Modeling suggests fossil fuel emissions have been driving increased land carbon uptake since the turn of the 20th Century
ApproachWe use historical reconstructions of gross primary productivity (GPP) from offline and fully coupled state-of-the-art Earth system models (ESMs). Offline reconstructions are drawn from 11 terrestrial biosphere simulators in the Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP Version 1; https://doi.org/10.3334/ORNLDAAC/1225) (ref. 11,39). MsTMIP uses a constrained simulation protocol (driving data, vegetation cover, boundary conditions, and steady-state spin-up protocol are all standardized; only model structure varies). All runs use observation-based driving d ata to replicate 1901 to 2010 historical conditions34 with a semi-factorial design where time-varying factors of climate (SG1), land cover and land use change (LULCC) (SG2), [CO2] (SG3), and nitrogen deposition (BG1) are sequentially enabled after steady-state (RG1)—the reference run11,39 based on a repeated cycle of a randomized 30-yr block of trend-free weather—is achieved (MsTMIP simulation name in parenthesis). MsTMIP models used for this study are CLM, CLM4VIC, DLEM, GTEC, ISAM, LPJ-wsl, ORCHIDEE-LSCE, SiBCASA, TEM6, VEGAS2.1 and VISIT. Only CLM, CLM4VIC, DLEM and TEM6 simulate BG1, i.e., have carbon-nitrogen coupling. Fully coupled reconstructions for 1901 to 2010 for 13 ESMs (Extended Data Table 1) are taken from the CMIP5 archive (http://cmip-pcmdi.llnl.gov/cmip5/data_portal.html). CMIP5, the fifth phase of the Coupled Model Intercomparison Project, serves as a central repository for ESM simulations that inform the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) (https://ipcc.ch/report/ar5/). CMIP5 is designed to provide a multi-model framework to investigate model differences in carbon cycle and cloud-based feedbacks as well as climate predictability and the range in ESM responses from similar forcing inputs12. For this study ESMs are chosen based on simulation experiment availability. Each ESM has, at a minimum, a preindustrial control (piControl) and historical (historical) experiments (CMIP5 simulation experiment name in parenthesis; see Extended Data Table 1). In addition, experiments that differ from piControl or historical by a single factor (or factor set, e.g., all anthropogenic forcings) are also included: (1) with greenhouse gas forcing only (historicalGHG), (2) with natural forcing only (historicalNat), (3) with LULCC only and/or (4) with anthropogenic forcings only (variants of historicalMisc). Finally, two fully coupled carbon/climate experiments are included if available: (1) the carbon cycle sees preindustrial levels of CO2 but the radiation code sees historical conditions (esmFdbk2) and (2) the radiation code sees preindustrial levels of CO2 but the carbon cycle sees historical CO2 (esmFixClim2). In all cases only one realization per ESM is used; land carbon cycling is driven by model structure and therefore insensitive to initial conditions. For both offline and coupled reconstructions we analyze annual values as global aggregates as well as by grid cell. For mapped values CMIP5 reconstructions are resampled to a half-degree spatial resolution to match the MsTMIP land mask and use the barren mask from a contemporary upscaled eddy covariance product2. Our results are based on integration over the full ensemble of ESMs—the consensus ensemble mean40—using “one-model-one-vote” (ref. 41) and assume that an unweighted multi-model mean is the best estimate42,43,44. We note that not all reconstructions share the same set of simulation experiments. Uncertainty is calculated as ensemble spread based on bootstrapping with 1000 bootstrap replicates and is expressed as 90% confidence intervals throughout.
AttributionAttribution is based on differencing9. For CMIP5 and MsTMIP, we assume the difference between two simulations is solely attributable to the relevant factor, e.g., subtracting SG2 from SG3 recovers the effect of time-varying [CO2] as both MsTMIP simulations are identical apart from SG3 having dynamic [CO2] enabled. Similarly, in CMIP5, subtracting piControl from historicalNat GPP recovers the effect of natural forcings only as both simulations are identical apart from historicalNat including changes in natural forcings–solar irradiance and volcanic aerosols. For climatic factors (near-surface air temperature, precipitation, and downwelling shortwave radiation) the effect of each is recovered using a machine learning-based emulator of MsTMIP run SG1 (time-varying climate only). Here the random forest algorithm45 is trained with SG1 GPP as the target and near-surface air temperature, precipitation, and downwelling shortwave radiation as explanatory variables. The explanatory variables are those used in forcing all offline MsTMIP runs and are identical across all MsTMIP models. Training is done by grid cell at monthly time step for each MsTMIP model individually. For the SG1 emulation (Extended Data Fig. 4) median variance explained, in a least-squares sense based on those observations not used in training (out-of-bag data points), is at least 93% across all models and all grid cells. As the climate space used to force RG1 is a randomized subset of that used to force SG1 (ref. 33,34) the random forest algorithm does not have to extrapolate beyond the limits of the training data. As such the emulation of RG1 shows equivalent skill (Extended Data Fig. 4) with median variance explained of 96%. For both emulations, skill across space is highly similar with the lowest skill in the Indonesian tropics and Australia and the highest skill across the extratropical Northern Hemisphere. The effect of each climatic factor is then calculated by differencing after sequentially enabling downwelling shortwave radiation, precipitation, and then near-surface air temperature in the emulator. As an illustration, the effect of downwelling shortwave radiation is recovered based on the difference between two emulations: the RG1 case (all climate explanatory variables use RG1 climate) subtracted from the emulation where SG1 downwelling shortwave radiation is paired with RG1 precipitation and RG1 near-surface air temperature. While the emulator offers a computationally inexpensive approach to attribute changes in GPP to individual climate drivers it is not process-aware but rather maps changes in climate to instantaneous changes in GPP. As such, the emulator provides only a first-order assessment (interaction terms are excluded) of how each climate driver impacts GPP without having to formally execute additional MsTMIP simulations. It is important to note that climatic effects cannot be precisely attributed to anthropogenic or natural forcings. In MsTMIP prescribed forcing data is based on historical information, i.e., contains a mix of anthropogenic changes as well as natural variability in Earth’s climate system. In total we attribute changes in GPP from 1901 to 2010 to seven single factors and two factor sets: climate, LULCC, [CO2], nitrogen deposition, near-surface air temperature, precipitation, and downwelling shortwave radiation as well as all natural forcings (changes in solar irradiance and volcanic aerosols) vs. all anthropogenic forcings (with emphasis on well-mixed greenhouse gases and LULCC; see Extended Data Table 1). We note that attributed changes at 1901, the start of the analysis period, are not a priori zero as anthropogenic impacts on carbon cycling predate 1901.
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