Recent trends and drivers of regional sources and sinks of carbon dioxide

Abstract

The land and ocean absorb on average over half of the anthropogenic emissions of carbon dioxide (CO2) every year. These CO2 “sinks” are modulated by climate change and variability. Here we use a suite of nine Dynamic Global Vegetation Models (DGVMs) 5 and four Ocean Biogeochemical General Circulation Models (OBGCMs) to quantify the global and regional climate and atmospheric CO2 – driven trends in land and oceanic CO2 exchanges with the atmosphere over the period 1990–2009, attribute these trends to underlying processes, and quantify the uncertainty and level of model agreement. The models were forced with reconstructed climate fields and observed 10 global atmospheric CO2; Land Use and Land Cover Changes are not included for the DGVMs. Over the period 1990–2009, the DGVMs simulate a mean global land carbon sink of −2.4±0.7 PgCyr−1 with a small significant trend of −0.06±0.03 PgCyr−2 (increasing sink). Over the more limited period 1990–2004, the ocean models simulate a mean ocean sink of −2.2±0.2 PgCyr−1 with a trend in the net C uptake that 15 is indistinguishable from zero (−0.01±0.02 PgCyr−2). The two ocean models that extended the simulations until 2009 suggest a slightly stronger, but still small trend of −0.02±0.01 PgCyr−2. Trends from land and ocean models compare favourably to the land greenness trends from remote sensing, atmospheric inversion results, and the residual land sink required to close the global carbon budget. Trends in the land sink 20 are driven by increasing net primary production (NPP) whose statistically significant trend of 0.22±0.08 PgCyr−2 exceeds a significant trend in heterotrophic respiration of 0.16±0.05 PgCyr−2 – primarily as a consequence of wide-spread CO2 fertilisation of plant production. Most of the land-based trend in simulated net carbon uptake originates from natural ecosystems in the tropics (−0.04±0.01 PgCyr−2), with almost no 25 trend over the northern land region, where recent warming and reduced rainfall offsets the positive impact of elevated atmospheric CO2 on carbon storage. The small uptake trend in the ocean models emerges because climate variability and change, and in particular increasing sea surface temperatures, tend to counteract the trend in ocean uptake driven by the increase in atmospheric CO2. Large uncertainty remains in the magnitude and sign of modelled carbon trends in several regions, and on the influence of land use and land cover changes on regional trends

Evolution and variation of atmospheric carbon dioxide concentration over terrestrial ecosystems as derived from eddy covariance measurements

“…It was estimated, by fitting a harmonic model to monthly-mean ACTE, seasonal amplitude of ACTE increased over the 10-year period at rates of 0.60 ppm yr À1 . The increase in amplitude has been attributed to increasing assimilation and release of CO 2 by terrestrial ecosystem and the advance of spring drawdown to climate warming (Keeling et al, 1996;Piao et al, 2008), suggested that terrestrial ecosystems in Northern Hemisphere were experiencing greater CO 2 uptake during the growing season and greater CO 2 release during period outside the growing season (Sitch et al, 2015).…”

Soil Methanotrophy Model (MeMo v1.0): a process-based model to quantify global uptake of atmospheric methane by soil

“…The use of MeMo as a predictive tool to estimate the past and future global CH 4 soil sink relies strongly on the use of soil moisture from standard climate models, such as output from land surface models or dynamic global vegetation models (DGVMs). Therefore, gaps in the Dorigo et al (2011) data set were filled using soil moisture data from an ensemble of nine DGVMs (TRENDY; Sitch et al, 2015). The R99 model parameterizes the effect of soil moisture on CH 4 uptake fluxes as a function of precipitation and evaporation, and therefore R99 was forced using monthly data sets of precipitation (CRU3.1; Harris et al, 2014) and evapotranspiration (TRENDY; Sitch et al, 2015).…”

“…Therefore, gaps in the Dorigo et al (2011) data set were filled using soil moisture data from an ensemble of nine DGVMs (TRENDY; Sitch et al, 2015). The R99 model parameterizes the effect of soil moisture on CH 4 uptake fluxes as a function of precipitation and evaporation, and therefore R99 was forced using monthly data sets of precipitation (CRU3.1; Harris et al, 2014) and evapotranspiration (TRENDY; Sitch et al, 2015). Temperature forcing is constrained by global data sets for surface temperature as a proxy for soil temperature (CRU3.1; Harris et al, 2014).…”

Global evaluation of gross primary productivity in the JULES land surface model v3.4.1

“…The reduced ability of the land surface to absorb increased anthropogenic CO 2 emissions in the future has been shown by models and inferred from observations (Friedlingstein et al, 2006;Canadell et al, 2007; Published by Copernicus Publications on behalf of the European Geosciences Union. 2652 D. Slevin et al: Global evaluation of gross primary productivity in the JULES land surface model v3.4.1 Friedlingstein et al, 2014;Sitch et al, 2015). Friedlingstein et al (2006) and Friedlingstein et al (2014) have suggested that a major source of model uncertainty is the land C cycle which can affect the ability of Earth system models (ESMs; also known as coupled carbon-cycle-climate models) to reliably simulate future atmospheric CO 2 concentrations and climate (Dalmonech et al, 2014).…”

“…The Joint UK Land Environment Simulator (JULES) has been evaluated on various scales: point Slevin et al, 2015;Ménard et al, 2015), regional (Galbraith et al, 2010;Burke et al, 2013;Chadburn et al, 2015) and globally as part of model-intercomparison studies (Anav et al, 2015;Sitch et al, 2015). Evaluating simulated GPP on a range of scales and its sensitivity to spatial resolution and meteorological data is essential for informing future model developments.…”