Presentation on theme: "What’s quasi-equilibrium all about?"— Presentation transcript:
1 What’s quasi-equilibrium all about? Laura Davies, University of Reading, UK.Supervisors:Bob Plant, Steve Derbyshire (Met Office)
2 Why is convection important? Focus on deep convectionMajor transport of heat, moisture and momentum.CumulonimbusFair weather cumulusFocus on this!
3 What is deep convection? Develop on organised, long-lived systems such as squall lines and MCSs.
4 What is deep convection? Provide energy to large-scale circulations eg Hadley cell.Convection interacts and modulates MJO.
5 What is deep convection? Effects the radiation budget of the earth.
6 Convection meets NWPConvective systems are a major contributor to global circulations of heat, mass and momentumRepresentation depends on scale of modelHigh resolution models explicitly resolve cloudsLarge scale models require parameterisationParameterisations represent the mean effect of the sub-grid scale cloud process on the large scale flowFor validity this requires assumptions to be made about the mean convection
7 Parameterisation basics Arakawa and Schubert (1974)Key assumption
8 The assumptionsScale separation in time and space between cloud processes and large scale flowConvection acts on smaller and faster scales than the large scale flowAVHRR visible (University of Dundee)Convective ensembleAnalogous to the equation of statep=ρRT
9 Motivation Model compared to observations (Yang & Slingo 2001) Longer systematic life cycle…memory?
10 Large eddy models setup LEM run as a CRM explicitly resolves cloud-scale dynamics but parameterises sub-grid processesLargest eddies are responsible for majority of transport so are explicitly resolvedInitialised with profiles of θ and qvNon-rotating, no wind shear1 km resolutionBalanced in terms ofmoist static energyVary the lengthof the cycle
11 Control run Control run: Applying constant surface fluxes until equilibrium achievedA long timeRepeating 3hr cyclesTime-varying portion of runInitial convective response is strongly influenced by the control run. It was found a portion of ~ 6hrs need to be removed. This suggests ‘memory’ within the system.
12 Equilibrium response Long timescale - 36 hrs Decay consistentwith forcingFluctuations around an equilibrium mass fluxRapid triggering but variable magnitude and timing
13 Non-equilibrium response Short timescale - 3 hrAll times show convective activityGradual triggeringPeak response highly variable cycle-to-cycle
14 Effect of forcing timescale Increased variability in mass flux responseSimilar response for precipitation.Increased variability in mean totalprecipitation per cycle.
16 Diurnal cycle depends on… Khairoutdinov and Randell (2006)Localised solar heating due to surfaceSea-breezesCold pools and gust frontsDry linesHorizontal convective rollsStirling and Petch(2004)Earlier rain eventsLand useSoil moistureCold pools in boundary layerHumidity of free troposphere
17 Experimental set up Variables to damp Perturb key thermodynamic variables by damping them back to the horizontal meanDamp on the convective timescale ~ 15 minsInvestigate effect on variability in 3 hr runVariables to dampHorizontal winds, u, vVertical wind, wMoisture, qvTemperature, θ
18 Damping u, vReduced precipitationIncreased precipitationDamping u, v reduces the variability in the precipitation where the variability was stronger.It increases the variability where it was weaker.
19 At top of boundary layer Damped u, vAt top of boundary layer(925 m)At surfaceVertical profile of horizontalwind speedAt time of maximumforcingNon-dampedX-section of horizontalwind speed (m/s)
21 At time of maximum forcing Damped θNon-dampedNear start of dampingX-section θ’2 (K2) at 3 kmAt time of maximum forcingTowards end of simulation
22 Cloud distribution Mean number of clouds Non-damped Damped θ 2.4 km Increasing height2.4 km12.4 kmClouds defined as buoyant, moist and upward moving
23 ConclusionsIncluding convective parameterisations in numerical models is essential.However results suggest that making an equilibrium assumption might not always be valid.At short forcing timescales the convection is not a direct function of the forcing.The time-history of the system affects the current amount of convection. The system has an element of memory.Experiments are starting to consider what variables may cause this memory.u, v and θ are initial suggestions but experiments need to be carefully constructed.
24 Discussion pointsHow do we make a parameterisation that works for all forcing timescales eg.What key variables provide the memory in the convective system?At what height do they have greatest effect?Is their spatial variability also a key factor?