Geology topics

Lianhong Gu

Publications and source records attributed to Lianhong Gu.

3 recordsLinked to original sources

USGS2020

Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2

Atmospheric carbon dioxide concentration ([CO 2 ]) is increasing, which increases leaf‐scale photosynthesis and intrinsic water‐use efficiency. These direct responses have the potential to increase plant growth, vegetation biomass, and soil organic matter; transferring carbon from the atmosphere into terrestrial ecosystems (a carbon sink). A substantial global terrestrial carbon sink would slow the rate of [CO 2 ] increase and thus climate change. However, ecosystem CO 2 responses are complex or confounded by concurrent changes in multiple agents of global change and evidence for a [CO 2 ]‐driven terrestrial carbon sink can appear contradictory. Here we synthesize theory and broad, multidisciplinary evidence for the effects of increasing [CO 2 ] (iCO 2 ) on the global terrestrial carbon sink. Evidence suggests a substantial increase in global photosynthesis since pre‐industrial times. Established theory, supported by experiments, indicates that iCO 2 is likely responsible for about half of the increase. Global carbon budgeting, atmospheric data, and forest inventories indicate a historical carbon sink, and these apparent iCO 2 responses are high in comparison to experiments and predictions from theory. Plant mortality and soil carbon iCO 2 responses are highly uncertain. In conclusion, a range of evidence supports a positive terrestrial carbon sink in response to iCO 2 , albeit with uncertain magnitude and strong suggestion of a role for additional agents of global change.

New Phytologist↗

USGS2015

Incorporating microbial dormancy dynamics into soil decomposition models to improve quantification of soil carbon dynamics of northern temperate forests

Soil carbon dynamics of terrestrial ecosystems play a significant role in the global carbon cycle. Microbial-based decomposition models have seen much growth recently for quantifying this role, yet dormancy as a common strategy used by microorganisms has not usually been represented and tested in these models against field observations. Here we developed an explicit microbial-enzyme decomposition model and examined model performance with and without representation of microbial dormancy at six temperate forest sites of different forest types. We then extrapolated the model to global temperate forest ecosystems to investigate biogeochemical controls on soil heterotrophic respiration and microbial dormancy dynamics at different temporal-spatial scales. The dormancy model consistently produced better match with field-observed heterotrophic soil CO 2 efflux ( R H ) than the no dormancy model. Our regional modeling results further indicated that models with dormancy were able to produce more realistic magnitude of microbial biomass (<2% of soil organic carbon) and soil R H (7.5 ± 2.4 Pg C yr −1 ). Spatial correlation analysis showed that soil organic carbon content was the dominating factor (correlation coefficient = 0.4–0.6) in the simulated spatial pattern of soil R H with both models. In contrast to strong temporal and local controls of soil temperature and moisture on microbial dormancy, our modeling results showed that soil carbon-to-nitrogen ratio (C:N) was a major regulating factor at regional scales (correlation coefficient = −0.43 to −0.58), indicating scale-dependent biogeochemical controls on microbial dynamics. Our findings suggest that incorporating microbial dormancy could improve the realism of microbial-based decomposition models and enhance the integration of soil experiments and mechanistically based modeling.

Journal of Geophysical Research G: Biogeosciences↗

USGS2010

A model‐data intercomparison of CO2 exchange across North America: Results from the North American Carbon Program site synthesis

Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO 2 exchange from remote sensing and other spatiotemporal data. Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO 2 exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans ∼220 site‐years, 10 biomes, and includes two large‐scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models' ability to simulate the seasonal cycle of CO 2 exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was ∼10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model‐data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step.

Journal of Geophysical Research: Biogeosciences↗