Evaluating two land surface models for Brazil using a full carbon cycle benchmark with uncertainties. Issue 1 (13th August 2021)

• Record Type: Journal Article
• Authors: Caen, Auguste; Smallman, T. Luke; de Castro, Aline Anderson; Robertson, Eddy; von Randow, Celso; Cardoso, Manoel; Williams, Mathew
• Other Names: Brown Foster guestEditor.; Peters Wouter guestEditor.; Koren Gerbrand guestEditor.; Koven Charlie guestEditor.
• Is Part Of: Climate resilience and sustainability. Volume 1:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-08-13
• Subjects: Amazon -- biomass -- carbon cycle -- Cerrado -- LAI -- land surface model -- mean transit time -- model‐data fusion -- net ecosystem exchange; Climatic changes -- Periodicals; Sustainability -- Periodicals; Climatic changes; Sustainability; Climate change mitigation$2fast$0(OCoLC)fst01749583; Periodicals; 338.927
• DOI: 10.1002/cli2.10
• Languages: English
• ISSNs: 2692-4587

Abstract

Abstract: Forecasts of tropical ecosystem C cycling diverge among models due to differences in simulation of internal processes such as turnover, or transit times, of carbon pools. Estimates of these processes for the recent past are needed to test model representations, and so build confidence in model forecasts within and across biomes. Here, we evaluate carbon cycle process representation in two land surface models [Joint UK Land Environment Simulator (JULES) and Integrated Model of Land Surface Processes (INLAND)] for the period 2001–10 across Brazilian biomes. Model outputs are evaluated using the ILAMB system. Probabilistic benchmarking data were created using the carbon data model framework that assimilates observational times series of leaf area index and maps of woody biomass and soil C. New custom uncertainty metrics assess if models are within benchmark uncertainties. Simulations are better in homogeneous areas of vegetation type, and are less robust at ecotones between biomes, likely due to disturbance effects and parameter errors. Gross biosphere‐atmosphere fluxes are robustly modelled across Brazil. However, benchmark uncertainty is too high on net ecosystem exchange to provide an accurate evaluation of the models. The LSMs have significant differences in internal carbon allocation and the dynamics of the different C pools. JULES models dead C stocks more accurately while living C stocks are best resolved for INLAND. JULES' over‐estimate of the C wood pool results from over‐estimation of both inputs to wood and the transit time of wood. INLAND's under‐estimate of dead C stocks arises from an under‐estimate of the transit time of dead organic matter. The models are better at simulating annual averages than seasonal variation of fluxes. Analyses of monthly net C exchanges show that INLAND correctly simulates seasonality, but over‐estimates amplitudes, whereas JULES correctly simulates the annual amplitudes, but is out of phase with the benchmark. Abstract : Forecasts of tropical C cycling diverge among models due to differences in ecosystem representation. Using regional carbon cycle data linked in an analytical framework, we evaluate process representation in two land surface models across Brazilian biomes. We found that the models broadly reproduced the benchmark C fluxes between biosphere and atmosphere, but the processes underpinning these fluxes differed between models and often were at odds with the benchmark data. We provide recommendations for how to improve the models for better forecasts. Legend: The mean annual cycle of GPP (gross primary production) across the Cerrado biome of Brazil. The red line how the predictions for the INLAND model; the green line for the JULES model. The black line shows the benchmark estimate with 95% confidence intervals (dashed grey).