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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.

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

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  1. 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

  2. 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!

  3. 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!

  4. 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!

  5. 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!

  6. 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

  7. 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?

  8. 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?

  9. Observations used • AmeriFlux standardized Level 2 data

  10. 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.

  11. 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.

  12. 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).

  13. 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.

  14. Models used ~30 years for each, covering ~1980s-2000s

  15. Water Cycle Surface Coupling Small circles are AmeriFlux sites, same color key Models show too dominant positive correlation of LHF vs. surface soil moisture

  16. 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

  17. Scatter across stations, model grid boxes Positive model bias in r(SM1,LHF) is evident

  18. Scatter across stations, model grid boxes Positive model bias in r(SM1,LHF) is evident Spatial correlation across stations is not bad (except GLDAS)

  19. Surface flux variability Standard deviation of daily LHF is low in models

  20. Surface flux variability Standard deviation of daily LHF is low in models Scaling could also be contributing to this bias

  21. 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…

  22. Daily variability of latent heat flux Spatial correlation across stations is quite low

  23. 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?

  24. Moisture coupling index This is the first “leg” of land feedback onto the atmosphere

  25. Moisture coupling index This is the first “leg” of land feedback onto the atmosphere Previous biases compensate partially

  26. Moisture coupling index This is the first “leg” of land feedback onto the atmosphere Previous biases compensate partially CFSR crop kludge in evidence

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

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

  29. 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

  30. Models poorer at SM:SHF relation Low spatial correlations in thermal coupling index

  31. Models poorer at SM:SHF relation Low spatial correlations in thermal coupling index Suggests models do not reproduce the pattern of SM:PBL links

  32. Decomposing the SM:SHF relation Models put strong negative correlations nearly everywhere

  33. Decomposing the SM:SHF relation Models put strong negative correlations nearly everywhere Flux towers show weak or even positive correlations

  34. Sensible heat flux variability No model shows the ability to reproduce the observed pattern of SHF variability over the US

  35. 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!

  36. Surface soil moisture memory NCEP weaker than AmeriFlux, NASA stronger

  37. Surface soil moisture memory NCEP weaker than AmeriFlux, NASA stronger Consistency issues, e.g. depth of measurements

  38. 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):

  39. Memory in latent heat fluxes Models are anticorrelated spatially with soil moisture memory, while the flux towers are not (except northern Great Plains)!

  40. Memory in sensible heat fluxes Models generally weaker than for LHF, but strong areas over Great Plains generally east of maxima for LHF.

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

  42. Conclusions Need to quantify how spatial scale differences, measurement heights/depths affect direct comparisons. Nevertheless, spatial patterns for water cycle indices are promisingly good.

  43. 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.

  44. 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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