Modeling vegetation dynamics and climate the global perspective. Christine Delire CNRM/GAME (Meteo-France / CNRS)

Transcription

1 Modeling vegetation dynamics and climate the global perspective Christine Delire CNRM/GAME (Meteo-France / CNRS) Levis, Wileys&Sons Advanced Review, 2010

2 Scales global regional local Factors Climate Soil Biotic factors biosphere ecosystem population species Adapted from I. Chuine.

3 NASA Earth Observations Nov Oct

4 Outline 1. Vegetation-Climate interactions and feedbacks a. Biogeophysical feedbacks b. Biogeochemical feedbacks 2. Modeling vegetation dynamics at large scale: DGVMs 3. Coupled climate-vegetation models: biogeophysical feedbacks a. Mean state b. Variability c. Sahel 20 th century d. Sahara mid-holocene 4. Coupled climate-vegetation models: biogeochemical feedbacks 4

5 1. Vegetation climate interactions and feedbacks Climate change affects vegetation through : precipitation, temperature, radiation air composition (CO2, O2 ) Changes in vegetation affect climate through : albedo evapotranspiration rugosity Biogeophysical feedbacks exchange of CO2, O2, CH4, N, Biogeochemical feedbacks (adapted from Foley et al., Frontiers in Ecology, 2003) 5

6 Biophysical feedbacks. Mid - high latitudes: snow Albedo Boreal Forest Tundra Solar Radiation Solar Radiation S S! Low B S! Higher T albédo SAlbedo T B > T T 6

7 Snow albedo feedback Forest Tundra «Cold» Arctic case Warming Foley et al, Science,

8 Observed arctic heating Alaska Chapin et al, Science 2005 Observed heating since the 1960 ies can t be explained by changes in atmospheric circulation, cloud cover or sea ice. Main reason: Lengthening of the snow-free season (reduced albedo) Northward expansion of shrubs 8

9 Biophysical feedback: Tropics: evapotranspiration, rugosity, albedo Ascending air Humid convection Favors precipitation (water recycling) High evapotranspiration H Low albedo Solar radiation Higher Albedo Descending air Dry Convection Lower Evapotranspiration H low rugosity Higher wind 9

10 Biophysical feedbacks: Tropics: evapotranspiration, rugosity, albedo NYTimes 2007, «Rabbit fence» in Australia Clouds above natural vegetation areas 10

11 Biogeochemical feedbacks: C, N, CH 4, O 2 O2 CO2 CO2 Anoxic decomposition, CH4 Lake / wetland Permafrost Adapted from Levis, Wileys&Sons Advanced Review,

12 Biogeochemical feedbacks: terrestrial carbon cycle (19 32' N, ' W) Keeling, C.D. Increase in annual mean annual cycle linked to photosynthetic activity and respiration May-Sept : growing season N hemisphere: photosynthèse > respiration: Biosphere sink of CO 2 Oct - April : senescence - dormancy N hemisphere : respiration > photosynthese: Biosphere source of CO 2 12

13 Biogeochemical feedbacks: terrestrial carbon cycle Effect of increased atmospheric CO 2 fertilisation effect increased water use efficiency (WUE) Effect of increased temperature: Respiration by plants increases Soil microbial decomposition increases increased vegetation density, increased uptake of CO 2 = negative feedback Increased release of CO 2 = positive feedback Effect of climate change: combination of biogeochemical and biophysical feedbacks Terrestrial biosphere may become a net source of carbon 13

14 2. Modeling Vegetation dynamics at large scale: Dynamic Global Vegetation Models (DGVM) Climate change affects vegetation : precipitation, temperature, radiation air composition (CO2, O2 ) Changes in vegetation affect climate through: albedo evapotranspiration rugosity exchange of CO2, O2, CH4 adapted from Foley et al.,

15 Historical perspective: Global climate models (GCM): GCM in 1990s : Simple biophysical parameterizations of vegetation: biomes with albedo, roughness, stomatal resistance from look-up tables ex: Amazon deforestation numerical experiments Paleo-climate study For different climates (LGM, mid-holocene): development of equilibrium vegetation models (EVM) based on bioclimatological envelope, Concept of plant functional type (PFT) Ex: BIOME (Prentice et al,.) Ecology: plant succession models, biogeochemistry models DGVM (end of 1990s) simplified succession and biogeochemistry, interactive biogeography of EVM, Biophysics from GCMs 15

16 Example: IBIS (Integrated BIosphere Simulator) Foley et al, 1996; Kucharik et al., 2000 CO 2 Based on conservation of mass (H2O, C) and energy Upper canopy wood H 2 O Uses concept of plant functional types (12 PFTs) Light and water competition between PFTs --> succession Water and carbon cycling in vegetation and soils 6 soil layers Lower canopy roots Organic matter water / ice 16

17 Climate allowing, 12 plant functionnal type (pft) can coexist in each gridcell in IBIS 1: tropical broadleaf evergreen trees 2: tropical broadleaf drought-deciduous trees 3: warm-temperate broadleaf evergreen trees 4: temperate conifer evergreen trees 5: temperate broadleaf cold-deciduous trees 6: boreal conifer evergreen trees 7: boreal broadleaf cold-deciduous trees 8: boreal conifer cold-deciduous trees 9: evergreen shrubs 10: cold-deciduous shrubs 11: warm (c4) grasses 12: cool (c3) grasses pfts defined by form: tree, shrub, grass phenology: evergreen - deciduous physiology: conifers - C3 - C4 photosynthesis climatic zone: tropical, temperate, boreal 17

18 inputs: soil texture map topography Air temperature and humidity precipitations, Wind speed Solar radiation (visible and infrared), Atmospheric CO 2 IBIS Output examples : Biomes Leaf area index per PFT Fraction of tree, grass in the gridcell Net Primary Productivity, biomass (vegetation and sol) Soil microbial decomposition Temperature and soil moisture, evapotranspiration Surface runoff, deep drainage, water table height 18

19 Simplified Dynamics: - representation of a «mean individual» area averaged - dynamics is the result of the carbon balance per pft - dispersal processes not represented Delire et al, Glob Biogeochem Cyc,

20 Dynamics Initial Condition Humid conditions --> competition for light Dry conditions --> competition for water saplings Forest Grassland 20

21 Dynamique Initial Condition Dry / wet seasons Savanna 21

22 Simulated vegetation types climatic observations IBIS (Kucharik et al., GBC, 2000) 22

23 Net primary productivity Simulation Satellite Running et al,

24 Soil Carbon 24

25 Application at smaller scale: France Temperate Broadleaf cold-deciduous trees Cheaib et al, Ecology Letters, 2012 Beech Fagus sylvatica Sessile Oak Quercus Petraea Pedonculate Oak Quercus robur 25

26 Application at smaller scale: France Temperate Broadleaf cold-deciduous trees Cheaib et al, Ecology Letters,

27 Current distribution simulations and models evaluation Pinus sylvestris TSS = 0.48 TSS = 0.30 TSS = 0.25 BIOMOD Nancy NBM STASH TSS = 0.26 TSS = 0.26 Current distribution (IFN) PHENOFIT TSS = 0.03 LPJ TSS = 0.07 Needleleaf Evergreen Cheaib, 2010 ORCHIDEE IBIS

28 DGVMS differ in level of complexity to represent processes in plants: photosynthesis, respiration at the leaf level, Allocation, phenology Vertical (2 canopies?), horizontal resolution (several PFTs / gridcell) Number / definition of PFTs Competition rules Area based / individual based, cohorts Nutrients (Nitrogen, Phosphorus) Work in progress: Adaptative models Seed dispersal / plant migration Soil development Plant mortality, response to extreme events Insect disturbance 28

29 Example: area based vs individual based model LPJ (IBIS type) LPJ-GUESS Forest NE Poland Smith et al., Ecol Applications,

30 Active research area: 30% of land area covered by crops Land use changes during 21 st century will affect climate more than natural vegetation changes Need of vegetation models representing land cover changes due to human activities. Problem: modeling human behavior: timing of planting, harvesting, irrigation, fertilization, tilling, rotation, etc Ramankutty et al, 2005, 30

31 Up to now: Climatic observations DVGM? GCMs DGVM NPP Vegetation distribution 31

32 3. Coupled climate - vegetation models : biophysical feedbacks a. Mean state Vegetation types, offline model example CCM3/IBIS ocean Climatic observations polar desert desert tundra open shrubland DVGM dense shrubland grassland/steppe Vegetation types, coupled model savanna mixed F. boreal deciduous F. AGCM (CCM3) DVGM (IBIS) boreal evergreen F. temperate deciduous F. temperate evergreen coniferous F. temperate evergreen broadleaf F. tropical deciduous F tropical evergreen F. Delire et al, Glob Biogeochem Cyc,

33 Feedback example Vegetation types, coupled model Dynamic vegetation feedback: Cold bias increased by 1C when vegetation dynamics is turned on JJA Temperature simulated - observed Delire et al,

34 b. Variability? Vegetation dynamics introduces slow processes in the climatic system (ex: yrs transition between a grassland and a forest) should affect climate variability. Test with CCM3/IBIS and fixed sea-surface temperatures Fixed vegetation a. Lag-one correlation coefficient of yearly precipitation c. Dynamic vegetation Lag-one correlation coefficient of yearly precipitation Delire et al, J Climate, 2004 Vegetation dynamics enhances low-frequency variability of the precipitation in transition zones between wet and dry climate 34

35 Sahel precipitation, Observations (CRU) Annual rainfall anomalies Static vegetation simulation AGCM (GENESIS) DVGM (IBIS) Dynamic vegetation simulation Wang et al, Clim Dyn, 2004 Precipitation anomalies - triggered by sea-surface temperatures anomalies - less persistent without feedbacks from vegetation dynamics 35

36 Sahel precipitation, Observations (CRU) Annual rainfall anomalies AGCM (GENESIS) DVGM (IBIS) Dynamic vegetation simulation 2 different versions of the model with different internal variability of rainfall Wang et al, Clim Dyn, 2004 Too high internal variability overrides long-term memory of vegetation 36

37 Mid Holocene Sahel & Sahara - Multiple Equilibriums? Evidence for 3 periods with Green & wet Sahara during last 120 kyrs in response to orbital forcing 2 equilibriums? demenocal, comment on Tjallingii et al, NatureGeoSc, 2008 Mid-Holocene, evidence for vegetation collapse in response to smooth orbital forcing Foley et al, 2003 from demenocal et al, QuatScRev,

38 Mid Holocene Sahel & Sahara - Multiple Equilibria, sensitivity to initial conditions? Vegetation cover Incoming solar radiation Monsoon rains albedo evapotranspiration Mechanism proposed by Brovkin et al, (JGR 1998), Claussen, (GCB,1998), Claussen et al, (GRL1999): with box model & biome-gcm model o strong positive feedback from vegetation cover o In present-day climate: 2 stable states : dry Sahel, wet Sahel, sensitivity to initial conditions o Mid-Holocene: only 1 state: wet Sahel/Sahara In Other models : very weak vegetation feedback, or negative vegetation feedback Feedback strength very much model dependant 2006: Liu et al (GRL, 2006) propose alternative mechanism: low frequency climate variability 38

39 Biophysical effects of landuse: Cooling after deforestation in midlatitudes: ex Frost followed the Plow, Bonan, Ecol Applic, 1999 In the Amazon, complete deforestation leeds to increased Temp and decreased Precip. But contrasting effects depending on the strength of the deforestation Effect of irrigation? Cooling? Effect on river discharge? Depends Ramankutty et al, 2005, 39

40 4. Coupled climate - vegetation models : biogeochemical feedbacks CO 2 21st century simulations of climate-vegetation-carbon cycle Cox et al., Nature 2000 Coupled ocean - vegetation atmosphere - carbone cycle simulation Fixed vegetation simulation Simulation with no climatic effect of CO 2, only fertilisation effect on vegetation Changes in vegetation 40

41 21st century simulations of climate-vegetation -carbon cycle CO 2 fertilisation CO2 Precip? T Changes in vegetation cover Soil microbial decomposition Biospheric sink decreases 41

42 Future - present CO 2 Amazon forest dieback Cox et al., Theor. Appl. Clim

43 Comparison of several coupled climate-carbon models CO 2 ~ agree for 20 th century 300 ppmv spread in 2100 ~ Compensation between land uptake and ocean uptake highest land, lowest ocean uptake reverse Friedlingstein et al, J of Climate,

44 Biogeochemical feedbacks: other processes? Nitrogen cycle: weakens fertilization feedback weakens T soil decomposition feedback Phosphorus:? CH4? High latitudes lakes and wetlands under climate change. thawing of permafrost increased productivity thermokarst lakes anoxic decomposition, release of CH4 44

45 Biogeochemical feedbacks of land-use Estimated cumulative CO 2 emissions since 1750 : 155 Pg (320 Pg from fossil fuels) Currently feedback same order of magnitude as climate-carbon feedbacks Biofuels? Fertilizers : increased plant productivity increased CO 2 uptake dead zones (Mississippi) release of N 2 O 45

46 Conclusions Global models with biospheric processes have lots of feedback loops The strength of these feedbacks is still sometimes poorly known, or poorly modeled Necessary simplifications (ex: PFTs) tend to strengthen feedbacks Necessity to think about numerical scientific method 46

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