Post on 03-Feb-2016
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Investigation of Microphysical Parameterizations of Snow and Investigation of Microphysical Parameterizations of Snow and Ice in Arctic Clouds During M-PACE through Model-Ice in Arctic Clouds During M-PACE through Model-
Observation ComparisonsObservation Comparisons
Amy SolomonAmy Solomon1212
In collaboration with:In collaboration with:
Hugh MorrisonHugh Morrison33, Ola Persson, Ola Persson1212, Matt Shupe, Matt Shupe1212, Jian-Wen Bao, Jian-Wen Bao11
11NOAA/Earth System Research Laboratory, Boulder, ColoradoNOAA/Earth System Research Laboratory, Boulder, Colorado
22CIRES/University of Colorado, Boulder, ColoradoCIRES/University of Colorado, Boulder, Colorado33National Center for Atmospheric Research, Boulder, ColoradoNational Center for Atmospheric Research, Boulder, Colorado
Investigation of Microphysical Parameterizations of Snow and Investigation of Microphysical Parameterizations of Snow and Ice in Arctic Clouds During M-PACE through Model-Ice in Arctic Clouds During M-PACE through Model-
Observation ComparisonsObservation Comparisons
Amy SolomonAmy Solomon1212
In collaboration with:In collaboration with:
Hugh MorrisonHugh Morrison33, Ola Persson, Ola Persson1212, Matt Shupe, Matt Shupe1212, Jian-Wen Bao, Jian-Wen Bao11
11NOAA/Earth System Research Laboratory, Boulder, ColoradoNOAA/Earth System Research Laboratory, Boulder, Colorado
22CIRES/University of Colorado, Boulder, ColoradoCIRES/University of Colorado, Boulder, Colorado33National Center for Atmospheric Research, Boulder, ColoradoNational Center for Atmospheric Research, Boulder, Colorado
Models that include prognostic equations for cloud water, rain, ice, and snow mixing ratio and number concentration (two-moment) successfully model horizontally extensive, persistent mixed-phase Arctic stratocumulus
Observations
Two-Moment
One-Moment
(After Morrison and Pinto 2006)
Impact of Microphysical Parameterization on Impact of Microphysical Parameterization on Simulations of Arctic StratocumulusSimulations of Arctic StratocumulusImpact of Microphysical Parameterization on Impact of Microphysical Parameterization on Simulations of Arctic StratocumulusSimulations of Arctic Stratocumulus
Climate models fail to produce Arctic clouds that maintain liquid water at low temperatures (e.g., Prenni et al. 2006, Inoue et al.
2006, Sandvik et al. 2007, Klein et al. 2008)
Since cloud liquid water causes an increase in the downwelling longwave radiation and a decrease in the incoming shortwave radiation
Inadequate simulations of Arctic clouds causes significant errors in the modeled surface energy budget
ObjectiveObjectivessObjectiveObjectivess
Process-oriented validation of microphysical parameterizations
Specifically, in this study:
Verify the simulations of mixed-phase clouds and boundary layer structure during M-PACE with the WRF V2.2
Comparisons of LWP/IWPComparisons of SWD/LWDVertical structure and temporal variability of LWC and IWCVariability of vertical velocitiesVariability of LWP/IWP
Investigate the sensitivity of the model simulations to the parameterization of snow/ice
Sensitivity to mid-latitude N0s
Sensitivity to two-moment N0s
Sensitivity to approximate observed N0i
Prognostic variables include mixing ratios and number concentrations of cloud ice, cloud droplets, snow, graupel, and rain Hydrometeors have the form of a complete gamma size distribution:(D) = N0DPce-D
= cN(Pc+d+1)1/d
q(Pc + 1) = the Slope Parameter
N0 = NPc+1 is the Intercept Parameter
Pc is the Spectral Parameter (Pc=0 for ice, snow, rain, graupel)
Bulk density of ice = 0.5 g cm-3 Bulk density of snow = 0.1 g cm-3
(Now available as a standard option in WRF V3)
Microphysical Microphysical SchemeSchemeMicrophysical Microphysical SchemeScheme
Control Set-Control Set-upupControl Set-Control Set-upup
Weather Research Forecast Model V2.2Nested 18/6/1 km horizontal grids50 vertical levels (20 levels below 800 hPa)Morrison Two-Moment microphysicsCAM Radiation3D PBL mixing in the 1km grid (YSU in 18/6 km)NOAH LSM
Control Set-up and Horizontal Structure in 1 km Control Set-up and Horizontal Structure in 1 km DomainDomainControl Set-up and Horizontal Structure in 1 km Control Set-up and Horizontal Structure in 1 km DomainDomain
Weather Research Forecast Model V2.2Nested 18/6/1 km horizontal grids50 vertical levels (20 levels below 800 hPa)Morrison Two-Moment microphysicsCAM Radiation3D PBL mixing in the 1km grid (YSU in 18/6 km)NOAH LSM
Impact of Horizontal Resolution on the Maintenance of Impact of Horizontal Resolution on the Maintenance of Liquid WaterLiquid WaterImpact of Horizontal Resolution on the Maintenance of Impact of Horizontal Resolution on the Maintenance of Liquid WaterLiquid Water
1 KM 50 KM24
-hou
r av
erag
e1-
hour
ave
rage
Vertical Velocity and Growth of Cloud Vertical Velocity and Growth of Cloud DropletsDropletsVertical Velocity and Growth of Cloud Vertical Velocity and Growth of Cloud DropletsDroplets
Hourly-averaged 2M vertical velocity at Barrow from Oct 2004 9 12Z to 10 11Z, in units of cm s-1
Hourly-averaged 2M vapor deposition with LWC is shown with dashed blue contours, in units of g m-3
W (cm/s) Vapor Droplets (g/m3/hour)
STD(W) at different averaging periods
Retrievals T
wo-M
oment
Validation of Dominant Timescales for Vertical Validation of Dominant Timescales for Vertical Motions and Ice/Liquid InteractionsMotions and Ice/Liquid InteractionsValidation of Dominant Timescales for Vertical Validation of Dominant Timescales for Vertical Motions and Ice/Liquid InteractionsMotions and Ice/Liquid Interactions
Correlation between IWP and LWP at different averaging periodsModel underestimates variability on
timescales shorter than 2 minutes
And overestimates variability on timescales between 2-30 minutes
An aliasing of fast processes to longer timescales?
STD(W) at different averaging periods
Retrievals T
wo-M
oment
Validation of Dominant Timescales for Vertical Validation of Dominant Timescales for Vertical Motions and Ice/Liquid InteractionsMotions and Ice/Liquid InteractionsValidation of Dominant Timescales for Vertical Validation of Dominant Timescales for Vertical Motions and Ice/Liquid InteractionsMotions and Ice/Liquid Interactions
Correlation between IWP and LWP at different averaging periods
Observations show significant correlations between IWP and LWP for timescales shorter than 12 minutes
The model shows significant correlations between IWP and LWP on all timescales
Another indication that the model is aliasing fast processes to longer timescales?
Validation of Liquid and Ice Water Validation of Liquid and Ice Water ContentContentValidation of Liquid and Ice Water Validation of Liquid and Ice Water ContentContent
Retrievals Morrison 2M MicrophysicsIW
C
LW
C
Validation of Water Paths and Surface Validation of Water Paths and Surface RadiationRadiationValidation of Water Paths and Surface Validation of Water Paths and Surface RadiationRadiation
SW
LW
P
IWP
LW
Observations2M Morrison1M Morrison
Validation of Cloud Droplet, Snow, and Ice Size Validation of Cloud Droplet, Snow, and Ice Size DistributionsDistributionsValidation of Cloud Droplet, Snow, and Ice Size Validation of Cloud Droplet, Snow, and Ice Size DistributionsDistributions
Two-moment scheme does a good job of simulating the observed cloud droplet and snow size distributions
In general, N0 is not known apiori and varies in space and time!
N0 for ice is underestimated by an order of magnitude…
Aircraft measurements Two-Moment
(After McFarquhar et al. 2007)
N0i?
N0s?
Mixing Ratio of Snow
Mixing Ratio of Cloud Droplets
Two-moment N0s (solid) One-moment N0s=8.e5 m-4 (dash)
Averaged for LWC>0.01 g m-3 at Barrow, Alaska
Impact of Specifying Two-Moment Impact of Specifying Two-Moment
NN0s0s
Impact of Specifying Two-Moment Impact of Specifying Two-Moment
NN0s0s
Two-moment N0i (solid)One-moment N0i=5.e7 m-4 (dash) at Barrow, Alaska
Aircraft Measurements of ice number concentration (d >53 microns):Mean (white line) and +/- one standard deviation (grey shading)
Impact of Specifying Observed Impact of Specifying Observed
NN0i0i
Impact of Specifying Observed Impact of Specifying Observed
NN0i0i24-hour mean ice number concentration 24-hour mean snow number concentration
FindingFindingss
FindingFindingss
1) Morrison two-moment microphysics does a good job of simulating the LWC in Arctic stratocumulus observed during M-PACE, as well as, the dominant initiation and growth mechanisms for cloud water, snow, and ice.
2) Cloud water forms at the cloud base and grows in updrafts. Coarser resolution runs (>18 km) do not resolve this process because the vertical velocity needed to lift the cloud water is underestimated, resulting in clouds that form closer to the ground.
3) Specifying N0s to typical mid-latitude values depletes the cloud liquid water--The
two-moment scheme is able to reproduce the observed LWC because it does a good
job of predicting N0s, which is advantageous since N0 is poorly constrained by
observations and varies in space and time.
4) Ice number concentration is underestimate by an order of magnitude in the two-moment simulation and there is an indication that variability is being aliased to longer (> 2 minutes) timescales.
5) Specifying the ice size distribution observed during this period in a one-moment simulation acts to deplete the liquid water, indicating that the vapor deposition to ice may be occurring too rapidly in the model.
6) Specifying the two-moment snow size distribution in a one-moment simulation results in similar snow mixing ratio and number concentration but a depletion of cloud water due to the coupling between mixing ratio and number concentration in the one-
moment scheme.
Structure of Arctic clouds
Mixed-phase clouds dominate the low-cloud fraction within the Arctic during the colder three-quarters of the year (Curry et al. 2000; Intrieri et al. 2002; Uttal et al. 2002; Wang et al. 2005)
Arctic low-level mixed-phase clouds tend to be long-lived and are not observed to glaciate quickly due to the Bergeron process (Pinto 1998; Hobbs and Rangno 1998; Curry et al. 2000)
Liquid layer were observed in the M-PACE experiment at temperatures down to -34°C (Verlinde et al. 2007)
Mixed-phase clouds are observed to Mixed-phase clouds are observed to occur in regions of both strong and occur in regions of both strong and weak surface forcing, indicating that the weak surface forcing, indicating that the cause of these cold, ice-precipitating cause of these cold, ice-precipitating clouds is microphysical in natureclouds is microphysical in nature ((Harrington et al. 1999; Morrison et al. 2005)Harrington et al. 1999; Morrison et al. 2005)
(After Shupe et al. 2006)
Observations of Mixed-Phase Arctic Observations of Mixed-Phase Arctic StratocumulusStratocumulusObservations of Mixed-Phase Arctic Observations of Mixed-Phase Arctic StratocumulusStratocumulus
Case Studied: Mixed-Phase Arctic Cloud Case Studied: Mixed-Phase Arctic Cloud ExperimentExperiment Case Studied: Mixed-Phase Arctic Cloud Case Studied: Mixed-Phase Arctic Cloud ExperimentExperiment
A B
160ºW 150ºW 140ºW
Intensive Observations Oct 6-11 2004:Intensive Observations Oct 6-11 2004:Measurements at DOE ARM NSA SiteMeasurements at DOE ARM NSA Site+ High Spectral Resolution Lidar+ High Spectral Resolution Lidar+ Atmospheric Emitted Radiance + Atmospheric Emitted Radiance InterferometerInterferometer+ Radiosonde launches+ Radiosonde launches+ Two Instrumented Aircraft with a + Two Instrumented Aircraft with a Compliment of Cloud Physics ProbesCompliment of Cloud Physics Probes
A strengthening high-pressure system north of Alaska
Caused air to flow from pack ice over the open Beaufort Sea to the North Slope of Alaska
Forcing roll clouds that extended from the pack ice to the North Slope of Alaska
These clouds are aligned closely to the direction of the boundary layer winds
With wavelength of 10-15km and a PBL 1km over the ocean and <1km inland
These clouds were mixed phase with total water content dominated by liquid hydrometeors throughout the cloud layer
MODIS visible image Oct 9 0Z 2004