Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric CO2

Abstract

Uncertainties in the response of vegetation to rising atmospheric CO concentrations contribute to the large spread in projections of future climate change. Climate-carbon cycle models generally agree that elevated atmospheric CO concentrations will enhance terrestrial gross primary productivity (GPP). However, the magnitude of this CO fertilization effect varies from a 20 per cent to a 60 per cent increase in GPP for a doubling of atmospheric CO concentrations in model studies. Here we demonstrate emergent constraints on large-scale CO fertilization using observed changes in the amplitude of the atmospheric CO seasonal cycle that are thought to be the result of increasing terrestrial GPP. Our comparison of atmospheric CO measurements from Point Barrow in Alaska and Cape Kumukahi in Hawaii with historical simulations of the latest climate-carbon cycle models demonstrates that the increase in the amplitude of the CO seasonal cycle at both measurement sites is consistent with increasing annual mean GPP, driven in part by climate warming, but with differences in CO fertilization controlling the spread among the model trends. As a result, the relationship between the amplitude of the CO seasonal cycle and the magnitude of CO fertilization of GPP is almost linear across the entire ensemble of models. When combined with the observed trends in the seasonal CO amplitude, these relationships lead to consistent emergent constraints on the CO fertilization of GPP. Overall, we estimate a GPP increase of 37 ± 9 per cent for high-latitude ecosystems and 32 ± 9 per cent for extratropical ecosystems under a doubling of atmospheric CO concentrations on the basis of the Point Barrow and Cape Kumukahi records, respectively.

Photosynthetic productivity and its efficiencies in ISIMIP2a biome models: benchmarking for impact assessment studies

“…Apparently, the CO 2 fertilization effect played an important role, but observational evidences provide inconsistent implications for the model sensitivity as noted previously (Kolby Smith et al 2015, Campbell et al 2017. We need to seek additional data and metrics to reduce the uncertainty; for example, Wenzel et al (2016) implied that SCA is a useful metric to constrain the simulated GPP trend. Spatial variability in the simulated GPP trends (figure S4) has additional mechanistic implications.…”

“…Several metrics related to GPP properties were examined. Seasonal-cycle amplitude (SCA) of GPP, which reflects vegetation activity and affects atmospheric CO 2 (Graven et al 2013, Wenzel et al 2016, was defined as the difference between the maximum and minimum monthly values for each year. Resource-use efficiencies of GPP are expected to provide insights into underlying mechanisms and limiting factors of photosynthetic production.…”

Land-use and land-cover change carbon emissions between 1901 and 2012 constrained by biomass observations

“…The soil carbon changes after LULCC is also indirectly impacted by initial biomass, since the dead roots and remaining aboveground debris turn into soil organic carbon after land clearing, which takes longer to return into the atmosphere. In addition, it is not necessary to account for all factors when applying an emergent constraint approach (e.g., Cox et al, 2013;Kwiatkowski et al, 2017;Wenzel et al, 2016). The regression between E c LUC and biomass in 1901 in the models in our study is satisfying (e.g., r 2 = 0.66 on a global scale; Fig.…”

Towards improved and more routine Earth system model evaluation in CMIP

“…For example, Hall and Qu (2006) used the observable variation in the seasonal cycle of the snow albedo as a proxy for constraining the unobservable feedback strength to climate warming, and Cox et al (2013) and found a good correlation between the carbon cycle-climate feedback and the observable sensitivity of interannual variations in the CO 2 growth rate to temperature variations in an ensemble of models, enabling the projections to be constrained with observations. Other examples include constraints on the CO 2 fertilization effect (Wenzel et al, 2016a), equilibrium climate sensitivity and clouds (Fasullo et al, 2015;Fasullo and Trenberth, 2012;Klein and Hall, 2015;Sherwood et al, 2014), the austral jet stream , total column ozone (Karpechko et al, 2013), and sea ice (Mahlstein and Knutti, 2012;Massonnet et al, 2012). One should keep in mind, however, that the "emergent constraint" approach is based on relationships which are uncovered in models themselves.…”