Coronal electron temperature in the protracted solar minimum, the cycle 24 mini maximum, and over centuries

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106800/1/jgra50869.pd

Global stability of enzymatic chain of full reversible Michaelis-Menten reactions

International audienceWe consider a chain of metabolic reactions catalyzed by enzymes, of reversible Michaelis-Menten type with full dynamics, i.e. not reduced with any quasi- steady state approximations. We study the corresponding dynamical system and show its global stability if the equilibrium exists. If the system is open, the equilibrium may not exist. The main tool is monotone systems theory. Finally we study the implications of these results for the study of coupled genetic-metabolic systems

Investigation of Blade-row Flow Distributions in Axial-flow-compressor Stage Consisting of Guide Vanes and Rotor-blade Row

A 30-inch tip-diameter axial-flow compressor stage was investigated with and without rotor to determine individual blade-row performance, interblade-row effects, and outer-wall boundary-layer conditions. Velocity gradients at guide-vane outlet without rotor approximated design assumptions, when the measured variation of leaving angle was considered. With rotor in operation, Mach number and rotor-blade effects changed flow distribution leaving guide vanes and invalidated design assumption of radial equilibrium. Rotor-blade performance correlated interpolated two-dimensional results within 2 degrees, although tip stall was indicated in experimental and not two-dimensional results. Boundary-displacement thickness was less than 1.0 and 1.5 percent of passage height after guide vanes and after rotor, respectively, but increased rapidly after rotor when tip stall occurred

Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction

Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models.DM was supported by the Portuguese Foundationfor Science and Technologythrough a post-doc fellowship (ref: SFRH/BPD/111519/ 2015). This study was supported by the PortugueseFoundationfor Science and Technology (FCT) under the scope of the strategic fundingof UID/BIO/04469/2013 unitand COMPETE2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE-01-0145FEDER-000004) fundedby EuropeanRegional Development Fund under the scope of Norte2020Programa Operacional Regional do Norte. This project has received fundingfrom the European Union’s Horizon 2020 research and innovation programme under grant agreementNo 686070. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

A topographically controlled tipping point for complete Greenland ice sheet melt

A major impact of anthropogenic climate change is the crossing of tipping points, which may have severe consequences such as the complete mass loss of the Greenland ice sheet (GrIS). At present, the GrIS is losing mass at an accelerated rate, largely due to a steep decrease in its surface mass balance (SMB; the balance between snow accumulation and surface ablation from melt and associated runoff). Previous work on the magnitude and nature of a threshold for GrIS complete melt remains controversial. Here, we explore a potential SMB threshold for complete melt of the GrIS; the impact and interplay of surface melt and glacial isostatic adjustment (GIA) in determining this threshold; and whether the GrIS exhibits characteristics commonly associated with tipping points, such as sensitivity to external forcing. To this end, we force the Community Ice Sheet Model v.2 (CISM2) by cycling different SMB climatologies previously calculated at multiple elevation classes with the Community Earth System Model v.2 (CESM2) in a two-way coupled CESM2–CISM2 transient simulation of the global climate and GrIS under high CO2 forcing. The SMB calculation in CESM2 has been evaluated with contemporary observations and high-resolution modelling and includes an advanced representation of surface melt and snow–firn processes. We find a positive SMB threshold for complete GrIS melt of 230 ± 84 Gt yr−1, corresponding to a 60 % decrease in SMB and to a global mean warming of +3.4 K compared to pre-industrial CESM2–CISM2 simulated values. In our simulations, a small change in the initial SMB forcing (from 255 to 230 Gt yr−1) and global mean warming above pre-industrial levels (from +3.2 to +3.4 K) causes an abrupt change in the GrIS final volume (from 50 % mass to nearly complete deglaciation). This nonlinear behaviour is caused by the SMB–elevation feedback, which responds to changes in surface topography due to surface melt and GIA. The GrIS tips from ∼ 50 % mass towards nearly complete melt when the impact of melt-induced surface lowering outweighs that of GIA-induced bedrock uplift and the (initially positive) SMB becomes and remains negative for at least a few thousand years. We also find that the GrIS tips towards nearly complete melt when the ice margin in the central west unpins from a coastal region with high topography and SMB. We show that if we keep the SMB fixed (i.e. no SMB–elevation feedback) in this relatively confined region, the ice sheet retreat is halted and nearly complete GrIS melt is prevented even though the initial SMB forcing is past the threshold. Based on the minimum GrIS configuration in previous paleo-ice-sheet modelling studies, we suggest that the surface topography in the central west might have played a role in preventing larger GrIS loss during the last interglacial period ∼ 130–115 kyr BP.</p

A High-End Estimate of Sea Level Rise for Practitioners

Sea level rise (SLR) is a long-lasting consequence of climate change because global anthropogenic warming takes centuries to millennia to equilibrate for the deep ocean and ice sheets. SLR projections based on climate models support policy analysis, risk assessment and adaptation planning today, despite their large uncertainties. The central range of the SLR distribution is estimated by process-based models. However, risk-averse practitioners often require information about plausible future conditions that lie in the tails of the SLR distribution, which are poorly defined by existing models. Here, a community effort combining scientists and practitioners builds on a framework of discussing physical evidence to quantify high-end global SLR for practitioners. The approach is complementary to the IPCC AR6 report and provides further physically plausible high-end scenarios. High-end estimates for the different SLR components are developed for two climate scenarios at two timescales. For global warming of +2°C in 2100 (RCP2.6/SSP1-2.6) relative to pre-industrial values our high-end global SLR estimates are up to 0.9 m in 2100 and 2.5 m in 2300. Similarly, for a (RCP8.5/SSP5-8.5), we estimate up to 1.6 m in 2100 and up to 10.4 m in 2300. The large and growing differences between the scenarios beyond 2100 emphasize the long-term benefits of mitigation. However, even a modest 2°C warming may cause multi-meter SLR on centennial time scales with profound consequences for coastal areas. Earlier high-end assessments focused on instability mechanisms in Antarctica, while here we emphasize the importance of the timing of ice shelf collapse around Antarctica. This is highly uncertain due to low understanding of the driving processes. Hence both process understanding and emission scenario control high-end SLR

Experimental protocol for sea level projections from ISMIP6 stand-alone ice sheet models

Projection of the contribution of ice sheets to sea level change as part of the Coupled Model Intercomparison Project Phase 6 (CMIP6) takes the form of simulations from coupled ice sheet–climate models and stand-alone ice sheet models, overseen by the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). This paper describes the experimental setup for process-based sea level change projections to be performed with stand-alone Greenland and Antarctic ice sheet models in the context of ISMIP6. The ISMIP6 protocol relies on a suite of polar atmospheric and oceanic CMIP-based forcing for ice sheet models, in order to explore the uncertainty in projected sea level change due to future emissions scenarios, CMIP models, ice sheet models, and parameterizations for ice–ocean interactions. We describe here the approach taken for defining the suite of ISMIP6 stand-alone ice sheet simulations, document the experimental framework and implementation, and present an overview of the ISMIP6 forcing to be used by participating ice sheet modeling groups

Desempenho de Cultivares de Soja Transgênica (Intacta e Rr1) na Macrorregião Sojícola 1, Avaliadas na Safra 2013/14 ela Rede Soja Sul de Pesquisa.

A Rede Soja Sul de Pesquisa, composta por empresas de melhoramento e de pesquisa (CCGL Tecnologia, Coodetec, GDM Genética do Brasil, Embrapa Clima Temperado, Embrapa Trigo, Fepagro, Geneze Sementes, Nidera Sementes, Syngenta Seeds, TMG, Instituto Federal de Sertão e Fundação Pró-Sementes), conduz ensaios que avaliam, no mesmo ambiente e manejo, o desempenho agronômico de cultivares registradas por diferentes obtentores. Assim, o objetivo deste trabalho foi avaliar o rendimento de grãos de cultivares de soja das tecnologias Intacta e RR1, em ambientes da Macrorregião sojícola 1

Insights into the vulnerability of Antarctic glaciers from the ISMIP6 ice sheet model ensemble and associated uncertainty

The Antarctic Ice Sheet represents the largest source of uncertainty in future sea level rise projections, with a contribution to sea level by 2100 ranging from −5 to 43 cm of sea level equivalent under high carbon emission scenarios estimated by the recent Ice Sheet Model Intercomparison for CMIP6 (ISMIP6). ISMIP6 highlighted the different behaviors of the East and West Antarctic ice sheets, as well as the possible role of increased surface mass balance in offsetting the dynamic ice loss in response to changing oceanic conditions in ice shelf cavities. However, the detailed contribution of individual glaciers, as well as the partitioning of uncertainty associated with this ensemble, have not yet been investigated. Here, we analyze the ISMIP6 results for high carbon emission scenarios, focusing on key glaciers around the Antarctic Ice Sheet, and we quantify their projected dynamic mass loss, defined here as mass loss through increased ice discharge into the ocean in response to changing oceanic conditions. We highlight glaciers contributing the most to sea level rise, as well as their vulnerability to changes in oceanic conditions. We then investigate the different sources of uncertainty and their relative role in projections, for the entire continent and for key individual glaciers. We show that, in addition to Thwaites and Pine Island glaciers in West Antarctica, Totten and Moscow University glaciers in East Antarctica present comparable future dynamic mass loss and high sensitivity to ice shelf basal melt. The overall uncertainty in additional dynamic mass loss in response to changing oceanic conditions, compared to a scenario with constant oceanic conditions, is dominated by the choice of ice sheet model, accounting for 52 % of the total uncertainty of the Antarctic dynamic mass loss in 2100. Its relative role for the most dynamic glaciers varies between 14 % for MacAyeal and Whillans ice streams and 56 % for Pine Island Glacier at the end of the century. The uncertainty associated with the choice of climate model increases over time and reaches 13 % of the uncertainty by 2100 for the Antarctic Ice Sheet but varies between 4 % for Thwaites Glacier and 53 % for Whillans Ice Stream. The uncertainty associated with the ice–climate interaction, which captures different treatments of oceanic forcings such as the choice of melt parameterization, its calibration, and simulated ice shelf geometries, accounts for 22 % of the uncertainty at the ice sheet scale but reaches 36 % and 39 % for Institute Ice Stream and Thwaites Glacier, respectively, by 2100. Overall, this study helps inform future research by highlighting the sectors of the ice sheet most vulnerable to oceanic warming over the 21st century and by quantifying the main sources of uncertainty.1 Introduction 2 Data and methods 2.1 Model ensemble 2.2 Dynamic mass loss 2.3 Emulation of results 2.4 Glaciers contributing most to dynamic sea level rise 3 Results 3.1 Sensitivity to ocean-induced melt 3.2 Sources of uncertainty 4 Discussion 5 Conclusion