Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations

Abstract

Stocks of soil organic carbon represent a large component of the carbon cycle that may participate in climate change feedbacks, particularly on decadal and centennial timescales. For Earth system models (ESMs), the ability to accurately represent the global distribution of existing soil carbon stocks is a prerequisite for accurately predicting future carbon–climate feedbacks. We compared soil carbon simulations from 11 model centers to empirical data from the Harmonized World Soil Database (HWSD) and the Northern Circumpolar Soil Carbon Database (NCSCD). Model estimates of global soil carbon stocks ranged from 510 to 3040 Pg C, compared to an estimate of 1260 Pg C (with a 95% confidence interval of 890–1660 Pg C) from the HWSD. Model simulations for the high northern latitudes fell between 60 and 820 Pg C, compared to 500 Pg C (with a 95% confidence interval of 380–620 Pg C) for the NCSCD and 290 Pg C for the HWSD. Global soil carbon varied 5.9 fold across models in response to a 2.6-fold variation in global net primary productivity (NPP) and a 3.6-fold variation in global soil carbon turnover times. Model–data agreement was moderate at the biome level (<i>R</i>² values ranged from 0.38 to 0.97 with a mean of 0.75); however, the spatial distribution of soil carbon simulated by the ESMs at the 1&deg; scale was not well correlated with the HWSD (Pearson correlation coefficients less than 0.4 and root mean square errors from 9.4 to 20.8 kg C m^&minus;2). In northern latitudes where the two data sets overlapped, agreement between the HWSD and the NCSCD was poor (Pearson correlation coefficient 0.33), indicating uncertainty in empirical estimates of soil carbon. We found that a reduced complexity model dependent on NPP and soil temperature explained much of the 1&deg; spatial variation in soil carbon within most ESMs (<i>R</i>² values between 0.62 and 0.93 for 9 of 11 model centers). However, the same reduced complexity model only explained 10% of the spatial variation in HWSD soil carbon when driven by observations of NPP and temperature, implying that other drivers or processes may be more important in explaining observed soil carbon distributions. The reduced complexity model also showed that differences in simulated soil carbon across ESMs were driven by differences in simulated NPP and the parameterization of soil heterotrophic respiration (inter-model <i>R</i>² = 0.93), not by structural differences between the models. Overall, our results suggest that despite fair global-scale agreement with observational data and moderate agreement at the biome scale, most ESMs cannot reproduce grid-scale variation in soil carbon and may be missing key processes. Future work should focus on improving the simulation of driving variables for soil carbon stocks and modifying model structures to include additional processes

Interactions between land use change and carbon cycle feedbacks

“…LULCC does not directly impact soil carbon in CESM (Table 3), which likely represents an underestimate of the real response [Levis et al, 2014;Todd-Brown et al, 2013]. Indeed, although the spatial pattern of the impact of climate and LULCC on land carbon is different, in many regions the magnitude of impact is similar (Figure 3).…”

Responses of peat carbon at different depths to simulated warming and oxidizing

“…Such results should be very useful for modeling scientists to calibrate their models. Since the current carbon models for peatlands do not include into consideration the carbon loss from subsurface and deep soils, therefore probably under-estimating the real carbon loss from peatlands (Bond-Lamberty and Thomson, 2010;Davidson and Janssens, 2006;Fan et al, 2014;McGuire et al, 2012;Rowson et al, 2013;Todd-Brown et al, 2013;Watts et al, 2014).…”

Fog and rain in the Amazon

“…In the more humid Western part of the basin, surface incoming radiation, evapotranspiration, and photosynthesis all tend to peak in the dry season (15-17), whereas GCMs simulate peaks of those fluxes in the wet season (10, 11). Those issues might be related to the representation of convection (1,2,4,5,13,14) and vegetation water stress (6)(7)(8)(15)(16)(17) in GCMs.We here show that we can represent the Amazonian climate using a strategy opposite to GCMs in which we resolve convection and parameterize the large-scale circulation (Methods). The simulations lack many of the biases observed in GCMs and more accurately capture the differences between the dry and wet season of the Amazon in surface heat fluxes and precipitation.…”

“…Hence, the climate of the Amazon is of particular importance for the fate of global CO 2 concentration in the atmosphere (1). Besides the difficulty of estimating carbon pools (1-3), our incapacity to correctly predict CO 2 fluxes in the continental tropics largely results from inaccurate simulation of the tropical climate (1,2,4,5). More frequent and more intense droughts in particular are expected to affect the future health of the Amazon and its capacity to act as a major carbon sink (6)(7)(8).…”