Land atmosphere interaction metrics and examples
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Land atmosphere interaction – metrics and examples. Bart van den Hurk (KNMI/IMAU). Next time assignment (16 May). Identify a new topic that involves land use-climate feedback, and describe the feedback processes using the diagram qualitatively, e.g. green roofs in cities irrigation

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Land atmosphere interaction metrics and examples

Land atmosphere interaction – metrics and examples

Bart van den Hurk

(KNMI/IMAU)

Land atmosphere interaction


Next time assignment 16 may

Next time assignment (16 May)

  • Identify a new topic that involves land use-climate feedback, and describe the feedback processes using the diagram qualitatively, e.g.

    • green roofs in cities

    • irrigation

    • crop disease

    • ...

Land atmosphere interaction


Metrics and examples

Metrics and examples

  • How to ‘measure’ land-atmosphere feedback?

    • From budget analysis: feedback numbers (p, )

    • From statistical analysis: coupling coefficient 

    • From physical analysis: anecdotal evidence from (model) experiments

  • Diagnosis of  from ensemble model experiments

    • Global

    • Regional (European)

  • Some (model) experiments explaining mechanisms

    • Charney’s feedback hypothesis

    • Regional climate model simulations Europe and USA

    • Penman-Monteith coupled to PBL model

    • Convective triggering

Land atmosphere interaction


The coupling coefficient

The coupling coefficient 

  • What is the contribution of the interactive land-atmosphere coupling on the hydrological cycle?

  • How to answer?

    • Simple. Simulate the hydrological cycle without interactive land-atmosphere coupling and compare.

  • How?

    • Simple. Replace interactive landsurface flux/state by something that is prescribed and not interactive.

  • How to distinguish between interactive and prescribed landsurface?

    • Ensemble simulations

Land atmosphere interaction


The coupling coefficient1

The coupling coefficient 

  • Two ensembles of 16 model simulations, with varying atmosphere for each member

    • One ensemble (W) ‘normal’ (full coupling between land and atmosphere)

      • soil moisture from member one is written every time step

    • One ensemble (S) with varying atmosphere prescribed soil moisture

      • soil moisture from each member is read from W1

  • Evaluate ensemble variance of precipitation

Koster et al, 2004, Science

Land atmosphere interaction


Effect on precipitation

Effect on precipitation

All simulations in ensemble

respond to the land surface

boundary condition in the

same way

strong coupling

Simulations in ensemble

have no coherent response

to the land surface

boundary condition

weak coupling

Koster et al, 2004, Science

Land atmosphere interaction


Definition of simplified version

Definition of  (simplified version)

  • Take variance of precipitation across ensemble, P2

  • Compare P2 from ensemble W with ensemble S

  • If P2(W)  P2(S)    0, low coupling

  • If P2(S) disappears    1, strong coupling

Koster et al, 2004, Science

Land atmosphere interaction


Major result mean over 12 gcms

Major result, mean over 12 GCMs

Koster et al, 2004, Science

Land atmosphere interaction


When strong positive hydrological feedback likely

When strong positive hydrological feedback likely?

ET→P

W→ET

climate transition

zones

sensitivity

Arid

Humid

wet

dry

Land atmosphere interaction


Similar exp not an ensemble but multiyear simulation

Similar exp, not an ensemble but multiyear simulation

  • Compare two multi-year simulations:

    • One normal (‘coupled’) simulation

    • One ‘uncoupled’ simulation with fixed land cond’s

  • See what is the effect on variability of T, P

soil water

precipitation

Land atmosphere interaction


Land atmosphere coupling in europe climate change scenario

Land-atmosphere coupling in Europe: climate change scenario

Precipitation

  • Change in interannual variability

  • T,p2(future) - T,p2(control)

Temperature

Coupled

Uncoupled

Seneviratne et al, 2006, Nature

Land atmosphere interaction


Temperature extremes in europe

Temperature extremes in Europe

  • Various runs with an RCM covering 1959-2006

    • a cooling period 1959-1980

    • a warming between 1981-2006

  • Cut land-atmosphere interaction by prescribing time-filtered soil moisture using different time scales

  • Check the trends in these periods for different filtering time scales

IAV = interannual

variability removed

Jaeger et al (2010)

Land atmosphere interaction


Results in trends

Results in trends

Thus: cutting land-atmosphere

interaction affects trends in Tmax

Also feedbacks with cloud cover

play a role

Land atmosphere interaction


Chasing the coupling mechanisms other model experiments with on off switches

Chasing the coupling mechanisms: other model experiments with on/off switches

2 different land surface models giving different precipitation

(USGS better than SiB)

Kanamitsu and Mo, 2003, J.Clim

Land atmosphere interaction


Is it the local land atmosphere feedback

Is it the local land-atmosphere feedback?

  • Also different flow patterns (USGS follows more western and more humid pathway)

  • Additional experiments

    • 20km RCMs (local physics) nested in 50km models giving lateral boundary conditions

      • USGS-USGS

      • USGS-SiB

      • SiB-USGS

      • SiB-SiB

Kanamitsu and Mo, 2003, J.Clim

Land atmosphere interaction


Results

Results

P

E

land USGS, atmUSGS

Atmospheric

forcing has

larger impact

on both E and

P than land

surface model

land USGS, atmSiB

land SiB, atmUSGS

Kanamitsu and Mo, 2003, J.Clim

Land atmosphere interaction


Coupled penman monteith equation

Coupled Penman-Monteith equation

  • Consider the following system:

  • And the Penman-Monteith equation:

A = (1-)Sin + Lin-Lout

D = qsat(T) – q

 = dqsat/dT

Van Heerwaarden et al, 2010

Land atmosphere interaction


Derivative dle dt

Derivative dLE/dt

Forcing

Feedback

Heating/moistening through advection

Change of radiation in time

Changes in aerodynamic coupling

due to stability effects

Heating/moistening through

boundary layer growth

Soil moisture depletion

Longwave cooling

Soil heating

A = (1-)Sin + Lin-Lout

D = qsat(T) – q

 = dqsat/dT

Van Heerwaarden et al, 2010

Land atmosphere interaction


Results for cabauw and niamey

Results for Cabauw and Niamey

Van Heerwaarden et al, 2010

Land atmosphere interaction


Physical processes related to precipitation

Physical processes related to precipitation

  • Cloud formation

  • Factors generating rising air

Land atmosphere interaction


Rising air and condensation

Rising air and condensation

Rising air cools with 1 K per 100m ………..

Until it becomes so cold that it starts to condensate…

rising air

rising air

water vapour condensates to droplets

water vapour molecules

And a cloud is born!!!

Land atmosphere interaction

sheets from Pier Siebesma, KNMI


Conditions for precipitation

Conditions for precipitation

Necessary (but not sufficient) condition:

Low Level Moisture Convergence

Land atmosphere interaction


Examples of moist convergence

Examples of Moist Convergence

Mid-latitudes : Low pressure Systems

Tropics: Intertropical Convergence Zone (ITCZ)

Land atmosphere interaction


Causes of rising air 1 orography

Causes of rising air:1. Orography

Lenticularis above Mount Etna

seen from Taormina, Sicily Italy.

Land atmosphere interaction


2 convection

2. Convection

  • The sun heats the soil so that……..

  • Thermals are formed….

  • that rise because of buoyancy….

  • And a cloud forms as a wig on top of an invisible man

24-07-2006 12:30 Amsterdam:

cumulus humilis or “fair weather” cumulus

www.sky-catcher.nl

Land atmosphere interaction


Condensational heating allows cumulus to grow

Condensational Heating allows cumulus to grow

  • Wolken top (~3 km)

  • Humidity condensates into cloud water…..

  • And produces latent heat

  • Which serves as onboard fuel that allows the cloud to rise further…..

  • With ~5 m/s….

  • Until the cloud is stopped by a temperature inversion.

  • Wolken basis (~1km)

24-07-2006 Amsterdam: cumulus mediocris.

15:30

Land atmosphere interaction


Poor man s cloud model adiabatic ascent

Poor man’s cloud model: adiabatic ascent

Mean profile

“Level of zero kinetic energy”

Inversion

Level of neutral buoyancy (LNB)

non-well

mixed

layer

height

Level of free convection (LFC)

Lifting condensation level (LCL)

well mixed layer

Land atmosphere interaction


Convection in the sub tropics

Convection in the (sub)tropics

}

Hadley

circulation

Land atmosphere interaction


Cartoon of hadley circulation

Cartoon of Hadley Circulation

Tropopause 10km

Subsidence

~0.5 cm/s

inversion

10 m/s

Cloud base

~500m

  • Shallow Convective Clouds

  • No precipitation

  • Vertical turbulent transport

  • No net latent heat production

  • Fuel Supply Hadley Circulation

  • Stratocumulus

  • Interaction with radiation

  • Deep Convective Clouds

  • Precipitation

  • Vertical turbulent transport

  • Net latent heat production

  • Engine Hadley Circulation

Land atmosphere interaction


3 large scale lifting through fronts

3. Large Scale Lifting through fronts

}

Occuring at mid-latitudes

}

Land atmosphere interaction


Convection and land atmosphere coupling

Convection and land-atmosphere coupling

  • Findell and Eltahir (2003) did a systematic analysis of soil moisture – precipitation feedback

  • Start with atm. sounding of 6:00 am

  • Use simple Land-PBL model driven by obs. soundings

  • Diagnose convective triggering or shallow cumulus formation for 2 runs (dry and wet soil)

  • Classes of cases:

    • soil has no impact (atm. controlled)

    • convection favoured over wet soils

    • convection favoured over dry soils

Land atmosphere interaction


Soil pbl feedback

Soil-PBL feedback

  • Dry soils favoring convection (negative feedback)

  • Wet soils favoring convection (positive feedback)

dry

wet

PBL growth

reaches LCL

LCL

LCL

Build-up of MSE

gives convective

potential

LCL

LCL

Land atmosphere interaction


Findell s diagnostics

Findell’s diagnostics

  • Convective Triggering Potential (CTP)

  • Dewpoint depression in low levels (HIlow):

    • too wet: rain always likely

    • too dry: no moisture available

Land atmosphere interaction


Findell s map of feedback

Findell’s map of feedback

Land atmosphere interaction


Soil moisture precipitation feedback diagnosed from rcm

Soil moisture – precipitation feedbackdiagnosed from RCM

  • Comparison RCM at 25km (convection parameterized) to a 2km run (resolved convection)

  • Three runs: CTL, dry soil, wet soil.

precipitation

time (UTC)

PCTL

positive feedback

(Pwet-Pdry)

PCTL

negative feedback

Hohenegger et al, 2009

Land atmosphere interaction


Cloud cover

Cloud cover

resolved

parameterized

morning cloud cover

radiative cooling

stabilization

difficult for convection

wet

higher pbl moisture content

extra fuel in convective

closure scheme

dry

spec.hum.

Hohenegger et al, 2009

Land atmosphere interaction


Next week design an experiment

Next week: Design an experiment

  • Consider the 2003 heatwave in Europe.

  • And consider a GCM with which you can make seasonal forecasts

    • What kind of land surface processes could affect this heatwave (extended in time, intensified)

    • What kind of experiment would you design to demonstrate this role of land surface processes?

Land atmosphere interaction


More information

More information

  • Bart van den Hurk

    • [email protected]

Land atmosphere interaction


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