gettelman ecmwf sm2 · 2015-11-09 · Community#Atmosphere#Model#(CAM5)# Dynamics Boundary Layer...
Transcript of gettelman ecmwf sm2 · 2015-11-09 · Community#Atmosphere#Model#(CAM5)# Dynamics Boundary Layer...
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Cloud Evalua*on and Impacts in Large Scale Models
A. Ge&elman, NCAR
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Outline Main Story: From iden;fying biases to process improvement using the ‘weather’ scale • Mo;va;on: the impact of clouds for weather & climate: focus mostly on climate examples
• Example of a cloud parameteriza;on “suite” • How we simulate and observe clouds • Evalua;on methods: some examples
– The tradi;onal way – Best prac;ces
• Summary of Best Prac;ces for Evalua;on • Challenges Warning: Focus is on climate.
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Mo;va;on: Weather
Hypothesis: “most impacts of extreme weather occur through cloud processes.” Excep;ons: windstorms, heat and cold. • Precipita;on (or lack thereof)= flooding, drought
• Severe Thunderstorms, Hail, Tornadoes • Tropical Cyclones • Ice storms
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Mo;va;on: Climate
Hypothesis: “clouds are the largest uncertainty in our ability to predict future climate change.” • Cloud Radia;ve Effects • Cloud Feedbacks • In addi;on to understanding the frequency of extreme weather
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Climate Feedbacks: Cannot be Directly Observed
Mean CMIP3 (2006) CMIP5 (2013)
Planck Water Vapor
Lapse Rate Cloud Albedo
IPCC, 2013 (Ch 9, Hartmann et al 2013) Fig 9.43
Planck ε = σT4 (-‐) Water Vapor +T & RH=C à +H2O (+) Lapse Rate (-‐) Albedo (snow, ice) +T à less snow, ice -‐T à more snow, ice (+) Clouds: Complicated (+)
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Cloud Radia;ve Effects
IPCC 2013 (Boucher et al 2013) Fig 7.7
Rcloudy -‐ Rclear
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S. Ocean Cloud Biases: CMIP5
IPCC AR5, 2013: Fig 9.5
CERES (old) CERES (new: EBAF) Model Mean
Kay et al., 2014
CESM
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How do we represent clouds in models?
• Turbulence parameteriza;ons [Yesterday] – Deep, Shallow convec;on – Boundary layer turbulence (moist)
• Condensa;on, Large scale closure [Richard] • Microphysics (precipita;on) [Jason] + Lots of Plumbing
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Current state of cloud Schemes
Parameteriza;ons (Tendency Generators)
Dynamical core
Connec;ons
Model developer just hanging on!
Deep Convec;on
Microphysics Condensa;on/Frac;on
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Building a unified cloud scheme
• Can we do be&er with clouds? • Connec;ons (plumbing) are oren a problem • Goal: more unified and scale-‐insensi;ve codes
– Not “scale aware”: want to do the same thing at all scales
• How? Unified Schemes.
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Community Atmosphere Model (CAM5)
Dynamics
Boundary Layer
Macrophysics
Microphysics Shallow Convection
Deep Convection
Radiation
Aerosols
Clouds (Al), Condensate (qv, qc)
Mass, Number Conc
A, qc, qi, qv rei, rel
Surface Fluxes Precipitation
Detrained qc,qi
Clouds & Condensate: T, Adeep, Ash
A = cloud fraction, q=H2O, re=effective radius (size), T=temperature (i)ce, (l)iquid, (v)apor
Finite Volume Cartesian
3-‐Mode Liu, Ghan et al
2 Moment Morrison & Ge&elman Ice supersatura;on Diag 2-‐moment Precip
Crystal/Drop Ac;va;on
Park et al: Equil PDF Zhang & McFarlane
Park & Bretherton
Bretherton & Park
CAM5.1-‐5.3: IPCC AR5 version (Neale et al 2010)
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Community Atmosphere Model (CAM5.5)
Dynamics
Unified Turbulence
Radiation
Aerosols
Clouds (Al), Condensate (qv, qc)
Mass, Number Conc
A, qc, qi, qv rei, rel
Surface Fluxes
Precipitation
Clouds & Condensate: T, Adeep, Ash
A = cloud fraction, q=H2O, re=effective radius (size), T=temperature (i)ce, (l)iquid, (v)apor
4-‐Mode Liu, Ghan et al
2 Moment Morrison & Ge&elman Ice supersatura;on Prognos;c 2-‐moment Precip
Crystal/Drop Ac;va;on
Zhang-‐ McFarlane or UNICON
Baseline for CMIP6 model (CAM6)
Convection
CLUBB
Sub-‐Step Microphysics
Finite Volume Cartesian
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Community Atmosphere Model (0.25°)
Dynamics
Unified Turbulence
Radiation
Aerosols
Clouds (Al), Condensate (qv, qc)
Mass, Number Conc
A, qc, qi, qv rei, rel
Surface Fluxes
Precipitation
Clouds & Condensate: T, Adeep, Ash
A = cloud fraction, q=H2O, re=effective radius (size), T=temperature (i)ce, (l)iquid, (v)apor
Spectral Element Cubed Sphere: Variable Resolu;on Mesh
4-‐Mode Liu, Ghan et al
2 Moment Morrison & Ge&elman Ice supersatura;on Prognos;c 2-‐moment Precip
Crystal/Drop Ac;va;on
Working on this as an op;on
CLUBB (includes deep convec;on)
Sub-‐Step Microphysics
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Community Atmosphere Model (CAM5.X++)
Dynamics
Unified Turbulence
Microphysics
Sub Columns
Radiation
Aerosols Mass, Number Conc
A, qc, qi, qv rei, rel
Surface Fluxes
Clouds & Condensate: T, Adeep, Ash
A = cloud fraction, q=H2O, re=effective radius (size), T=temperature (i)ce, (l)iquid, (v)apor
Spectral Element Cubed Sphere
4-‐Mode Liu, Ghan et al
2 Moment Morrison & Ge&elman Ice supersatura;on Prognos;c 2-‐moment Precip
Crystal/Drop Ac;va;on
Now in development: Sub-‐columns
CLUBB
Averaging
Sub-‐Step
Precipitation
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Simula;ng and Observing Clouds
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Ceci n’est pas un nuage
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How does a model see clouds?
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CRM/LES Convec;ve Cloud
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Mesoscale Convec;ve Cloud
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GCM Convec;ve Cloud
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This is a (picture of a) Cloud How do we observe it?
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Conceptual Picture
Houze et al., BAMS, 1989
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Convec;ve Cloud
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Aircrar Convec;ve Cloud
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Radar Convec;ve Cloud
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Lidar (CALIPSO) Convec;ve Cloud
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Radar (CloudSat) Convec;ve Cloud
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IR (MODIS) Convec;ve Cloud
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Vis/IR (MISR) Convec;ve Cloud
This is just cloud frac;on: try it with LWP, re, etc !
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Tradi;onal Evalua;on Methods
• Climate Evalua;on – Use the easy variables: Cloud Frac;on – Means or Climatology
• Weather Evalua;on – Forecast: Looks okay – Composites – Forecast Skill Metrics
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Tradi;onal View: Model – CloudSat (Radar+ Lidar)
Climate Evalua;on: Cloud frac;on…
Model
CloudSat
Difference
MISR
Biases change with data set. May even change sign!
CAM5.4
CAM5.4 -‐ CLOUDSAT
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Mean Metrics
CAM5.4
CAM5.4 – GPCP
Example: Precipita;on
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Weather: Forecast Verifica;on
h&p://www.ecmwf.int/en/forecasts/charts/medium/brier-‐skill-‐score-‐weather-‐parameters
Brier (1950) skill score, or cost func;on
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Weather Forecast Evalua;on Observed and simulated Temperature and Dewpoint
In a Mesoscale model over Texas Votes for your favorite boundary layer scheme anyone?
Hu et al 2010, JAM Reference Redacted (see me later)
Scheme1 Scheme2 Scheme3
2m-‐Temp
Dewpoint
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How do we do this be&er?
Issue: these evalua;ons are loosely related to specific processes. • Evalua;on of Variability, Climate Modes • Process based evalua;on (weather, climate) • Hindcast experiments for Weather, Climate Models – Mul;ple forecasts and forecast increments – Case studies in par;cular regions
• Satellite simulators
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Variability: Diurnal Cycle of Precip TRMM: OBS
CAM5.5 (new)
CAM5.3 (Old)
Thanks to R. Neale, C-‐C. Chen
JJA Precipita;on Rate
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Variability: Tropical Waves Symmetric OLR Spectrum (Wheeler & Kiladis, 1999)
CAM5.5 NOAA-‐OLR
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Process Rate Based Evalua;on: Autoconversion
Observa;ons = Calcula;ons with detailed model and observed size distribu;ons from S. E. Pacific (Terai and Wood, 2013) Current Autoconversion, Alterna;ve Schemes, Fixed Drop Number
Ge&elman, 2015, submi&ed to ACP
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Global Model Forecasts Use a climate model like a weather model. Simulate individual cases. • Specified Dynamics simula;on: 2008-‐2011 • CESM1.2 (CAM5.3): GEOS-‐5 Meteorology, 200km resolu;on (equator)
– Winds and Temps forced – Water species (q, clouds, aerosols) model calculated – Climate is reasonably in balance (-‐1.6 Wm-‐2 TOA)
• Output columns along (and around) HIPPO flight tracks • Sample CESM box containing point & adjacent grid boxes • Do every 10s. Model ;mestep is 1800s (oversample model)
HIPPO Observa;on (10s=3km, 360s~100km)
CESM Grid Box (100km @ 60N)
Hippo flight track with 10s obs ( )
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NSF G-‐V HIPPO Experiment ‘HIAPER Pole to Pole Observa;ons’: mul;ple deployments (different seasons) • Measured mass of liquid & ice and par;cle number concentra;ons • Selected 2 flights with microphysics data in S. Ocean or S. Pacific
Subtropical Winter
S. Ocean in Fall
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Sec;on along H4RF05 (Jun) Flight Track
Blue Shading: CESM Ice clouds
Gray Shading: CESM Liquid Clouds
GV Al;tude Pressure
+ HIPPO Ice (Ni>0) + HIPPO Liq (Nc >0) + HIPPO Liq T<0C
La;tude
◊ + ◊ + + ◊
Green Shading: CESM Liquid <0°C
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Across S. Ocean (H3RF06) April
Obs=Liquid Model = Ice
CAM5.3
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Across S. Ocean (H3RF06) April
Obs=Liquid Model = Ice
CAM5.4 (+ New Mixed Phase Ice Nuclea;on [Hoose et al. 2010])
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Frequency of occurrence of different hydrometeors at cloud top. Solid = Satellite observa;ons. (DARDAR) Dashed = CAM5.4 Ge~ng some super-‐cooled liquid water (SLW), not quite enough Liquid looks good (too much Ice)
Supercooled liquid in CESM
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CESM2 development
Current (CESM1.2-‐CAM5.3)
CAM5.4 (new ice nuclea;on)
CESM2α
DJF SW Cloud Radia;ve Effect Bias v. Satellite (CERES) Bias = too much Absorbed Solar (ASR) Free running (Fixed SST) simula;ons
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Instrument Simulators
• Designed to “simulate” retrievals of a variety of satellites: includes MODIS, MISR, CloudSat, CALIPSO, ISCCP
• Why? Be&er comparisons between models and observa;ons
• Mostly Cloud frac;on, but also cloud microphysics from MODIS
• CFMIP Observa;on Simulator Package (COSP) – CFMIP = Cloud Feedback Model Intercomparison Project
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Why?
• Different instruments have different sensi;vi;es
• Need to sample the model correctly to compare apples to apples
• Examples: – Cloud Frac;on – Liquid Water Path
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Tradi;onal View: Model – CloudSat (Radar+ Lidar)
Cloud frac;on…
Model
CloudSat
Difference
CAM5.4
CAM5.4-‐ CLOUDSAT
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SIMULATOR VIEW Model – CloudSat
Cloud frac;on…
Model
CloudSat
Difference
CloudSat Product only. Fewer clouds (no Lidar). Model higher in tropics.
CAM5.4
CAM5.4-‐ CLOUDSAT
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Cloud Frac;on Differences (τ>0.3)
Model-‐ISCCP
Model-‐MISR
Model-‐MODIS Different bias against different instruments
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LWP: Wrong Message
Tradi;onal comparison of Model LWP field against Microwave Satellite Observa;ons of LWP. Model is low. But cloud forcing looks okay, and the cloud frac;on looks okay. What is going on? Same problem with comparison with MODIS LWP retrievals…
Model
SSMI: UWisc
Difference
CAM5.4
CAM5.4-‐ UWisc
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LWP: Correct Message
Use of the MODIS simulator for LWP: implies an Adiaba;c assump;on for low clouds. The model is not Adiaba;c, but assuming it is Adiaba;c increases LWP, especially over land and storm tracks Now the model is slightly HIGHER than observa;ons (+20%) rather than -‐50% LOW.
COSP Simulated Model
MODIS
Difference
CAM5.4
CAM5.4-‐ MODIS
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Simulate Reflec;vity Simulate observed quan;;es: in this case, Reflec;vity (Z ∝ D-‐6)
Cumula;ve Frequency by Al;tude Diagram (CFAD)
Shows modes of variability and regimes in models and observa;ons Here: thin, low clouds too extensive and high, too much moderate drizzle
Reflec;vity Reflec;vity
Al;tud
e (km)
Frequency (percent) Frequency (percent)
MODEL CLOUDSAT
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Complexi;es
• Sub-‐grid scales are hard to observe – Hard to make model and observa;ons consistent
• Global constraints on clouds are good (& bad) – Benefit of the climate scale – Integral of regimes: zero in on regimes, analogues – Easy to get the right clouds for the wrong reasons
• Simulators provide an integrated view – Some;mes hard to disentangle processes
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Summary Best prac;ces for cloud observa;ons: 1. Use forecast mode for verifica;on 2. Simulate the observa;on with the model (simulators).
Find the cri;cal processes: focus on these processes (field studies). Here: mixed phase, auto-‐conversion
Challenges • Scales: Cloud scales hard to observe, harder to simulate • No easy way to get simula;ons to work across scales
– We are trying: Some dogs are be&er than others for this – Fewer dogs may be be&er (less plumbing)
• Improving processes some;mes breaks key metrics: the challenge of rippling compensa;ng errors – Some dogs work/play be&er with others
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Thanks!
This is how René Magri&e sees a cloud… The Empire of Lights, R. Magri&e, Peggy Guggenheim Museum, Venice