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Impacts of leaf phenology and water table on interannual variability of carbon fluxes in subboreal uplands and wetlands

Impacts of leaf phenology and water table on interannual variability of carbon fluxes in subboreal uplands and wetlands. Implications for regional fluxes in the upper Midwest USA. Ankur R Desai, Benjamin N Sulman University of Wisconsin-Madison D. Scott Mackay

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Impacts of leaf phenology and water table on interannual variability of carbon fluxes in subboreal uplands and wetlands

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  1. Impacts of leaf phenology and water table on interannual variability of carbon fluxes in subboreal uplands and wetlands Implications for regional fluxes in the upper Midwest USA Ankur R Desai, Benjamin N Sulman University of Wisconsin-Madison D. Scott Mackay State University of New York-Buffalo Ameriflux/ChEAS PIs Ameriflux Meeting 2008

  2. Motivation • Interannual variation (IAV) in carbon fluxes from land to atmosphere is significant at most flux sites • Key to understanding how climate affects ecosystems comes from modeling IAV • IAV (years-decade) is currently poorly modeled, while hourly, seasonal, and even successional (century) are better

  3. Predicting NEE (Ricciuto et al)

  4. Climate Drivers of Carbon Flux • Temperature • Precipitation • Radiation • [CO2]

  5. Interannual ^ Climate Drivers of Carbon Flux • Temperature -> Phenology • Precipitation -> Drought • Radiation -> Light Quality • [CO2] -> Acclimation

  6. Interannual NEE at ChEAS

  7. Questions • What controls IAV of NEE in subboreal uplands? • Hypothesis: Phenology -> Growing season start, end, or length affects GPP • Piao et al (2008) -> Autumn warming and Rh • What controls IAV of NEE in subboreal wetlands? • Hypothesis: Phenology + Water table affects ER • Ise et al (2008) -> Decomposition and moisture • What controls IAV of regional NEE in subboreal North America? • Can a very simple model be constructed to explain IAV? • Can we make do a parameter opimization more attuned to IAV? • Hypothesis: MCMC overfits to hourly data

  8. Optimization HOURLY IAV

  9. Phenology • Five sites with 5-8 years of data • 1 regional (LEF), 1 wetland (LCR), 3 uplands (SYL, UMB, WCR) • Assimilate 1st 4 years of data

  10. Phenology Model • Twice daily model, annually resetting pools • Driven by PAR, Air and Soil T, VPD • LUE based GPP model f(PAR,T,VPD) • Three respiration pools f(Air T, Soil T, GPP) • Model 1. NOLEAF • Constant leaf on and leaf off days • Model 2. LEAF (Phenology) • Sigmoidal Threshold GDD (base 10) function for leaf on • Sigmoidal Threshold Daily Mean Soil Temp function for leaf off • 17 parameters, 3 are fixed • Output: NEE, ER, GPP, LAI

  11. Hourly HOURLY NOLEAF HOURLY LEAF IAV NOLEAF IAV LEAF

  12. Interannual HOURLY NOLEAF HOURLY LEAF IAV NOLEAF IAV LEAF

  13. NEE-Leaf

  14. GPP ER Leaf

  15. Precipitation and Water Table • Sulman et al (in prep) Biogeosciences - see Ameriflux poster

  16. Shrub Wetland Flux Response

  17. Three Wetlands

  18. Three Wetlands

  19. Regional NEE • See NACP poster in Feb.

  20. Conclusions • Autumn soil temperature appears to be a major control on interannual variability in subboreal upper Midwest USA flux tower site annual NEE • Due mainly to effect of growing season length for GPP • Only detectable using a modified MCMC cost function that penalizes for poor fit to cumulative annual NEE • Additionally, growing season average water table strongly affects ER in wetlands • GPP effect in both uplands and wetlands -> related to precipitation deficits? • Regional NEE is messy • Thanks: DOE NICCR, DOE TCP, NASA CC, NOAA CPO, USDA/USFS NRS, NSF, UW Foundation, ChEAS

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