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Land-atmosphere feedbacks over North America: How well do weather and climate models represent reality?. Paul Dirmeyer , Ahmed Tawfik, Holly Norton and Jiexia Wu Center for Ocean-Land-Atmosphere Studies George Mason University Fairfax, Virginia, USA. Predictability and Prediction.

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Paul Dirmeyer , Ahmed Tawfik, Holly Norton and Jiexia Wu Center for Ocean-Land-Atmosphere Studies

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Paul dirmeyer ahmed tawfik holly norton and jiexia wu center for ocean land atmosphere studies

Land-atmosphere feedbacks over North America: How well do weather and climate models represent reality?

Paul Dirmeyer, Ahmed Tawfik, Holly Norton and Jiexia Wu

Center for Ocean-Land-Atmosphere Studies

George Mason University

Fairfax, Virginia, USA


Predictability and prediction

Predictability and Prediction

  • Land states (namely soil moisture*) can provide predictability in the window between deterministic (weather) and climate (O-A) time scales.

Atmosphere (Weather)

Predictability

Land

Ocean (Climate)

Time

~10 days ~2 months

*Snow too!


Predictability and prediction1

Predictability and Prediction

  • Land states (namely soil moisture*) can provide predictability in the window between deterministic (weather) and climate (O-A) time scales.

  • To have an effect, must have:

    • Memory of initial land states

Atmosphere (Weather)

Predictability

Land

Ocean (Climate)

Time

~10 days ~2 months

*Snow too!


Predictability and prediction2

Predictability and Prediction

  • Land states (namely soil moisture*) can provide predictability in the window between deterministic (weather) and climate (O-A) time scales.

  • To have an effect, must have:

    • Memory of initial land states

    • Sensitivity of fluxes to land states, atmosphere to fluxes

Atmosphere (Weather)

Predictability

Land

Ocean (Climate)

Time

~10 days ~2 months

*Snow too!


Predictability and prediction3

Predictability and Prediction

  • Land states (namely soil moisture*) can provide predictability in the window between deterministic (weather) and climate (O-A) time scales.

  • To have an effect, must have:

    • Memory of initial land states

    • Sensitivity of fluxes to land states, atmosphere to fluxes

    • Sufficient variability

Atmosphere (Weather)

Predictability

Land

Ocean (Climate)

Time

~10 days ~2 months

*Snow too!


L a feedback stands on 2 legs

L-A feedback stands on 2 legs

∆P ∆SM ∆Fluxes∆PBL ∆P

Feedback path: Terrestrial leg

Atmospheric leg

Arid Humid

Arid Humid

Arid Humid

ET→P

SM→ET,SH

SH→PBL


L a feedback stands on 2 legs1

L-A feedback stands on 2 legs

∆P ∆SM ∆Fluxes∆PBL ∆P

Feedback path: Terrestrial leg

Atmospheric leg

Arid Humid

Arid Humid

Arid Humid

ET→P

SM→ET,SH

SH→PBL

  • Terrestrial – When/where does soil moisture (vegetation, snow, etc.) control the partitioning of net radiation into sensible and latent heat fluxes?


L a feedback stands on 2 legs2

L-A feedback stands on 2 legs

∆P ∆SM ∆Fluxes∆PBL ∆P

Feedback path: Terrestrial leg

Atmospheric leg

Arid Humid

Arid Humid

Arid Humid

ET→P

SM→ET,SH

SH→PBL

  • Terrestrial – When/where does soil moisture (vegetation, snow, etc.) control the partitioning of net radiation into sensible and latent heat fluxes?

  • Atmosphere – When/where do surface fluxes significantly affect boundary layer growth, clouds and precipitation?


Observations used

Observations used

  • AmeriFlux standardized Level 2 data


Observations used1

Observations used

  • AmeriFlux standardized Level 2 data

    • “Surface soil moisture” measurements vary in depth between stations from 2.5 cm to a 0-30cm average.


Observations used2

Observations used

  • AmeriFlux standardized Level 2 data

    • “Surface soil moisture” measurements vary in depth between stations from 2.5 cm to a 0-30cm average.

    • Sensible and latent heat flux (eddy covariance) measurements taken from 2.5m-70m aloft, depending on site.


Observations used3

Observations used

  • AmeriFlux standardized Level 2 data

    • “Surface soil moisture” measurements vary in depth between stations from 2.5 cm to a 0-30cm average.

    • Sensible and latent heat flux (eddy covariance) measurements taken from 2.5m-70m aloft, depending on site.

  • All data averaged to daily (missing if ≤36 half-hourly reports are present for fluxes, ≤10 for soil moisture).


Observations used4

Observations used

  • AmeriFlux standardized Level 2 data

    • “Surface soil moisture” measurements vary in depth between stations from 2.5 cm to a 0-30cm average.

    • Sensible and latent heat flux (eddy covariance) measurements taken from 2.5m-70m aloft, depending on site.

  • All data averaged to daily (missing if ≤36 half-hourly reports are present for fluxes, ≤10 for soil moisture).

  • Station must have >100 daily reports during JJA to be included in the analysis.


Models used

Models used

~30 years for each, covering ~1980s-2000s


Water cycle surface coupling

Water Cycle Surface Coupling

Small circles are AmeriFlux sites, same color key

Models show too dominant positive correlation of LHF vs. surface soil moisture


Water cycle surface coupling1

Water Cycle Surface Coupling

Small circles are AmeriFlux sites, same color key

Models show too dominant positive correlation of LHF vs. surface soil moisture

Could be at least partly a scaling issue – how much? TBD


Scatter across stations model grid boxes

Scatter across stations, model grid boxes

Positive model bias in r(SM1,LHF) is evident


Scatter across stations model grid boxes1

Scatter across stations, model grid boxes

Positive model bias in r(SM1,LHF) is evident

Spatial correlation across stations is not bad (except GLDAS)


Surface flux variability

Surface flux variability

Standard deviation of daily LHF is low in models


Surface flux variability1

Surface flux variability

Standard deviation of daily LHF is low in models

Scaling could also be contributing to this bias


Surface flux variability2

Surface flux variability

Standard deviation of daily LHF is low in models

Scaling could also be contributing to this bias

But the spatial patterns are a bigger problem…


Daily variability of latent heat flux

Daily variability of latent heat flux

Spatial correlation across stations is quite low


Daily variability of latent heat flux1

Daily variability of latent heat flux

Spatial correlation across stations is quite low

Is this a problem originating in the land models, the AGCMs, or both?


Moisture coupling index

Moisture coupling index

This is the first “leg” of land feedback onto the atmosphere


Moisture coupling index1

Moisture coupling index

This is the first “leg” of land feedback onto the atmosphere

Previous biases compensate partially


Moisture coupling index2

Moisture coupling index

This is the first “leg” of land feedback onto the atmosphere

Previous biases compensate partially

CFSR crop kludge in evidence


Generally good patterns

Generally good patterns

Positive bias is there, but the linear fit is promising (20%- 40% of variance explained) given all the model problems.


Thermal coupling index

Thermal coupling index

SM SHF is the pathway by which soil moisture controls boundary layer growth (cf. A. Betts work)


Thermal coupling index1

Thermal coupling index

SM SHF is the pathway by which soil moisture controls boundary layer growth (cf. A. Betts work)

Obs show weaker coupling than models


Models poorer at sm shf relation

Models poorer at SM:SHF relation

Low spatial correlations in thermal coupling index


Models poorer at sm shf relation1

Models poorer at SM:SHF relation

Low spatial correlations in thermal coupling index

Suggests models do not reproduce the pattern of SM:PBL links


Decomposing the sm shf relation

Decomposing the SM:SHF relation

Models put strong negative correlations nearly everywhere


Decomposing the sm shf relation1

Decomposing the SM:SHF relation

Models put strong negative correlations nearly everywhere

Flux towers show weak or even positive correlations


Sensible heat flux variability

Sensible heat flux variability

No model shows the ability to reproduce the observed pattern of SHF variability over the US


Sensible heat flux variability1

Sensible heat flux variability

No model shows the ability to reproduce the observed pattern of SHF variability over the US

This needs model development attention!


Surface soil moisture memory

Surface soil moisture memory

NCEP weaker than AmeriFlux, NASA stronger


Surface soil moisture memory1

Surface soil moisture memory

NCEP weaker than AmeriFlux, NASA stronger

Consistency issues, e.g. depth of measurements


Surface soil moisture memory2

Surface soil moisture memory

Dirmeyer et al., 2013: J. Climate, 8495-.

NCEP weaker than AmeriFlux, NASA stronger

Consistency issues, e.g. depth of measurements

Pattern is poor (seen this before):


Memory in latent heat fluxes

Memory in latent heat fluxes

Models are anticorrelated spatially with soil moisture memory, while the flux towers are not (except northern Great Plains)!


Memory in sensible heat fluxes

Memory in sensible heat fluxes

Models generally weaker than for LHF, but strong areas over Great Plains generally east of maxima for LHF.


Conclusions

Conclusions

Need to quantify how spatial scale differences, measurement heights/depths affect direct comparisons.


Conclusions1

Conclusions

Need to quantify how spatial scale differences, measurement heights/depths affect direct comparisons.

Nevertheless, spatial patterns for water cycle indices are promisingly good.


Conclusions2

Conclusions

Need to quantify how spatial scale differences, measurement heights/depths affect direct comparisons.

Nevertheless, spatial patterns for water cycle indices are promisingly good.

Energy cycle indices are disappointing.


Conclusions3

Conclusions

Need to quantify how spatial scale differences, measurement heights/depths affect direct comparisons.

Nevertheless, spatial patterns for water cycle indices are promisingly good.

Energy cycle indices are disappointing.

First step toward identifying poorly-modeled coupled processes that require focused model development.


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