Observed and simulated climate sensitivity
of large-scale forest productivityTree-rings and vegetation models
NACP meeting 2013
Flurin Babst1,3, Ben Poulter1,2, Valerie Trouet3, Kun Tan2, Burkhard Neuwirth4, Rob Wilson5, Marco Carrer6, Michael Grabner7, Willy Tegel8, Tom Levanic9, Momchil Panayotov10, Carlo Urbinati11, Olivier Bouriaud12, Philippe Ciais2, David Frank11Swiss Federal Research Institute WSL, Switzerland2LSCE CNRS, France3Laboratory of Tree-ring Research, University of Arizona, USA4DeLaWi TreeRingAnalyses, Windeck, Germany5School of GeoSciences, University of Edinburgh, UK6Forest Ecology Research Unit, University of Padova, Italy
7Universität für Bodenkultur Vienna, Austria8University of Freiburg, Germany9Slovenian Forestry Institute Ljubljana, Slovenia10University of Forestry Sofia, Bulgaria11Universita Politechnica delle Marche Ancona, Italy12Forest Research and Management ICAS, Romania
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MotivationForests 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?
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Tree-ring networkTree-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: 1920-1970
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Monthly climate response
Monthly climate data:CRU 3.0, 1901-2006 (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 temperatureii) monthly precipitation
from previous April to current September
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Climate signalsSelf-organizing maps (SOMs) to divide the network into clusters of sites with similar climate responses.
SOM grid: T
signal
P signal M signal
Babst et al. 2013, GEB
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Latitude / altitude
Limiting factors for tree growth can be estimated as a function of latitude and elevation (temperature)
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Novel application of tree-ring data:
Large-scale validation of vegetation models
T
P
Tree-rings vs. DGVMs
Babst et al. 2013, GEB
Seasonality%
site
s wi
th si
gn. p
os.
corre
latio
ns
T
tree-
rings
mod
el
P
temperate coniferstemperate broadleaf boreal conifers
boreal conifers
temperate conifers
Tan et al. in review, ERL
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Conclusions I
Nemani et al. 2003, Science
Beer et al. 2010, Science
Babst et al. 2013, GEB
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Conclusions II 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.
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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Thank you!
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Carry-over effectsAll temperature and precipitation limited sites
Climate conditions (bootstrapped) leading to contemporaneous or lagged growth extremes.
Babst et al. 2012, ERL
