LINDSEY NOLANWILLIAM COLLINS

PETA-APPS TEAM MEETING OCTOBER 1 , 2009

Stochastic Physics Update: Simulating the Climate Systems

Accounting for Key Uncertainties in Atmospheric Convection

Overview

Formulation of problemParameter Perturbations

Convective Momentum Transport Entrainment/Dilution

Stochastic aspectModeling

CMMAP data from CRMs Interactive Ensembles

Future plansIncorporation into PetaApps

Global models and cloud-scale physics

Unresolved sub-grid processes affect large-scale Mass Energy Momentum

100 km

100

km

http://visibleearth.nasa.gov/view_rec.php?id=2710http://collaboration.cmc.ec.gc.ca/science/rpn/gem

Local to Global Scales

Cloud scale physics affects global climate and

climate variabilityExample: Walker Circulation

United Nations Environmental Program, GRID-Arendal Maps and Graphics Library

Uncertainty & climate change

Climate sensitivity is sensitive to small-scale processes

Ensemble of single model with perturbed physics Clouds Precipitation

Murphy et al, Nature 2004Hadley Center

Physics uncertainties

Concentrate on convective processes Convective momentum transport/drag/friction (CMT) Entrainment/dilution

These processes were added in CAM 3.5These 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

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

ObservationsNew modelOld model

Neale et al, J. Climate, 2008

Convective Momentum Transport

Courtesy of Jadwiga Richter, NCAR

Entrainment

Based on Tolford model (1975), Raymond and Blythe (1986,1992), Neale et al (date)

Reducing buoyancyEntraining air at all levelsParameterizations – coming soon…

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

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

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

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

Potential Problems

Gravity waves – should be dampedNeed to monitor energy lost by atmosphereComputing issues?

Potential Problems, part 2

Generation of gravity wavesA 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/

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.

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