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

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

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

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)

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

base cloud

updraft

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

(long-lasting, feedback on cloud dynamics?)

IPCC 2007; Synthesis Report

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

- 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.

-

Rosenfeld et al.

Science, 2008

Example of hypothesized aerosol-

microphysics- dynamics

interactions in deep convection

Koren et al. (2010)

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.

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

between clouds and their environment

(“system-dynamics approach”).

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)

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

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.

Cloud water and drizzle/rain fields

Ice field Solid: polluted Dashed: pristine

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

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

• 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

from about 20 Wm

in one-moment

simulations.

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

less stringent constraints relative to CRE

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

Tropical Western Pacific – International

Cloud Experiment (TWP-ICE)

-

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?

-

•  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)

PRISTINE (SOLID LINES)

POLLUTED (DOTTED LINES)

•  Impact on surface precipitation

ACTIVE MONSOON SUPPRESSED MONSOON BASE FRZ GRP OBS

PRISTINE (SOLID LINES)

POLLUTED (DOTTED LINES) ACTIVE MONSOON

SUPPRESSED MONSOON

PRISTINE (SOLID LINES)

POLLUTED (DOTTED LINES)\

OBSERVED

IMPACT ON

MICROPHYSICS

PRISTINE (SOLID)

POLLUTED (DOTTED)

DROPLET CONCENTRATION ICE CONCENTRATION

ICE WATER CONTENT LIQUID WATER CONTENT

DROPLET EFF RADIUS ICE EFF RADIUS

PRISTINE (SOLID LINES) POLLUTED (DOTTED LINES)

•  Impact on TOA radiative fluxes

TOA upwelling SW

BASE FRZ GRP OBS

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

Given a standard deviation of 10 W m

in aerosol indirect effect, statistical significance at 95% level

roughly requires:

3 W m

 50 ensemble members 2 W m

 100 ensemble members 1 W m

 400 ensemble members Size of

indirect

effect

• 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

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…

DJF Precipitation Rate (mm hr

)

From M. Khairoutdinov

DJF Outgoing Longwave Radiation, OLR (W m

)

From M. Khairoutdinov

DJF Absorbed Solar Radiation (W m

)

From M. Khairoutdinov

• 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

Thank you.

We acknowledge funding from CMMAP,

NOAA, and DOE ARM/ASR.