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Michael Raupach and Pep Canadell CSIRO Marine and Atmospheric Research, Canberra, Australia

Explore vulnerabilities in global carbon and water cycles, regional scale issues, and the impacts on vegetation responses. Learn about dynamic systems frameworks and examples like biosphere-human systems. Understand potential vulnerabilities in the water cycle and the effects of climate change.

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Michael Raupach and Pep Canadell CSIRO Marine and Atmospheric Research, Canberra, Australia

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  1. A dynamical-system perspective on carbon and water vulnerabilities: views at global and local scales Michael Raupach and Pep Canadell CSIRO Marine and Atmospheric Research, Canberra, Australia Global Carbon Project (IGBP-IHDP-WCRP-Diversitas) Canberra, 5-9 June 2006

  2. Outline • Vulnerabilities in the global carbon cycle • Vulnerabilities in the global water cycle • Regional scale vulnerabilities (mainly Australia) • Water cycle • Vegetation responses • A dynamical systems framework • Example: biosphere-human system

  3. Global atmospheric carbon budget http://lgmacweb.env.uea.ac.uk/e415/co2/carbon_budget.html Corinne LeQuere Data Sources: • Land Use: Houghton (1999) Tellus • Fossil Fuel: Marland et al (2005) CDIAC • Ocean: Buitenhuis et al (2005) GBC • Atmosphere: Keeling and Whorf (2005) CDIAC • Terrestrial: difference

  4. Emissions, CO2, temperature 150-year records of: • Anthropogenic CO2 emissions from fossil fuel burning • Changing atmospheric CO2 concentrations • Changing global mean temperatures (from instrumental record with effects of urbanisation removed)

  5. Present radiative forcing IPCC AR4, WG1 SPM, second draft (24-mar-2006)

  6. The changing carbon cycle 1850-2100 Atmospheric CO2 NOW Land C uptake Ocean C uptake temperature implication: 2 to 3 degC • C4MIP = Coupled Climate Carbon Cycle Model Intercomparison Experiment • Intercomparison of 8 coupled climate-carbon cycle models • Uncertainty (range among predictions) is comparable with uncertainty from physical climate models and emission scenarios Friedlingstein et al. 2006, in press present land sink (2 to 3 GtC/y) becomes a source

  7. Terrestrial C vulnerabilities Drivers: A: atmospheric composition B: climate C: land use

  8. Vulnerable land and ocean carbon pools (2000-2100) Gruber et al. (2004) In: Field CB, Raupach MR (eds.) (2004) The Global Carbon Cycle: Integrating Humans, Climate and the Natural World. Island Press, Washington D.C. 526 pp.

  9. Vulnerabilities in the carbon cycle: a simple model • Dynamic equations for 8 state variables

  10. Forcing: CO2 emission flux A2 A1 FEmis (PgC/y) B2 B1 data

  11. Temperature, CO2 : data and predictions TA (degK) • Global temperature record • Amospheric CO2 record • Climate sensitivity to CO2 : CO2 = 0.008 K/ppm CA (ppm)

  12. Vulnerability of peatland and frozen C:effect on CO2 • CP0 = 400 PgC, CF0 = 500 PgC, kPT = kFT = 0.001 [y1 K1] CA (ppm) A1 + vulnerable peatland C, frozen C: extra 100 ppm of atmospheric CO2 A2 A1 B2 B1

  13. Vulnerability of peatland and frozen C:effect on temperature • CP0 = 400 PgC, CF0 = 500 PgC, kPT = kFT = 0.001 [y1 K1] TA (degK) A1 + vulnerable peatland C, frozen C: extra 0.8 degK warming A2 A1 B2 B1

  14. Outline • Vulnerabilities in the global carbon cycle • Vulnerabilities in the global water cycle • Regional scale vulnerabilities (mainly Australia) • Water cycle • Vegetation responses • A dynamical systems framework • Example: biosphere-human system

  15. Potential vulnerabilities in the water cycle 1. Changes in global mean precipitation 2. Changes in large-scale spatial distribution of precipitation 3. Changes in temporal distribution of precipitationInterannual variability, seasonal cycling, frontal and convective rainfall 4. Changes in partition of precipitationCompetition for soil water (transpiration, soil evaporation, runoff, drainage)

  16. Response of global precipitation to global temperature change(IPCC Third Assessment Report, WG1) 1.2% per deg C Figure 9.18: Equilibrium climate and hydrological sensitivities from AGCMs coupled to mixed-layer ocean components; blue diamonds from SAR, red triangles from models in current use (LeTreut and McAvaney, 2000 and Table 9.1) Source: IPCC (2001) Climate Change 2001: The Scientific Basis, p. 560

  17. Global equilibrium evaporation? ??? 1.2% per C at 28 C Equilibrium evaporation: Raupach (2001) QJRMS Raupach (2000) BLM • Physical result: For any semi-closed system supplied with energy, the evaporation rate settles to equilibrium evaporation in the long-term limit • High generality: any mixing, any spatial distribution of evaporating surfaces • Hypothesis: the main evaporating parts of the atmosphere are approximately thermodynamically closed, and therefore evaporate at the equilibrium rate. • Global water balance: • A = available energy flux,  = dimensionless slope of saturation humidity • A simple sum: • Choosing T: Global average Bowen ratio = 7/24 = 0.29; 1/ = 0.29 at 28 oC

  18. Spatial distribution of precipitation:Present global and continental water budgets • Global precipitation = evaporation (PrecGlobe = EvapGlobe) • (AreaGlobePrecGlobe = AreaOceanPrecOcean + AreaLandPrecLand) (likewise for Evap) • (PrecLand = EvapLand + RunoffLand) (likewise for ocean)

  19. Spatial distribution of precipitationPrecipitation change through 21st century = (Y2100 - Y2000)/Y2000 (%) DJF JJA Canadian: CGCM1 Hadley: HadCM2 US National Assessment of the Potential Consequences of Climate Variability and Change (2003)http://www.usgcrp.gov/usgcrp/nacc/background/scenarios/found/fig20.html

  20. Observed precipitation trends (1900 to 2000) IPCC (2001) Third Assessment

  21. Partition of precipitation Quasi-steady-state water balance: a similarity approach • Time averaged water balance in the steady state: • Dependent variables: E (mean total evaporation) R (mean total runoff)Independent variables: P (mean precipitation) = water supply Q (mean potential evaporation) = water demand • Similarity assumptions (Fu 1981, Zhang et al 2004): • Solution (Fu 1981, Zhang et al 2004) finds E and R (with parameter a):

  22. Steady water balance: similarity approach dry wet wet dry • Normalise with potential evap Q:plot E/Q against P/Q • Normalise with precipitation P:plot E/P against Q/P a=2,3,4,5 a=2,3,4,5

  23. Steady water balance: similarity approach a=2,3,4,5 dry wet • E/Q as a function of P/Q • Sensitivity of runoff to P to Q

  24. Outline • Vulnerabilities in the global carbon cycle • Vulnerabilities in the global water cycle • Regional scale vulnerabilities (mainly Australia) • Water cycle • Vegetation responses • A dynamical systems framework • Example: biosphere-human system

  25. Annual mean temperature: Australia and global land

  26. Australian climate variability over 100 yearsRainfall Sources: • Lavery, B., Joung, G. and Nicholls, N. (1997). An extended high-quality historical rainfall dataset for Australia. Aust. Meteorol. Mag 46, 27-38 • BoM climate data set (http://www.bom.gov.au/cgi-bin/silo/reg/cli_chg/timeseries.cgi) • SILO gridded data set (Queensland Department of Natural Resources, Mines and Energy) • BoM gridded data set (Jones, Plummer et al 2005, part of Australian Water Availability Project)

  27. Correlation between temperature and rainfall Maximum temperature and rainfallCloudless days are rainfree and hot Minimum temperature and rainfallCloudless nights are rainfree and cool

  28. Water and carbon balances: dynamic model • Dynamic model is of general form dx/dt = f(x, u, p) • All fluxes (fi) are functions fi(state vector, met forcing, params) • Governing equations for state vector x = (W, Ci): • Soil water W: • Carbon pools Ci: • Simple (and conventional) phenomenological equations specify all f(x, u, p) • Carbon allocation (ai) specified by an analytic solution to optimisation of NPP

  29. Test area: Murrumbidgee basin Murrumbidgee basin

  30. J F M A M J J A S O N D 81 82 83 84 Murrumbidgee Relative Soil Moisture (0 to 1) 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05

  31. J F M A M J J A S O N D 81 82 83 84 Murrumbidgee Total Evaporation (mm d-1) 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05

  32. Predicted and observed discharge 11 unimpaired catchments in Murrumbidgee basin • 25-year mean: Jan 1981 to December 2005Prior model parameters set roughly for Adelong, no spatial variation Goobarragandra:410057 Adelong:410061

  33. Australia: vegetation greenness trends 1990-2005 NDVI, FC NDVI (various) fraction cover FCfrom GlobCarbon LAI

  34. Summary so far • Vulnerabilities in the global carbon cycle • BGC vulnerabilities comparable with physical climate and human dimensions • Quantitative analysis using perturbation of simple carbon-climate model • Example: vulnerability to peatland, frozen C is ~ 100 ppm or 0.8 degK • Vulnerabilities in the global water cycle • Four kinds of change in water availability through precipitation: Global mean Spatial distribution Temporal distribution Partition • Regional scale vulnerabilities (mainly Australia) • Current trends are not the same as trends over past 100 years • Consequences of hot droughts for water availablity and vegetation state

  35. Outline • Vulnerabilities in the global carbon cycle • Vulnerabilities in the global water cycle • Regional scale vulnerabilities (mainly Australia) • Water cycle • Vegetation responses • A dynamical systems framework • Example: biosphere-human system

  36. Modelling water, carbon and nutrient cycles:Dynamical systems framework • Variables: x = {xr} = set of stores (r) including all water, C, N, P, … stores f = {frs} = set of fluxes (affecting store r by process s)m = set of forcing climate and surface variablesp = set of process parameters • Stores obey mass balances (conservation equations) of form (for store r) • Equilibrium solutions: • Fluxes are described by scale-dependent phenomenological equations of form

  37. Basic dynamical systems theory:equilibrium points and local stability • Dynamical system: • Equilibrium points satisfy: • Determine local stability near equilibrium points by solving the linearised system around an equilibrium point xQ: • Solutions: • Stability criteria: • all λm have negative real parts => xQ is a stable equilibrium point • Imaginary parts of λm determine oscillatory behaviour of solution near xQ

  38. Dynamics at small and large scales f F f F x prob(x) prob(x) x • Most of the systems we study have small-scale and large-scale dynamics • Often we need to infer large-scale dynamics from small-scale dynamics • Small-scale dynamics Large-scale dynamics • Relationship between phenomenological laws [f(x)] at small and large scales: x x

  39. Simplified terrestrial biogeochemical model • Pools: (x1, x2) = (plant C, soil C) • Parameters: q1 = 1, q2 = 1 = scales for limitation of production by x1 and x2 k1 = 0.2, k2 = 0.1 = rate constants for fast, slow pools s1 = 0.01 = seed production (constant) • This is the test model used in the Optimisation Intercomparison (OptIC): comparative evaluation of parameter estimation and data assimilation methods for determining parameters in BGC models (see GlobalCarbonProject.org)

  40. Simplified terrestrial BGC model: trajectories

  41. Simplified terrestrial BGC model: equilibrium points A: stable B: unstable C: stable • At equilibrium, x2 and x1 satisfy • Either 1 or 3 equilibrium points (A, B, C)

  42. Simplified terrestrial BGC model:cubic defining the equilibrium points • Three equilibrium points: A (stable) B (unstable) C (stable) • If seed production s1 = 0: point A is at the origin (stable "extinction") • If seed production s1 > 0: point A has x1Q (A) > 0 (stable "quiescence") C A, B B A

  43. Simplified BGC model:effect of random forcing • "Log-Markovian" random forcing F(t)(Mean = F0, SDev/mean = 0.5) • k1 = 0.2, s1 = 0.01 • k1 = 0.4, s1 = 0.01 • k1 = 0.5, s1 = 0.01 • k1 = 0.5, s1 = 0 • Forcing F(t) • System flips randomly between active and quiescent stable states • "Blip and Flip" chaos • NOT Lorenzian chaos

  44. Final summary • Vulnerabilities in the global carbon cycle • BGC vulnerabilities comparable with physical climate and human dimensions • Quantitative analysis using perturbation of simple carbon-climate model • Example: vulnerability to peatland, frozen C is ~ 100 ppm or 0.8 degK • Vulnerabilities in the global water cycle • Four kinds of change in water availability through precipitation:Global mean, spatial distribution, temporal distribution, partition • Regional scale vulnerabilities (mainly Australia) • Consequences of hot droughts for water availablity and vegetation state • Dynamical systems • Equilibria, stability, cycles, trajectories, thresholds, phase transitions • Example: simplified BGC model (used in OptIC project) • "Flip and blip" chaos is some circumstances

  45. Hilary Talbot

  46. Wetland and frozen terrestrial C pools • 200-800 PgC in wetlands and peatlands • Tropical, temperate, boreal • CO2, CH4 exchanges both important • Vulnerable: ~ 100 PgCeq • 200-800 PgC in frozen soils • Warming => melting • CO2, CH4 exchanges both important • Vulnerable: ~ 100 PgCeq Gruber et al. (2004, SCOPE-GCP)

  47. The nitrogen gap Production of New N to 2100 • Modelled terrestrial sink through 21st century (CO2 + climate): • 260 to 530 PgC • 16 to 34% of anthropogenic emissions • N required: 2.3 to 16.9 PgN • N available: 1.2 to 6.1 PgN • Vulnerability (as foregone terrestrial C uptake):~ 200 to 500 PgC Hungate et al. (2003) Science

  48. Vulnerabilities in the carbon cycle: a simple model • Aim of analysis: study process perturbations in carbon cycle modelling Given a trajectory XR(t) from integration of the reference model, can we find properties of a similar perturbed model, if the reference and perturbed phenomenological laws FR(XR) and FP(XP) are similar in some sense? • Reference model: • Simple C model which approximately replicates mean of C4MIP simulations • Perturbed models: • Same simple model, including C release from peatland C, frozen C • How results are interpreted: • Difference XP(t)  XR(t) is a measure of the vulnerability associated with extra processes included in FP(XP) beyond FR(XR) • BUT XR(t) from simple model is not an independent carbon-climate prediction

  49. Vulnerabilities in the carbon cycle: a simple model • Phenomenological equations

  50. Is terrestrial C currently vulnerable?Observed vegetation greenness trends (2) 1980s: d(NDVI)/dt Summer 1982-1991 • Gains from earlier onset of growing season are almost cancelled out by hotter and drier summers which depress assimilation • Suggests a decreasing net terrestrial C sink 1990s: d(NDVI)/dt Summer 1994-2002 Angert et al. 2005; Dai et al. 2005; Buermann et al. 2005; Courtesy Inez Fung 2005

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