn/gem

4. Local to Global Scales Cloud scale physics affects global climate and climate variability Example: Walker Circulation United Nations Environmental Program, GRID-Arendal Maps and Graphics Library

5. Uncertainty & climate change Climate sensitivity is sensitive to small-scale processes Ensemble of single model with perturbed physics  Clouds  Precipitation Murphy et al, Nature 2004 Hadley Center

6. Physics uncertainties Concentrate on convective processes  Convective momentum transport/drag/friction (CMT)  Entrainment/dilution These processes were added in CAM 3.5 These processes have large effects on unforced variability. Current assumptions and treatments:  There is large uncertainty in the treatment of these processes  Parameters governing convection are same in all synoptic systems  Parameter settings are obtained from limited experiments

7. Effects of cumulus momentum transport Benefits:  CMT comparable to other term in angular momentum budget  Capture seasonal migration of ITCZ  Improved precipitation in tropics  ENSO periodicity and magnitude  Include perturbations to pressure field in and around convective area Observations New model Old model Neale et al, J. Climate, 2008

8. Convective Momentum Transport Courtesy of Jadwiga Richter, NCAR

9. Entrainment Based on Tolford model (1975), Raymond and Blythe (1986,1992), Neale et al (date) Reducing buoyancy Entraining air at all levels Parameterizations – coming soon…

10. Effects of entrainment Mass exchange in convective plumes Focus on mixing at cloud edge Reduces buoyancy of updrafts www.srh.noaa.gov/ohx/educate/dry_entrainment.gif

11. Physics Perturbations Relax assumptions in current climate models:  Time invariance  Spatial invariance Explicit representation of treatment of key uncertainties:  Entrainment of dry air into convective plumes  Convective Momentum Transport Represent as stochastic processes

12. Stochastic Physics At present, parameterizations in CAM obeys:  Parametric variance: 0  Variance timescale: infinity  Variance lengthscale: infinity We will relax all three conditions:  Parametric variance: >0 (mode = standard CAM value)  Variance timescale: <= synoptic  Variance lengthscale: <= synoptic Series of experiments:  Idealized: artificial values of variance properties  “Realistic”: variance from global cloud resolving modeling

13. Next steps Conduct highly idealized perturbed-physics experiments  CAM with AquaPlanet Introduce autoregressive processes with characteristic length and time scales Determine correct length and time scales from CMMAP results using the multi-scale modeling framework

14. Potential Problems Gravity waves – should be damped Need to monitor energy lost by atmosphere Computing issues?

15. Potential Problems, part 2 Generation of gravity waves A model incorporating this variability may not produce a stable present-day climate (i.e. not in energy balance Technical Issues  Cloud resolving models haven’t looked at full range of atmospheric variability http://pcl.physics.uwo.ca/science/gravitywaves/

16. Future Plans IE: varying physics but same dynamics Perturbed physics will introduce new source of “weather noise” We will investigate whether there is a “red” component of this new noise that affects the ocean. We will quantify the effects of the stochastic physics variability on the coupled climate using IE.

17. Conclusion Understand large impact of observed physics noise on climate and climate change Advance representation of natural variability of clouds and atmospheric processes in climate models Theoretical grounding – obtain variability from process models http://eol.jsc.nasa.gov/scripts/sseop/photo.pl?mission=ISS007&roll=E&frame=10807