Vanessa Haverd_Multiple observation types reduce uncertainty in Australia's terrestrial carbon and water cycles
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Vanessa Haverd_Multiple observation types reduce uncertainty in Australia's terrestrial carbon and water cycles
- 1. The Australian Terrestrial Carbon Budget V. Haverd, M.R. Raupach, P.R. Briggs, J.G. Canadell, S. J. Davis, P. Isaac, R. M. Law, C. P. Meyer, G. P . Peters, C. Pickett-Heaps, S. Roxburgh, B. Sherman, E. van Gorsel , R. Viscarra Rossel, Z. Wang Vanessa Haverd | Research Scientist 14 February 2012 CMAR Acknowledgements: TERN OzFlux and ACCSP
- 2. Introduction Australian Continent First attempt at full carbon budget (C-CO2) of • 1990-2011 period Contribution to RECCAP (REgional Carbon Cycle Assessment and Processes) project (Canadell et al., 2011) Places contributions from the terrestrial biosphere and anthropogenic emissions along side each other Territorial C = Biospheric C + Fossil Fuel C + Harvested Wood Products C −dCT / dt = − dCB / dt − dCFF / dt − dCHWP / dt = ( FNPP + FRH + FFire + FLUC + FTransport +FHarvest ) + ( FFF + FFF , Export ) − dCHWP / dt
- 5. Contributions to Biospheric Carbon Accumulation
- 6. Net Primary Production (NPP) and Net Ecosystem Production (NEP = NPP – Heterotrophic Respiration) Obtained using BIOS2 = CABLE-SLI-CASAcnp in AWAP operational framework SLI = Soil-Litter- CASAcnp = AWAP = Australian Water CABLE = Community Availability Project Atmosphere-Biosphere-Land Iso Biogeochemical Exchange model model Continental processing Soil hydrology, soil evaporation Soil and plant framework Water, energy, carbon fluxes C, N, P dynamics Met and soil data Wang et al. (2011) Haverd et al. (2010) Wang et al. (2010) Model-Data Fusion Raupach et al. (2009) Non-zero NEP results from climate variability and rising CO2 Disturbance effects (particularly fire and LUC) accounted for by other methods
- 7. Observations for BIOS2 constraints and evaluation
- 8. Multiple constraints on Australian terrestrial Net Primary Production : Eddy flux data provide the tightest constraint error bars = uncertainty from propagated parameter uncertatinties (1σ) Prior estimate Eddy fluxes Streamflow Litterfall Eddy fluxes + Litterfall Streamflow + Litterfall Streamflow + Eddy fluxes Eddy fluxes + Litterfall + Streamflow 0 1 2 3 4 NPP (GtC y-1)
- 9. BIOS2 evaluation: Gross Primary Production from 12 OzFlux sites Map: long term mean (BIOS2) Boxes: annual cycle (Ozflux sites)
- 10. BIOS2 evaluation: comparison with Viscarra Rossel obs-based Soil C
- 11. NPP: spatial variability 1 Tropics 2 Savanna 3 Warm 6 Desert Temperate 5 4 Mediterranean Cool Temperate NPP (1990-2011) Bioclimatic Classification
- 12. A century of continental precip, NPP and NEP
- 13. Percentile Rank of 22-y mean Precip, NPP and NEP Precip NPP NEP
- 14. Percentile Rank of 22-y mean Precip, NPP and NEP Precip NPP NEP
- 15. Percentile Rank of 22-y mean Precip, NPP and NEP Precip NPP NEP
- 16. Percentile ranks of the 1990-2011 mean precipitation, NPP and NEP, relative to all other 22-y periods starting in successive years from 1911.
- 17. Responses of NPP and NEP to rising CO2
- 18. Effects of variable climate and rising CO2 on mean (1990-2011) NPP and NEP Net Net Primary Ecosystem Production Production
- 19. Fire: Comparison of GFED3 and NGGI CO2-C Gross Fire Emissions estimates. (i) Annual fluxes per state per vegetation class; (ii) Total annual Australian continental fire emissions 1997-2009.
- 21. Gross Fire emissions of C-CO2 by region • Tropics and savanna account for 80% of the total emissions • South-eastern temperate region contributes significantly in years with extreme fire events such as 2003 (28 %) and 2006 (15 %). • Total gross fire emissions (127 Tg C y-1) are comparable to Australian territorial emissions from the burning of fossil fuels (96 Tg Cy-1) • The net C-CO2 emissions from biomass burning are much smaller (26 TgCy-1)
- 22. Land Use Change
- 23. Net effect of emissions from traded fossil fuels (2004)
- 24. Key Findings •Australia’s NBP of 36 ± 35 TgC y-1 offsets fossil fuel emissions (95 ± 6 TgC y-1) by 38 ± 36 %. •The interannual variability in NEP and hence NBP exceeds Australia's total carbon emissions by fossil fuel consumption •Gross fire emissions account for 6% of continental NPP, approximately the same as the 1σ interannual variability in NPP •Net fire emissions account for only 1 % of NPP. •Land use change emissions similar to net fire emissions, accounting for 1% of NPP. •Fossil fuel export ~1.5 times territorial emissions (1990-2011). •Fossil fuel export ~2.5 times territorial emissions (2009-2010).
- 25. Vanessa Haverd Thank you Research Scientist t +61 2 6246 5981 e vanessa.haverd@csiro.au CMAR
- 26. BIOS2 results: water balance and net 1500 primary production Precip (mm y-1) i 1500 ET (mm y ) -1 ii Parameter + forcing error Parameter error 1000 1000 Best estimate Forcing error Best estimate 500 500 0 0 Soil evaporation fraction iii Runoff / precip fraction iv 1.0 0.4 0.5 0.2 0.0 0.0 NPP (g m-2 d-1) v NPP recurrent fraction vi 1.0 2 0.5 1 0 0.0 Global Global Desert Desert Tropics Tropics Mediterr Mediterr Australia Australia Savanna Savanna Cool Temp Cool Temp Warm Temp Warm Temp 1 2 3 4 5 6 A 1 2 3 4 5 6 A
- 27. Observations for BIOS2 constraints and evaluation
- 29. BIOS2 evaluation: monthly mean carbon and water fluxes (OzFlux)
- 30. BIOS2 evaluation: long term ET from streamflow and carbon pool data
- 31. NPP and ET: NPP (g m-2 d-1) comparing with 3 i 3pg AussieGrass BiosEquil other estimates 2 Century CenW dLdP Miami-oz 12 mean NPP Miami 1 Olson estimates for Australia RFBN from (Roxburgh et al TMS Vast 0 2004) ET (mm y-1) ii AWAP 1000 AWRA Guerschman NDTI etlook 7 mean ET 500 MODIS estimates for Australia 0 (King et al 2012) Desert Tropics Mediterr Australia Savanna Cool Temp Warm Temp 1 2 3 4 5 6 A
- 32. Multiple constraints on Australian terrestrial Net Primary Production : Eddy flux data provide the tightest constraint error bars = uncertainty from propagated parameter uncertatinties (1σ) Prior estimate Eddy fluxes Streamflow Litterfall Eddy fluxes + Litterfall Streamflow + Litterfall Streamflow + Eddy fluxes Eddy fluxes + Litterfall + Streamflow 0 1 2 3 4 NPP (GtC y-1)
- 33. BIOS2 evaluation: Gross Primary Production from 12 OzFlux sites Map: long term mean (BIOS2) Boxes: annual cycle (Ozflux sites)
- 34. Flux components of Net Biosphere Production (i) Fluxes out of Aust. (ii) Process contributions (iii) Fluxes into biosphere to accumulation of atmosphere from Aust. biospheric C territory
- 35. Flux components of Land-Atmosphere- Exchange (i) Fluxes out of Aust. (ii) Process contributions (iii) Fluxes into biosphere to accumulation of atmosphere from Aust. biospheric C territory
- 36. BiosEquil Long-term NPP: 2 Century CenW comparing with dLdP Miami-oz Miami 1 previous estimates Olson RFBN TMS 0 Vast 12 mean NPP NPP (g m-2 -1 -1) d ET (mm y ) 3pg 3 i estimates for Australia ii AussieGrass BiosEquil AWAP from (Roxburgh et al 1000 Century AWRA 2 CenW 2004) Guerschman dLdP NDTI Miami-oz etlook Miami MODIS 500 1 Olson RFBN TMS 0 Vast 0 ET (mm y-1) ii AWAP 1000 Desert AWRA Tropics Mediterr Australia Savanna Guerschman NDTI Cool Temp Warm Temp etlook MODIS 500 0 Desert Tropics Mediter Austra Savan Coo Wa
- 37. Temperature Precip NPP NPP NEP NEP Rising CO2 Preindustrial CO2
Editor's Notes
- The impact of each of three data sets (leaf –NPP (litter-fall), streamflow and eddy flux data) and combinations thereof on the long-term mean Australian continental NPP estimate and its uncertainty. Each data set individually leads to a reduction in uncertainty compared with the prior estimate, although with quite different values, reflecting possible biases in the model and/or observations for the particular observable. The estimates are more convergent when 2 data sets are used simultaneously, and the estimate constrained by all three is a compromise between the results obtained using each data set individually. The error bars in Figure 3 indicate that eddy flux data provide a stronger constraint than leaf-NPP, even though leaf NPP observations more widely distributed (Figure 2). This reflects the high precision of the eddy flux measurements, compared with disparate litterfall observations which do not share a common methodology and are subject to large errors from fine scale heterogeneity. Long-term evaporation from streamflow provides a relatively weak constraint because in most regions of Australia, it is largely driven by rainfall (continentally, evaporation accounts for 90% of precipitation).
- BIOS2 paper, Figure 6: Continental long term GPP (map) and mean annual cycle (averaged over years of obs) of GPP’ (BIOS2 and obs, left axis) and LAI (right axis), at 12 OzFlux sites.
- BIOS2 paper, Figure 6: Continental long term GPP (map) and mean annual cycle (averaged over years of obs) of GPP’ (BIOS2 and obs, left axis) and LAI (right axis), at 12 OzFlux sites.
- Here, the top two panes of the previous slide (NPP and ET) are shown with all elements greyed out. Superimposed in colour on the NPP pane are the various Roxburgh results. Superimposed on the ET pane are most of the WIRADA ET estimates. The old CABLE results from WIRADA will be removed for publication. Also left out of the WIRADA results are those of Yongqiang, which have some problems. All ET results are for the period Jan 2000-Dec 2005, except etlook which is for 2002/07-2005/06.
- The impact of each of three data sets (leaf –NPP (litter-fall), streamflow and eddy flux data) and combinations thereof on the long-term mean Australian continental NPP estimate and its uncertainty. Each data set individually leads to a reduction in uncertainty compared with the prior estimate, although with quite different values, reflecting possible biases in the model and/or observations for the particular observable. The estimates are more convergent when 2 data sets are used simultaneously, and the estimate constrained by all three is a compromise between the results obtained using each data set individually. The error bars in Figure 3 indicate that eddy flux data provide a stronger constraint than leaf-NPP, even though leaf NPP observations more widely distributed (Figure 2). This reflects the high precision of the eddy flux measurements, compared with disparate litterfall observations which do not share a common methodology and are subject to large errors from fine scale heterogeneity. Long-term evaporation from streamflow provides a relatively weak constraint because in most regions of Australia, it is largely driven by rainfall (continentally, evaporation accounts for 90% of precipitation).
- BIOS2 paper, Figure 6: Continental long term GPP (map) and mean annual cycle (averaged over years of obs) of GPP’ (BIOS2 and obs, left axis) and LAI (right axis), at 12 OzFlux sites.
- Here, the top two panes of the previous slide (NPP and ET) are shown with all elements greyed out. Superimposed in colour on the NPP pane are the various Roxburgh results. Superimposed on the ET pane are most of the WIRADA ET estimates. The old CABLE results from WIRADA will be removed for publication. Also left out of the WIRADA results are those of Yongqiang, which have some problems. All ET results are for the period Jan 2000-Dec 2005, except etlook which is for 2002/07-2005/06.