Abstract

  • assimilate observation-based time series of woody vegetation carbon densities into bookkeeping model (BKM)
  • disentangle C fluxes from woody vegetation into anthropogenic and environmental contributions
  • data assimilation vastly improves BKM
  • global woody vegetation C sink is weaker and more suceptible to interannual variations and extrem events than estimated by other models
  • model data integration important

Introduction

  • the Global Carbon Project annually updates the Global Carbon Budget (GCB)
  • net land-atmosphere exchange of CO2 anthroppgenic fluxes from land use and land-use induced land cover change activities (LULCC) $+$ fluxes due to environmental processes = $E_{LUC} + S_{LAND}$
  • $E_{LUC}$ estimated with semi-empirical BKMs
    • $\pm65\,\%$ uncertainty (for one standard deviation)
      • different assumptions on amount of C contained per unit area (C density) contributes much to uncertainty
      • based on contemporary C stocks, leads to error simulation the past (bookkeeping error)
    • simulate emissions due to LULCC in absence of environmental fluxes by combining assumptions on amount of C contained in vegetation and soils and empirical decay functions
  • $S_{LAND}$ estimated with process based dynamic global vegetation models (DGVMs)
    • $\pm19\,\%$
      • different parametrizations
      • whether and how vegetation and soil processes are captured (found from model intercomparison project TRENDY)
    • account additionally (to BKM) for environmental effects on different C pools, simulate biogeochemical processes such as photosynthesis
    • loss of additional sink capacity (LASC): larger forested areas unde rpre-industrial land cover allow more C accumulation under favorable conditions (elevated CO$_2$), DGVMs without LULCC do not correct for that
  • satellite data can reduce uncertainties, but comes with limitations
    • only gross fluxes/sub-component fluxes
    • difficult to distringuish anthropogenic from environmental fluxes
    • restriction to committed fluxes: biomass loss considered flux, except when fate explicitly tracked (legacy fluxes)
  • propose approach to overcome BKP and satellite limitations
    • decompose observed fluxes into $E_{LUC}$ and $S_{LAND}$
    • based on time series of global woody vegetation C densities (2000-20019)
    • time series assimilated into BKM BLUE (Bookkeeping of Land Use Emissions) model

Results

A model-data integration framework for separating anthropogenic and environmental carbon fluxes

  • $C_{B,trans} = $net effects of anthropogenic and environmeltal changes based on time series mention earlier
  • $C_{B,fixed} = $effects of anthropogenic changes based on Land Use Harmonization 2 (LUH2) dataset
  • $S_{LAND,B} = \Delta C_{B, trans} - \Delta C_{B,fixed}$
    • should not be used to compute GCB, for instance litter, dead wood and soil dynamics are ignored; delayed fluxes happen here immediately (decomposition)
    • no bookkeeping error after 2000

Anthropogenic effects (LULCC) on global woody vegetation carbon

  • transient run shows higher C stocks, probably because of effects of elevated CO$_2$
  • leads also to higher emissions caused by clearing and harvesting
  • such emsissions not outbalanced by higher C uptake
  • major land-use transitions in BLUE: clearing, harvest, abandonment

Environmental effects on global woody vegetation carbon

  • TRENDY simulations assume current CO$_2$ levels in the past and hence overestimate C biomass
  • interannual variabilty mainly due to environmental effects
  • $S_{LAND,B}$ shows smaller sink than TRENDY estimates
  • interannual variabbility (IAV) computed as coefficient of variation
  • strong positive correlation between annual mean air temperature anomaly and and annualt anomaly in forest biomass
  • eastern North America (NAM): air temperature and radioation limiting effects of growth, western NAM: precipitation and soil moisture
  • higher temperature also increase heterotrophic respiration, working agains ositive growth effect
  • DGVMs underestimate negative effects of drought on sink strength, often heat and water stress on plant productivity not modeled
  • forest degradation implicit in the data now, has strong effect

Discussion

  • integration of observation into models crucial
  • budget imbalance ($B_{\mathrm{IM}}$) is measure of uncertainty in estimated terms of GCB, describes difference between emissions and sinks on land, ocean, and atmosphere
  • atmospheric growth rate can ($G_{ATM}$) be estimated with confidence
  • natural C sinks on land and ocean uncertain
  • $S_{LAND}$ as sink should increase due to C fertilization and hence $B_{\mathrm{IM}}$ should increase, but it does not, hence $S_{LAND}$ probably not accurate
  • possible improvements:
    • include non-woody vegetation C
    • sub-annual time scales: capture intra-annual response to extreme events
    • different measurements should be consistent in space and time

Methods

…

My ideas

  • instead of computing correlations on model output data, try causal inference computations