From Gaps to the Globe: Vegetation dynamics in global carbon cycle models Ben Poulter SOFIE Winter School LSCE-PKU

1.Documenting biome and community changes 2.Challenges in scaling ecological knowledge 3.Survey of forest gap models 4.Future of global vegetation modeling Overview of todays lecture

Documenting biome and community change Evidence for recent changes in plant communities 1.Global extent, from boreal to temperate to tropical 30-years of boreal greening and browning Bunn et al 2007 EOS Saatchi et al 2013 PNAS Successive (2005 & 2010) droughts in Amazonia

Documenting biome and community change Evidence for recent changes in plant communities 1.Global extent, from boreal to temperate to tropical 2.An array of drivers (CO 2, climate, nutrients, disturbance, management) Allen et al 2010 FEM Observed forest mortality from drought and temperature stress Forest retreat from sea-level rise Poulter et al 2009 OCM Altitudinal migration from inc. T Lenoir et al 2008 Science

Documenting biome and community change Evidence for recent changes in plant communities 1.Global extent, from boreal to temperate to tropical 2.An array of drivers (CO 2, climate, nutrients, disturbance, management) 3.Acceleration of population dynamics in response to changing resources Van Mantgem et al 2009 Science 87% increase in mortality 53% increase in recruitment Wright et al TREE Amazon Basin Pacific NW

Documenting biome and community change To address these issues, we need vegetation models -Simplicity -Occam's razor remains good advice for reasonable hypothesis testing -Constrained by datasets for model calibration and benchmarking -Computational limitations (I/O, disk space…), even with super computers…. -Scalability in space and time -Principles of vegetation dynamics should transferable spatially -Vegetation interactions should be temporally flexible (i.e., ‘non-analogs’) -Similarity -Models should reflect reality -Benchmarked under a range of circumstances (observations and experiments) Evidence for recent changes in plant communities 1.Global extent, from boreal to temperate to tropical 2.An array of drivers (CO 2, climate, nutrients, disturbance, management) 3.Acceleration of population dynamics in response to changing resources

1.Documenting biome and community changes 2.Challenges in scaling ecological knowledge 3.Survey of forest gap models 4.Future of global vegetation modeling Overview of todays lecture

Challenges in scaling ecological knowledge DGVM models -Biogeography with dynamic vegetation -Carbon, water, energy, and nutrient cycles Classes of terrestrial models Biogeography models -Equilibrium with climate (BIOME) Biogeochemistry models -Light-use efficiency (CASA) -Satellite era only Land surface models -Biophysics (SiB) -Satellite era only Dynamic Global Veg. Models -Prognostic/forward models -Numerical equations -Deterministic

Challenges in scaling ecological knowledge A history of dynamic global vegetation models (DGVM) (years)

1.Documenting biome and community changes 2.Challenges in scaling ecological knowledge 3.Survey of forest gap models 4.Future of global vegetation modeling Overview of today’s lecture

The forest gap model Key features of the forest gap model 1.The ‘gap’ or ‘patch’ represents forest stand dynamics in a landscape 2.The size of the gap is defined as the largest area that can be influenced by a single tree (~ m 2 ) 3.Three ecological processes: Establishment (birth) - stochastic Growth - deterministic Mortality – stochastic 1.Spatial position of a tree is not considered 2.Forest gap models must be run times to represent a landscape Stochasticity & successive repetitions 1972: The first forest gap model, JABOWA (Botkin et al 1972) Written in FORTRAN IV, ran on IBM 2741/TSS r.7 w. 50 kb memory

The forest gap model Introduction to JABOWA -Almost all forest gap models are based on JABOWA -JABOWA uses an individual-tree based approach to track establishment, growth, mortality Establishment: -Assumes unlimited seed source -Initializes the only state variable (diameter, ~0.5 cm) -Options: light, water, temperature -Random establishment ( individuals ha -1 ) Growth: -Potential tree diameter change scaled by -Temperature, soil moisture, light availability, nutrients -Change in diameter updated annually -Triggers allometric changes in height, LAI Botkin 1972 Bugmann 2001

The forest gap model

Introduction to JABOWA Stochastic Mortality 1. Background mortality -Age-related mortality (2% survive to max. tree age) 2. Stress / growth mortality (combined) -<0.01 cm for any given year Bugmann 2001 Model simulations & output variables 1.Species composition (10-30 species) 2.Basal area and wood volume or biomass 3.Stand density dynamics 4.Forest management and thinning or harvest 5.….

From FORET to FORECE to FORCLIM to FORSKA (and ZELIG) The forest gap model The framework of establishment, growth, mortality, is the same for all gap models FORET (Shugart 1977) Addition of resprouting or coppicing module specific for S. Appalachian forests 33 species in first version (up from 13 in JABOWA) FORECE (Kienast 1989) Introduced size dependent masting, frost events Scaling factor for CO2 effect on photosynthesis Browsing module to simulate deer-related mortality FORENA (Soloman 1986) Soil moisture / freezing threshold First application of climate change impacts (1986) FORSKA (Prentice) Chilling requirement Mechanistic based wood allocation FORCLIM (Bugmann 1986) Based on FORECE, but with fewer parameters Inclusion of cohorts Mechanistic hydrology ZELIG (Urban 1991) FORET derived Light effects from neighboring patches

Lischke 1998 The forest gap model Computational efficiency 1.Tracking all individual trees is memory intensive Solved by introduction of cohorts with same age &diameter (FORCLIM: Bugmann 1996) 2. Stochastic repetitions Solved by aggregating patches into height distribution (TREEMIG: Lischke 1998) Only 1 simulation is needed to represent the landscape Limitations of the forest gap model approach 3. Empirical growth models -Very little biology or biogeochemistry -Not valid under unique atmospheric conditions (CO 2, O 3, climate,…)

The forest gap model A closer look at GAP model growth and allocation -Fixed potential growth rates -Fixed carbon allocation ratios -Fixed parameters rather than processes -Sensitive to shape of response functions A DGVM is a process-based model 2 Key Developments 1.Species are replaced by plant functional types (PFTs) 2.Detailed ‘fast’ physiological calculations performed on the ‘mean individual’ 1.The fast processes are scaled to the number of individuals to set up slow processes 2.A single, deterministic simulation represents landscape-scale processes Prentice 2004 Emergence of dynamic global vegetation models

The forest gap model Plant functional type fractional coverage (-) Poulter et al GMD Plant functional types Grouping of 1000's plant species with similar functional traits Non-phylogenetic classification Fixed physiological and structural traits Physiognomy Tree or grass Photosynthetic pathway C3 or C4 Phenology Leaf type, deciduousness Rooting distribution Farquhar temperature and CO2 specificity C:N ratios Bioclimatic limits Resilience to fire Phenology ….

The forest gap model The ‘mean individual’ & establishment, growth, mortality 1.Establishment of seedlings is limited by space or light Maximum establishment rate is scaled deterministically New seedlings merge with the existing tree population and biomass redistributed 2.Growth occurs via physiological processes for photosynthesis, carbon allocation, respiration 3.Mortality of seedlings is caused by several factors; growth efficiency, temperature stress, light competition, disturbance Reduction of individuals adjusts existing biomass pools Prentice 2004

1.Documenting biome and community changes 2.Challenges in scaling ecological knowledge 3.Survey of forest gap models 4.Future of global vegetation modeling Overview of todays lecture

Limitations of the mean individual approach No explicit age structure as a consequence of merging approach Forest recovery from disturbance is too fast because of averaging of new with existing individuals -Implications for natural and human disturbance (fire, windthrow, ice storms, insects, disease) PFT approach reduces functional diversity causing models to be too sensitive to climate change PFT traits are also fixed in time and space, where variability and adaptability is observed Future of global vegetation modeling No age initialization With age initialization LPJ (pipe model) ORCHIDEE (resource model) ORCHIDEE (FM model)

Recent Advances 1.Ecosystem Demography model – Deterministic solution for gap dynamics – Grid cell has ‘tiles’ that represent age cohorts – But highly parameterized and unable to run globally 2.LPJ-GUESS – Biogeochemical and species based model – Uses gap approach with cohorts – 20 to 100 replications needed 3.ORCHIDEE-FM – Scaling of stand biomass increment to distribution of diameters using growth and yield concept – Only suitable for even age cohorts 4.TREEM-LPJ – Coupling biogeochemistry to height-structured TreeMig 5.GFDL-Princeton – Combining sub-grid cell tiling with mean individual Future of global vegetation modeling

6th Ginkgo Workshop, 2010 Lake Geneva Matterhorn Valais South Study sites ~30 km Study Area: Valais, Switzerland Future of global vegetation modeling

Sensitivity of modeling approaches to climate variability and change

Summary -Dynamic vegetation is a key component of understanding the past, present and future of the earth system -To meet the 3 S’s, trade-offs in model complexity and computing efficiency are required -AR5 IPCC Report includes static veg. models Simulation of loss of tree cover (green) with a 3 degree warming Change in NPP (LUE model) Liu, 2010, QI Paleovegetation Future- vegetation Scholze, 2006 PNAS Zhao and Running 2010 Science

Summary DGVM trends in LAI from migration and growing season length Observed Attribution to climate, CO2, …

Summary A history of dynamic global vegetation models (DGVM) (years)

Recommended reading Forest Gap Models Botkin, D.B., Janak, J.F. and Wallis, J.R., Some ecological consequences of a computer model of forest growth. J. Ecol., 60(3): Bugmann, H., A review of gap models. Clim. Change, 51: Lischke, H., Loffler, T.J. and Fischlin, A., Aggregation of Individual Trees and Patches in Forest Succession Models: Capturing Variability with Height Structured, Random, Spatial Distributions. Theor. Popul. Biol., 54: Dynamic global vegetation models Prentice, I.C., Bondeau, A., Cramer, W., Harrison, S.P., Hickler, T., Lucht, W., Sitch, S., Smith, B. and Sykes, M.T., Dynamic global vegetation modeling: Quantifying terrestrial ecosystem responses to large-scale environmental change. In: P. Canadell, D.E. Pataki and L.F. Pitelka (Editors), Terrestrial Ecosystems in a Changing World. Springer-Verlag, Berlin, Heidelberg, DE, pp Sitch, S., Smith, B., Prentice, I.C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J.O., Levis, S., Lucht, W., Sykes, M.T., Thonicke, K. and Venevsky, S., Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biol., 9: