Using tree ring databases to evaluate regional climate drivers of productivity variability in ORCHIDEE-FM model Kun Tan1, Flurin Babst2, Ben Poulter1, Philippe Ciais1, David Frank2, Thomas Launois1, Valentin Bellassen1 1 LSCE, Lab. des Sciences du Climat et de l’Environnement, Gif sur Yvette Cedex, France 2 WSL, Swiss Federal Research Institute, Birmensdorf, Switzerland

Model introduction ORCHIDEE-Forest-Management version - simulate individual growth of trees in an even-aged stand Specific add-ons to the standard version age-related decline NPP age-related limitation of LAI in young stands age-related allocation ratio between stem and coarse roots, branch mortality and coarse woody debris litter compartment Forest management module (simulates three main processes) Annual time step Distributes the annual stand-level wood increment to individual trees (using modeled or observed increment) Unmanaged => natural mortality due to self-thinning Managed => the timing and intensity of thinning or clear-cuts. Bellassen et al., 2010, Ecological Modelling, doi:10.1016/j.ecolmodel.2010.07.008

Model introduction Advantage of FM version versus standard version Ability to simulate annual ring width of individual trees, and its variability within a stand Better representation of tree and stand growth and mortality

Tree-ring modeling at 5 eddy-covariance sites Climate data CRUNCEP: 1901-2009, 0.5×0.5deg, 6 hourly time step WCLIMCRU: 1901-2005, 1km×1km, monthly time step Tree ring measurements at 5 sites cored by WSL

Tree-ring modeling at 5 eddy-covariance sites 1. Comparisons between observed and ORCHIDEE-FM simulated TR width

Tree-ring modeling at 5 eddy-covariance sites 2. Comparisons between observed and ORCHIDEE-FM simulated woody NPP Drought 1976 Shortcomings of the model: Over-estimated NPP for young trees Underestimates lag effects of droughts

Comparing tree-ring series with modeled NPP at 1000 sites over Europe Conifers: PCAB - Picea abies PISY - Pinus sylvestris ABAL - Abies alba LADE - Larix decidua PICE - Pinus cembra Broadleaves: FASY - Fagus sylvatica QURO - Quercus robur QUPE - Quercus petraea PFTs: BoNE - boreal needleleaf evergreen TeNE - temperate needleleaf evergreen TeBS - temperate broadleaves summergreen

Comparing tree-ring series with modeled NPP at 1000 sites over Europe Models, forcing data, and soil depth data used for NPP simulations Forcing data CRUNCEP: 0.5×0.5deg, 6 hourly time step WCLIMCRU: 1km×1km, monthly time step Soil depth: JRC-ESDB data: almost less than 1 m and never over 1.5 m

Comparing tree-ring series with modeled NPP at 1000 sites over Europe ORCHIDEE has a peak NPP-temperature a few months before the obs. tree ring Modeled NPP is too sensitive to temperature & precipitation/soil moisture High lagged correlations with previous years climate but not addressed in models Fig. Percentage of sites per PFTs with significant (p = 0.05) positive (top two) and negative (bottom one) climate correlations from previous April (A’) to current September (S)

Comparing tree-ring series with modeled NPP at 1000 sites over Europe Fig. Mean monthly NPP from models for the PFTs during 1920-1970

Comparing tree-ring series with modeled NPP at 1000 sites over Europe Fig. Spatial distributions of spring (MAM) and summer (JJA) temperature and precipitation anomaly, and tree ring width (TRW) anomaly and modeled annual NPP (mean ORCHIDEE modeled NPP - ORC mean NPP and mean LPJ modeled NPP - LPJ mean NPP) anomaly in 1959

Comparing tree-ring series with modeled NPP at 1000 sites over Europe Fig. Spatial distributions of spring (MAM) and summer (JJA) temperature and precipitation anomaly, and tree ring width (TRW) anomaly and modeled annual NPP (mean ORCHIDEE modeled NPP - ORC mean NPP and mean LPJ modeled NPP - LPJ mean NPP) anomaly in 1976

Comparing tree-ring series with modeled NPP at 1000 sites over Europe Conclusions Models are overly sensitive, especially to spring temperature, may come from Phenology Vcmax sensitivity to temperature in cool regions Vcmax sensitivity to precipitation in dry regions Over sensitive to summer water stress (deep roots, access to ground waters) Models could not address high lagged correlations with previous years climate Seasonal plots show large difference between ORCHIDEE and LPJ for winter NPP for conifers – different lagged correlations; both ORCHIDEE and LPJ show similar seasonal NPP, but different magnitudes for broadleaves – similar lagged correlations

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