Abstract
AbstractThe terrestrial biosphere is currently slowing down global warming by absorbing about 30% of human emissions of carbon dioxide (CO2). The largest flux of the terrestrial carbon uptake is gross primary production (GPP) defined as the production of carbohydrates by photosynthesis. Elevated atmospheric CO2 concentration is expected to increase GPP (āCO2 fertilization effectā). However, Earth system models (ESMs) exhibit a large range in simulated GPP projections. In this study, we combine an existing emergent constraint on CO2 fertilization with a machine learning approach to constrain the spatial variations of multimodel GPP projections. In a first step, we use observed changes in the CO2 seasonal cycle at Cape Kumukahi to constrain the global mean GPP at the end of the 21st century (2091ā2100) in Representative Concentration Pathway 8.5 simulations with ESMs participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) to 171 Ā± 12 Gt C yrā1, compared to the unconstrained model range of 156ā247 Gt C yrā1. In a second step, we use a machine learning model to constrain gridded future absolute GPP and gridded fractional GPP change in two independent approaches. For this, observational data are fed into the machine learning algorithm that has been trained on CMIP5 data to learn relationships between presentāday physically relevant diagnostics and the target variable. In a leaveāoneāmodelāout crossāvalidation approach, the machine learning model shows superior performance to the CMIP5 ensemble mean. Our approach predicts an increased GPP change in northern high latitudes compared to regions closer to the equator.
Highlights
The response of the terrestrial carbon cycle to changes in atmospheric CO2 and climate is a major source of uncertainty in climate projections (Bodman et al, 2013; Booth et al, 2012; M. Collins et al, 2013)
We use observed changes in the CO2 seasonal cycle at Cape Kumukahi to constrain the global mean gross primary production (GPP) at the end of the 21st century (2091ā2100) in Representative Concentration Pathway 8.5 simulations with Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) to 171 Ā± 12 Gt C yrā1, compared to the unconstrained model range of 156ā247 Gt C yrā1
In Wenzel, Cox, et al (2016), the observed atmospheric CO2 concentration at Cape Kumukahi, Hawaii (KUM; 19.5Ā°N, 154.8Ā°W) (Keeling et al, 2005) was used to constrain the GPP change in the 1%BGC run resulting from a doubling of atmospheric CO2 concentrations to 32 Ā± 9% for extratropical ecosystems (30ā90Ā°N)
Summary
The response of the terrestrial carbon cycle to changes in atmospheric CO2 and climate is a major source of uncertainty in climate projections (Bodman et al, 2013; Booth et al, 2012; M. Collins et al, 2013). The response of the terrestrial carbon cycle to changes in atmospheric CO2 and climate is a major source of uncertainty in climate projections Collins et al, 2013). Future terrestrial carbon sensitivity is mainly driven by two feedback mechanisms: the concentrationācarbon and the climateācarbon feedbacks Collins et al, 2013; Friedlingstein et al, 2006; Gregory et al, 2009). The first one is connected to the CO2 fertilization effect (Walker et al, 2020) where elevated atmospheric CO2 concentrations increase photosynthesis rates and generally lead to a higher terrestrial carbon uptake, constituting a negative feedback. The second one, the climateācarbon feedback, is driven by temperature and precipitation changes leading to a smaller land
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