The horizontal diffusion issue in CRM simulations of moist convection

The horizontal diffusion issue in CRM simulations of moist convection

Wolfgang Langhans

Institute for Atmospheric and Climate Science, ETH Zurich

June 9, 2009

Outline

1 Introduction and Motivation

2 Objectives

3 Preliminary results

4 Preliminary conclusions

Wolfgang Langhans Group retreat/Bergell June 9, 2009 2 / 25

Introduction and Motivation

Modeling the European summer climate

“Indeed, differences in parameterizations between RCMs appear more important than differences in synoptic climatology between AGCMs . . . ”

Vidale et al. (2007)

Wolfgang Langhans Group retreat/Bergell June 9, 2009 4 / 25

Modeling the European summer climate

“The prominent role of physical parameterizations (e.g., convection, land-surface atmosphere exchange, radiation, and clouds) may explain the large model spread in summer . . . ”

Frei et al. (2006)

Sensitivity to convection parameterizations

Soil moisture-precipitation feedback

Hohenegger et al. (accepted)

Wolfgang Langhans Group retreat/Bergell June 9, 2009 6 / 25

Cloud-resolving modeling

| {z }

∆x ∼ 100 km

=⇒

|{z}

∆x ∼ 1 km

Objectives & scientific questions

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Goals

The overarching goals of this PhD project are

A to develop a cloud-resolving regional climate modeling capability, and

• How is the predicted convective preciptiation related to model components?

• What is the impact of horizontal resolution?

B to advance our knowledge about physical climate feedbacks and improving climate scenarios.

• How do mountain circulations interact with the diurnal cycle of moist convection?

Preliminary results

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Model setup

Version: CLM 4.3 Dynamics:

3rd order RK scheme (Wicker and Skamarock 1998, 2002)

5th order advection, pos. definite qx advection

Physics:

prognostic TKE-based turbulence scheme

no cumulus scheme graupel scheme TERRA_ML

Topographic correction scheme for radiation

Large Alpine domain:

501 × 451 × 45 gridpoints d ϕ = d λ = 0.02^◦, dt = 30 s

Sensitivity to horizontal diffusion

Given as M_HD= −α∇²(∇²ψ), ψ = u, v , w , q_v,q_c,q_i,t⁰,p⁰

Q Q

Q Q

Q Q

Q Q

Q Q

Q Q

Q Q

QQ

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Sensitivity to horizontal diffusion

Given as M_HD= −α∇²(∇²ψ), ψ = u, v , w , q_v,q_c,q_i,t⁰,p⁰

Q Q

Q Q

Q Q

Q Q

Q Q

Q Q

Q Q

QQ

Sensitivity to horizontal diffusion

Given as M_HD= −α∇²(∇²ψ), ψ = u, v , w , q_v,q_c,q_i,t⁰,p⁰ Q

Q Q

Q Q

Q Q

Q Q

Q Q

Q Q

Q QQ

Wolfgang Langhans Group retreat/Bergell June 9, 2009 12 / 25

Small-scale fluctuations

Vertical velocity at ∼ 5 km MSL

DIFFUSED UNDIFFUSED

Power spectra of vertical velocity

a.m. p.m.

10⁰ 10¹

10² 10³

10¹ 10² 10³ 10⁴ 10⁵

wavelength n ∆x variance (m3/s2)

B22A B22DIFFMASK

06 - 10 UTC 11.7.06

10⁰ 10¹

10² 10³

10¹ 10² 10³ 10⁴ 10⁵

wavelength n ∆x variance (m3/s2)

B22A B22DIFFMASK

12 - 16 UTC 11.7.06

⇐=

10⁰ 10¹

10² 10³

10¹ 10² 10³ 10⁴ 10⁵

wavelength n ∆x variance (m3/s2)

B22A B22DIFFMASK

06 - 10 UTC 12.7.06

10⁰ 10¹

10² 10³

10¹ 10² 10³ 10⁴ 10⁵ 10⁶

wavelength n ∆x variance (m3/s2)

B22A B22DIFFMASK

12 - 16 UTC 12.7.06

⇐=

Wolfgang Langhans Group retreat/Bergell June 9, 2009 14 / 25

Power spectra of vertical velocity

a.m. p.m.

10⁰ 10¹

10² 10³

10¹ 10² 10³ 10⁴ 10⁵

wavelength n ∆x variance (m3/s2)

B22A B22DIFFMASK

06 - 10 UTC 11.7.06

10⁰ 10¹

10² 10³

10¹ 10² 10³ 10⁴ 10⁵

wavelength n ∆x variance (m3/s2)

B22A B22DIFFMASK

12 - 16 UTC 11.7.06

⇐=

10⁰ 10¹

10² 10³

10¹ 10² 10³ 10⁴ 10⁵

wavelength n ∆x variance (m3/s2)

B22A B22DIFFMASK

06 - 10 UTC 12.7.06

10⁰ 10¹

10² 10³

10¹ 10² 10³ 10⁴ 10⁵ 10⁶

wavelength n ∆x variance (m3/s2)

B22A B22DIFFMASK

12 - 16 UTC 12.7.06

⇐=

Power spectra of vertical velocity

a.m. p.m.

10⁰ 10¹

10² 10³

10¹ 10² 10³ 10⁴ 10⁵

wavelength n ∆x variance (m3/s2)

B22A B22DIFFMASK

06 - 10 UTC 11.7.06

10⁰ 10¹

10² 10³

10¹ 10² 10³ 10⁴ 10⁵

wavelength n ∆x variance (m3/s2)

B22A B22DIFFMASK

12 - 16 UTC 11.7.06

⇐=

10⁰ 10¹

10² 10³

10¹ 10² 10³ 10⁴ 10⁵

wavelength n ∆x variance (m3/s2)

B22A B22DIFFMASK

06 - 10 UTC 12.7.06

10⁰ 10¹

10² 10³

10¹ 10² 10³ 10⁴ 10⁵ 10⁶

wavelength n ∆x variance (m3/s2)

B22A B22DIFFMASK

12 - 16 UTC 12.7.06

⇐=

Wolfgang Langhans Group retreat/Bergell June 9, 2009 14 / 25

Power spectra of vertical velocity

a.m. p.m.

10⁰ 10¹

10² 10³

10¹ 10² 10³ 10⁴ 10⁵

wavelength n ∆x variance (m3/s2)

B22A B22DIFFMASK

06 - 10 UTC 11.7.06

10⁰ 10¹

10² 10³

10¹ 10² 10³ 10⁴ 10⁵

wavelength n ∆x variance (m3/s2)

B22A B22DIFFMASK

12 - 16 UTC 11.7.06

⇐=

10⁰ 10¹

10² 10³

10¹ 10² 10³ 10⁴ 10⁵

wavelength n ∆x variance (m3/s2)

B22A B22DIFFMASK

06 - 10 UTC 12.7.06

10⁰ 10¹

10² 10³

10¹ 10² 10³ 10⁴ 10⁵ 10⁶

wavelength n ∆x variance (m3/s2)

B22A B22DIFFMASK

12 - 16 UTC 12.7.06

⇐=

Numerical stability

u-velocity at ∼ 1.3 km above ground

DIFFUSED UNDIFFUSED

Adr ia

⇐=

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Mean diurnal cycle of hydrometeors

DIFFUSED UNDIFFUSED

CLOUD WATER

RAIN

GRAUPEL

CLOUD WATER

RAIN

GRAUPEL

Heat budget

total

DIFFUSED UNDIFFUSED

latent heating

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Example: Vertical cross-section

12 UTC 12 July 2006, southern Alpine rim

Potential temperature

W E

Latent heating

W E

Heat budget

Turbulent flux divergence

DIFFUSED UNDIFFUSED

Radiative flux divergence

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Example: Vertical cross-section

12 UTC 12 July 2006, southern Alpine rim

Turbulence

W E

Radiation * 10

W E

Heat budget

3D advection

DIFFUSED UNDIFFUSED

Vertical advection

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Example: Vertical cross-section

12 UTC 12 July 2006, southern Alpine rim

3D advection

W E

Vertical advection

W E

Preliminary conclusions

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Preliminary conclusions

First simulations of a convective period in July 2006 have been conducted.

A budget analysis tool for potential temperature and moisture scalars has been implemented into COSMO.

The amount of convective precipitation is related to computational horizontal diffusion.

stronger diffusion → less convective precipitation t’ diffusion most influential

Affects averaged heat and moisture budgets Optimum setup: q0.0, t’0.0, u=0.25-0.4 (G. Zängl)

Preliminary conclusions

First simulations of a convective period in July 2006 have been conducted.

A budget analysis tool for potential temperature and moisture scalars has been implemented into COSMO.

The amount of convective precipitation is related to computational horizontal diffusion.

stronger diffusion → less convective precipitation t’ diffusion most influential

Affects averaged heat and moisture budgets Optimum setup: q0.0, t’0.0, u=0.25-0.4 (G. Zängl)

Wolfgang Langhans Group retreat/Bergell June 9, 2009 24 / 25

Preliminary conclusions

First simulations of a convective period in July 2006 have been conducted.

A budget analysis tool for potential temperature and moisture scalars has been implemented into COSMO.

The amount of convective precipitation is related to computational horizontal diffusion.

stronger diffusion → less convective precipitation t’ diffusion most influential

Affects averaged heat and moisture budgets Optimum setup: q0.0, t’0.0, u=0.25-0.4 (G. Zängl)

Thanks for your attention!

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