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Brad Christoffersen University of Arizona

Controls on evapotranspiration (ET) and its seasonality in select land surface models In Support of The LBA- Model Intercomparison Project (MIP). Brad Christoffersen University of Arizona. Motivation. Rising atmospheric CO 2.

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Brad Christoffersen University of Arizona

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  1. Controls on evapotranspiration (ET) and its seasonality in select land surface modelsIn Support of The LBA-Model Intercomparison Project (MIP) Brad Christoffersen University of Arizona

  2. Motivation Rising atmospheric CO2 • Amazonian forests are a locus for potential positive feedback to climate change. (Betts et al. 2004) Forest dieback Drought frequency Stomatal closure

  3. Motivation Rising atmospheric CO2 • Amazonian forests are a locus for potential positive feedback to climate change (Betts et al. 2004) • Land surface models have typically predicted water-limited control on ET across the seasonally dry Amazon (Shuttleworth 1991, Bonan et al. 1998, Dickinson et al. 2006) Forest dieback Drought frequency Stomatal closure

  4. Motivation Rising atmospheric CO2 • Amazonian forests are a locus for potential positive feedback to climate change (Betts et al. 2004) • Land surface models have typically predicted water-limited control on ET across the seasonally dry Amazon (Shuttleworth 1991, Bonan et al. 1998, Dickinson et al. 2006) • Recent eddy tower syntheses reveal strong net radiation and little precipitation control on ET (Hasler and Avissar 2007, Juarez et al. 2007, Fisher et al. in press) Forest dieback Drought frequency Stomatal closure

  5. LBA-MIP Participating Models • Ecosystem Process Models: • SSiB2, SiB2, SiB3, SiB-CASA, Biome-BGC, VISIT • Dynamic Vegetation Models: • LPJ, HyLand, Jules-TRIFFID, CLM-DGVM, Orchidee, IBIS, LM3V • Parameter Models: • 5PM, SPA-DALEC • Strictly Soil-Vegetation-Atmosphere Model: • NOAH • Corresponding GCMs or Mesoscale Models: • CSU-SiB3, SPEEDY-LPJ, HadCM3-Jules, CCSM-CLM, IPSL-CM4-Orchidee, Eta-NOAH

  6. Water Dynamics in Land Surface Models: Central Questions • How do model-predicted seasonal / diurnal patterns in ET intercompare with each other and with data? • What is the relative importance of radiation and available soil moisture as controls on ET? Long-term goal  Identify key model mechanisms associated w/ model-model and model-data differences

  7. Tapajós K67 Site

  8. Tapajós K67 Site

  9. Tapajós K67 Site

  10. Tapajós K67 Site

  11. Net Radiation Controls on ET – Observed and Modeled

  12. Net Radiation Controls on ET – Observed and Modeled Adapted from Hasler and Avissar 2007

  13. Net Radiation Controls on ET – Observed and Modeled Adapted from Hasler and Avissar 2007

  14. Varying Strength of Rnet control on ET across models (wet season) 1:1 H&A LSR MIP LSR CLM3.5 (x4) SiB3 IBIS LPJ Mean Daily LE (W m-2) Mean Daily Net Radiation (W m-2)

  15. Most Models Lag ~2hrs behind observed diurnal cycles in LE flux CLM3.5 (x4) SiB3 IBIS Data Hourly LE (W m-2) Hour

  16. Conclusions • Considerable cross-model variance in predicted daily (and seasonal) patterns of ET • Diurnal cycles of ET often lag those observed in data • Controls on ET - Models in Semi-Agreement: • Net radiation exhibits dominant control on ET in absence of water stress • Increased soil moisture storage capacity in models  shifts ET peak to dry season (in phase with net radiation)

  17. Future Directions • What model mechanisms give rise to these differences? • What is the quantitative partitioning of relative controls of radiation and soil moisture on ET? • Explore empirical bucket model capability of caputuring seasonal and interannual variability in modeled soil moisture.

  18. Thanks! The LBA-MIP Team Scott UofA Gustavo NASA Ben, Potsdam Ian, CSU Brad UofA Julio INPA David Edinburgh Hewlley UFV Fanny Potsdam Lindsey UT-Austin Natalia UofA Margriet Vrije U. Marcos UFV Laura

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