Impacts of land cover and climate data selection on understanding terrestrial carbon dynamics and the CO 2 airborne fraction

Abstract. Terrestrial and oceanic carbon cycle processes remove ~55 % of global carbon emissions, with the remaining 45 %, known as the "airborne fraction", accumulating in the atmosphere. The long-term dynamics of the component fluxes contributing to the airborne fraction are challenging to interpret, but important for informing fossil-fuel emission targets and for monitoring the trends of biospheric carbon fluxes. Climate and land-cover forcing data for terrestrial ecosystem models are a largely unexplored source of uncertainty in terms of their contribution to understanding airborne fraction dynamics. Here we present results using a single dynamic global vegetation model forced by an ensemble experiment of climate (CRU, ERA-Interim, NCEP-DOE II), and diagnostic land-cover datasets (GLC2000, GlobCover, MODIS). For the averaging period 1996–2005, forcing uncertainties resulted in a large range of simulated global carbon fluxes, up to 13 % for net primary production (52.4 to 60.2 Pg C a−1) and 19 % for soil respiration (44.2 to 54.8 Pg C a−1). The sensitivity of contemporary global terrestrial carbon fluxes to climate strongly depends on forcing data (1.2–5.9 Pg C K−1 or 0.5 to 2.7 ppmv CO2 K−1), but weakening carbon sinks in sub-tropical regions and strengthening carbon sinks in northern latitudes are found to be robust. The climate and land-cover combination that best correlate to the inferred carbon sink, and with the lowest residuals, is from observational data (CRU) rather than reanalysis climate data and with land-cover categories that have more stringent criteria for forest cover (MODIS). Since 1998, an increasing positive trend in residual error from bottom-up accounting of global sinks and sources (from 0.03 (1989–2005) to 0.23 Pg C a−1 (1998–2005)) suggests that either modeled drought sensitivity of carbon fluxes is too high, or that carbon emissions from net land-cover change is too large.

[1]  Philippe Ciais,et al.  Update on CO2 emissions , 2010 .

[2]  Niklaus E. Zimmermann,et al.  Plant functional type mapping for earth system models , 2011 .

[3]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[4]  I. C. Prentice,et al.  Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model , 2003 .

[5]  Contributions of carbon cycle uncertainty to future climate projection spread , 2009 .

[6]  Maosheng Zhao,et al.  Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009 , 2010, Science.

[7]  S. Seneviratne,et al.  Recent decline in the global land evapotranspiration trend due to limited moisture supply , 2010, Nature.

[8]  Markus Reichstein,et al.  Uncertainties of modeling gross primary productivity over Europe: A systematic study on the effects of using different drivers and terrestrial biosphere models , 2007 .

[9]  T. McMahon,et al.  Updated world map of the Köppen-Geiger climate classification , 2007 .

[10]  E. T. Linacre,et al.  Estimating the net-radiation flux , 1968 .

[11]  Paul Berrisford,et al.  The ERA-Interim Archive , 2009 .

[12]  P. Ciais,et al.  Multiple constraints on regional CO2 flux variations over land and oceans , 2005 .

[13]  Stephen Sitch,et al.  Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics , 2005 .

[14]  Steffen Fritz,et al.  Identifying and quantifying uncertainty and spatial disagreement in the comparison of Global Land Cover for different applications , 2008 .

[15]  I. C. Prentice,et al.  Carbon balance of the terrestrial biosphere in the Twentieth Century: Analyses of CO2, climate and land use effects with four process‐based ecosystem models , 2001 .

[16]  Pete Smith,et al.  Integrating plant–soil interactions into global carbon cycle models , 2009 .

[17]  I. C. Prentice,et al.  Evaluation of the terrestrial carbon cycle, future plant geography and climate‐carbon cycle feedbacks using five Dynamic Global Vegetation Models (DGVMs) , 2008 .

[18]  Wolfgang Knorr,et al.  Is the airborne fraction of anthropogenic CO2 emissions increasing? , 2009 .

[19]  Philippe Ciais,et al.  Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades , 2007 .

[20]  Nicolas Gruber,et al.  Trends and regional distributions of land and ocean carbon sinks , 2009 .

[21]  W. Lucht,et al.  Terrestrial vegetation and water balance-hydrological evaluation of a dynamic global vegetation model , 2004 .

[22]  C. D. Keeling,et al.  Atmospheric CO 2 records from sites in the SIO air sampling network , 1994 .

[23]  K. Taylor.  Summarizing multiple aspects of model performance in a single diagram , 2001 .

[24]  Ke Zhang,et al.  Sensitivity of pan-Arctic terrestrial net primary productivity simulations to daily surface meteorology from NCEP-NCAR and ERA-40 reanalyses , 2007 .

[25]  Philippe Ciais,et al.  Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models , 2002, Nature.

[26]  Wolfgang Cramer,et al.  A simulation model for the transient effects of climate change on forest landscapes , 1993 .

[27]  M. Kanamitsu,et al.  NCEP–DOE AMIP-II Reanalysis (R-2) , 2002 .

[28]  T. D. Mitchell,et al.  An improved method of constructing a database of monthly climate observations and associated high‐resolution grids , 2005 .

[29]  N. Meinshausen,et al.  Greenhouse-gas emission targets for limiting global warming to 2 °C , 2009, Nature.

[30]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[31]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

[32]  R. Houghton.  The annual net flux of carbon to the atmosphere from changes in land use 1850–1990* , 1999 .

[33]  Maosheng Zhao,et al.  Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses , 2006 .

[34]  N. Ramankutty,et al.  Characterizing patterns of global land use: An analysis of global croplands data , 1998 .

[35]  A. Grainger.  Difficulties in tracking the long-term global trend in tropical forest area , 2008, Proceedings of the National Academy of Sciences.

[36]  John S. Woollen,et al.  NCEP-DOE AMIP-II reanalysis (R-2). Bulletin of the American Meteorological Society . , 2002 .

[37]  Frédéric Achard,et al.  GLOBCOVER : The most detailed portrait of Earth , 2008 .

[38]  A. Belward,et al.  GLC 2000 : a new approach to global land cover mapping from Earth observation data , 2005 .

[39]  Iwan Supit,et al.  A simple method for generating daily rainfall data , 1986 .

[40]  P. Cox,et al.  Impact of changes in diffuse radiation on the global land carbon sink , 2009, Nature.

[41]  Valerie Trouet,et al.  Ensemble reconstruction constraints on the global carbon cycle sensitivity to climate , 2010, Nature.

[42]  N. McDowell,et al.  A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests , 2010 .

[43]  A. Sterl,et al.  The ERA‐40 re‐analysis , 2005 .

[44]  Mathias Disney,et al.  Impact of land cover uncertainties on estimates of biospheric carbon fluxes , 2008 .

[45]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[46]  Jeffrey A. Hicke,et al.  NCEP and GISS solar radiation data sets available for ecosystem modeling: Description, differences, and impacts on net primary production , 2005 .

[47]  C. Müller,et al.  Modelling the role of agriculture for the 20th century global terrestrial carbon balance , 2007 .

[48]  Corinne Le Quéré,et al.  Trends in the sources and sinks of carbon dioxide , 2009 .

[49]  P. Cox,et al.  How positive is the feedback between climate change and the carbon cycle? , 2003 .