Impact of microphysics on cloud-system resolving model simulations of deep convection and SpCAM

Full text

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Impact of microphysics on cloud-system resolving model simulations of deep convection and SpCAM

Hugh Morrison and Wojciech Grabowski NCAR* (MMM Division, NESL)

Marat Khairoutdinov Stony Brook University

*NCAR is sponsored by the National Science Foundation

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Outline

1. Motivation

- Indirect aerosol effects

2. CSRM simulations of convective radiative quasi- equilibrium

3. CSRM simulations of real case study (TWP-ICE)

4. SpCAM simulations with new microphysics scheme

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Microphysics plays a key role in cloud, climate and weather models

Stephens (2005)

- Latent heating/cooling

(condensation, evaporation, deposition, sublimation, freezing, melting)

- Condensate loading

(mass of the condensate carried by the flow)

- Precipitation

(fallout of larger particles)

- Coupling with surface processes

(moist downdrafts leading to surface-wind gustiness, cloud shading)

- Radiative transfer

(mostly mass for absorption/emission of LW, particle size also important for SW)

- Cloud-aerosol-precipitation interactions

(aerosol affect clouds: indirect aerosol effects, but clouds process aerosols as well)

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maritime (“clean”) continental (“polluted”) cloud

base cloud

updraft

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Ship tracks: spectacular example of indirect effects caused by ship exhausts acting as CCN

(long-lasting, feedback on cloud dynamics?)

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IPCC 2007; Synthesis Report

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Issues:

- Difficulty of current observational techniques in untangling relationship between aerosols and

clouds on spatial and temporal scales relevant to climate:

correlation versus causality

- Traditional general circulation models cannot resolve the cloud dynamics that are critical to cloud-aerosol-precipitation interactions

parameterized microphysics in

parameterized clouds  parameterization

2

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- Aerosol indirect effects are especially uncertain for deep convective clouds because of the complexity of microphysical processes (both liquid and ice) and close coupling between cloud-scale dynamics and

microphysics.

- High resolution cloud models (GCRMs and MMF) can resolve deep-convective and mesoscale motion and

therefore are better suited to the problem.

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-

Rosenfeld et al.

Science, 2008

Example of hypothesized aerosol-

microphysics- dynamics

interactions in deep convection

Koren et al. (2010)

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single-cloud reasoning versus

cloud-ensemble reasoning

Arguably, the cloud-ensemble reasoning is more appropriate for climate.

Another way to think about the problem: single-process reasoning (e.g.,

microphysics) versus the system-dynamics approach. Only the latter includes all the feedbacks and forcings in the system.

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Convective-radiative quasi-equilibrium is the simplest system that includes interactions

between clouds and their environment

(“system-dynamics approach”).

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Convective-radiative quasi-equilibrium mimicking planetary energy budget using a 2D cloud-system resolving model

61 levels 100 columns (200 columns)

Surface temperature = 15° C Surface relative humidity = 85%

Surface albedo = 0.15 solar input

342 Wm-2

horizontal distance

height

Grabowski J. Climate 2006, Grabowski and Morrison J. Climate 2010 (submitted)

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Numerical model:

Dynamics: 2D super-parameterization model (Grabowski 2001)

Radiation: NCAR’s Community Climate System Model (CCSM) (Kiehl et al 1994) in the Independent Column Approximation (ICA) mode

100-200 columns (Δx=1-2km) and 61 levels (stretched; 12 levels below 2 km; top at 18-24 km)

Grabowski 2006; Grabowski and Morrison 2010

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Simulations with the new two-moment bulk microphysics:

Warm-rain scheme of Morrison and Grabowski (JAS 2007,

2008a) predicts concentrations and mixing ratios of cloud water and rain water; relatively sophisticated CCN activation scheme, contrasting pristine and polluted CCN spectra, and better

representation of the homogeneity of subgrid-scale mixing.

Ice scheme of Morrison and Grabowski (JAS 2008b; 2010)

predicts concentrations and two mixing ratios of ice particles to keep track of mass grown by diffusion and by riming;

heterogeneous and homogeneous ice nucleation with the same IN

characteristics for pristine and polluted conditions.

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Cloud water and drizzle/rain fields

Ice field Solid: polluted Dashed: pristine

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Grabowski

J. Climate 2006 (G06)

Grabowski and Morrison

J. Climate 2010 (submitted) (GM10)

Solid: polluted Dashed: pristine

Horizontal bars: standard deviation of temporal evolution (measure of statistical significance of the difference)

Cloud fraction profiles

G06 – 1-moment microphysics

GM10 – 2-moment microphysics

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Pot. temperature profiles in the lower troposphere:

Dashed: domain-averaged

Solid: within raining regions only

Mean of rainy grids

Domain mean

G06 GM10 GM10: 1-moment rain

Deviation from surface temperature

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• Idealized convective-radiative quasi-equilibrium simulations using the two-moment bulk microphysics result in the mean

atmospheric state similar to previous simulations with one-moment microphysics.

•  Bowen ratio: two-moment microphysics has a different impact on cold-pool temperature and moisture due to smaller rate of rain

evaporation.

•  Precipitation: Little difference in atm. radiative cooling between PRISTINE and POLLUTED little impact of aerosol on surface precipitation

•  TOA net shortwave: between PRISTINE and POLLUTED is down to about 9 Wm

-2

from about 20 Wm

-2

in one-moment

simulations.

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Next we move to a less idealized, time-evolving framework…

less stringent constraints relative to CRE

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16-day, 2D simulations of TWP-ICE, using observed large-scale forcing

•  similar setup to other GCSS case studies

97 levels 200 columns

Surface temperature = 29° C

Prescribed large-scale forcing of T, qv, 6 hr nudging of u to observations

horizontal distance

height

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Tropical Western Pacific – International

Cloud Experiment (TWP-ICE)

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-

Question: how does parameterization of

microphysics and model resolution in a CSRM impact simulation of aerosol effects on clouds and

precipitation for tropical deep convection?

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-

•  BASE Baseline configuration (Morrison and Grabowski 2007; 2008a,b)

•  FRZ  Heterogeneous droplet freezing of Bigg (1953) replaced by Barklie and Gokhale (1959), ~ factor of 100 reduction in freezing rate

•  GRPL  Graupel density decreased by ~ factor of 3

•  Resolution  Horizontal grid spacing varied from 2 km to 500 m

Aerosol

specification, similar to

Fridlind et al.

(2010, in prep)

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PRISTINE (SOLID LINES)

POLLUTED (DOTTED LINES)

•  Impact on surface precipitation

ACTIVE MONSOON SUPPRESSED MONSOON BASE FRZ GRP OBS

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PRISTINE (SOLID LINES)

POLLUTED (DOTTED LINES) ACTIVE MONSOON

SUPPRESSED MONSOON

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PRISTINE (SOLID LINES)

POLLUTED (DOTTED LINES)\

OBSERVED

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IMPACT ON

MICROPHYSICS

PRISTINE (SOLID)

POLLUTED (DOTTED)

DROPLET CONCENTRATION ICE CONCENTRATION

ICE WATER CONTENT LIQUID WATER CONTENT

DROPLET EFF RADIUS ICE EFF RADIUS

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PRISTINE (SOLID LINES) POLLUTED (DOTTED LINES)

•  Impact on TOA radiative fluxes

TOA upwelling SW

BASE FRZ GRP OBS

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What is the role of internal variability in explaining these differences?

•  Run 5-member ensemble of simulations (pristine and polluted) with different initial seed for random noise

ACTIVE MONSOON SUPPRESSED MONSOON

W m-2 /µm hr-1 ENSEMBLE

SPREAD

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Given a standard deviation of 10 W m

-2

in aerosol indirect effect, statistical significance at 95% level

roughly requires:

3 W m

-2

 50 ensemble members 2 W m

-2

 100 ensemble members 1 W m

-2

 400 ensemble members Size of

indirect

effect

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• Precipitation: little impact of aerosol over timescales longer than a few days, consistent with “systems dynamics” reasoning and

results for CRE

•  Radiation: impact of aerosol difficult to discern from large internal variability ensemble approach

Caution is needed when quantifying indirect effects in GCSS- type modeling frameworks as used here, less problematic for 3D??

•  Sensitivity to microphysics and resolution: nearly all tests lie within the ensemble spread

Summary of TWP-ICE results

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Microphysics and aerosol indirect effects in MMF

•  Recent effort to incorporate 2-moment microphysics scheme (Morrison et al. 2009) into SpCAM that predicts cloud particle number concentration and allows coupling with aerosol

•  Parallel effort underway (led by PNNL) to incorporate cloud-aerosol interaction in SpCAM using a more

complicated framework (Explicit Clouds-Parameterized Pollutants)

•  Preliminary results using 2-moment scheme and comparison with default SpCAM microphysics

2-moment scheme is out of the box, no tuning…

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DJF Precipitation Rate (mm hr

-1

)

From M. Khairoutdinov

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DJF Outgoing Longwave Radiation, OLR (W m

-2

)

From M. Khairoutdinov

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DJF Absorbed Solar Radiation (W m

-2

)

From M. Khairoutdinov

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• Overall results: not greatly different with 2-moment and 1- moment microphysics

•  Computational cost: ~ factor of 2 with 2-moment efforts underway to increase efficiency (e.g., reducing # of prognostic variables)

•  Some tuning of 2-moment scheme is required to increase TOA reflected solar radiation and achieve radiative balance

•  Aerosol indirect effects: coupling of 2-moment scheme to CAM aerosol is underway to simulate indirect effects in SpCAM

•  Uncertainties: shallow clouds (Cu, Sc), due to general difficulty

of representing these clouds in SpCAM, and specifically because

droplet activation is mostly driven by sub-grid vertical motion in

these clouds explicit coupling with sub-grid scheme

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Thank you.

We acknowledge funding from CMMAP,

NOAA, and DOE ARM/ASR.

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