Engineering topics

Luus, Kristina

Publications and source records attributed to Luus, Kristina.

3 records

DOE OSTI2019

Decadal biomass increment in early secondary succession woody ecosystems is increased by CO 2 enrichment

Increasing atmospheric CO 2 stimulates photosynthesis which can increase net primary production (NPP), but at longer timescales may not necessarily increase plant biomass. Here we analyse the four decade-long CO 2 -enrichment experiments in woody ecosystems that measured total NPP and biomass. CO 2 enrichment increased biomass increment by 1.05 ± 0.26 kg C m –2 over a full decade, a 29.1 ± 11.7% stimulation of biomass gain in these early-secondary-succession temperate ecosystems. This response is predictable by combining the CO 2 response of NPP (0.16 ± 0.03 kg C m –2 y –1 ) and the CO 2 -independent, linear slope between biomass increment and cumulative NPP (0.55 ± 0.17). Furthermore an ensemble of terrestrial ecosystem models fail to predict both terms correctly. Allocation to wood was a driver of across-site, and across-model, response variability and together with CO 2 -independence of biomass retention highlights the value of understanding drivers of wood allocation under ambient conditions to correctly interpret and predict CO 2 responses.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

FACE-MDS Phase 2: Model Output

Model output from Phase 2 of the Free Air CO2 Enrichment Model Data Synthesis (FACE-MDS) project. Results from 12 models simulating six elevated CO2 experiments using the protocols, met data, and other data supplied in the accompanying dataset: Phase 2 Free Air CO2 Enrichment Model-Data Synthesis: Meteorological Data and Protocols. File naming convention can be found in the file 'facemds-file-table-modeldata.xml' and information about variable naming convention can be found in the file 'facemds-standard-name-table-modvars.xml'. We followed the ALMA v3 convention for meteorological variables and developed a hierarchical naming scheme for ecosystem variables. Additional info can be found in the protocols in the accompanying meteorological dataset (doi:10.15485/1480328). We replaced the original naming convention mentioned in the protocol with the one described in 'facemds-standard-name-table-modvars.xml'.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Challenging terrestrial biosphere models with data from the long-term multifactor Prairie Heating and CO 2 enrichment experiment

Multi-factor experiments are often advocated as important for advancing terrestrial biosphere models (TBMs), yet to date such models have only been tested against single-factor experiments. We applied 10 TBMs to the multi-factor Prairie Heating and CO 2 Enrichment (PHACE) experiment in Wyoming, USA. Our goals were to investigate how multi-factor experiments can be used to constrain models, and to identify a road map for model improvement. We found models performed poorly in ambient conditions; there was a wide spread in simulated above-ground net primary productivity (range: 31-390 g C m -2 yr -1 ). Comparison with data highlighted model failures particularly in respect to carbon allocation, phenology, and the impact of water stress on phenology. Performance against single-factors was also relatively poor. In addition, similar responses were predicted for different reasons across models: there were large differences among models in sensitivity to water stress and, among the nitrogen cycle models, nitrogen availability during the experiment. Models were also unable to capture observed treatment effects on phenology: they over-estimated the effect of warming on leaf onset and did not allow CO 2 -induced water savings to extend growing season length. Observed interactive (CO 2 x warming) treatment effects were subtle and contingent on water stress, phenology and species composition. Since the models did not correctly represent these processes under ambient and single-factor conditions, little extra information was gained by comparing model predictions against interactive responses. Finally, we outline a series of key areas in which this and future experiments could be used to improve model predictions of grassland responses to global change.

54 ENVIRONMENTAL SCIENCES↗