Presentation on theme: "Robin Hogan, Andrew Barrett"— Presentation transcript:
1Evaluation and improvement of mixed-phase cloud schemes using radar and lidar observations Robin Hogan, Andrew BarrettDepartment of Meteorology, University of ReadingRichard ForbesECMWF
2Overview Why are mixed-phase clouds so poorly captured in GCMs? These clouds are potentially a key negative feedback for climateGetting these clouds right requires the correct specification of turbulent mixing, radiation, microphysics, fall speed, sub-grid structure etc.Vertical resolution is a key issue for representing thin liquid layersCan we devise a scale-independent parameterization?Use a 1D model and long-term cloud radar and lidar observationsEasy to perform many sensitivity studies to changed physicsAbstract: 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.
3Mixed-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 so more persistentHigher albedo -> negative climate feedback (Mitchell et al. 1989)Depends on questionable model physics!Decrease in subtropical stratocumulusLower albedo -> positive feedback on climate
4How well do models capture mid-level clouds? CloudSat & Calipso (Hogan, Stein, Garcon, Delanoe, Forbes, Bodas-Salcedo, in prep.)How well do models capture mid-level clouds?Models miss at least a third of mid-level cloudsGround-based radar and lidar (Illingworth, Hogan et al. 2007)ISCCP and CERES (Zhang et al. 2005)t<3.63.6<t<23
5Important processes in altocumulus Longwave cloud-top coolingSupercooled droplets formCooling induces upside-down convective mixingSome droplets freezeIce particles grow at expense of liquid by Bergeron-FindeisenIce particles fall out of layerHlEMost previous studies (e.g. Xie et al. 2008) in Arctic: surface fluxes importantMany 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?
6Observations of long-lived liquid layer Liquid at –20CRadar reflectivity (large particles)Lidar backscatter (small particles)Estimate ice water content from radar reflectivity factor and temperatureEstimate liquid water content from microwave radiometer using scaled adiabatic method
721 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?
81D “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
10Evaluation of EMPIRE control model More supercooled liquid than Met Office but still seriously underestimatedNote that ice & liquid cloud fraction is diagnosed so errors not so fundamental
11Effect of turbulent mixing scheme Quite a small effect!
12Effect 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
13Effect 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?
14Summary of sensitivity tests Main model sensitivities appear to be: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?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)
16Resolution dependence Best NWP resolutionTypical NWP resolution
17Effect 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
18Effect 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%
20Parameterization at work New parameterization works well over full range of model resolutionsTypically applied only at cloud top, which can be identified objectively
21Standard ice particle size distribution log(N)“Marshall-Palmer” inverse exponential 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?
22Parameterized 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
23Adjusted 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) suggest N0 smaller for low water contentMuch better growth rate and fall speedNeed to account for ice shattering!Fall speedIce water content
24Conclusions Why are mixed-phase clouds so poorly captured in GCMs? Two key effects that lead to ice growth too fast at cloud topSub-grid structure in the verticalStrong resolution dependence near cloud top; can be parameterized to allow liquid layers that only partially fill the layer verticallyWe have parameterized effect on liquid occurrence and ice growthError in assumed ice size distributionMore realistic size distribution has fewer, larger crystals at cloud topLower ice growth and faster fall speeds so liquid depleted more slowlyImplications for large scale modelsNWP: Richard Forbes shown large surface temperature errors unless cloud-top ice growth scaled back: now has physical basisClimate: urgent need to re-evaluate mixed-phase cloud contribution to climate sensitivity using models with better physics
27Radiative properties Using Edwards and Slingo (1996) radiation code Water content in different phase can have different radiative impact
28Ice particle size distribution Large ice crystals are more massive and grow faster than smaller crystalsSmall crystals have largest impact on growth rate
29Cloudnet processing Illingworth, Hogan et al. (BAMS 2007) Use radar, lidar and microwave radiometer to estimate ice and liquid water content on model grid
30Mixed-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