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Abstract A significant warming effect on arctic tundra is greening. Although this increase in predominantly woody vegetation has been linked to increases in gross primary productivity, increasing temperatures also stimulate ecosystem respiration. We present a novel analysis from small-scale plot measurements showing that the shape of the temperature- and light-dependent sink-to-source threshold (where net ecosystem exchange (NEE) equals zero) differs between two tussock tundra ecosystems differing in leaf area index (LAI). At the higher LAI site, the threshold is exceeded (i.e the ecosystem becomes a source) at relatively higher temperatures under low light but at lower temperatures under high light. At the lower LAI site, the threshold is exceeded at relatively lower temperatures under low light but at higher temperatures under high light. We confirmed this response at a single site where LAI was experimentally increased. This suggests the carbon balance of the tundra may be sensitive to small increases in temperature under low light, but that this effect may be significantly offset by increases in LAI. Importantly, we found that this LAI effect is reversed under high light, and so in a warming tundra, greater vegetation cover could have a progressively negative effect on net carbon uptake.
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Optimal model complexity for terrestrial carbon cycle prediction
Abstract. The terrestrial carbon cycle plays a critical role in modulating the interactions of climate with the Earth system, but different models often make vastly different predictions of its behavior. Efforts to reduce model uncertainty have commonly focused on model structure, namely by introducing additional processes and increasing structural complexity. However, the extent to which increased structural complexity can directly improve predictive skill is unclear. While adding processes may improve realism, the resulting models are often encumbered by a greater number of poorly determined or over-generalized parameters. To guide efficient model development, here we map the theoretical relationship between model complexity and predictive skill. To do so, we developed 16 structurally distinct carbon cycle models spanning an axis of complexity and incorporated them into a model–data fusion system. We calibrated each model at six globally distributed eddy covariance sites with long observation time series and under 42 data scenarios that resulted in different degrees of parameter uncertainty. For each combination of site, data scenario, and model, we then predicted net ecosystem exchange (NEE) and leaf area index (LAI) for validation against independent local site data. Though the maximum model complexity we evaluated is lower than most traditional terrestrial biosphere models, the complexity range we explored provides universal insight into the inter-relationship between structural uncertainty, parametric uncertainty, and model forecast skill. Specifically, increased complexity only improves forecast skill if parameters are adequately informed (e.g., when NEE observations are used for calibration). Otherwise, increased complexity can degrade skill and an intermediate-complexity model is optimal. This finding remains consistent regardless of whether NEE or LAI is predicted. Our COMPLexity EXperiment (COMPLEX) highlights the importance of robust observation-based parameterization for land surface modeling and suggests that data characterizing net carbon fluxes will be key to improving decadal predictions of high-dimensional terrestrial biosphere models.
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- Award ID(s):
- 1942133
- PAR ID:
- 10314190
- Date Published:
- Journal Name:
- Biogeosciences
- Volume:
- 18
- Issue:
- 8
- ISSN:
- 1726-4189
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.5194/bg-18-2727-2021
