160 research outputs found

    Coupled climate–carbon cycle simulation of the Last Glacial Maximum atmospheric CO2 decrease using a large ensemble of modern plausible parameter sets

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    During the Last Glacial Maximum (LGM), atmospheric CO2 was around 90 ppmv lower than during the pre-industrial period. The reasons for this decrease are most often elucidated through factorial experiments testing the impact of individual mechanisms. Due to uncertainty in our understanding of the real system, however, the different models used to conduct the experiments inevitably take on different parameter values and different structures. In this paper, the objective is therefore to take an uncertainty-based approach to investigating the LGM CO2 drop by simulating it with a large ensemble of parameter sets, designed to allow for a wide range of large-scale feedback response strengths. Our aim is not to definitely explain the causes of the CO2 drop but rather explore the range of possible responses. We find that the LGM CO2 decrease tends to predominantly be associated with decreasing sea surface temperatures (SSTs), increasing sea ice area, a weakening of the Atlantic Meridional Overturning Circulation (AMOC), a strengthening of the Antarctic Bottom Water (AABW) cell in the Atlantic Ocean, a decreasing ocean biological productivity, an increasing CaCO3 weathering flux and an increasing deep-sea CaCO3 burial flux. The majority of our simulations also predict an increase in terrestrial carbon, coupled with a decrease in ocean and increase in lithospheric carbon. We attribute the increase in terrestrial carbon to a slower soil respiration rate, as well as the preservation rather than destruction of carbon by the LGM ice sheets. An initial comparison of these dominant changes with observations and paleoproxies other than carbon isotope and oxygen data (not evaluated directly in this study) suggests broad agreement. However, we advise more detailed comparisons in the future, and also note that, conceptually at least, our results can only be reconciled with carbon isotope and oxygen data if additional processes not included in our model are brought into play

    The mPower Study, Parkinson Disease Mobile Data Collected Using Researchkit

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    Current measures of health and disease are often insensitive, episodic, and subjective. Further, these measures generally are not designed to provide meaningful feedback to individuals. The impact of high-resolution activity data collected from mobile phones is only beginning to be explored. Here we present data from mPower, a clinical observational study about Parkinson disease conducted purely through an iPhone app interface. The study interrogated aspects of this movement disorder through surveys and frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from large enrollment and repeated measurements on many individuals, these data may help establish baseline variability of real-world activity measurement collected via mobile phones, and ultimately may lead to quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection modules are available through an open source license for use in studies of other conditions. We hope that releasing data contributed by engaged research participants will seed a new community of analysts working collaboratively on understanding mobile health data to advance human health

    Potassium- and Rubidium-Passivated Alloyed Perovskite Films: Optoelectronic Properties and Moisture Stability.

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    Halide perovskites passivated with potassium or rubidium show superior photovoltaic device performance compared to unpassivated samples. However, it is unclear which passivation route is more effective for film stability. Here, we directly compare the optoelectronic properties and stability of thin films when passivating triple-cation perovskite films with potassium or rubidium species. The optoelectronic and chemical studies reveal that the alloyed perovskites are tolerant toward higher loadings of potassium than rubidium. Whereas potassium complexes with bromide from the perovskite precursor solution to form thin surface passivation layers, rubidium additives favor the formation of phase-segregated micron-sized rubidium halide crystals. This tolerance to higher loadings of potassium allows us to achieve superior luminescent properties with potassium passivation. We also find that exposure to a humid atmosphere drives phase segregation and grain coalescence for all compositions, with the rubidium-passivated sample showing the highest sensitivity to nonperovskite phase formation. Our work highlights the benefits but also the limitations of these passivation approaches in maximizing both optoelectronic properties and the stability of perovskite films.Engineering and Physical Sciences Research Council (grant number: EP/M005143/1

    Climatically controlled reproduction drives interannual growth variability in a temperate tree species

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    Climatically controlled allocation to reproduction is a key mechanism by which climate influences tree growth and may explain lagged correlations between climate and growth. We used continent‐wide datasets of tree‐ring chronologies and annual reproductive effort in Fagus sylvatica from 1901 to 2015 to characterise relationships between climate, reproduction and growth. Results highlight that variable allocation to reproduction is a key factor for growth in this species, and that high reproductive effort (‘mast years’) is associated with stem growth reduction. Additionally, high reproductive effort is associated with previous summer temperature, creating lagged climate effects on growth. Consequently, understanding growth variability in forest ecosystems requires the incorporation of reproduction, which can be highly variable. Our results suggest that future response of growth dynamics to climate change in this species will be strongly influenced by the response of reproduction.Additional co-authors: Ernst van der Maaten, Marieke van der Maaten‐Theunissen, Lena Muffler, Renzo Motta, Catalin‐Constantin Roibu, Ionel Popa, Tobias Scharnweber, Robert Weigel, Martin Wilmking, Christian S Zan

    Modelling tropical forest responses to drought and El Niño with a stomatal optimization model based on xylem hydraulics.

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    The current generation of dynamic global vegetation models (DGVMs) lacks a mechanistic representation of vegetation responses to soil drought, impairing their ability to accurately predict Earth system responses to future climate scenarios and climatic anomalies, such as El Niño events. We propose a simple numerical approach to model plant responses to drought coupling stomatal optimality theory and plant hydraulics that can be used in dynamic global vegetation models (DGVMs). The model is validated against stand-scale forest transpiration (E) observations from a long-term soil drought experiment and used to predict the response of three Amazonian forest sites to climatic anomalies during the twentieth century. We show that our stomatal optimization model produces realistic stomatal responses to environmental conditions and can accurately simulate how tropical forest E responds to seasonal, and even long-term soil drought. Our model predicts a stronger cumulative effect of climatic anomalies in Amazon forest sites exposed to soil drought during El Niño years than can be captured by alternative empirical drought representation schemes. The contrasting responses between our model and empirical drought factors highlight the utility of hydraulically-based stomatal optimization models to represent vegetation responses to drought and climatic anomalies in DGVMs.This article is part of a discussion meeting issue 'The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'

    Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2.

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    Future climate change and increasing atmospheric CO2 are expected to cause major changes in vegetation structure and function over large fractions of the global land surface. Seven global vegetation models are used to analyze possible responses to future climate simulated by a range of general circulation models run under all four representative concentration pathway scenarios of changing concentrations of greenhouse gases. All 110 simulations predict an increase in global vegetation carbon to 2100, but with substantial variation between vegetation models. For example, at 4 °C of global land surface warming (510-758 ppm of CO2), vegetation carbon increases by 52-477 Pg C (224 Pg C mean), mainly due to CO2 fertilization of photosynthesis. Simulations agree on large regional increases across much of the boreal forest, western Amazonia, central Africa, western China, and southeast Asia, with reductions across southwestern North America, central South America, southern Mediterranean areas, southwestern Africa, and southwestern Australia. Four vegetation models display discontinuities across 4 °C of warming, indicating global thresholds in the balance of positive and negative influences on productivity and biomass. In contrast to previous global vegetation model studies, we emphasize the importance of uncertainties in projected changes in carbon residence times. We find, when all seven models are considered for one representative concentration pathway × general circulation model combination, such uncertainties explain 30% more variation in modeled vegetation carbon change than responses of net primary productivity alone, increasing to 151% for non-HYBRID4 models. A change in research priorities away from production and toward structural dynamics and demographic processes is recommended.The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7 2007-2013) under Grant 238366. R.B., R.K., R.D., A.W., and P.D.F. were supported by the Joint Department of Energy and Climate Change/Department for Environment, Food and Rural Affairs Met Office Hadley Centre Climate Programme (GA01101). A.I. and K.N. were supported by the Environment Research and Technology Development Fund (S-10) of the Ministry of the Environment, Japan. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for the Coupled Model Intercomparison Project (CMIP), and we thank the climate modeling groups responsible for the GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M models for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This work has been conducted under the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP). The ISI-MIP Fast Track project was funded by the German Federal Ministry of Education and Research (BMBF) with project funding Reference 01LS1201A.This is the author accepted manuscript. The final version is available from PNAS via http://dx.doi.org/10.1073/pnas.122247711
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