Presentation on theme: "Clouds and their turbulent environment"— Presentation transcript:
1 Clouds and their turbulent environment Robin Hogan, Andrew Barrett, Natalie HarveyHelen Dacre, Richard Forbes (ECMWF)Department of Meteorology, University of Reading
2 OverviewPart 1: Why can’t models simulate mixed-phase altocumulus clouds?These clouds are potentially a key negative feedback for climateGetting these clouds right requires the correct specification of turbulent mixing, radiation, microphysics and sub-grid distributionWe use a 1D model and long-term cloud radar and lidar observationsPart 2: Can models simulate boundary-layer type, and hence the associated mixing and clouds?Important for pollution transport and evolution of weather systemsWe use long-term Doppler lidar observations to evaluate the scheme in the Met Office modelAbstract: This talk will tackle two aspects of evaluating and improving the representation of clouds in weather and climate models using observational data, both of which involve the challenging problem of understanding the interaction between clouds and their turbulent environment. The first part will address the problem of why virtually all models grossly underestimate the occurrence of mid-level altocumulus clouds, which represent an important uncertainty in climate prediction. These clouds are difficult to simulate because are often much thinner than the vertical resolution of the model, and it is necessary to correctly represent the processes of longwave cloud-top cooling, turbulent mixing and the exchange of water between the ice, liquid and vapour phases. We have developed a flexible 1D model that can address this problem by running it at a cloud radar and lidar site to see which parameterizations lead to the best agreement with observations. Two key bits of physics are needed to improve the occurrence of these clouds: firstly, a new parameterization for the sub-grid vertical distribution of thermodynamic quantities enables thin liquid water layers to be represented correctly in models with a coarse vertical resolution. Secondly, we show that an improved representation of the ice particle size distribution leading on from the work of Delanoe and Hogan (2008) provides a much better prediction of the rate at which ice crystals grow and fall out of the cloud. The second part of the talk will demonstrate how ground-based Doppler lidar can be used to classify the boundary layer into nine different types (e.g. stratus-topped stable layer, decoupled stratocumulus, cumulus-capped etc). It is demonstrated that Doppler lidar diagnostics such as vertical velocity variance and skewness are able to diagnose whether boundary-layer turbulence is being generated from surface heating or cloud-top radiative cooling, which is key to distinguishing between types such as cumulus clouds and broken stratocumulus. A two-year dataset is then used to evaluate the predictions of the Met Office model, one of many models that diagnose a boundary-layer type in order to decide which mixing scheme to use, which is important for the transport of pollution and the evolution of weather systems.
3 Mixed-phase altocumulus clouds Small supercooled liquid cloud dropletsLow fall speedHighly reflective to sunlightOften in layers only m thickLarge ice particlesHigh fall speedMuch less reflective for a given water content
4 Mixed-phase cloud radiative feedback Change to cloud mixing ratio on doubling of CO2Tsushima et al. (2006)Increase in polar boundary-layer and mid-latitude mid-level cloudsClouds more likely to be liquid phase: lower fall speed and more persistentHigher albedo -> negative feedbackBut this result depends on questionable model physics!Decrease in subtropical stratocumulusLower albedo -> positive feedback on climate
5 Important processes in altocumulus Longwave cloud-top coolingSupercooled droplets formCooling induces upside-down convective mixingSome droplets freezeIce particles grow at expense of liquid by Bergeron-Findeisen mechanismIce particles fall out of layerMany models have prognostic cloud water content, and temperature-dependent ice/liquid split, with less liquid at colder temperaturesImpossible to represent altocumulus clouds properly!Newer models have separate prognostic ice and liquid mixing ratiosAre they better at mixed-phase clouds?
6 How well do models get mixed-phase clouds? Ground-based radar and lidar (Illingworth, Hogan et al. 2007)CloudSat and Calipso (Hogan, Stein, Garcon and Delanoe, in preparation)Models typically miss a third of mid-level cloudsThis is cloud fraction – what about cloud water content?
7 Observations of long-lived liquid layer Radar reflectivity (large particles)Lidar backscatter (small particles)Radar Doppler velocityLiquid at –20C
8 Cloudnet processing Illingworth, Hogan et al. (BAMS 2007) Use radar, lidar and microwave radiometer to estimate ice and liquid water content on model grid
9 21 altocumulus days at Chilbolton Met Office models (mesoscale and global) have most sophisticated cloud schemeSeparate prognostic liquid and iceBut these models have the worst supercooled liquid water content and liquid cloud fractionWhat are we doing wrong in these schemes?
10 1D “EMPIRE” model Single column model High vertical resolution Default: Dz = 50mFive prognostic variablesu, v, θl, qt and qiDefault: follows Met Office modelWilson & Ballard microphysicsLocal and non-local mixingExplicit cloud-top entrainmentFrequent radiation updates (Edwards & Slingo scheme)Advective forcing using ERA-InterimFlexible: very easy to try different parameterization schemesCoded in matlabEach configuration compared to set of 21 Chilbolton altocumulus daysVariables conserved under moist adiabatic processes:Total water (vapour plus liquid):Liquid water potential temperature
12 Evaluation of EMPIRE control model More supercooled liquid than Met Office but still seriously underestimated
13 Effect of turbulent mixing scheme Quite a small effect!
14 Effect of vertical resolution Take EMPIRE and change physical processes within bounds of parameterized uncertaintyAssess change in simulated mixed-phase cloudsSignificantly less liquid at 500-m resolutionExplains poorer performance of Met Office modelThin liquid layers cannot be resolved
15 Effect of ice growth rate Liquid water distribution improves in response to any change that reduces the ice growth rate in the cloudChange could be: reduced ice number concentration, increased ice fall speed, reduced ice capacitanceBut which change is physically justifiable?
16 Summary of sensitivity tests Main model sensitivities appear to be:Ice cloud fractionIn most models this is a function of ice mixing ratio and temperatureWe have found from Cloudnet observations that the temperature dependence is unnecessary, and that this significantly improves the ice cloud fraction in clouds warmer than –30C (not shown)Vertical resolutionCan we parameterize the sub-grid vertical distribution to get the same result in the high and low resolution models?Ice growth rateIs there something wrong with the size distribution assumed in models that causes too high an ice growth rate when the ice water content is small?
18 Resolution dependence Best NWP resolutionTypical NWP resolution
19 Effect 1: thin clouds can be missed Consider a 500-m model level at the top of an altocumulus cloudConsider prognostic variables ql and qt that lead to ql = 0θlqtqlTP1But layer is well mixed which means that even though prognostic variables are constant with height, T decreases significantly in layerTherefore a liquid cloud may still be present at the top of the layerGridbox-mean liquid can be parameterizedP2
20 Effect 2: Ice growth too high at cloud top Diffusional growth:qi = ice mixing ratio, ice diameterRHi = relative humidity with respect to iceqi zero at cloud top: growth too highdqiRHiqidtP1Assume linear qi profile to enable gridbox-mean growth rate to be estimated: significantly lower than beforeP2100%
22 Parameterization at work New parameterization works well over full range of model resolutionsTypically applied only at cloud top, which can be identified objectively
23 Standard ice particle size distribution log(N)Inverse exponential fit used in all situationsSimply adjust slope to match ice water contentWilson and Ballard scheme used by Met OfficeSimilar schemes in many other modelsN0 = 2x106Increasing ice water contentDBut how does calculated growth rate versus ice water content compare to calculations from aircraft spectra?
24 Parameterized growth rates log(N)Ice growth rateDRatio of parameterization to aircraft spectraN0 = constantIce clouds with low water content:Ice growth rate too highFall speed too lowLiquid clouds depleted too quickly!Fall speedIce water content
25 Adjusted growth rates log(N) New ice size distribution leads to better agreement in liquid water contentIce growth rateDRatio of parameterization to aircraft spectraN0 ~ IWC3/4Delanoe and Hogan (2008) result suggests N0 smaller for low water contentMuch better agreement for growth rate and fall speedFall speedIce water content
26 Mixed-phase clouds: summary Mixed-phase clouds drastically underestimated in climate models, particularly those that have the most sophisticated physics!Very difficult to simulate persistent supercooled layersExperiments with a 1D model evaluated against observations show:Strong resolution dependence near cloud top; can be parameterized to allow liquid layers that only partially fill the layer verticallyMore realistic ice size distribution has fewer, larger crystals at cloud top: lower ice growth and faster fall speeds so liquid depleted more slowlyMany other experiments have examined importance of radiation, turbulence, fall speed etc.Next step: apply new parameterizations in a climate modelWhat is the new estimate of the cloud radiative feedback?
27 Part 2 Boundary layer type from Doppler lidar Turbulent mixing in the boundary layer transports:Pollutants away from surface: important for healthWater: important for cloud formation, and hence climate and weather forecastingHeat and momentum: important for evolution of weather systemsMixing represented in four ways in models:Local mixing (shear-driven mixing)Non-local mixing (buoyancy-driven with strong capping inversion)Convection (buoyancy-driven without strong capping inversion)Entrainment (exchange across tops of stratocumulus clouds)Models must diagnose boundary-layer type to decide scheme to useGetting the clouds right is a key part of this diagnosisDoppler lidar can measure many important boundary layer propertiesCan we objectively diagnose boundary-layer type?
28 How is the boundary layer modelled? Met Office model has explicit boundary-layer types (Lock et al. 2000)Unstable profile: Non-local schemeBuoyancy-driven mixing: diffusivity profile determined by parcel ascents/descentsStable profile: Local mixing schemeShear-driven mixing only: diffusivity K is a function of local Richardson number RiShallow cumulus schemeIf cumulus present, mixing determined by mass-flux schemeEntrainment schemeIf stratocumulus is present, entrainment velocity is parameterized explicitly
29 Turbulence from Doppler lidar Input of sensible heat “grows” a new cumulus-capped boundary layer during the day (small amount of stratocumulus in early morning)Convection is “switched off” when sensible heat flux goes negative at 1800Surface heating leads to convectively generated turbulenceTurbulence from Doppler lidarHogan et al. (QJRMS 2009)
30 Skewness Can diagnose the source of turbulence Stratocumulus cloud HeightPotential temperatureLongwave coolingShortwave heatingCloudNegatively buoyant plumes generated at cloud top: upside-down convection and negative skewnessPositively buoyant plumes generated at surface: normal convection and positive skewnessSkewnessCan diagnose the source of turbulence
31 Boundary-layer types from observations Lock type IqvLock type III
32 Probabilistic decision tree Test surface sensible heat fluxTest skewnessTest skewness & velocity varianceUse lidar backscatterStable cloudlessClear well mixedForced Cu under ScCloudy well mixedDecoupled ScDecoupled Sc over CuCumulusStable stratusDecoupled Sc over stable
33 Example day: 18 October 2009Most probable boundary-layer typeII: Stratocu over stable surface layerIIIb: Stratocumulus-topped mixed layerIb: StratusUsually the most probable type has a probability greater than 0.9Now apply to two years of data and evaluate the type in the Met Office modelHarvey, Hogan and Dacre (2012)
34 Comparison to Met Office model WinterSpringModel has:Too little stableToo little well-mixedToo much cumulusNote:Model cumulus needs to be >400 m thickUse radar to apply this criterion to obsHarvey, Hogan and Dacre (2012)SummerAutumn
35 Comparison with Met Office versus season and time of day ObsWinterSpringSummerAutumnModel
36 Forecast skill 6x6 contingency table is difficult to analyse Most skill scores operate on a 2x2 table: a (hits), b (false alarms), c (misses), d (correct negatives)Instead consider each decision separatelyUse symmetric extremal dependence index (SEDI) of Ferro & Stephenson (2011): many desirable properties (equitable, robust for rare events etc)Where hit rate H = a/(a+c) and false alarm rate F = b/(b+d)
37 Forecast skill: stability Surface layer stable?Model very skilful (but basically predicting day versus night)Better than persistence (predicting yesterday’s observations)dcrandom
38 Forecast skill: cumulus baCumulus present (given the surface layer is unstable)?Much less skilful than in predicting stabilitySignificantly better than persistencedcrandom
39 Forecast skill: decoupled baDecoupled (as opposed to well-mixed)?Not significantly more skilful than a persistence forecastdcrandom
40 Forecast skill: multiple cloud layers? baForecast skill: multiple cloud layers?dcCumulus under statocumulus as opposed to cumulus alone?Not significantly more skilful than a random forecastMuch poorer than cloud occurrence skill (SEDI )random
41 Forecast skill: Nocturnal stratocu Stratocumulus present (given a stable surface layer)?Marginally more skilful than a persistence forecastMuch poorer than cloud occurrence skill (SEDI )badcrandom
42 Summary and future work Doppler lidar opens a new possibility to evaluate boundary layer schemesModel rather poor at predicting boundary layer typeIn addition to boundary-layer type, can we evaluate the diagnosed diffusivity profile – this is what matters for evolution of weather?How do models perform over oceans or urban areas?How can boundary layer schemes be improved?Combination of radar-lidar retrievals and 1D modelling demonstrated that shortcomings of altocumulus models could be identified and fixedThe same strategy could be applied to the boundary layer
48 Ice particle size distribution Large ice crystals are more massive and grow faster than smaller crystalsSmall crystals have largest impact on growth rate
49 Skewness Skewness defined as Positive in convective daytime boundary layersAgrees with aircraft observations of LeMone (1990) when plotted versus the fraction of distance into the boundary layerUseful for diagnosing source of turbulence