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Journal Article: Publisher's Accepted Manuscript
Journal Name:
Natural Resource Modeling
Additional Journal Information:
Journal Volume: 1; Journal Issue: 2; Related Information: CHORUS Timestamp: 2017-10-20 17:33:04; Journal ID: ISSN 0890-8575
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Country unknown/Code not available

Citation Formats

Loehle, Craig. OPTIMIZING ECOSYSTEM SIMULATION MODEL PERFORMANCE. Country unknown/Code not available: N. p., 2017. Web. doi:10.1111/j.1939-7445.1987.tb00015.x.
Loehle, Craig. OPTIMIZING ECOSYSTEM SIMULATION MODEL PERFORMANCE. Country unknown/Code not available. doi:10.1111/j.1939-7445.1987.tb00015.x.
Loehle, Craig. Fri . "OPTIMIZING ECOSYSTEM SIMULATION MODEL PERFORMANCE". Country unknown/Code not available. doi:10.1111/j.1939-7445.1987.tb00015.x.
author = {Loehle, Craig},
abstractNote = {},
doi = {10.1111/j.1939-7445.1987.tb00015.x},
journal = {Natural Resource Modeling},
number = 2,
volume = 1,
place = {Country unknown/Code not available},
year = {Fri Jun 23 00:00:00 EDT 2017},
month = {Fri Jun 23 00:00:00 EDT 2017}

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This content will become publicly available on June 23, 2018
Publisher's Accepted Manuscript

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  • Free Air CO2 Enrichment (FACE) experiments provide a remarkable wealth of data to test the sensitivities of terrestrial ecosystem models (TEMs). In this study, a broad set of 11 TEMs were compared to 22 years of data from two contrasting FACE experiments in temperate forests of the south eastern US the evergreen Duke Forest and the deciduous Oak Ridge forest. We evaluated the models' ability to reproduce observed net primary productivity (NPP), transpiration and Leaf Area index (LAI) in ambient CO2 treatments. Encouragingly, many models simulated annual NPP and transpiration within observed uncertainty. Daily transpiration model errors were often relatedmore » to errors in leaf area phenology and peak LAI. Our analysis demonstrates that the simulation of LAI often drives the simulation of transpiration and hence there is a need to adopt the most appropriate of hypothesis driven methods to simulate and predict LAI. Of the three competing hypotheses determining peak LAI (1) optimisation to maximise carbon export, (2) increasing SLA with canopy depth and (3) the pipe model the pipe model produced LAI closest to the observations. Modelled phenology was either prescribed or based on broader empirical calibrations to climate. In some cases, simulation accuracy was achieved through compensating biases in component variables. For example, NPP accuracy was sometimes achieved with counter-balancing biases in nitrogen use efficiency and nitrogen uptake. Combined analysis of parallel measurements aides the identification of offsetting biases; without which over-confidence in model abilities to predict ecosystem function may emerge, potentially leading to erroneous predictions of change under future climates.« less
  • Standard approaches in dendroclimatology used to determine climate-tree growth relationships at individual alpine treeline sites have primarily focused on empirically based statistical reconstructions. While such statistical relationships produce highly significant results, it is not possible to explore the underlying biophysiology in the links between climate and forest growth. Use of a deterministic forest ecosystem process model (FOREST-BGC) allows an evaluation of the impact of growing season and prior year meteorological conditions on phenological parameters such as net canopy photosynthesis (PSN) and net carbon gain (NETC). These variables were modeled over the course of a year and were statistically related tomore » tree growth at an upper treeline site in the Sierra Nevada Mountains of California. The predicted growth increments over a 40-yr period exhibit trends similar to the measured variation in increment growth and perform better (R[sup 2][sub adj] = 0.62) than regression models based on monthly/seasonal mean temperature and precipitation totals (R[sup 2][sub adj] = 0.52). The standard principal component based approach, while producing results similar to the components identified in the forest ecosystem (FOREST-BGC) analysis, provided a better reconstruction of increment growth (R[sup 2][sub adj] = 0.79). However, site- and species-specific tuning of the FOREST-BGC model could make this approach a viable alternative to standard response function analysis and potentially a valuable tool for pursuing a theoretically based explanation of treeline processes. 40 refs., 6 figs., 1 tab.« less
  • Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246 ± 6 g C m -2 yr -1), most models produced higher NPP (309 ± 12 g C m -2 yr -1) over the permafrost region during 2000–2009.more » By comparing the simulated gross primary productivity (GPP) with a flux tower-based database, we found that although mean GPP among the models was only overestimated by 10% over 1982–2009, there was a twofold discrepancy among models (380 to 800 g C m -2 yr -1), which mainly resulted from differences in simulated maximum monthly GPP (GPP max). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation by the enzyme Rubisco at 25°C (Vc max_25), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO 2 concentration. In conclusion, these results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPP max as well as their sensitivity to climate change.« less
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