Engineering Papers⌕ Search

Engineering topics

Huntzinger, Deborah

Publications and source records attributed to Huntzinger, Deborah.

9 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Impacts of benchmarking choices on inferred model skill of the Arctic–Boreal terrestrial carbon cycle

Abstract Land surface models require continuous validation against observations to improve and reduce simulation uncertainty. However, inferred model performance can be heavily influenced by subjective choices made in the selection and application of observational data products. A key area often misrepresented by models is the Arctic–Boreal region, which is a potential tipping point region in Earth’s climate system due to large permafrost carbon stocks that are vulnerable to release with climate warming. We use the International Land Model Benchmarking (ILAMB) framework to evaluate how the model skill of TRENDY-v9 models varies based on the choice of observational-based benchmark and how benchmarks are applied in model evaluation. This analysis uses global datasets integrated into ILAMB and new, regionally-specific observational products from the Arctic–Boreal Vulnerability Experiment. Our results cover the overall time period of 1979–2019 and show that model scores can vary substantially depending on the data product applied, with higher model scores indicating better model performance against observations. The lowest model scores occur when benchmarked against regional, compared to global, datasets. We also evaluate observed and modeled functional relationships between ecosystem respiration and air temperature and between gross primary production and precipitation. Here, we find that the magnitude and shape of the responses are strongly impacted by the choice of observational dataset and the approach used to construct the functional relationship benchmark. These results suggest that model evaluation studies could conclude a false sense of model skill if only using a single benchmark data product or if not applying regional data products when performing a regional model analysis. Collectively, our findings highlight the influence of benchmarking choices on model evaluation and point to the need for benchmarking guidelines when assessing model skill.

Poe, Jeralyn (ORCID:0000000318495278)↗
DOE OSTI2024

AGU Publications Updates Authorship Policy to Foster Greater Equity and Transparency in Global Research Collaborations

AGU Publications encourages research collaborations between regions, countries, and communities. When well-resourced researchers complete research or field work in low-resourced settings while excluding local communities or researchers from the process, this can be referred to as parachute science or helicopter research. To help address concerns of parachute science and to promote greater equity and transparency in global research collaborations, AGU Publications has updated its authorship policy across its scholarly journals. The implementation of this policy follows a successful 18-month pilot at JGR: Biogeosciences. For research completed in low-resourced regions, authors are encouraged to include a disclosure statement pertaining to the ethical and scientific considerations of their research collaborations.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Update on Our Action Plan for Equity, Inclusion, and Diversity in Publishing at JGR: Biogeosciences

We made a commitment to better include underrepresented members of our community in the publication pipeline of JGR: Biogeosciences. This commitment consists of regular updates on our policies and practices, and concrete actions we intend to implement over the next year. So far, our progress to tackle biases and ensure equitable research in the biogeosciences has focused on improving diversity of our associate editor and reviewer pools, increasing awareness of unconscious bias in peer-review, and promoting inclusion in global collaborations. In this update, we explore manuscript submissions and manuscript decisions by gender, and we present a pilot that aims to promote ethical and equitable global collaborations in resource-poor settings. Here, we end our editorial by presenting our next set of actions that we plan on completing over the next year, which include a more thorough analysis of reviewer demographics.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Field-experiment constraints on the enhancement of the terrestrial carbon sink by CO2 fertilization

How much elevated atmospheric CO2 concentration (eCO2) contributes to the terrestrial carbon sink (NEP) is a debated issue (Friedlingstein et al., 1995; Field & Raupach, 2004; Schimel et al., 2015; Keenan et al., 2016; Huntzinger et al., 2017). The results from Free-Air CO2-Enrichment (FACE) experiments at the site scale show that eCO2 generally increase plant growth and net ecosystem CO2 uptake for a few years after exposure to eCO2, but the effects were highly variable across sites (Jastrow et al., 2005; De Graaff et al., 2006), which poses a challenge for scaling up these local observations. Here, we extract the simulated short-term responses of NEP to eCO2 from global carbon models using a substitute pulse-response approach, and compare them with site-scale responses from FACE experiments in temperate ecosystems. In 12 gridded terrestrial ecosystem models examined, the large-scale NEP sensitivity to eCO2 across the Northern Hemisphere (NH) temperate region is linearly related to the site-scale sensitivity across different ecosystems. We use this emergent relationship, together with the observed response to eCO2 in the FACE site-scale data to estimate a data constrained effect of eCO2 on the NH temperate NEP of 0.73 ± 0.22 Pg C yr-1 per hundred parts per million (ppm) of CO2 increase during the past five decades. This is 26% higher than the mean of the model ensemble. Among the models, those with carbon-nitrogen interactions underestimate the sensitivity of NEP to eCO2, suggesting overly strong nitrogen-limitations on carbon uptake. We further identify that the NEP sensitivity to eCO2 at NH temperate scale is linearly correlated with that at global scale across the models. Extrapolating the constrained NH eCO2 effect to the global terrestrial carbon sink provides an estimate of 4.1 ± 1.5 Pg C yr-1 [100 ppm]-1. Large uncertainties remain regarding the global response broadly inferred from temperate FACE data, due to the lack of observations in boreal and tropical regions, emphasizing the importance of deploying new FACE experiments in those regions.

Liu, Yongwen↗
DOE OSTI2019

Divergence in land surface modeling: linking spread to structure

Divergence in land carbon cycle simulation is persistent and widespread. Regardless of model intercomparison project, results from individual models diverge significantly from each other and, in consequence, from reference datasets. Here we link model spread to structure using a 15-member ensemble of land surface models from the Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP) as a test case. Our analysis uses functional benchmarks and model structure as predicted by model skill in a machine learning framework to isolate discrete aspects of model structure associated with divergence. We also quantify how initial conditions prejudice present-day model outcomes after centennial-scale transient simulations. Overall, the functional benchmark and machine learning exercises emphasize the importance of ecosystem structure in correctly simulating carbon and water cycling, highlight uncertainties in the structure of carbon pools, and advise against hard parametric limits on ecosystem function. We also find that initial conditions explain 90% of variation in global satellite-era values—initial conditions largely predetermine transient endpoints, historical environmental change notwithstanding. As MsTMIP prescribes forcing data and spin-up protocol, the range in initial conditions and high levels of predetermination are also structural. Our results suggest that methodological tools linking divergence to discrete aspects of model structure would complement current community best practices in model development.

58 GEOSCIENCES↗
DOE OSTI2016

Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901-2005

Here, despite the importance of net primary productivity (NPP) and net biome productivity (NBP), estimates of NPP and NBP for China are highly uncertain. To investigate the main sources of uncertainty, we synthesized model estimates of NPP and NBP for China from published literature and the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). The literature-based results showed that total NPP and NBP in China were 3.35 ± 1.25 and 0.14 ± 0.094 Pg C yr –1 , respectively. Classification and regression tree analysis based on literature data showed that model type was the primary source of the uncertainty, explaining 36% and 64% of the variance in NPP and NBP, respectively. Spatiotemporal scales, land cover conditions, inclusion of the N cycle, and effects of N addition also contributed to the overall uncertainty. Results based on the MsTMIP data suggested that model structures were overwhelmingly important (>90%) for the overall uncertainty compared to simulations with different combinations of time-varying global change factors. The interannual pattern of NPP was similar among diverse studies and increased by 0.012 Pg C yr –1 during 1981–2000. In addition, high uncertainty in China's NPP occurred in areas with high productivity, whereas NBP showed the opposite pattern. Our results suggest that to significantly reduce uncertainty in estimated NPP and NBP, model structures should be substantially tested on the basis of empirical results. To this end, coordinated distributed experiments with multiple global change factors might be a practical approach that can validate specific structures of different models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

A framework for benchmarking land models

Land models, which have been developed by the modeling community in the past few decades to predict future states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosystem responses and feedback to climate change. Benchmarking is an emerging procedure to measure performance of models against a set of defined standards. This paper proposes a benchmarking framework for evaluation of land model performances and, meanwhile, highlights major challenges at this infant stage of benchmark analysis. The framework includes (1) targeted aspects of model performance to be evaluated, (2) a set of benchmarks as defined references to test model performance, (3) metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies, and (4) model improvement. Land models are required to simulate exchange of water, energy, carbon and sometimes other trace gases between the atmosphere and land surface, and should be evaluated for their simulations of biophysical processes, biogeochemical cycles, and vegetation dynamics in response to climate change across broad temporal and spatial scales. Thus, one major challenge is to select and define a limited number of benchmarks to effectively evaluate land model performance. The second challenge is to develop metrics of measuring mismatches between models and benchmarks. The metrics may include (1) a priori thresholds of acceptable model performance and (2) a scoring system to combine data–model mismatches for various processes at different temporal and spatial scales. The benchmark analyses should identify clues of weak model performance to guide future development, thus enabling improved predictions of future states of ecosystems and climate. The near-future research effort should be on development of a set of widely acceptable benchmarks that can be used to objectively, effectively, and reliably evaluate fundamental properties of land models to improve their prediction performance skills.

Biophysical Processes↗
DOE OSTI2012

North American Carbon Project (NACP) Regional Interim Synthesis: Terrestrial Biospheric Model Intercomparison

Understanding of carbon exchange between terrestrial ecosystems and the atmosphere can be improved through direct observations and experiments, as well as through modeling activities. Terrestrial biosphere models (TBMs) have become an integral tool for extrapolating local observations and understanding to much larger terrestrial regions. Although models vary in their specific goals and approaches, their central role within carbon cycle science is to provide a better understanding of the mechanisms currently controlling carbon exchange. Recently, the North American Carbon Program (NACP) organized several interim-synthesis activities to evaluate and inter-compare models and observations at local to continental scales for the years 2000 to 2005. Here, we compare the results from the TBMs collected as part of the regional and continental interim-synthesis (RCIS) activities. The primary objective of this work is to synthesize and compare the 19 participating TBMs to assess current understanding of the terrestrial carbon cycle in North America.

54 ENVIRONMENTAL SCIENCES↗