The Response of Marine Boundary Layer Clouds to Climate Change in a Hierarchy of Models

The Response of Marine Boundary Layer Clouds to Climate Change in a

Hierarchy of Models

Chris Jones Department of Applied Math

Advisor: Chris BrethertonDepartments of Applied Math and Atmospheric Sciences

VOCALS RF05, 72W, 20S

Overview

• Introduction: Marine boundary layer (MBL) clouds and climate sensitivity

• Idealized local case studies in a hierarchy of models

• The well-mixed MBL from observations• Comparison of model responses to changes in

CO2 and temperature• Summary of proposed future work

Earth’s Radiation Budget:R = Absorbed Solar Radiation – Outgoing Longwave Radiation

(NASA)

Marine boundary layer clouds especially important because…

1. They’re reflective at visible wavelengths

MBL clouds


(images courtesy of Chris Bretherton)



2. They cover a lot of area

CloudFraction

Cloud forcing = R(clear sky) – R(all sky)

Global net cloud radiative forcing ~ -20 W m-2 (Loeb et al, 2009)

Compared to CO2 ~ 2 W m-2


Marine boundary layer clouds especially important because

1. They’re shiny (reflect incoming solar radiation)


3. They’re hard to realistically represent in global climate models• Interplay between

dynamics and physics• Nonlinear• Turbulent• Physics must be

parameterized

Climate Change: Response to radiative forcingR = Absorbed Solar Radiation – Outgoing Longwave Radiation

Δ 𝑅=Δ𝑄+𝜆 Δ𝑇 𝑠: Global mean equilibrium surface temperature change (“sensitivity to ”)

Example: If results in more low cloud, that means more reflected solar radiation, less warming ( is smaller for a given ) and thus a negative cloud feedback

If radiation budget is perturbed by a radiative forcing (e.g., doubling CO2), the Earth’s mean surface temperature adjusts until balance is restored:

Feedback parameter

W m-2 K-1 (Planck)

Cloud contribution most uncertain (next slide)

Cloud feedbacks dominate climate sensitivity uncertainty in GCMs

Clouds dominate overall climate feedback uncertainty

Clouds:- Positive feedback, - Large spread between models

Bony et al. (2006)




Bony et al. (2006)



Low clouds dominate cloud feedback uncertainty

Soden and Vecchi (2011)


Bony et al. (2006)

Parameterizations of Physical Processes Make Profound Impact

3.2K climate sensitivity 4.0 K climate sensitivity

(Gettelman et al., 2011)

UW turbulence and shallow convection parameterizations largely responsible for increase in climate sensitivity from CAM4 to CAM5 – can our analysis help explain this?

Equilibrium response to 2xCO2

Objectives of This Research• Use a localized, idealized column-oriented analysis of prototypical MBL cloud

regimes to identify and evaluate MBL cloud-climate radiative response mechanisms• Hierarchy of models:

– Large eddy simulation (LES): high resolution cloud resolving model – closest we have to “observations” in local climate change simulations

– Single-column model (SCM): ties results to GCM– Mixed-layer model (MLM): simplified model for interpretive purposes

• Seek to relate SCM back to parent GCM• Scientific Relevance: Understanding mechanisms of change in GCMs is pre-requisite

for constraining through observation and/or improving parameterizations.• Mathematical Relevance: Investigate impacts of various parts of model formulation

(e.g., subgrid parameterizations, model resolution, applied large-scale forcings); to what extent can models be used to interpret the behavior of other models?

Case studies drawn from CGILS Intercomparison

• S12: Shallow Stratocumulus (Sc)• Well-mixed BL

• S11: Transition between Sc and shallow cumulus (Cu)• Onset of BL decoupling• Cu rising into Sc

• S6: Shallow Cu

Zhang et al (2010)

Hierarchy of modelsGCM (CAM5)

SCM (SCAM5)

LES (SAM)

Image courtesy of NOAA

(S6, courtesy of Peter Blossey)

SCAM5 Vertical Resolution

MLM

Primitive equations for liquid static energy () and total water mixing ratio () in this

study

Large-scale advection Subsidence Tendencies due to physical processes, e.g.,• Precipitation• Radiation and clouds• Microphysics• Turbulence

Dynamics

• Moist static energy • Water mixing ratio • Inversion (cloud top)

Δ𝑞𝑡

(Stevens, 2007)

Mixed-layer model equations


Advective cooling/drying

Entrainment

surface fluxes Radiation

Precipitation

Δ𝑞𝑡

(Stevens, 2007)

October 2008-November 2008

(http://www.atmos.washington.edu/~robwood/VOCALS/vocals_uw.html)

How reasonable is the well-mixed assumption?

Previous project studied the extent of well-mixed vs. decoupled boundary layers using aircraft data from VOCALS field experiment

• Classified flight legs as well-mixed or decoupled based on gradient of moisture and temperature quantities

Decouplingmetric (s)

Subcloud layer

Cloud layer

𝛿𝑞𝛿𝜃ℓWell-mixed Decoupled

Profile-based decoupling classification: Well-mixed if g kg-1 and KApproximately 30% of region was well-mixed.Well-mixed regions correspond to shallower boundary layers.These conditions are met at S12 location.

Jones et al. (2011)

Case setup and proposed sensitivity studies

Simulation setup• Diurnally averaged

summertime insolation• Models run to steady-state• Large-scale forcings

specified from observations:– Horizontal divergence– Subsidence– Sea surface temperature– Wind profile

CGILS sensitivity studies• Control (CTL)

– Mimics current climate

• 4xCO2 concentration (4xCO2):– Captures “fast” adjustment

• Uniform +2K temp. increase: – Captures temperature-

mediated response– Reduced subsidence (P2K)– Subsidence as in CTL (P2K

OM0)

S12 Results: Cloud FractionLES Results

from CGILSintercomparison

MLM Results

Preliminary S12 Results: Profiles

SAM LES:

Liquid static energy Moisture Cloud liquid

MLM:

SAM LES:

Liquid static energy Moisture Cloud liquid

MLM:

SCAM5:(L80)

• All models exhibit similar steady-state mean sensitivities:• 4xCO2 has lower inversion, thinner cloud (positive cloud feedback)• P2K deepens and thickens relative to control (negative cloud feedback)• P2K OM0 thinner than P2K and slightly thinner than CTL (positive cloud

feedback)• Subsidence (large scale dynamics) plays dominant role in P2K response

Preliminary S12 Results: Summary [m] [g m-2] [Wm-2]

SAM (LES) -111 -13 +28SCAM5 (SCM) -176 -12 +54MLM -68 -9 +14

SAM (LES) +109 +2 -2SCAM5 (SCM) +70 +1 -7MLM +114 +32 -30

4xCO2

P2K

SAM (LES) -38 -9 +20SCAM5 (SCM) +5 -5 +18MLM -4 -4 +8

P2K OM0

MLM 4xCO2 Sensitivity Mechanism:

Increased down-welling LW radiationdecreased cloud top radiative

cooling (~10% decrease) Less turbulence (i.e., less

entrainment)Lower zi

Cloud thickness decreases

CTL

4xCO2

CTL

4xCO2

SCAM5 S12 Resolution SensitivityDefault CAM5 Resolutiondoesn’t sustain a cloud Higher resolution does

Clou

d fra

ction

Future Work– Apply MLM to interpreting other LESs involved in CGILS

case study– Fully investigate SCAM5 S12 behavior

• What’s driving the resolution sensitivity?– Expand analysis to other locations (MLM may not apply)– Parameter-space representation with SCAM

• Use SST, Free troposphere lapse rate, CO2 and/or subsidence as control parameters

– Find a way to relate the local cloud response in SCAM to the sensitivity in its parent GCM

Questions?(MODIS satellite image)

Additional slides

Future Work (plenty to keep me busy)

– Apply MLM to interpreting other LESs involved in CGILS case study (hypothesis: by tuning entrainment efficiency, can I reproduce their mean properties / sensitivities?)

– Dig into roots of SCAM5 S12 sensitivity (interpret w/MLM when appropriate)• What’s driving the resolution sensitivity?

– Expand analysis to other locations (MLM may not apply)– Parameter-space representation with SCAM, following approach of

Caldwell and Bretherton (2009) MLM study• Use SST, Free troposphere lapse rate, CO2 and/or subsidence as control

parameters– Find a way to relate the local cloud response in SCAM to the

sensitivity in its parent GCM

Additional Slides

• CRF, adjusted CRF, etc.

SCAM5 Default Resolution vs. VOCALS radar strip

SAM LES Equations

Khairoutdinov and Randall (2003)

• Prognostic TKE SGS model• Diagnostic cloud water, cloud ice, rain,

and snow• Periodic horizontal domain, surface

fluxes from Monin-Obukhov similarity theory

• ISCCP cloud simulator• Parallel (MPI)

The proposal (remember the proposal? This is a presentation about the proposal …)

• Use MLM to interpret output from other LESs (can “tune” parameterizations and entrainment closure as needed)

• Investigate sensitivities in each model for each location• Map out primitive parameter-space representation

using SCM (like CB09)• Ultimately, most concerned with SCAM, b/c it connects

directly to GCM – to what extent can we use this analysis to shed light on the low cloud-climate mechanisms in CAM5?

Primitive equations for liquid static energy () and total water mixing ratio () in this

study

Large-scale advection Subsidence Tendencies due to physical processes, e.g.,• Precipitation• Radiation and clouds• Microphysics• Surface fluxes• Turbulence

Primitive equations

• LES:

• SCAM:

Mixed-layer model equationsPrognostic equations:

Entrainment closure:

• (Moist static energy)• (total water mixing ratio)• : Inversion height

• (vertical turbulent flux of x)• (radiation flux)• (precipitation)

• A: entrainment efficiency

Mixed-layer model equationsPrognostic equations:

Mixed Layer Assumptions:• Vertically uniform profiles below inversion• Surface fluxes from bulk transfer model• Inversion flux given by • No turbulence above inversion• Precipitation parameterized following Wood et al• Radiation from RRTMG radiative transfer model• Subsidence, large scale divergence, SST, surface

pressure, and free troposphere h, q specified at all times

Mixed-layer model equations:

Advection(cooling,drying) Entrainment warming/drying

Latent heat flux Precipitation

Radiative coolingSensible heat fluxsubsidence

EPIC 2001 (Bretherton, et al.)

Contributing Mechanisms for MBL BalanceSubsidence

Advection

Mixed-layer model:

• Well mixed q and h moist thermo variables => vertically uniform.– Bulk aerodynamic formulas for surface flux– Inversion fluxes based on thermo jumps

Advection(cooling,drying) Entrainment warming/drying

Latent heat flux Precipitation

Radiative coolingSensible heat fluxsubsidence

Sc (top) vs. Cu (bottom) MBL structure (Stevens et al 2007; Stevens 2006)

MLM time series for S12

Relevant previous column modeling studies

• Caldwell and Bretherton• Zhang and Bretherton• …

Model run specifics

• Grid resolution– CESM 1.0 (CAM5): 1 deg = 0.9 deg x 1.25 deg x 30

levels– (i.e., ~100 km x 137 km x … [variable])

• Time steps (?)• Length of integration• Numerics / miscellaneous

Outline• Introduction

– Climate sensitivity, feedbacks, and cloud radiative forcing– Why are low clouds important (to climate system, climate sensitivity)?– What has been done, and where does this study fit in?– Feedback flow chart (?)

• Proposal for this study: Localized case studies using a hierarchy of models– CGILS cases– Primitive equations– An assortment of models

• GCM (global models, under-resolved,…)• SCM (single column of the GCM)• LES (high-resolution column model – resolve largest, most energetic eddies, models subgrid)• MLM (idealized reduced order model that uses

– Decoupling work pepper VOCALS throughout

• MLM comparison with LES for S12 (and maybe SCAM?)• Proposed dissertation topic

Outline• Introduction

– What is climate sensitivity and why do we care?– Why are low clouds important (to climate system, climate sensitivity)?– What has been done, and where does this study fit in?– Feedback flow chart (?)

• Proposal for this study– CGILS cases– Primitive equations– An assortment of models

• GCM (global models, under-resolved,…)• SCM (single column of the GCM)• LES (high-resolution column model – resolve largest, most energetic eddies, models subgrid)• MLM (idealized reduced order model that uses

– Decoupling work pepper VOCALS throughout

• MLM comparison with LES for S12 (and maybe SCAM?)• Proposed dissertation topic

Our approach:• Consensus that we need better understanding of the processes

underlying low-cloud response to climate change (i.e., GCM intercomparison studies demonstrate clearly the global average low cloud response is a big uncertainty, but individual models differ in parameterizations of cloud processes, and climate-change output diverges widely between models)

• Use IDEALIZED LOCAL CASE STUDIES (drawn from CGILS intercomparison) to investigate cloud sensitivity in a hierarchy of models (LES, SCM, and MLM) to climate-change inspired tests, with the goals of:– Understanding mechanisms behind cloud sensitivity (i.e., do LES and SCM

agree? Can this behavior be constrained by observations? Is improved parameterization, informed by LES necessary?)

– Connecting these back to the GCM behavior of a given model.

Proposal: use a hierarchy of models to investigate low cloud response to climate perturbations

• Local analysis:– Focus on 3 regions used in CGILS intercomparison

study representing 3 low cloud regimes with idealized large scale forcings

– Use 3 types of column models to investigate cloud sensitivity to a variety of perturbations:

• Ultimate goal: Connect these back to GCM

Subcloud legs (actual cloud base – “well-mixed” cloud base)

drizzle

Profiles Decouplingmetric (s)

Surface layer

Cloud layer


C-130 flight path (grey)Cloud base (lidar-derived)LCL (“well-mixed cloud base”)

Radar reflectivity(drizzle proxy)

(courtesy of Rob Wood)

We use vertical profiles and subcloud level legs

Inversion Jumps• Lock (2009) and others

have suggested high values of

induce strong entrainment and Sc cloud breakup.

• Strong entrainment might also favor decoupling.

Δ𝑞𝑡 Inversion base

Inversion “top”

Decoupling not correlated with inversion jump parameter

• Use REx C-130 profiles to calculate jumps/decoupling, adjacent subcloud legs to calculate cloud fraction. Restrict to flights before 10:00 LT in left panel.

• κ > 0.4 often (but not always) goes with broken cloud. • For κ < 0.5 there is no obvious correlation of κ and decoupling.• POC and non-POC distributions overlap

Blue = well-mixedRed = decoupledHollow = POCDash = Lock (2009) LES results

Shiny clouds

MODIS Visible Image

Marine Boundary Layer (MBL) clouds:

CGILS Cases (focus on S12 this talk)

• S12: Shallow Stratocumulus (Sc)• Well-mixed BL => mixed-layer

model appropriate• Focus of remainder of this talk

• S11: Transition between Sc and shallow cumulus (Cu)• Onset of BL decoupling• Cu rising into Sc

• S6: Shallow Cu


horizontal advection

Entrainment

surface fluxes Radiation

Precipitation

Δ𝑞𝑡

Marine Boundary Layer (MBL) Clouds

(Infrared satellite image, courtesy of Rob Wood)

Marine Boundary Layer (MBL) Clouds

NASA MODIS Satellite Image

Questions?

Marine boundary layer clouds:

1. Reflect incoming solar radiation2. Cover a large fraction of the surface

MODIS visible satellite image

Reflective

Clouds in climate models

- change in low cloud amount

for 2CO2

from Stephens (2005)

GFDL

CCM

model number

Decouplingmetric (s)

Subcloud layer

Cloud layer


Approximately 30% of profiles in VOCALS-REx were well-mixed (blue)

thickness the cloud would have if it was well-mixed


Feedback parameter

Δ 𝑅=Δ𝑄+𝜆 Δ𝑇 𝑠

Radiative forcing (e.g., increased CO2)

: Global mean equilibrium surface temperature change


If radiation budget is perturbed by a radiative forcing , the Earth’s mean surface temperature adjusts until balance is restored:

Feedback parameter

W m-2 K-1 (Planck)

Cloud contribution most uncertain


(Infrared satellite image, courtesy of Rob Wood)





feedback parameter

Δ 𝑅=Δ𝑄+𝜆 Δ𝑇 𝑠

Radiative forcing (e.g., increased CO2)

Climate sensitivity : Global mean equilibrium surface temperature change due to 2xCO2


Likewise, less low cloud => positive feedback (amplifies warming)

If radiation budget is perturbed by a radiative forcing , the Earth’s mean surface temperature adjusts until balance is restored:



Low clouds dominate cloud feedback uncertainty


Bony et al. (2006) Soden and Vecchi (2011)


(NASA)

Marine boundary layer clouds especially important because ...

MBL clouds

IPCC (2007)

The Models• LES (high resolution): System for Atmospheric Model (SAM)

– High resolution cloud resolving model– Largest, most energetic eddies resolved– Subgrid-scale turbulence is modeled– The closest we have to “observations” for climate change simulations– Parallel effort by Peter Blossey and Chris Bretherton for CGILS LES intercomparision

• SCM (single column of global model): SCAM5 (CAM5 GCM, operating in single column mode)– Single grid column from the GCM– Approximately 1 degree horizontal resolution, 30 vertical levels– Parameterize subgrid physical processes

• MLM (idealized, interpretive model):– Idealized reduced order model applicable in Sc region (S12) when MBL remains “well-mixed”– When applicable, good for diagnosing / interpreting sensitivities in other models


(NASA MODIS visible satellite image in Eastern Pacific)



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The Response of Marine Boundary Layer Clouds to Climate Change in a Hierarchy of Models