Integrating plant–soil interactions into global carbon cycle models

Abstract

Summary1. Plant-soil interactions play a central role in the biogeochemical carbon (C), nitrogen (N) and hydrological cycles. In the context of global environmental change, they are important both in modulating the impact of climate change and in regulating the feedback of greenhouse gas emissions (CO 2 , CH 4 and N 2 O) to the climate system. 2. Dynamic global vegetation models (DGVMs) represent the most advanced tools available to predict the impacts of global change on terrestrial ecosystem functions and to examine their feedbacks to climate change. The accurate representation of plant-soil interactions in these models is crucial to improving predictions of the effects of climate change on a global scale. 3. In this paper, we describe the general structure of DGVMs that use plant functional types (PFTs) classifications as a means to integrate plant-soil interactions and illustrate how models have been developed to improve the simulation of: (a) soil carbon dynamics, (b) nitrogen cycling, (c) drought impacts and (d) vegetation dynamics. For each of these, we discuss some recent advances and identify knowledge gaps. 4. We identify three ongoing challenges, requiring collaboration between the global modelling community and process ecologists. First, the need for a critical evaluation of the representation of plant-soil processes in global models; second, the need to supply and integrate knowledge into global models; third, the testing of global model simulations against large-scale multifactor experiments and data from observatory gradients. 5. Synthesis. This paperreviews how plant-soil interactions are represented in DGVMs that use PFTs and illustrates some model developments. We also identify areas of ecological understanding and experimentation needed to reduce uncertainty in future carbon coupled climate change predictions.

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

“…This uncertainty in PFT distributions results from both incomplete bioclimatic information to define the fundamental niche, and to the complexity of modeling species competitive interactions that define the realized niche. To better represent present-day managed and natural land cover, four plant functional type datasets were created using a uniform methodology that combined Köppen-Geiger climate zones (delineated with climate data from the Global Historical Climatological Network v2.0 (Peel et al, 2007)) with physiognomy and phenology-type, and managed or natural grasslands, from land-cover data provided by MODIS (V004 and V005), GLC2000, and GlobCover (Table 2; Poulter et al, 2011). The satellite derived PFT fractions were prescribed directly to the maximum annual FPAR variable in LPJ, which defines the fraction of photosynthetic active radiation (FPAR) absorbed by each PFT and is equal to that PFT's fractional coverage.…”

“…Clearly, improvements in terrestrial carbon-cycle model simulations, especially a rigorous quantification of associated uncertainties, are required to provide a more robust interpretation and quantification of the airborne fraction. In addition to numerical descriptions of ecological processes (Ostle et al, 2009;Sitch et al, 2008) and model parameters (Zaehle et al, 2005), climate and land-cover forcing used in model simulations are a large source of uncertainty in dynamic global vegetation modeling (Hicke, 2005;Jung et al, 2007;Quaife et al, 2008;McGuire et al, 2001). Different methods used to create time series of gridded climate data from meteorological stations introduce uncertainty that propagates through ecosystem models (Zhao et al, 2006).…”

Evaluating the Community Land Model in a pine stand with shading manipulations and <sup>13</sup>CO<sub>2</sub> labeling

“…Global and regional observations of land surface fluxes, states, and dynamic vegetation change offer insights into the large-scale interactions between the land surface and atmosphere and hence facilitate model improvements at relevant scales in space and time (Beer et al, 2010;Luo et al, 2012;Randerson et al, 2009). However, to better quantify and reduce uncertainties arising from deficiencies in model process representation, parameters, driver data sets, and initial conditions, there has been significant effort to evaluate and to calibrate LSMs against site-scale ob-servations and experimental manipulations (Baldocchi et al, 2001;De Kauwe et al, 2014;Hanson et al, 2004;Ostle et al, 2009;Raczka et al, 2013;Richardson et al, 2012;Schaefer et al, 2012;Schwalm et al, 2010;Stoy et al, 2013;Williams et al, 2009;Zaehle et al, 2014). Further, model development from these focused site-scale studies, especially in close collaboration with experimentalists, can inform and prioritize new experiments and observations that are specifically designed to advance understanding of critical terrestrial ecosystems and processes (Shi et al, 2015).…”

Rapid transfer of photosynthetic carbon through the plant-soil system in differently managed species-rich grasslands

“…However, the underlying process of C mineralization is primarily governed by the activity of soil biota which are very responsive to plant C inputs and the transfer of recent photosynthetic C to soil via roots and their exudates (Olsson and Johnson, 2005;Ostle et al, 2007;Kuzyakov, 2010). In general, our understanding of the short-term transfer of C between plants and soil biota remains limited, although it is widely recognized that this interaction plays a key role in the C cycle and soil C sequestration (Ostle et al, 2009b;Bardgett et al, 2009;Paterson et al, 2009).…”