Tree-rings and vegetation models NACP meeting 2013 Flurin Babst 1,3, Ben Poulter 1,2, Valerie Trouet 3, Kun Tan 2, Burkhard Neuwirth 4, Rob Wilson 5, Marco Carrer 6, Michael Grabner 7, Willy Tegel 8, Tom Levanic 9, Momchil Panayotov 10, Carlo Urbinati 11, Olivier Bouriaud 12, Philippe Ciais 2, David Frank 1 1 Swiss Federal Research Institute WSL, Switzerland 2 LSCE CNRS, France 3 Laboratory of Tree-ring Research, University of Arizona, USA 4 DeLaWi TreeRingAnalyses, Windeck, Germany 5 School of GeoSciences, University of Edinburgh, UK 6 Forest Ecology Research Unit, University of Padova, Italy 7 Universität für Bodenkultur Vienna, Austria 8 University of Freiburg, Germany 9 Slovenian Forestry Institute Ljubljana, Slovenia 10 University of Forestry Sofia, Bulgaria 11 Universita Politechnica delle Marche Ancona, Italy 12 Forest Research and Management ICAS, Romania

Forests worldwide currently assimilate approximately 25% of the anthropogenic fossil fuel emissions (Friedlingstein et al. 2010, Nature Geoscience).  Understanding the climatic drivers of forest growth at a large scale. Nemani et al. 2003, Science Beer et al. 2010, Science  Empirical observations? 2

Tree-ring data can help to: i)Assess the climate response of forests at large scales. ii)Evaluate the climate sensitivity of dynamic global vegetation models ~ 1000 sites 36 species Common period:

Monthly climate data: CRU 3.0, (Mitchell & Jones, 2005)  Downscaled to 1 x 1 km resolution temperature precipitation  Climate correlation functions for all sites  Basis for further analyses Pinus cembra: Correlations between radial growth and i)monthly temperature ii)monthly precipitation from previous April to current September 4

Self-organizing maps (SOMs) to divide the network into clusters of sites with similar climate responses. SOM grid: T signal P signalM signal Babst et al. 2013, GEB 5

 Limiting factors for tree growth can be estimated as a function of latitude and elevation (temperature) 6

Novel application of tree-ring data: Large-scale validation of vegetation models T P Tree-rings vs. DGVMs 7 Babst et al. 2013, GEB

% sites with sign. pos. correlations T tree-rings model P temperate coniferstemperate broadleafboreal conifers temperate conifers Tan et al. in review, ERL 8

9 Nemani et al. 2003, Science Beer et al. 2010, Science Babst et al. 2013, GEB

 DGVMs show a stronger drought sensitivity than tree-rings.  Seasonality in climate response of DGVMs differs strongly from observations.  Lag-effects are not considered in simulations.  Tree-ring network does not provide absolute productivity. 10 DGVMs: - Improve seasonality and include carry-over effects Tree-rings: -Work towards absolute biomass increment -Combinations with other in-situ measurements (e.g. eddy-fluxes) Outlook:

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All temperature and precipitation limited sites Climate conditions (bootstrapped) leading to contemporaneous or lagged growth extremes. 15 Babst et al. 2012, ERL