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Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ-GUESS, ORCHIDEE, SiB-3, and SiB-CASA. All models were wrapped in a software framework driven with common forcing data, spin-up, and run protocols specified by the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901-2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open-source, cloud-based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges., Competing Interests: The authors declare no conflicts of interest relevant to this study., (© 2022 The Authors. Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union.)

Terrestrial ecosystems carbon and water cycles are tightly coupled through photosynthesis and evapotranspiration processes. The ratios of carbon stored to carbon uptake and water loss to carbon gain are key ecophysiological indicators essential to assess the magnitude and response of the terrestrial plant to the changing climate. Here, we use estimates from 10 terrestrial ecosystem models to quantify the impacts of climate, atmospheric CO 2 concentration, and nitrogen (N) deposition on water use efficiency (WUE), and carbon use efficiency (CUE). We find that across models, WUE increases over the 20 th Century particularly due to CO 2 fertilization and N deposition and compares favorably to experimental studies. Also, the results show a decrease in WUE with climate for the last 3 decades, in contrasts with up-scaled flux observations that demonstrate a constant WUE. Modeled WUE responds minimally to climate with modeled CUE exhibiting no clear trend across space and time. The divergence between simulated and observationally-constrained WUE and CUE is driven by modeled NPP and autotrophic respiration, nitrogen cycle, carbon allocation, and soil moisture dynamics in current ecosystem models. We suggest that carbon-modeling community needs to reexamine stomatal conductance schemes and the soil-vegetation interactions for more robust modeling of carbon and water cycles.

Elevated atmospheric CO2 concentration (eCO2) has the potential to increase vegetation carbon storage if increased net primary production causes increased long-lived biomass. Model predictions of eCO2 effects on vegetation carbon storage depend on how allocation and turnover processes are represented. We used data from two temperate forest free-air CO2 enrichment (FACE) experiments to evaluate representations of allocation and turnover in 11 ecosystem models. Observed eCO2 effects on allocation were dynamic. Allocation schemes based on functional relationships among biomass fractions that vary with resource availability were best able to capture the general features of the observations. Allocation schemes based on constant fractions or resource limitations performed less well, with some models having unintended outcomes. Few models represent turnover processes mechanistically and there was wide variation in predictions of tissue lifespan. Consequently, models did not perform well at predicting eCO2 effects on vegetation carbon storage. Our recommendations to reduce uncertainty include: use of allocation schemes constrained by biomass fractions; careful testing of allocation schemes; and synthesis of allocation and turnover data in terms of model parameters. Data from intensively studied ecosystem manipulation experiments are invaluable for constraining models and we recommend that such experiments should attempt to fully quantify carbon, water and nutrient budgets., (© 2014 The Authors. New Phytologist © 2014 New Phytologist Trust.)

We analysed the responses of 11 ecosystem models to elevated atmospheric [CO2 ] (eCO2 ) 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 eCO2 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 eCO2 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 eCO2 , 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., (© 2014 The Authors. New Phytologist © 2014 New Phytologist Trust.)

Hypertriglyceridemia may contribute to the development of atherosclerosis by increasing expression of cell adhesion molecules (CAMs). Although the cellular expression of CAMs is difficult to assess clinically, soluble forms of CAMs (sCAMs) are present in the circulation and may serve as markers for CAMs. In this study, we examined the association between sCAMs and other risk factors occurring with hypertriglyceridemia, the effect of triglyceride reduction on sCAM levels, and the role of soluble vascular cell adhesion molecule-1 (sVCAM-1) in monocyte adhesion in vitro. Compared with normal control subjects (n=20), patients with hypertriglyceridemia and low HDL (n=39) had significantly increased levels of soluble intercellular adhesion molecule-1 (sICAM-1) (316+/-28.8 versus 225+/-16.6 ng/mL), sVCAM-1 (743+/-52.2 versus 522+/-43.6 ng/mL), and soluble E-selectin (83+/-5.9 versus 49+/-3.6 ng/mL). ANCOVA showed that the higher sCAM levels in patients occurred independently of diabetes mellitus and other risk factors. In 27 patients who received purified n-3 fatty acid (Omacor) 4 g/d for > or =7 months, triglyceride level was reduced by 47+/-4.6%, sICAM-1 level was reduced by 9+/-3.4% (P=.02), and soluble E-selectin level was reduced by 16+/-3.2% (P<.0001), with the greatest reduction in diabetic patients. These results support previous in vitro data showing that disorders in triglyceride and HDL metabolism influence CAM expression and treatment with fish oils may alter vascular cell activation. In a parallel-plate flow chamber, recombinant sVCAM-1 at the concentration seen in patients significantly inhibited adhesion of monocytes to interleukin-1-stimulated cultured endothelial cells under conditions of flow by 27.5+/-7.2%. Thus, elevated sCAMs may negatively regulate monocyte adhesion.