Reliability Ensemble Averaging of 21st century projections of terrestrial net primary productivity reduces global and regional uncertainties

Abstract

Abstract.Multi-model averaging techniques provide opportunities to extract additional information from large ensembles of simulations. In particular, present-day model skill can be used to evaluate their potential performance in future climate simulations. Multi-model averaging methods have been used extensively in climate and hydrological sciences, but they have not been used to constrain projected plant productivity responses to climate change, which is a major uncertainty in earth system modelling.Here, we use three global observation-orientated estimates of current net primary productivity (NPP) to 15 perform a reliability ensemble averaging (REA) using 30 global simulations of the 21 st century change in NPP based on the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) "business as usual" emissions scenario. We find that the three REAs support an increase in global NPP by the end of the 21 st century (2090s) compared to the 2000s, which is 4 -6% stronger than the ensemble ISIMIP mean value of 23.7 Pg C y -1 . Using REA also leads to a 43 -67% reduction in the global uncertainty of 21 st century NPP projection, which strengthens confidence in the resilience of the CO 2 -fertilization effect to 20 climate change. This reduction in uncertainty is especially clear for boreal ecosystems. Conversely, the large uncertainty that remains on the sign of the response of NPP in semi-arid regions points to the need for better observations and model development in these regions.

Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO2 Flux and its Inter‐Annual Variability

“…This suggests that further model developments are likely needed to address structural uncertainties and missing processes, which will then need to be followed up with additional parameter DA experiments to ensure increasing complexity does not degrade model skill (Famiglietti et al., 2021). We know for example that certain important processes for sparsely vegetated, mixed shrub‐ and grass‐dominated dryland ecosystems, such as wildfires (Exbrayat et al., 2018; Lasslop et al., 2016; Whitley et al., 2017) and biological soil crust C cycling (Belnap et al., 2016), are currently not represented in most TBMs. Exbrayat et al.…”

“…Exbrayat et al. (2018) showed using a Bayesian parameter DA experiment that model simulations with fire had faster carbon turnover times and increased C allocation to wood and root pools (rather than foliage) than the simulations without fire–all of which resulted in changes to GPP, net primary productivity, biomass and carbon use efficiency. Their results neatly demonstrate that errors due to missing model processes can be aliased onto the posterior parameter values.…”

“…Model benchmarking and optimization studies that have been performed in dryland regions indicate considerable model‐data discrepancies in vegetation dynamics, C and water fluxes (Dahlin et al., 2015; Exbrayat et al., 2018; Haverd et al., 2013; Lawal et al., 2019; MacBean et al., 2015; Renwick et al., 2019; Trudinger et al., 2016; Teckentrup et al., 2021 in review; Traore et al., 2014; Yang et al., 2021; Whitley et al., 2016). Whitley et al.…”

Resolving temperature limitation on spring productivity in an evergreen conifer forest using a model–data fusion framework

“…The CARbon DAta-MOdel fraMework (CARDAMOM; e.g., Bloom et al, 2016;Yin et al, 2020;Exbrayat et al, 2018;Smallman et al, 2017;López-Blanco et al, 2019;Famiglietti et al, 2021;Bloom et al, 2020;Yang et al, 2021a) uses carbon cycle and meteorolog-ical observations to constrain carbon fluxes, states and process controls represented in the DALEC2 model of terrestrial C cycling (Williams et al, 2005;Bloom and Williams, 2015). Specifically, CARDAMOM uses a Bayesian model-data fusion approach to optimize DALEC2 time-invariant parameters (such as leaf traits, allocation and turnover times) and the "initial" C and H 2 O conditions (namely biomass, soil and water states at the start of the model simulation period).…”

Effects of Temperature and Precipitation on Spatiotemporal Variations of Net Primary Productivity in the Qinling Mountains, China

“…Terrestrial vegetation is an important part of the global ecosystem, and is closely related to biodiversity. Terrestrial vegetation in ecosystems experiences severe changes due to global warming [3]. The five consecutive assessment reports from the Intergovernmental Panel on Climate Change (IPCC) (1990, 1995, 2001, 2007 and 2013) have all shown that global warming is unquestionably occurring, and that regions at high latitudes and high altitudes have been the most sensitive to that change [4].…”