Converging Climate Sensitivities of European Forests Between Observed Radial Tree Growth and Vegetation Models

Abstract The impacts of climate variability and trends on European forests are unevenly distributed across different bioclimatic zones and species. Extreme climate events are also becoming more frequent and it is unknown how they will affect feedbacks of CO2 between forest ecosystems and the atmosphere. An improved understanding of species differences at the regional scale of the response of forest productivity to climate variation and extremes is thus important for forecasting forest dynamics. In this study, we evaluate the climate sensitivity of aboveground net primary production (NPP) simulated by two dynamic global vegetation models (DGVM; ORCHIDEE and LPJ-wsl) against tree ring width (TRW) observations from about 1000 sites distributed across Europe. In both the model simulations and the TRW observations, forests in northern Europe and the Alps respond positively to warmer spring and summer temperature, and their overall temperature sensitivity is larger than that of the soil-moisture-limited forests in central Europe and Mediterranean regions. Compared with TRW observations, simulated NPP from ORCHIDEE and LPJ-wsl appear to be overly-sensitive to climatic factors. Our results indicate that the models lack biological processes that control time lags, such as carbohydrate storage and remobilization, that delay the effects of radial growth dynamics to climate. Our study highlights the need for re-evaluating the physiological controls on the climate sensitivity of NPP simulated by DGVMs. In particular, DGVMs could be further enhanced by a more detailed representation of carbon reserves and allocation that control year-to-year variation in plant growth.

[1]  David Frank,et al.  Toward consistent measurements of carbon accumulation: A multi-site assessment of biomass and basal area increment across Europe , 2014 .

[2]  F. Biondi,et al.  Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models , 2015, Science.

[3]  P. Ciais,et al.  Reconstruction and attribution of the carbon sink of European forests between 1950 and 2000 , 2011 .

[4]  Marc Wiedermann,et al.  Coincidences of climate extremes and anomalous vegetation responses: comparing tree ring patterns to simulated productivity , 2015 .

[5]  I. C. Prentice,et al.  BIOME3: An equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types , 1996 .

[6]  C. D. Keeling,et al.  Atmospheric CO 2 records from sites in the SIO air sampling network , 1994 .

[7]  A. Barbati,et al.  Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems , 2010 .

[8]  G. Wieser,et al.  Effects of atmospheric and climate change at the timberline of the Central European Alps , 2009, Annals of Forest Science.

[9]  Niklaus E. Zimmermann,et al.  Water-use efficiency and transpiration across European forests during the Anthropocene , 2015 .

[10]  G. Collatz,et al.  Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer , 1991 .

[11]  Pierre Friedlingstein,et al.  Uncertainties in CMIP5 Climate Projections due to Carbon Cycle Feedbacks , 2014 .

[12]  F. Woodward,et al.  Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate , 2010, Science.

[13]  David Frank,et al.  The influence of sampling design on tree‐ring‐based quantification of forest growth , 2014, Global change biology.

[14]  M. Hughes,et al.  An efficient forward model of the climate controls on interannual variation in tree-ring width , 2011 .

[15]  Benjamin Poulter,et al.  Observed forest sensitivity to climate implies large changes in 21st century North American forest growth. , 2016, Ecology letters.

[16]  Y. Xue,et al.  Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis , 2012 .

[17]  E. Davidson,et al.  Using model‐data fusion to interpret past trends, and quantify uncertainties in future projections, of terrestrial ecosystem carbon cycling , 2012 .

[18]  Daniela JacobJuliane,et al.  EURO-CORDEX: new high-resolution climate change projections for European impact research , 2013 .

[19]  M. Lomas,et al.  Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends , 2013, Global change biology.

[20]  P. Hari,et al.  Use of modeled photosynthesis and decomposition to describe tree growth at the northern tree line. , 2004, Tree physiology.

[21]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[22]  B. Poulter,et al.  Net biome production of the Amazon Basin in the 21st century , 2010 .

[23]  K. J. McCree,et al.  Equations for the Rate of Dark Respiration of White Clover and Grain Sorghum, as Functions of Dry Weight, Photosynthetic Rate, and Temperature1 , 1974 .

[24]  Juan Pedro Ferrio,et al.  Stable isotopes in tree rings: towards a mechanistic understanding of isotope fractionation and mixing processes from the leaves to the wood. , 2014, Tree physiology.

[25]  Niklaus E. Zimmermann,et al.  No growth stimulation of Canada’s boreal forest under half-century of combined warming and CO2 fertilization , 2016, Proceedings of the National Academy of Sciences.

[26]  Niklaus E. Zimmermann,et al.  Impacts of land cover and climate data selection on understanding terrestrial carbon dynamics and the CO 2 airborne fraction , 2011 .

[27]  J. Berry,et al.  A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species , 1980, Planta.

[28]  Gérard Dedieu,et al.  TURC: A diagnostic model of continental gross primary productivity and net primary productivity , 1996 .

[29]  D. Kneeshaw,et al.  Northeastern North America as a potential refugium for boreal forests in a warming climate , 2016, Science.

[30]  C. Körner,et al.  Growth and carbon relations of tree line forming conifers at constant vs. variable low temperatures , 2009 .

[31]  Ranga B. Myneni,et al.  Recent trends and drivers of regional sources and sinks of carbon dioxide , 2015 .

[32]  P. Jones,et al.  Updated high‐resolution grids of monthly climatic observations – the CRU TS3.10 Dataset , 2014 .

[33]  Philippe Ciais,et al.  Site‐ and species‐specific responses of forest growth to climate across the European continent , 2013 .

[34]  S. Josey.  Climate change impacts , 2012 .

[35]  J. Marshall,et al.  Seasonal variation in photosynthetic capacity of montane conifers , 2004 .

[36]  Stefan Brönnimann,et al.  Forward modelling of tree-ring width and comparison with a global network of tree-ring chronologies , 2013 .

[37]  R. Vautard,et al.  EURO-CORDEX: new high-resolution climate change projections for European impact research , 2014, Regional Environmental Change.

[38]  Christian Körner,et al.  Moving beyond photosynthesis: from carbon source to sink-driven vegetation modeling. , 2014, The New phytologist.

[39]  Atul K. Jain,et al.  Global Carbon Budget 2018 , 2014, Earth System Science Data.

[40]  Philippe Ciais,et al.  Modelling forest management within a global vegetation model—Part 1: Model structure and general behaviour , 2010 .

[41]  Sandy P. Harrison,et al.  Simulation of tree-ring widths with a model for primary production, carbon allocation, and growth , 2014 .

[42]  D. Nepstad,et al.  Mortality of large trees and lianas following experimental drought in an Amazon forest. , 2007, Ecology.

[43]  Laurent Misson,et al.  MAIDEN: a model for analyzing ecosystem processes in dendroecology , 2004 .

[44]  Martin P. Girardin,et al.  Response of tree growth to a changing climate in boreal central Canada: A comparison of empirical, process-based, and hybrid modelling approaches , 2008 .

[45]  I. E. Woodrow,et al.  A Model Predicting Stomatal Conductance and its Contribution to the Control of Photosynthesis under Different Environmental Conditions , 1987 .

[46]  B. Poulter,et al.  Modeling spatiotemporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties , 2015 .

[47]  P. Ciais,et al.  Europe-wide reduction in primary productivity caused by the heat and drought in 2003 , 2005, Nature.

[48]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[49]  Philippe Ciais,et al.  A tree-ring perspective on the terrestrial carbon cycle , 2014, Oecologia.

[50]  T. D. Mitchell,et al.  An improved method of constructing a database of monthly climate observations and associated high‐resolution grids , 2005 .

[51]  Xiao Jing Guo,et al.  Unusual forest growth decline in boreal North America covaries with the retreat of Arctic sea ice , 2014, Global change biology.

[52]  T. Wigley,et al.  On the Average Value of Correlated Time Series, with Applications in Dendroclimatology and Hydrometeorology , 1984 .

[53]  I. C. Prentice,et al.  A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system , 2005 .

[54]  D. Frank,et al.  Moisture stress of a hydrological year on tree growth in the Tibetan Plateau and surroundings , 2015 .

[55]  I. C. Prentice,et al.  Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model , 2003 .

[56]  S. Zaehle,et al.  Robust dynamics of Amazon dieback to climate change with perturbed ecosystem model parameters , 2010 .

[57]  Maosheng Zhao,et al.  Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009 , 2010, Science.