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