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

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1 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

2 Outline 1. Motivation - Indirect aerosol effects 2. CSRM simulations of convective radiative quasiequilibrium 3. CSRM simulations of real case study (TWP-ICE) 4. SpCAM simulations with new microphysics scheme

3 Microphysics plays a key role in cloud, climate and weather models - 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) Stephens (2005)

4 cloud base cloud updraft maritime ( clean ) continental ( polluted )

5 Ship tracks: spectacular example of indirect effects caused by ship exhausts acting as CCN (long-lasting, feedback on cloud dynamics?)

6 IPCC 2007; Synthesis Report

7 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

8 - 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.

9 Koren et al. (2010) Rosenfeld et al. Science, Example of hypothesized aerosolmicrophysicsdynamics interactions in deep convection

10 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.

11 Convective-radiative quasi-equilibrium is the simplest system that includes interactions between clouds and their environment ( system-dynamics approach ).

12 Convective-radiative quasi-equilibrium mimicking planetary energy budget using a 2D cloud-system resolving model solar input 342 Wm columns (200 columns) height 61 levels horizontal distance Surface temperature = 15 C Surface relative humidity = 85% Surface albedo = 0.15 Grabowski J. Climate 2006, Grabowski and Morrison J. Climate 2010 (submitted)

13 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 columns (Δx=1-2km) and 61 levels (stretched; 12 levels below 2 km; top at km) Grabowski 2006; Grabowski and Morrison 2010

14 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.

15 Cloud water and drizzle/rain fields Solid: polluted Dashed: pristine Ice field

16 Cloud fraction profiles Grabowski J. Climate 2006 (G06) Grabowski and Morrison J. Climate 2010 (submitted) (GM10) G06 1-moment microphysics GM10 2-moment microphysics Solid: polluted Dashed: pristine Horizontal bars: standard deviation of temporal evolution (measure of statistical significance of the difference)

17

18 Pot. temperature profiles in the lower troposphere: Dashed: domain-averaged Solid: within raining regions only G06 GM10 GM10: 1-moment rain Mean of rainy grids Domain mean Deviation from surface temperature

19 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.

20 Next we move to a less idealized, time-evolving framework less stringent constraints relative to CRE

21 16-day, 2D simulations of TWP-ICE, using observed large-scale forcing similar setup to other GCSS case studies Prescribed large-scale forcing of T, qv, 6 hr nudging of u to observations 200 columns height 97 levels horizontal distance Surface temperature = 29 C

22 Tropical Western Pacific International Cloud Experiment (TWP-ICE)

23 - 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?

24 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)

25 Impact on surface precipitation PRISTINE (SOLID LINES) POLLUTED (DOTTED LINES) ACTIVE MONSOON SUPPRESSED MONSOON BASE FRZ GRP OBS

26 ACTIVE MONSOON PRISTINE (SOLID LINES) POLLUTED (DOTTED LINES) SUPPRESSED MONSOON

27 PRISTINE (SOLID LINES) POLLUTED (DOTTED LINES)\ OBSERVED

28 DROPLET CONCENTRATION ICE CONCENTRATION DROPLET EFF RADIUS ICE EFF RADIUS IMPACT ON MICROPHYSICS PRISTINE (SOLID) POLLUTED (DOTTED) LIQUID WATER CONTENT ICE WATER CONTENT

29 Impact on TOA radiative fluxes TOA upwelling SW PRISTINE (SOLID LINES) POLLUTED (DOTTED LINES) BASE FRZ GRP OBS

30 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

31 Given a standard deviation of 10 W m -2 in aerosol indirect effect, statistical significance at 95% level roughly requires: Size of indirect effect 3 W m ensemble members 2 W m ensemble members 1 W m ensemble members

32 Summary of TWP-ICE results 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 GCSStype modeling frameworks as used here, less problematic for 3D?? Sensitivity to microphysics and resolution: nearly all tests lie within the ensemble spread

33 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

34 From M. Khairoutdinov DJF Precipitation Rate (mm hr -1 )

35 From M. Khairoutdinov DJF Outgoing Longwave Radiation, OLR (W m -2 )

36 From M. Khairoutdinov DJF Absorbed Solar Radiation (W m -2 )

37 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

38 Thank you. We acknowledge funding from CMMAP, NOAA, and DOE ARM/ASR.

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