Beyond Static Benchmarking: Using Experimental Manipulations to Evaluate Land Model Assumptions. Issue 10 (28th October 2019)

• Record Type: Journal Article
• Authors: Wieder, William R.; Lawrence, David M.; Fisher, Rosie A.; Bonan, Gordon B.; Cheng, Susan J.; Goodale, Christine L.; Grandy, A. Stuart; Koven, Charles D.; Lombardozzi, Danica L.; Oleson, Keith W.; Thomas, R. Quinn
• Is Part Of: Global biogeochemical cycles. Volume 33:Issue 10(2019:Oct.)
• Page Start: 1289
• Page End: 1309
• Publication Date: 2019-10-28
• Subjects: Community Land Model -- nitrogen enrichment -- elevated CO2 -- land model -- biogeochemistry; Biogeochemical cycles -- Periodicals; Electronic journals; 577.1405
• DOI: 10.1029/2018GB006141
• Languages: English
• ISSNs: 0886-6236

Abstract

Abstract: Land models are often used to simulate terrestrial responses to future environmental changes, but these models are not commonly evaluated with data from experimental manipulations. Results from experimental manipulations can identify and evaluate model assumptions that are consistent with appropriate ecosystem responses to future environmental change. We conducted simulations using three coupled carbon‐nitrogen versions of the Community Land Model (CLM, versions 4, 4.5, and—the newly developed—5), and compared the simulated response to nitrogen (N) and atmospheric carbon dioxide (CO2 ) enrichment with meta‐analyses of observations from similar experimental manipulations. In control simulations, successive versions of CLM showed a poleward increase in gross primary productivity and an overall bias reduction, compared to FLUXNET‐MTE observations. Simulations with N and CO2 enrichment demonstrate that CLM transitioned from a model that exhibited strong nitrogen limitation of the terrestrial carbon cycle (CLM4) to a model that showed greater responsiveness to elevated concentrations of CO2 in the atmosphere (CLM5). Overall, CLM5 simulations showed better agreement with observed ecosystem responses to experimental N and CO2 enrichment than previous versions of the model. These simulations also exposed shortcomings in structural assumptions and parameterizations. Specifically, no version of CLM captures changes in plant physiology, allocation, and nutrient uptake that are likely important aspects of terrestrial ecosystems' responses to environmental change. These highlight priority areas that should be addressed in future model developments. Moving forward, incorporating results from experimental manipulations into model benchmarking tools that are used to evaluate model performance will help increase confidence in terrestrial carbon cycle projections. Plain Language Summary: How do changes in the availability of nitrogen in soils or carbon dioxide in the atmosphere affect the amount of carbon that can be stored on land? Answering this question is critical, but it remains difficult for land models that are used to make climate change projections—in part because of limited understanding in how terrestrial ecosystems will respond to environmental change. Experimental manipulations that increase the availability of nitrogen or carbon dioxide, however, provide insights into how ecosystems are likely to respond to changes in resource availability. We expect that models should exhibit similar responses to those observed in the real world. Our results show that over the course of model development later versions of the Community Land Model do a better job of simulating the global carbon cycle and capturing appropriate ecosystem responses to nitrogen and carbon dioxide enrichment. This improves our confidence in the future carbon cycle projections made by more recent versions of the Community Land Model. Our results also identify assumptions in the model that are not well supported by observations and can help to prioritize future model developments. Key Points: Experimental manipulations provide critical insights into ecosystem responses to environmental change that can evaluate land models Parametric and structural changes to the Community Land Model version 5 improve the simulated response to environmental change Model assumptions related to nutrient acquisition strategies and trade‐offs between carbon and nitrogen limitation deserve further attention