| 1. |
- Georgiou, Katerina, et al.
(author)
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Emergent temperature sensitivity of soil organic carbon driven by mineral associations
- 2024
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In: Nature Geoscience. - 1752-0894. ; 17:3, s. 205-212
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Journal article (peer-reviewed)abstract
- Soil organic matter decomposition and its interactions with climate depend on whether the organic matter is associated with soil minerals. However, data limitations have hindered global-scale analyses of mineral-associated and particulate soil organic carbon pools and their benchmarking in Earth system models used to estimate carbon cycle–climate feedbacks. Here we analyse observationally derived global estimates of soil carbon pools to quantify their relative proportions and compute their climatological temperature sensitivities as the decline in carbon with increasing temperature. We find that the climatological temperature sensitivity of particulate carbon is on average 28% higher than that of mineral-associated carbon, and up to 53% higher in cool climates. Moreover, the distribution of carbon between these underlying soil carbon pools drives the emergent climatological temperature sensitivity of bulk soil carbon stocks. However, global models vary widely in their predictions of soil carbon pool distributions. We show that the global proportion of model pools that are conceptually similar to mineral-protected carbon ranges from 16 to 85% across Earth system models from the Coupled Model Intercomparison Project Phase 6 and offline land models, with implications for bulk soil carbon ages and ecosystem responsiveness. To improve projections of carbon cycle–climate feedbacks, it is imperative to assess underlying soil carbon pools to accurately predict the distribution and vulnerability of soil carbon.
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| 2. |
- Luo, Yiqi, et al.
(author)
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Toward more realistic projections of soil carbon dynamics by Earth system models
- 2016
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In: Global Biogeochemical Cycles. - 0886-6236. ; 30:1, s. 40-56
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Journal article (peer-reviewed)abstract
- Soil carbon (C) is a critical component of Earth system models (ESMs), and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the third to fifth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real-world processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. First, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by first-order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic soil organic C (SOC) dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth-dependent environmental controls, and other processes that strongly affect soil C dynamics. Second, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool- and flux-based data sets through data assimilation is among the highest priorities for near-term research to reduce biases among ESMs. Third, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. We recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable data sets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields.
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| 3. |
- De Kauwe, Martin G., et al.
(author)
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Forest water use and water use efficiency at elevated CO2: a model-data intercomparison at two contrasting temperate forest FACE sites
- 2013
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In: Global Change Biology. - : Wiley. - 1354-1013 .- 1365-2486. ; 19:6, s. 1759-1779
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Journal article (peer-reviewed)abstract
- Predicted responses of transpiration to elevated atmospheric CO2 concentration (eCO2) are highly variable amongst process-based models. To better understand and constrain this variability amongst models, we conducted an intercomparison of 11 ecosystem models applied to data from two forest free-air CO2 enrichment (FACE) experiments at Duke University and Oak Ridge National Laboratory. We analysed model structures to identify the key underlying assumptions causing differences in model predictions of transpiration and canopy water use efficiency. We then compared the models against data to identify model assumptions that are incorrect or are large sources of uncertainty. We found that model-to-model and model-to-observations differences resulted from four key sets of assumptions, namely (i) the nature of the stomatal response to elevated CO2 (coupling between photosynthesis and stomata was supported by the data); (ii) the roles of the leaf and atmospheric boundary layer (models which assumed multiple conductance terms in series predicted more decoupled fluxes than observed at the broadleaf site); (iii) the treatment of canopy interception (large intermodel variability, 215%); and (iv) the impact of soil moisture stress (process uncertainty in how models limit carbon and water fluxes during moisture stress). Overall, model predictions of the CO2 effect on WUE were reasonable (intermodel =approximately 28%+/- 10%) compared to the observations (=approximately 30%+/- 13%) at the well-coupled coniferous site (Duke), but poor (intermodel =approximately 24%+/- 6%; observations =approximately 38%+/- 7%) at the broadleaf site (Oak Ridge). The study yields a framework for analysing and interpreting model predictions of transpiration responses to eCO2, and highlights key improvements to these types of models.
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| 4. |
- Parton, Robert G., et al.
(author)
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Caveolae : The FAQs
- 2020
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In: Traffic. - : John Wiley & Sons. - 1398-9219 .- 1600-0854. ; 21:1, s. 181-185
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Journal article (peer-reviewed)abstract
- Caveolae are an abundant, but enigmatic, plasma membrane feature of vertebrate cells. In this brief commentary, the authors attempt to answer some key questions related to the formation and function of caveolae based on round‐table discussions at the first EMBO Workshop on Caveolae held in France in May 2019.
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| 5. |
- Walker, Anthony P., et al.
(author)
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Comprehensive ecosystem model-data synthesis using multiple data sets at two temperate forest free-air CO2 enrichment experiments: Model performance at ambient CO2 concentration
- 2014
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In: Journal of Geophysical Research - Biogeosciences. - 2169-8953 .- 2169-8961. ; 119:5, s. 937-964
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Journal article (peer-reviewed)abstract
- Free-air CO2 enrichment (FACE) experiments provide a remarkable wealth of data which can be used to evaluate and improve terrestrial ecosystem models (TEMs). In the FACE model-data synthesis project, 11 TEMs were applied to two decadelong FACE experiments in temperate forests of the southeastern U.S.the evergreen Duke Forest and the deciduous Oak Ridge Forest. In this baseline paper, we demonstrate our approach to model-data synthesis by evaluating the models' ability to reproduce observed net primary productivity (NPP), transpiration, and leaf area index (LAI) in ambient CO2 treatments. Model outputs were compared against observations using a range of goodness-of-fit statistics. Many models simulated annual NPP and transpiration within observed uncertainty. We demonstrate, however, that high goodness-of-fit values do not necessarily indicate a successful model, because simulation accuracy may be achieved through compensating biases in component variables. For example, transpiration accuracy was sometimes achieved with compensating biases in leaf area index and transpiration per unit leaf area. Our approach to model-data synthesis therefore goes beyond goodness-of-fit to investigate the success of alternative representations of component processes. Here we demonstrate this approach by comparing competing model hypotheses determining peak LAI. Of three alternative hypotheses(1) optimization to maximize carbon export, (2) increasing specific leaf area with canopy depth, and (3) the pipe modelthe pipe model produced peak LAI closest to the observations. This example illustrates how data sets from intensive field experiments such as FACE can be used to reduce model uncertainty despite compensating biases by evaluating individual model assumptions.
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| 6. |
- Walker, Anthony P., et al.
(author)
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Predicting long-term carbon sequestration in response to CO2 enrichment: How and why do current ecosystem models differ?
- 2015
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In: Global Biogeochemical Cycles. - 0886-6236. ; 29:4, s. 476-495
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Journal article (peer-reviewed)abstract
- Large uncertainty exists in model projections of the land carbon (C) sink response to increasing atmospheric CO2. Free-Air CO2 Enrichment (FACE) experiments lasting a decade or more have investigated ecosystem responses to a step change in atmospheric CO2 concentration. To interpret FACE results in the context of gradual increases in atmospheric CO2 over decades to centuries, we used a suite of seven models to simulate the Duke and Oak Ridge FACE experiments extended for 300 years of CO2 enrichment. We also determine key modeling assumptions that drive divergent projections of terrestrial C uptake and evaluate whether these assumptions can be constrained by experimental evidence. All models simulated increased terrestrial C pools resulting from CO2 enrichment, though there was substantial variability in quasi-equilibrium C sequestration and rates of change. In two of two models that assume that plant nitrogen (N) uptake is solely a function of soil N supply, the net primary production response to elevated CO2 became progressively N limited. In four of five models that assume that N uptake is a function of both soil N supply and plant N demand, elevated CO2 led to reduced ecosystem N losses and thus progressively relaxed nitrogen limitation. Many allocation assumptions resulted in increased wood allocation relative to leaves and roots which reduced the vegetation turnover rate and increased C sequestration. In addition, self-thinning assumptions had a substantial impact on C sequestration in two models. Accurate representation of N process dynamics (in particular N uptake), allocation, and forest self-thinning is key to minimizing uncertainty in projections of future C sequestration in response to elevated atmospheric CO2.
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| 7. |
- Zaehle, Soenke, et al.
(author)
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Evaluation of 11 terrestrial carbon-nitrogen cycle models against observations from two temperate Free-Air CO2 Enrichment studies
- 2014
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In: New Phytologist. - : Wiley. - 1469-8137 .- 0028-646X. ; 202:3, s. 803-822
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Journal article (peer-reviewed)abstract
- We analysed the responses of 11 ecosystem models to elevated atmospheric [CO2] (eCO(2)) at two temperate forest ecosystems (Duke and Oak Ridge National Laboratory (ORNL) Free-Air CO2 Enrichment (FACE) experiments) to test alternative representations of carbon (C)-nitrogen (N) cycle processes. We decomposed the model responses into component processes affecting the response to eCO(2) and confronted these with observations from the FACE experiments. Most of the models reproduced the observed initial enhancement of net primary production (NPP) at both sites, but none was able to simulate both the sustained 10-yr enhancement at Duke and the declining response at ORNL: models generally showed signs of progressive N limitation as a result of lower than observed plant N uptake. Nonetheless, many models showed qualitative agreement with observed component processes. The results suggest that improved representation of above-ground-below-ground interactions and better constraints on plant stoichiometry are important for a predictive understanding of eCO(2) effects. Improved accuracy of soil organic matter inventories is pivotal to reduce uncertainty in the observed C-N budgets. The two FACE experiments are insufficient to fully constrain terrestrial responses to eCO(2), given the complexity of factors leading to the observed diverging trends, and the consequential inability of the models to explain these trends. Nevertheless, the ecosystem models were able to capture important features of the experiments, lending some support to their projections.
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