Title: When things get MESI: The Manipulation Experiments Synthesis Initiative—A coordinated effort to synthesize terrestrial global change experiments
Abstract
Responses of the terrestrial biosphere to rapidly changing environmental conditions are a major source of uncertainty in climate projections. In an effort to reduce this uncertainty, a wide range of global change experiments have been conducted that mimic future conditions in terrestrial ecosystems, manipulating CO2, temperature, and nutrient and water availability. Syntheses of results across experiments provide a more general sense of ecosystem responses to global change, and help to discern the influence of background conditions such as climate and vegetation type in determining global change responses. Several independent syntheses of published data have yielded distinct databases for specific objectives. Such parallel, uncoordinated initiatives carry the risk of producing redundant data collection efforts and have led to contrasting outcomes without clarifying the underlying reason for divergence. These problems could be avoided by creating a publicly available, updatable, curated database. Here, we report on a global effort to collect and curate 57,089 treatment responses across 3644 manipulation experiments at 1145 sites, simulating elevated CO2, warming, nutrient addition, and precipitation changes. In the resulting Manipulation Experiments Synthesis Initiative (MESI) database, effects of experimental global change drivers on carbon and nutrient cycles are included, as well as ancillary data such as background climate, vegetation type, treatment magnitude, duration, and, unique to our database, measured soil properties. Our analysis of the database indicates that most experiments are short term (one or few growing seasons), conducted in the USA, Europe, or China, and that the most abundantly reported variable is aboveground biomass. We provide the most comprehensive multifactor global change database to date, enabling the research community to tackle open research questions, vital to global policymaking. The MESI database, freely accessible atdoi.org/10.5281/zenodo.7153253, opens new avenues for model evaluation and synthesis‐based understanding of how global change affects terrestrial biomes. We welcome contributions to the database on GitHub.
Abstract Numerous ecosystem manipulative experiments have been conducted since 1970/80 s to elucidate responses of terrestrial carbon cycling to the changing atmospheric composition (CO 2 enrichment and nitrogen deposition) and climate (warming and changing precipitation regimes), which is crucial for model projection and mitigation of future global change effects. Here, we extract data from 2,242 publications that report global change manipulative experiments and build a comprehensive global database with 5,213 pairs of samples for plant production (productivity, biomass, and litter mass) and ecosystem carbon exchange (gross and net ecosystem productivity as well as ecosystem and soil respiration). Information on climate characteristics and vegetation types of experimental sites as well as experimental facilities and manipulation magnitudes subjected to manipulative experiments are also included in this database. This global database can facilitate the estimation of response and sensitivity of key terrestrial carbon-cycling variables under future global change scenarios, and improve the robust projection of global change‒terrestrial carbon feedbacks imposed by Earth System Models.
Depsky, Nicholas; Bolliger, Ian; Allen, Daniel; Choi, Jun Ho; Delgado, Michael; Greenstone, Michael; Hamidi, Ali; Houser, Trevor; Kopp, Robert E.; Hsiang, Solomon(
, Geoscientific Model Development)
Sea level rise (SLR) may impose substantial economic costs to coastal communities worldwide, but characterizing its global impact remains challenging because SLR costs depend heavily on natural characteristics and human investments at each location – including topography, the spatial distribution of assets, and local adaptation decisions. To date, several impact models have been developed to estimate the global costs of SLR. Yet, the limited availability of open-source and modular platforms that easily ingest up-to-date socioeconomic and physical data sources restricts the ability of existing systems to incorporate new insights transparently. In this paper, we present a modular, open-source platform designed to address this need, providing end-to-end transparency from global input data to a scalable least-cost optimization framework that estimates adaptation and net SLR costs for nearly 10 000 global coastline segments and administrative regions. Our approach accounts both for uncertainty in the magnitude of global mean sea level (g.m.s.l.) rise and spatial variability in local relative sea level rise. Using this platform, we evaluate costs across 230 possible socioeconomic and SLR trajectories in the 21st century. According to the latest Intergovernmental Panel on Climate Change Assessment Report (AR6), g.m.s.l. is likely to rise during the 21st century by 0.40–0.69 m if late-century warming reaches 2 ∘C and by 0.58–0.91 m with 4 ∘C of warming (Fox-Kemper et al., 2021). With no forward-looking adaptation, we estimate that annual costs of sea level rise associated with a 2 ∘C scenario will likely fall between USD 1.2 and 4.0 trillion (0.1 % and 1.2 % of GDP, respectively) by 2100, depending on socioeconomic and sea level rise trajectories. Cost-effective, proactive adaptation would provide substantial benefits, lowering these values to between USD 110 and USD 530 billion (0.02 and 0.06 %) under an optimal adaptation scenario. For the likely SLR trajectories associated with 4 ∘C warming, these costs range from USD 3.1 to 6.9 trillion (0.3 % and 2.0 %) with no forward-looking adaptation and USD 200 billion to USD 750 billion (0.04 % to 0.09 %) under optimal adaptation. The Intergovernmental Panel on Climate Change (IPCC) notes that deeply uncertain physical processes like marine ice cliff instability could drive substantially higher global sea level rise, potentially approaching 2.0 m by 2100 in very high emission scenarios. Accordingly, we also model the impacts of 1.5 and 2.0 m g.m.s.l. rises by 2100; the associated annual cost estimates range from USD 11.2 to 30.6 trillion (1.2 % and 7.6 %) under no forward-looking adaptation and USD 420 billion to 1.5 trillion (0.08 % to 0.20 %) under optimal adaptation. Our modeling platform used to generate these estimates is publicly available in an effort to spur research collaboration and support decision-making, with segment-level physical and socioeconomic input characteristics provided at https://doi.org/10.5281/zenodo.7693868 (Bolliger et al., 2023a) and model results at https://doi.org/10.5281/zenodo.7693869 (Bolliger et al., 2023b).
Barker, Brandon L.; Harris, Chelsea E.; Warren, MacKenzie L.; O’Connor, Evan P.; Couch, Sean M.(
, The Astrophysical Journal)
Abstract
Observations of core-collapse supernovae (CCSNe) reveal a wealth of information about the dynamics of the supernova ejecta and its composition but very little direct information about the progenitor. Constraining properties of the progenitor and the explosion requires coupling the observations with a theoretical model of the explosion. Here we begin with the CCSN simulations of Couch et al., which use a nonparametric treatment of the neutrino transport while also accounting for turbulence and convection. In this work we use the SuperNova Explosion Code to evolve the CCSN hydrodynamics to later times and compute bolometric light curves. Focusing on Type IIP SNe (SNe IIP), we then (1) directly compare the theoretical STIR explosions to observations and (2) assess how properties of the progenitor’s core can be estimated from optical photometry in the plateau phase alone. First, the distribution of plateau luminosities (L50) and ejecta velocities achieved by our simulations is similar to the observed distributions. Second, we fit our models to the light curves and velocity evolution of some well-observed SNe. Third, we recover well-known correlations, as well as the difficulty of connecting any one SN property to zero-age main-sequence mass. Finally, we show that there is a usable, linear correlation between iron core mass andL50such that optical photometry alone of SNe IIP can give us insights into the cores of massive stars. Illustrating this by application to a few SNe, we find iron core masses of 1.3–1.5M⊙with typical errors of 0.05M⊙. Data are publicly available online on Zenodo: doi:10.5281/zenodo.6631964.
Tang, Weiyi; Ward, Bess B.; Beman, Michael; Bristow, Laura; Clark, Darren; Fawcett, Sarah; Frey, Claudia; Fripiat, François; Herndl, Gerhard J.; Mdutyana, Mhlangabezi; et al(
, Earth System Science Data)
Abstract. As a key biogeochemical pathway in the marine nitrogen cycle, nitrification (ammonia oxidation and nitrite oxidation) converts the most reduced form of nitrogen – ammonium–ammonia (NH4+–NH3) – into the oxidized species nitrite (NO2-) and nitrate (NO3-). In the ocean, these processes are mainly performed by ammonia-oxidizing archaea (AOA) and bacteria (AOB) and nitrite-oxidizing bacteria (NOB). By transforming nitrogen speciation and providing substrates for nitrogen removal, nitrification affects microbial community structure; marine productivity (including chemoautotrophic carbon fixation); and the production of a powerful greenhouse gas, nitrous oxide (N2O). Nitrification is hypothesized to be regulated by temperature, oxygen, light, substrate concentration, substrate flux, pH and other environmental factors. Although the number of field observations from various oceanic regions has increased considerably over the last few decades, a global synthesis is lacking, and understanding how environmental factors control nitrification remains elusive. Therefore, we have compiled a database of nitrification rates and nitrifier abundance in the global ocean from published literature and unpublished datasets. This database includes 2393 and 1006 measurements of ammonia oxidation and nitrite oxidation rates and 2242 and 631 quantifications of ammonia oxidizers and nitrite oxidizers, respectively. This community effort confirms and enhances our understanding of the spatial distribution of nitrification and nitrifiers and their corresponding drivers such as the important role of substrate concentration in controlling nitrification rates and nitrifier abundance. Some conundrums are also revealed, including the inconsistent observations of light limitation and high rates of nitrite oxidation reported from anoxic waters. This database can be used to constrain the distribution of marine nitrification, to evaluate and improve biogeochemical models of nitrification, and to quantify the impact of nitrification on ecosystem functions like marine productivity and N2O production. This database additionally sets a baseline for comparison with future observations and guides future exploration (e.g., measurements in the poorly sampled regions such as the Indian Ocean and method comparison and/or standardization). The database is publicly available at the Zenodo repository: https://doi.org/10.5281/zenodo.8355912 (Tang et al., 2023).
Keegans, James D.; Pignatari, Marco; Stancliffe, Richard J.; Travaglio, Claudia; Jones, Samuel; Gibson, Brad K.; Townsley, Dean M.; Miles, Broxton J.; Shen, Ken J.; Few, Gareth(
, The Astrophysical Journal Supplement Series)
Abstract
Type Ia supernova explosions (SN Ia) are fundamental sources of elements for the chemical evolution of galaxies. They efficiently produce intermediate-mass (withZbetween 11 and 20) and iron group elements—for example, about 70% of the solar iron is expected to be made by SN Ia. In this work, we calculate complete abundance yields for 39 models of SN Ia explosions, based on three progenitors—a 1.4M⊙deflagration detonation model, a 1.0M⊙double detonation model, and a 0.8M⊙double detonation model—and 13 metallicities, with22Ne mass fractions of 0, 1 × 10−7, 1 × 10−6, 1 × 10−5, 1 × 10−4, 1 × 10−3, 2 × 10−3, 5 × 10−3, 1 × 10−2, 1.4 × 10−2, 5 × 10−2, and 0.1, respectively. Nucleosynthesis calculations are done using the NuGrid suite of codes, using a consistent nuclear reaction network between the models. Complete tables with yields and production factors are provided online at Zenodo:Yields (https://doi.org/10.5281/zenodo.8060323). We discuss the main properties of our yields in light of the present understanding of SN Ia nucleosynthesis, depending on different progenitor mass and composition. Finally, we compare our results with a number of relevant models from the literature.
Van Sundert, Kevin, Leuzinger, Sebastian, Bader, Martin K.‐F., Chang, Scott X., De Kauwe, Martin G., Dukes, Jeffrey S., Langley, J. Adam, Ma, Zilong, Mariën, Bertold, Reynaert, Simon, Ru, Jingyi, Song, Jian, Stocker, Benjamin, Terrer, César, Thoresen, Joshua, Vanuytrecht, Eline, Wan, Shiqiang, Yue, Kai, and Vicca, Sara. When things get MESI: The Manipulation Experiments Synthesis Initiative—A coordinated effort to synthesize terrestrial global change experiments. Retrieved from https://par.nsf.gov/biblio/10487481. Global Change Biology 29.7 Web. doi:10.1111/gcb.16585.
Van Sundert, Kevin, Leuzinger, Sebastian, Bader, Martin K.‐F., Chang, Scott X., De Kauwe, Martin G., Dukes, Jeffrey S., Langley, J. Adam, Ma, Zilong, Mariën, Bertold, Reynaert, Simon, Ru, Jingyi, Song, Jian, Stocker, Benjamin, Terrer, César, Thoresen, Joshua, Vanuytrecht, Eline, Wan, Shiqiang, Yue, Kai, and Vicca, Sara.
"When things get MESI: The Manipulation Experiments Synthesis Initiative—A coordinated effort to synthesize terrestrial global change experiments". Global Change Biology 29 (7). Country unknown/Code not available: Global Change Biology. https://doi.org/10.1111/gcb.16585.https://par.nsf.gov/biblio/10487481.
@article{osti_10487481,
place = {Country unknown/Code not available},
title = {When things get MESI: The Manipulation Experiments Synthesis Initiative—A coordinated effort to synthesize terrestrial global change experiments},
url = {https://par.nsf.gov/biblio/10487481},
DOI = {10.1111/gcb.16585},
abstractNote = {Abstract Responses of the terrestrial biosphere to rapidly changing environmental conditions are a major source of uncertainty in climate projections. In an effort to reduce this uncertainty, a wide range of global change experiments have been conducted that mimic future conditions in terrestrial ecosystems, manipulating CO2, temperature, and nutrient and water availability. Syntheses of results across experiments provide a more general sense of ecosystem responses to global change, and help to discern the influence of background conditions such as climate and vegetation type in determining global change responses. Several independent syntheses of published data have yielded distinct databases for specific objectives. Such parallel, uncoordinated initiatives carry the risk of producing redundant data collection efforts and have led to contrasting outcomes without clarifying the underlying reason for divergence. These problems could be avoided by creating a publicly available, updatable, curated database. Here, we report on a global effort to collect and curate 57,089 treatment responses across 3644 manipulation experiments at 1145 sites, simulating elevated CO2, warming, nutrient addition, and precipitation changes. In the resulting Manipulation Experiments Synthesis Initiative (MESI) database, effects of experimental global change drivers on carbon and nutrient cycles are included, as well as ancillary data such as background climate, vegetation type, treatment magnitude, duration, and, unique to our database, measured soil properties. Our analysis of the database indicates that most experiments are short term (one or few growing seasons), conducted in the USA, Europe, or China, and that the most abundantly reported variable is aboveground biomass. We provide the most comprehensive multifactor global change database to date, enabling the research community to tackle open research questions, vital to global policymaking. The MESI database, freely accessible atdoi.org/10.5281/zenodo.7153253, opens new avenues for model evaluation and synthesis‐based understanding of how global change affects terrestrial biomes. We welcome contributions to the database on GitHub.},
journal = {Global Change Biology},
volume = {29},
number = {7},
publisher = {Global Change Biology},
author = {Van Sundert, Kevin and Leuzinger, Sebastian and Bader, Martin K.‐F. and Chang, Scott X. and De Kauwe, Martin G. and Dukes, Jeffrey S. and Langley, J. Adam and Ma, Zilong and Mariën, Bertold and Reynaert, Simon and Ru, Jingyi and Song, Jian and Stocker, Benjamin and Terrer, César and Thoresen, Joshua and Vanuytrecht, Eline and Wan, Shiqiang and Yue, Kai and Vicca, Sara},
}
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