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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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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 weather and climate models represent reality?

  • 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 weather and climate models represent reality?

  • 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 weather and climate models represent reality?

  • 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 weather and climate models represent reality?

  • 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 weather and climate models represent reality?

∆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 weather and climate models represent reality?

∆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 weather and climate models represent reality?

∆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 weather and climate models represent reality?

  • AmeriFlux standardized Level 2 data


Observations used1
Observations used weather and climate models represent reality?

  • 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 weather and climate models represent reality?

  • 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 weather and climate models represent reality?

  • 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 weather and climate models represent reality?

  • 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 weather and climate models represent reality?

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


Water cycle surface coupling
Water Cycle Surface Coupling weather and climate models represent reality?

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 weather and climate models represent reality?

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 weather and climate models represent reality?

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


Scatter across stations model grid boxes1
Scatter across stations, model grid boxes weather and climate models represent reality?

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

Spatial correlation across stations is not bad (except GLDAS)


Surface flux variability
Surface flux variability weather and climate models represent reality?

Standard deviation of daily LHF is low in models


Surface flux variability1
Surface flux variability weather and climate models represent reality?

Standard deviation of daily LHF is low in models

Scaling could also be contributing to this bias


Surface flux variability2
Surface flux variability weather and climate models represent reality?

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 weather and climate models represent reality?

Spatial correlation across stations is quite low


Daily variability of latent heat flux1
Daily variability of latent heat flux weather and climate models represent reality?

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 weather and climate models represent reality?

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


Moisture coupling index1
Moisture coupling index weather and climate models represent reality?

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

Previous biases compensate partially


Moisture coupling index2
Moisture coupling index weather and climate models represent reality?

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 weather and climate models represent reality?

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 weather and climate models represent reality?

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


Thermal coupling index1
Thermal coupling index weather and climate models represent reality?

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 weather and climate models represent reality?

Low spatial correlations in thermal coupling index


Models poorer at sm shf relation1
Models poorer at SM:SHF relation weather and climate models represent reality?

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 weather and climate models represent reality?

Models put strong negative correlations nearly everywhere


Decomposing the sm shf relation1
Decomposing the SM:SHF relation weather and climate models represent reality?

Models put strong negative correlations nearly everywhere

Flux towers show weak or even positive correlations


Sensible heat flux variability
Sensible heat flux variability weather and climate models represent reality?

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


Sensible heat flux variability1
Sensible heat flux variability weather and climate models represent reality?

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 weather and climate models represent reality?

NCEP weaker than AmeriFlux, NASA stronger


Surface soil moisture memory1
Surface soil moisture memory weather and climate models represent reality?

NCEP weaker than AmeriFlux, NASA stronger

Consistency issues, e.g. depth of measurements


Surface soil moisture memory2
Surface soil moisture memory weather and climate models represent reality?

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 weather and climate models represent reality?

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 weather and climate models represent reality?

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


Conclusions
Conclusions weather and climate models represent reality?

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


Conclusions1
Conclusions weather and climate models represent reality?

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 weather and climate models represent reality?

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 weather and climate models represent reality?

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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