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Carbonaceous particles, such as soot, make up a notable fraction of atmospheric particulate matter and contribute substantially to anthropogenic climate forcing, air pollution, and human health impacts. Thermal–optical analysis (TOA) is one of the most widespread methods used to speciate carbonaceous particles and divides total carbon (TC) into the operationally defined quantities of organic carbon (OC; carbon that has evolved during slow heating in an inert atmosphere) and elemental carbon (EC). While multiple studies have identified fundamental scientific reasons for uncertainty in distinguishing OC and EC, far fewer studies have reported on between-laboratory reproducibility. Moreover, existing reproducibility studies have focused on complex atmospheric samples. The real-time instruments used for regulatory measurements of the mass concentration of aircraft engine non-volatile particulate matter (nvPM) emissions are required to be calibrated to the mass of EC, as determined by TOA of the filter-sampled emissions of a diffusion flame combustion aerosol source (DFCAS). However, significant differences have been observed in the calibration factor for the same instrument based on EC content determined by different calibration laboratories. Here, we report on the reproducibility of TC, EC, and OC quantified using the same TOA protocol, instrument model (Model 5L, Sunset Laboratory), and software settings (auto-split-point: Calc405) across five different laboratories and instrument operators. Six unique data sets were obtained, with one laboratory operating two instruments. All samples were collected downstream of an aircraft engine after treatment with a catalytic stripper to remove volatile organics. Between-laboratory contributions made up a majority of the within-filter uncertainties for EC and TC, even for these relatively well-controlled samples. Overall, expanded (k = 2) uncertainties due to measurement reproducibility correspond to 17 %, 15 %, and 13 % of the nominal values for EC, OC, and TC, respectively, and 7.3 % in the EC / TC ratio. These values are lower than previous studies, including atmospheric samples without volatile organic removal; therefore, they likely represent lower limits for the uncertainties of the TOA method.

Abstract Neuromorphic sensors have inherently‐fast speeds and low data rates, which potentially make them ideal for the observation of transient sources, such as lightning and sprites. Particularly, for remote observations. In this article, we report the first observations of sprites from the ground with a neuromorphic sensor. These observations are accompanied by measurements with established instruments such as low‐light level and high‐frame rate cameras. We determine that neuromorphic sensors can capture sprites and determine their duration to an accuracy of roughly 6 ms. Average sprite durations were found to be 55 ms within our data set. We have also ascertained that sprites may be too dim for the neuromorphic sensors to resolve the internal spatiotemporal dynamics, at least without the aid of intensifiers.

Abstract A standing challenge in the study of animal movement ecology is the capacity to predict where and when an individual animal might occur on the landscape, the so‐called, utilisation distribution (UD). Under certain assumptions, the steady‐state UD can be predicted from a fitted exponential habitat selection function. However, these assumptions are rarely met. Furthermore, there are many applications that require the estimation of transient dynamics rather than steady‐state UDs (e.g. when modelling migration or dispersal). Thus, there is a clear need for computational tools capable of predicting UDs based on observed animal movement data. Integrated Step‐Selection Analyses (iSSAs), which integrates movement of the animal into habitat selection analyses, are widely used to study habitat selection and movement of wild animals, and result in a fully parametrised individual‐based model of animal movement, which we refer to as an integrated Step Selection Function (iSSF). An iSSF can be used to generate stochastic animal paths based on random draws from a series of Markovian redistribution kernels, each consisting of a selection‐free, but possibly habitat‐influenced, movement kernel and a movement‐free selection function. The UD can be approximated by a sufficiently large set of such stochastic paths. Here, we present a set of functions in R to facilitate the simulation of animal space use from fitted iSSFs. Our goal is to provide a general purpose simulator that is easy to use and is part of an existing workflow for iSSAs (within the amt R package). We demonstrate through a series of applications how the simulator can be used to address a variety of questions in applied movement ecology. By providing functions in amt and coded examples, we hope to encourage ecologists using iSSFs to explore their predictions and model goodness‐of‐fit using simulations, and to further explore mechanistic approaches to modelling landscape connectivity.

Abstract The specific activity of the $$\beta $$ β decay of $$^{39}$$ 39 Ar in atmospheric argon is measured using the DEAP-3600 detector. DEAP-3600, located 2 km underground at SNOLAB, uses a total of (3269 ± 24) kg of liquid argon distilled from the atmosphere to search for dark matter. This detector is well-suited to measure the decay of $$^{39}$$ 39 Ar owing to its very low background levels. This is achieved in two ways: it uses low background construction materials; and it uses pulse-shape discrimination to differentiate between nuclear recoils and electron recoils. With 167 live-days of data, the measured specific activity at the time of atmospheric extraction is (0.964 ± 0.001 $$_\textrm{stat}$$ stat ± 0.024 $$_\textrm{sys}$$ sys ) Bq/kg $$_\textrm{atmAr}$$ atmAr , which is consistent with results from other experiments. A cross-check analysis using different event selection criteria and a different statistical method confirms the result.

Climatic extreme events are expected to occur more frequently in the future, increasing the likelihood of unprecedented climate extremes (UCEs) or record-breaking events. UCEs, such as extreme heatwaves and droughts, substantially affect ecosystem stability and carbon cycling by increasing plant mortality and delaying ecosystem recovery. Quantitative knowledge of such effects is limited due to the paucity of experiments focusing on extreme climatic events beyond the range of historical experience. Here, we present a road map of how dynamic vegetation demographic models (VDMs) can be used to investigate hypotheses surrounding ecosystem responses to one type of UCE: unprecedented droughts. As a result of nonlinear ecosystem responses to UCEs that are qualitatively different from responses to milder extremes, we consider both biomass loss and recovery rates over time by reporting a time-integrated carbon loss as a result of UCE, relative to the absence of drought. Additionally, we explore how unprecedented droughts in combination with increasing atmospheric CO2 and/or temperature may affect ecosystem stability and carbon cycling. We explored these questions using simulations of pre-drought and post-drought conditions at well-studied forest sites using well-tested models (ED2 and LPJ-GUESS). The severity and patterns of biomass losses differed substantially between models. For example, biomass loss could be sensitive to either drought duration or drought intensity depending on the model approach. This is due to the models having different, but also plausible, representations of processes and interactions, highlighting the complicated variability of UCE impacts that still need to be narrowed down in models. Elevated atmospheric CO2 concentrations (eCO2) alone did not completely buffer the ecosystems from carbon losses during UCEs in the majority of our simulations. Our findings highlight the consequences of differences in process formulations and uncertainties in models, most notably related to availability in plant carbohydrate storage and the diversity of plant hydraulic schemes, in projecting potential ecosystem responses to UCEs. We provide a summary of the current state and role of many model processes that give way to different underlying hypotheses of plant responses to UCEs, reflecting knowledge gaps which in future studies could be tested with targeted field experiments and an iterative modeling–experimental conceptual framework.

Abstract The Aria cryogenic distillation plant, located in Sardinia, Italy, is a key component of the DarkSide-20k experimental program for WIMP dark matter searches at the INFN Laboratori Nazionali del Gran Sasso, Italy. Aria is designed to purify the argon, extracted from underground wells in Colorado, USA, and used as the DarkSide-20k target material, to detector-grade quality. In this paper, we report the first measurement of argon isotopic separation by distillation with the 26 m tall Aria prototype. We discuss the measurement of the operating parameters of the column and the observation of the simultaneous separation of the three stable argon isotopes: $${}^{36}\hbox {Ar}$$ 36 Ar , $${}^{38}\textrm{Ar}$$ 38 Ar , and $${}^{40}\textrm{Ar}$$ 40 Ar . We also provide a detailed comparison of the experimental results with commercial process simulation software. This measurement of isotopic separation of argon is a significant achievement for the project, building on the success of the initial demonstration of isotopic separation of nitrogen using the same equipment in 2019.

Climate projections from global circulation models (GCMs), part of the Coupled Model Intercomparison Project 6 (CMIP6), are often employed to study the impact of future climate on ecosystems. However, especially at regional scales, climate projections display large biases in key forcing variables such as temperature and precipitation. These biases have been identified as a major source of uncertainty in carbon cycle projections, hampering predictive capacity. In this study, we open the proverbial Pandora's box and peer under the lid of strategies to tackle climate model ensemble uncertainty. We employ a dynamic global vegetation model (LPJ-GUESS) and force it with raw output from CMIP6 to assess the uncertainty associated with the choice of climate forcing. We then test different methods to either bias-correct or calculate ensemble averages over the original forcing data to reduce the climate-driven uncertainty in the regional projection of the Australian carbon cycle. We find that all bias correction methods reduce the bias of continental averages of steady-state carbon variables. Bias correction can improve model carbon outputs, but carbon pools are insensitive to the type of bias correction method applied for both individual GCMs and the arithmetic ensemble average across all corrected models. None of the bias correction methods consistently improve the change in simulated carbon over time compared to the target dataset, highlighting the need to account for temporal properties in correction or ensemble-averaging methods. Multivariate bias correction methods tend to reduce the uncertainty more than univariate approaches, although the overall magnitude is similar. Even after correcting the bias in the meteorological forcing dataset, the simulated vegetation distribution presents different patterns when different GCMs are used to drive LPJ-GUESS. Additionally, we found that both the weighted ensemble-averaging and random forest approach reduce the bias in total ecosystem carbon to almost zero, clearly outperforming the arithmetic ensemble-averaging method. The random forest approach also produces the results closest to the target dataset for the change in the total carbon pool, seasonal carbon fluxes, emphasizing that machine learning approaches are promising tools for future studies. This highlights that, where possible, an arithmetic ensemble average should be avoided. However, potential target datasets that would facilitate the application of machine learning approaches, i.e., that cover both the spatial and temporal domain required to derive a robust informed ensemble average, are sparse for ecosystem variables.

Baltic Earth is an independent research network of scientists from all Baltic Sea countries that promotes regional Earth system research. Within the framework of this network, the Baltic Earth Assessment Reports (BEARs) were produced in the period 2019–2022. These are a collection of 10 review articles summarising current knowledge on the environmental and climatic state of the Earth system in the Baltic Sea region and its changes in the past (palaeoclimate), present (historical period with instrumental observations) and prospective future (until 2100) caused by natural variability, climate change and other human activities. The division of topics among articles follows the grand challenges and selected themes of the Baltic Earth Science Plan, such as the regional water, biogeochemical and carbon cycles; extremes and natural hazards; sea-level dynamics and coastal erosion; marine ecosystems; coupled Earth system models; scenario simulations for the regional atmosphere and the Baltic Sea; and climate change and impacts of human use. Each review article contains an introduction, the current state of knowledge, knowledge gaps, conclusions and key messages; the latter are the bases on which recommendations for future research are made. Based on the BEARs, Baltic Earth has published an information leaflet on climate change in the Baltic Sea as part of its outreach work, which has been published in two languages so far, and organised conferences and workshops for stakeholders, in collaboration with the Baltic Marine Environment Protection Commission (Helsinki Commission, HELCOM).

Land biosphere processes are of central importance to the climate system. Specifically, ecosystems interact with the atmosphere through a variety of feedback loops that modulate energy, water, and CO2 fluxes between the land surface and the atmosphere across a wide range of temporal and spatial scales. Human land use and land cover modification add a further level of complexity to land–atmosphere interactions. Dynamic global vegetation models (DGVMs) attempt to capture land ecosystem processes and are increasingly incorporated into Earth system models (ESMs), which makes it possible to study the coupled dynamics of the land biosphere and the climate. In this work we describe a number of modifications to the LPJ-GUESS DGVM, aimed at enabling direct integration into an ESM. These include energy balance closure, the introduction of a sub-daily time step, a new radiative transfer scheme, and improved soil physics. The implemented modifications allow the model (LPJ-GUESS/LSM) to simulate the diurnal exchange of energy, water, and CO2 between the land ecosystem and the atmosphere and thus provide surface boundary conditions to an atmospheric model over land. A site-based evaluation against FLUXNET2015 data shows reasonable agreement between observed and modelled sensible and latent heat fluxes. Differences in predicted ecosystem function between standard LPJ-GUESS and LPJ-GUESS/LSM vary across land cover types. We find that the emerging ecosystem composition and carbon fluxes are sensitive to both the choice of stomatal conductance model and the response of plant water uptake to soil moisture. The new implementation described in this work lays the foundation for using the well-established LPJ-GUESS DGVM as an alternative land surface model (LSM) in coupled land–biosphere–climate studies, where an accurate representation of ecosystem processes is essential.

The Earth system model EC-Earth3 for contributions to CMIP6 is documented here, with its flexible coupling framework, major model configurations, a methodology for ensuring the simulations are comparable across different high-performance computing (HPC) systems, and with the physical performance of base configurations over the historical period. The variety of possible configurations and sub-models reflects the broad interests in the EC-Earth community. EC-Earth3 key performance metrics demonstrate physical behavior and biases well within the frame known from recent CMIP models. With improved physical and dynamic features, new Earth system model (ESM) components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.

Arctic environmental change induces shifts in high-latitude plant community composition and stature with implications for Arctic carbon cycling and energy exchange. Two major components of change in high-latitude ecosystems are the advancement of trees into tundra and the increased abundance and size of shrubs. How future changes in key climatic and environmental drivers will affect distributions of major ecosystem types is an active area of research. Dynamic vegetation models (DVMs) offer a way to investigate multiple and interacting drivers of vegetation distribution and ecosystem function. We employed the LPJ-GUESS tree-individual-based DVM over the Torneträsk area, a sub-arctic landscape in northern Sweden. Using a highly resolved climate dataset to downscale CMIP5 climate data from three global climate models and two 21st-century future scenarios (RCP2.6 and RCP8.5), we investigated future impacts of climate change on these ecosystems. We also performed model experiments where we factorially varied drivers (climate, nitrogen deposition and [CO2]) to disentangle the effects of each on ecosystem properties and functions. Our model predicted that treelines could advance by between 45 and 195 elevational metres by 2100, depending on the scenario. Temperature was a strong driver of vegetation change, with nitrogen availability identified as an important modulator of treeline advance. While increased CO2 fertilisation drove productivity increases, it did not result in range shifts of trees. Treeline advance was realistically simulated without any temperature dependence on growth, but biomass was overestimated. Our finding that nitrogen cycling could modulate treeline advance underlines the importance of representing plant–soil interactions in models to project future Arctic vegetation change.

The physical and chemical properties of black carbon (BC) and organic aerosols are important for predicting their radiative forcing in the atmosphere. During the Soot Aerodynamic Size Selection for Optical properties (SASSO) project and a EUROCHAMP-2020 transnational access project, different types of light-absorbing carbon were studied, including BC from catalytically stripped diesel exhaust, an inverted flame burner, a colloidal graphite standard (Aquadag) and controlled flaming wood combustion. Brown carbon (BrC) was also investigated in the form of organic aerosol emissions from wood burning (pyrolysis and smouldering) and from the nitration of secondary organic aerosol (SOA) proxies produced in a photochemical reaction chamber. Here we present insights into the physical and chemical properties of the aerosols, with optical properties presented in subsequent publications. The dynamic shape factor (χ) of BC particles and material density (ρm) of organic aerosols was investigated by coupling a charging-free Aerodynamic Aerosol Classifier (AAC) with a Centrifugal Particle Mass Analyzer (CPMA) and a Scanning Mobility Particle Sizer (SMPS). The morphology of BC particles was captured by transmission electron microscopy (TEM). For BC particles from the diesel engine and flame burner emissions, the primary spherule sizes were similar, around 20 nm. With increasing particle size, BC particles adopted more collapsed/compacted morphologies for the former source but tended to show more aggregated morphologies for the latter source. For particles emitted from the combustion of dry wood samples, the χ of BC particles and the ρm of organic aerosols were observed in the ranges 1.8–2.17 and 1.22–1.32 g cm−3, respectively. Similarly, for wet wood samples, the χ and ρm ranges were 1.2–1.85 and 1.44–1.60 g cm−3, respectively. Aerosol mass spectrometry measurements show no clear difference in mass spectra of the organic aerosols in individual burn phases (pyrolysis or smouldering phase) with the moisture content of the wood samples. This suggests that the effect moisture has on the organic chemical profile of wood burning emissions is through changing the durations of the different phases of the burn cycle, not through the chemical modification of the individual phases. In this study, the incandescence signal of a Single Particle Soot Photometer (SP2) was calibrated with three different types of BC particles and compared with that from an Aquadag standard that is commonly used to calibrate SP2 incandescence to a BC mass. A correction factor is defined as the ratio of the incandescence signal from an alternative BC source to that from the Aquadag standard and took values of 0.821 ± 0.002 (or 0.794 ± 0.005), 0.879 ± 0.003 and 0.843 ± 0.028 to 0.913 ± 0.009 for the BC particles emitted from the diesel engine running under hot (or cold idle) conditions, the flame burner and wood combustion, respectively. These correction factors account for differences in instrument response to BC from different sources compared to the standardised Aquadag calibration and are more appropriate than the common value of 0.75 recommended by Laborde et al. (2012b) when deriving the mass concentration of BC emitted from diesel engines. Quantifying the correction factor for many types of BC particles found commonly in the atmosphere may enable better constraints to be placed on this factor depending on the BC source being sampled and thus improve the accuracy of future SP2 measurements of BC mass concentrations.

Global forests are the main component of the land carbon sink, which acts as a partial buffer to CO2 emissions into the atmosphere. Dynamic vegetation models offer an approach to projecting the development of forest carbon sink capacity in a future climate. Forest management capabilities are important to include in dynamic vegetation models to account for the effects of age and species structure and wood harvest on carbon stocks and carbon storage potential. This article describes the implementation of a forest management module containing even-age and clear-cut and uneven-age and continuous-cover management alternatives in the dynamic vegetation model LPJ-GUESS. Different age and species structure initialisation strategies and harvest alternatives are introduced. The model is applied at stand and European scales. Different management alternatives are applied in simulations of European beech (Fagus sylvaticus) and Norway spruce (Picea abies) even-aged monoculture stands in central Europe and evaluated against above-ground standing stem volume and harvested volume data from long-term experimental plots. At the European scale, an automated thinning and clear-cut strategy is applied. Modelled carbon stocks and fluxes are evaluated against reported data at the continent and country levels. Including wood harvest in regrowth forests increases the simulated total European carbon sink by 32 % in 1991–2015 and improves the fit to the reported European carbon sink, growing stock, and net annual increment (NAI). Growing stock (156 m3 ha−1) and NAI (5.4 m3 ha1 yr1) densities in 2010 are close to reported values, while the carbon sink density in 2000–2007 (0.085 kg C m−2 yr1) equates to 63 % of reported values, most likely reflecting uncertainties in carbon fluxes from soil given the unaccounted for forest land-use history in the simulations. The fit of modelled and reported values for individual European countries varies, but NAI is generally closer to reported values when including wood harvest in simulations.

Abstract The DEAP-3600 detector searches for the scintillation signal from dark matter particles scattering on a 3.3 tonne liquid argon target. The largest background comes from $$^{39}\text{ Ar }$$ 39 Ar beta decays and is suppressed using pulse-shape discrimination (PSD). We use two types of PSD estimator: the prompt-fraction, which considers the fraction of the scintillation signal in a narrow and a wide time window around the event peak, and the log-likelihood-ratio, which compares the observed photon arrival times to a signal and a background model. We furthermore use two algorithms to determine the number of photons detected at a given time: (1) simply dividing the charge of each PMT pulse by the mean single-photoelectron charge, and (2) a likelihood analysis that considers the probability to detect a certain number of photons at a given time, based on a model for the scintillation pulse shape and for afterpulsing in the light detectors. The prompt-fraction performs approximately as well as the log-likelihood-ratio PSD algorithm if the photon detection times are not biased by detector effects. We explain this result using a model for the information carried by scintillation photons as a function of the time when they are detected.

College opportunities now exist for young adults with intellectual disability. Because of this, it is common for these individuals' parents to express a desire for increased student agency (Miller et al., 2018). Yet, little is known about how parents feel about how to best support agency development for their young adult child. In the current study, authors surveyed 64 parents with a student attending an inclusive postsecondary education program to examine their perceived level of confidence related to supporting their student in developing agency. Further, we looked at parents' responses to their perception of the most important skills related to supporting agency development. Findings showed that parents felt least confident in supporting students' financial independence and felt supporting navigation of intimate relationships to be least important. Implications for practice and directions for future research are presented.

Transition-age adults with disabilities largely desire the same community employment and community participation outcomes as young adults without disabilities. One common barrier to the manifestation of these desires is the lack of reliable and accessible transportation in their communities. This study used the participatory action research method of photovoice to investigate the experiences of college students with intellectual and developmental disabilities (IDDs) accessing various transportation options within their community. Utilizing a critical constructivist theoretical framework, researchers worked with participants to co-construct situated knowledge. The overall aim of this study was to contribute to the extant literature on transportation barriers and facilitators for this population and to give emerging adults enrolled in an inclusive postsecondary education program an opportunity to share their lived experiences and raise critical consciousness related to their transportation experiences. Limitations and implications for future research, and practice are included.

Effluents from municipal wastewater treatment plants (MWTPs) are complex mixtures of chemicals including endocrine-disrupting compounds (EDCs) and 17α-ethynylestradiol (EE2). The objective of this study was to evaluate selected responses of two fish species, in two different years, exposed in situ to MWTP effluent. Biological markers of exposure (plasma vitellogenin (VTG) and antioxidant enzymes) were measured in two species of male fish, rainbow trout (Oncorhynchus mykiss) and fathead minnow (Pimephales promelas), caged at sites associated with wastewater outfall. The estrogenicity of the final effluent in 2010 was determined to be 17.0 + 0.4 ng/L estrogen equivalents (EEQ) and reduced to 7.5 + 2.9 ng/L EEQ after infrastructure upgrades. Pharmaceuticals and personal care products in the effluent and surface water in both years confirmed the exposures at each downstream site. Despite the presence of estrogenic compounds in the MWTP effluent, no effluent-caged male fish demonstrated plasma VTG induction. Minnows and trout that received an intraperitoneal injection of 5 mg/g EE2 showed VTG induction at both field sites. In 2012, the liver somatic index (LSI) of both species increased with exposure, as did changes in antioxidant enzymes, and reactive oxygen species (ROS) activity. Multiple biological mechanisms are modified by effluent exposure, and multiple endpoints are needed to assess risk. HIGHLIGHTS Effluent exposure resulted in induction of antioxidant enzymes and ROS activity in caged fish.; Despite measurable estrogen equivalents in the effluent, downstream caged male fathead minnow did not demonstrate VTG induction.; EE2 injected, control male fish exposed to wastewater or upstream reference conditions responded with elevated VTG concentrations.; Several factors may have reduced the VTG response in caged fish.;

Abstract Aria is a plant hosting a $${350}\,\hbox {m}$$ 350 m cryogenic isotopic distillation column, the tallest ever built, which is being installed in a mine shaft at Carbosulcis S.p.A., Nuraxi-Figus (SU), Italy. Aria is one of the pillars of the argon dark-matter search experimental program, lead by the Global Argon Dark Matter Collaboration. It was designed to reduce the isotopic abundance of $${^{39}\hbox {Ar}}$$ 39 Ar in argon extracted from underground sources, called Underground Argon (UAr), which is used for dark-matter searches. Indeed, $${^{39}\hbox {Ar}}$$ 39 Ar is a $$\beta $$ β -emitter of cosmogenic origin, whose activity poses background and pile-up concerns in the detectors. In this paper, we discuss the requirements, design, construction, tests, and projected performance of the plant for the isotopic cryogenic distillation of argon. We also present the successful results of the isotopic cryogenic distillation of nitrogen with a prototype plant.

The El Niño‐-Southern Oscillation (ENSO) influences the global climate and the variability in the terrestrial carbon cycle on interannual timescales. Two different expressions of El Niño have recently been identified: (i) central Pacific (CP) and (ii) eastern Pacific (EP). Both types of El Niño are characterised by above-average sea surface temperature anomalies at the respective locations. Studies exploring the impact of these expressions of El Niño on the carbon cycle have identified changes in the amplitude of the concentration of interannual atmospheric carbon dioxide (CO2) variability following increased tropical near-surface air temperature and decreased precipitation. We employ the dynamic global vegetation model LPJ-GUESS (Lund–Potsdam–Jena General Ecosystem Simulator) within a synthetic experimental framework to examine the sensitivity and potential long-term impacts of these two expressions of El Niño on the terrestrial carbon cycle. We manipulated the occurrence of CP and EP events in two climate reanalysis datasets during the latter half of the 20th and early 21st century by replacing all EP with CP and separately all CP with EP El Niño events. We found that the different expressions of El Niño affect interannual variability in the terrestrial carbon cycle. However, the effect on longer timescales was small for both climate reanalysis datasets. We conclude that capturing any future trends in the relative frequency of CP and EP El Niño events may not be critical for robust simulations of the terrestrial carbon cycle.

The nitrogen cycle and its effect on carbon uptake in the terrestrial biosphere is a recent progression in earth system models. As with any new component of a model, it is important to understand the behaviour, strengths, and limitations of the various process representations. Here we assess and compare five land surface models with nitrogen cycles that are used as the terrestrial components of some of the earth system models in CMIP6. The land surface models were run offline with a common spin-up and forcing protocol. We use a historical control simulation and two perturbations to assess the model nitrogen-related performances: a simulation with atmospheric carbon dioxide increased by 200 ppm and one with nitrogen deposition increased by 50 kgN ha−1 yr−1. There is generally greater variability in productivity response between models to increased nitrogen than to carbon dioxide. Across the five models the response to carbon dioxide globally was 5 % to 20 % and the response to nitrogen was 2 % to 24 %. The models are not evenly distributed within the ensemble range, with two of the models having low productivity response to nitrogen and another one with low response to elevated atmospheric carbon dioxide, compared to the other models. In all five models individual grid cells tend to exhibit bimodality, with either a strong response to increased nitrogen or atmospheric carbon dioxide but rarely to both to an equal extent. However, this local effect does not scale to either the regional or global level. The global and tropical responses are generally more accurately modelled than boreal, tundra, or other high-latitude areas compared to observations. These results are due to divergent choices in the representation of key nitrogen cycle processes. They show the need for more observational studies to enhance understanding of nitrogen cycle processes, especially nitrogen-use efficiency and biological nitrogen fixation.

Summary: Nutritional equivalency feeding studies with diets containing genetically modified grain continue to be required by some regulatory agencies as part of the safety assessment of new events. These studies have long been conducted using broiler chickens, as they are very sensitive to nutritional deficiencies and antinutrients in the diet owing to their very rapid growth rate. A new transgenic maize product containing event DP-2Ø2216-6 (DP202216) with enhanced grain yield potential and an herbicide-tolerance trait has been developed. The nutritional equivalency of grain produced from maize containing this event with conventional grain was evaluated in a 42-D feeding trial with male and female Ross 708 broiler chickens. Broilers consuming diets produced with grain from DP202216 maize plants performed as well and produced similar organ, carcass, and parts yields as broilers consuming diets produced with nontransgenic near-isogenic control grain. In addition, the ranges of observed performance and carcass and organ yield trait values from DP202216 and control groups were similar to the respective ranges observed for broilers fed diets containing nontransgenic commercially available maize grains. Based on these results, it is concluded that grain from DP202216 maize is nutritionally equivalent to grain from nontransgenic near-isogenic maize, consistent with previously published reports on whole-food (where the edible crop tissue is incorporated in the feed) animal feeding studies for other genetically modified events.

The length of time that carbon remains in forest biomass is one of the largest uncertainties in the global carbon cycle, with both recent historical baselines and future responses to environmental change poorly constrained by available observations. In the absence of large-scale observations, models used for global assessments tend to fall back on simplified assumptions of the turnover rates of biomass and soil carbon pools. In this study, the biomass carbon turnover times calculated by an ensemble of contemporary terrestrial biosphere models (TBMs) are analysed to assess their current capability to accurately estimate biomass carbon turnover times in forests and how these times are anticipated to change in the future. Modelled baseline 1985–2014 global average forest biomass turnover times vary from 12.2 to 23.5 years between TBMs. TBM differences in phenological processes, which control allocation to, and turnover rate of, leaves and fine roots, are as important as tree mortality with regard to explaining the variation in total turnover among TBMs. The different governing mechanisms exhibited by each TBM result in a wide range of plausible turnover time projections for the end of the century. Based on these simulations, it is not possible to draw robust conclusions regarding likely future changes in turnover time, and thus biomass change, for different regions. Both spatial and temporal uncertainty in turnover time are strongly linked to model assumptions concerning plant functional type distributions and their controls. Thirteen model-based hypotheses of controls on turnover time are identified, along with recommendations for pragmatic steps to test them using existing and novel observations. Efforts to resolve uncertainty in turnover time, and thus its impacts on the future evolution of biomass carbon stocks across the world's forests, will need to address both mortality and establishment components of forest demography, as well as allocation of carbon to woody versus non-woody biomass growth.

Abstract DEAP-3600 is a liquid-argon scintillation detector looking for dark matter. Scintillation events in the liquid argon (LAr) are registered by 255 photomultiplier tubes (PMTs), and pulseshape discrimination (PSD) is used to suppress electromagnetic background events. The excellent PSD performance of LAr makes it a viable target for dark matter searches, and the LAr scintillation pulseshape discussed here is the basis of PSD. The observed pulseshape is a combination of LAr scintillation physics with detector effects. We present a model for the pulseshape of electromagnetic background events in the energy region of interest for dark matter searches. The model is composed of (a) LAr scintillation physics, including the so-called intermediate component, (b) the time response of the TPB wavelength shifter, including delayed TPB emission at $${\mathcal {O}}$$ O (ms) time-scales, and c) PMT response. TPB is the wavelength shifter of choice in most LAr detectors. We find that approximately 10% of the intensity of the wavelength-shifted light is in a long-lived state of TPB. This causes light from an event to spill into subsequent events to an extent not usually accounted for in the design and data analysis of LAr-based detectors.

Background: Tafenoquine is active against a broad range of pathogens and accumulates extensively in the lung. We profiled the susceptibility of fungal pathogens to tafenoquine in vitro and in vivo. Methods: Minimum inhibitory concentrations [MICs] of medically important fungal pathogens were determined using conventional in vitro assays. The daily maximum tolerated dose [MTD] of tafenoquine was determined in neutropenic mice and the effect of two doses of tafenoquine [MTD and 0.5xMTD] on survival and fungal burden were assessed in Rhizopus and Aspergillus lung infections models. Results: Mean MICS against panels of yeasts and dimorphic/filamentous fungi were 4.5 and 8.3 ug/mL. The MTD of tafenoquine was 5 mg/kg/day. Against Aspergillus [MIC 16 ug/mL], tafenoquine did not increase survival or decrease fungal burden. Against Rhizopus, [MIC 4 ug/mL], tafenoquine decreased lung fungal burden [by 0.5 logs, P

This study aimed to understand the ways in which college students with intellectual and developmental disabilities (IDD) experience and develop their understanding of emotions and emotional wellness. Semi-structured interviews with college students with IDD were conducted. The research team utilized consensual qualitative research (CQR) to analyze interviews and came to consensus in generating domains, core ideas, and a cross-analysis to answer the research question, "What are the experiences of college students with IDD in developing an understanding of emotions and emotional wellness?" Findings suggest college students with IDD have experience developing and maintaining their emotional wellness, though they may experience barriers prior to and during college enrollment. Limitations and implications for future research are discussed.

Smith, Pope, and Kohler glaciers and the corresponding Crosson and Dotson ice shelves have undergone speedup, thinning, and rapid grounding-line retreat in recent years, leaving them in a state likely conducive to future retreat. We conducted a suite of numerical model simulations of these glaciers and compared the results to observations to determine the processes controlling their recent evolution. The model simulations indicate that the state of these glaciers in the 1990s was not inherently unstable, i.e., that small perturbations to the grounding line would not necessarily have caused the large retreat that has been observed. Instead, sustained, elevated melt at the grounding line was needed to cause the observed retreat. Weakening of the margins of Crosson Ice Shelf may have hastened the onset of grounding-line retreat but is unlikely to have initiated these rapid changes without an accompanying increase in melt. In the simulations that most closely match the observed thinning, speedup, and retreat, modeled grounding-line retreat and ice loss continue unabated throughout the 21st century, and subsequent retreat along Smith Glacier's trough appears likely. Given the rapid progression of grounding-line retreat in the model simulations, thinning associated with the retreat of Smith Glacier may reach the ice divide and undermine a portion of the Thwaites catchment as quickly as changes initiated at the Thwaites terminus.

Abstract The DEAP-3600 detector searches for dark matter interactions on a 3.3 tonne liquid argon target. Over nearly a decade, from start of detector construction through the end of the data analysis phase, well over 200 scientists will have contributed to the project. The DEAP-3600 detector will amass in excess of 900 TB of data representing more than 10$$^{10}$$ 10 particle interactions, a few of which could be from dark matter. At the same time, metadata exceeding 80 GB will be generated. This metadata is crucial for organizing and interpreting the dark matter search data and contains both structured and unstructured information. The scale of the data collected, the important role of metadata in interpreting it, the number of people involved, and the long lifetime of the project necessitate an industrialized approach to metadata management. We describe how the CouchDB and the PostgreSQL database systems were integrated into the DEAP detector operation and analysis workflows. This integration provides unified, distributed access to both structured (PostgreSQL) and unstructured (CouchDB) metadata at runtime of the data analysis software. It also supports operational and reporting requirements.

An automated multiplexed pumping system (MPS) for high-frequency water chemistry measurements at multiple locations previously showed the ability to increase spatial and temporal data resolution and improve understanding of biogeochemical processes in aquatic environments and at the land–water interface. The design of the previous system precludes its use in volume-limited applications in which highly frequent measurements requiring a large sample volume would significantly affect observed processes. A small-volume MPS was designed to minimize the sample volume while still providing high-frequency data. The system was tested for cross-contamination between multiple sources, and two applications of the technology are reported. Cross-contamination from multiple sources was shown to be negligible when using recommended procedures. Short-circuiting of flow in a bioreactor was directly observed using high-frequency porewater sampling in a well network, and the small-volume MPS showed high seasonal and spatial variability of nitrate removal in stream sediments, enhancing data collected from in situ mesocosms. The results show it is possible to obtain high-frequency data in volume-limited applications. The technology is most promising at the reach or transect scale for observing porewater solute dynamics over daily timescales, with data intervals

The response of peatland carbon accumulation to climate can be complex, with internal feedbacks and processes that can dampen or amplify responses to external forcing. Records of carbon accumulation from peat cores provide a record of carbon which persists as peat over long periods of time, demonstrating the long-term response of peatland carbon stocks to climatic events. Numerous records of long-term carbon accumulation exist globally. However, peatlands from oceanic climates, and particularly blanket bog, remain under-represented. Scottish bogs, which collectively have more than 475 separate palaeoecological records, may prove to be a valuable resource for studying the impact of environmental change on past rates of carbon accumulation. Here we present 12 records of carbon accumulation from the north of Scotland. We support these results with a further 43 records where potential carbon accumulation is inferred from published ages. These reveal a trend of high carbon accumulation in the early Holocene, declining in the mid-to-late Holocene. The trend is consistent with accumulation profiles from other northern peatlands and is likely to have been caused by climatic cooling. Considerable variability in carbon accumulation rates between locations is apparent for the mid-to-late Holocene. We attribute to hydrologically induced changes in carbon accumulation which are likely to be inconsistent between sites.

The Community Atmosphere–Biosphere Land Exchange model (CABLE) is a land surface model (LSM) that can be applied stand-alone and provides the land surface–atmosphere exchange within the Australian Community Climate and Earth System Simulator (ACCESS). We describe new developments that extend the applicability of CABLE for regional and global carbon–climate simulations, accounting for vegetation responses to biophysical and anthropogenic forcings. A land use and land cover change module driven by gross land use transitions and wood harvest area was implemented, tailored to the needs of the Coupled Model Intercomparison Project 6 (CMIP6). Novel aspects include the treatment of secondary woody vegetation, which benefits from a tight coupling between the land use module and the Population Orders Physiology (POP) module for woody demography and disturbance-mediated landscape heterogeneity. Land use transitions and harvest associated with secondary forest tiles modify the annually resolved patch age distribution within secondary vegetated tiles, in turn affecting biomass accumulation and turnover rates and hence the magnitude of the secondary forest sink. Additionally, we implemented a novel approach to constrain modelled GPP consistent with the coordination hypothesis and predicted by evolutionary theory, which suggests that electron-transport- and Rubisco-limited rates adjust seasonally and across biomes to be co-limiting. We show that the default prior assumption – common to CABLE and other LSMs – of a fixed ratio of electron transport to carboxylation capacity at standard temperature (Jmax, 0∕Vcmax, 0) is at odds with this hypothesis; we implement an alternative algorithm for dynamic optimisation of this ratio such that coordination is achieved as an outcome of fitness maximisation. The results have significant implications for the magnitude of the simulated CO2 fertilisation effect on photosynthesis in comparison to alternative estimates and observational proxies.These new developments enhance CABLE's capability for use within an Earth system model and in stand-alone applications to attribute trends and variability in the terrestrial carbon cycle to regions, processes and drivers. Model evaluation shows that the new model version satisfies several key observational constraints: (i) trend and interannual variations in the global land carbon sink, including sensitivities of interannual variations to global precipitation and temperature anomalies; (ii) centennial trends in global GPP; (iii) coordination of Rubisco-limited and electron-transport-limited photosynthesis; (iv) spatial distributions of global ET, GPP, biomass and soil carbon; and (v) age-dependent rates of biomass accumulation in boreal, temperate and tropical secondary forests.CABLE simulations agree with recent independent assessments of the global land–atmosphere flux partition that use a combination of atmospheric inversions and bottom-up constraints. In particular, there is agreement that the strong CO2-driven sink in the tropics is largely cancelled by net deforestation and forest degradation emissions, leaving the Northern Hemisphere (NH) extratropics as the dominant contributor to the net land sink.

Abstract We present possible observing scenarios for the Advanced LIGO, Advanced Virgo and KAGRA gravitational-wave detectors over the next decade, with the intention of providing information to the astronomy community to facilitate planning for multi-messenger astronomy with gravitational waves. We estimate the sensitivity of the network to transient gravitational-wave signals, and study the capability of the network to determine the sky location of the source. We report our findings for gravitational-wave transients, with particular focus on gravitational-wave signals from the inspiral of binary neutron star systems, which are the most promising targets for multi-messenger astronomy. The ability to localize the sources of the detected signals depends on the geographical distribution of the detectors and their relative sensitivity, and $$90\%$$ 90% credible regions can be as large as thousands of square degrees when only two sensitive detectors are operational. Determining the sky position of a significant fraction of detected signals to areas of 5–$$20~\mathrm {deg}^2$$ 20deg2 requires at least three detectors of sensitivity within a factor of $$\sim 2$$ ∼2 of each other and with a broad frequency bandwidth. When all detectors, including KAGRA and the third LIGO detector in India, reach design sensitivity, a significant fraction of gravitational-wave signals will be localized to a few square degrees by gravitational-wave observations alone.

Crosson and Dotson ice shelves are two of the most rapidly changing outlets in West Antarctica, displaying both significant thinning and grounding-line retreat in recent decades. We used remotely sensed measurements of velocity and ice geometry to investigate the processes controlling their changes in speed and grounding-line position over the past 20 years. We combined these observations with inverse modeling of the viscosity of the ice shelves to understand how weakening of the shelves affected this speedup. These ice shelves have lost mass continuously since the 1990s, and we find that this loss results from increasing melt beneath both shelves and the increasing speed of Crosson. High melt rates persisted over the period covered by our observations (1996–2014), with the highest rates beneath areas that ungrounded during this time. Grounding-line flux exceeded basin-wide accumulation by about a factor of 2 throughout the study period, consistent with earlier studies, resulting in significant loss of grounded as well as floating ice. The near doubling of Crosson's speed in some areas during this time is likely the result of weakening of its margins and retreat of its grounding line. This speedup contrasts with Dotson, which has maintained its speed despite increasingly high melt rates near its grounding line, likely a result of the sustained competency of the shelf. Our results indicate that changes to melt rates began before 1996 and suggest that observed increases in melt in the 2000s compounded an ongoing retreat of this system. Advection of a channel along Dotson, as well as the grounding-line position of Kohler Glacier, suggests that Dotson experienced a change in flow around the 1970s, which may be the initial cause of its continuing retreat.

To study global nitrogen (N) leaching from natural ecosystems under changing N deposition, climate, and atmospheric CO2, we performed a factorial model experiment for the period 1901–2006 with the N-enabled global terrestrial ecosystem model LPJ-GUESS (Lund–Potsdam–Jena General Ecosystem Simulator). In eight global simulations, we used either the true transient time series of N deposition, climate, and atmospheric CO2 as input or kept combinations of these drivers constant at initial values. The results show that N deposition is globally the strongest driver of simulated N leaching, individually causing an increase of 88 % by 1997–2006 relative to pre-industrial conditions. Climate change led globally to a 31 % increase in N leaching, but the size and direction of change varied among global regions: leaching generally increased in regions with high soil organic carbon storage and high initial N status, and decreased in regions with a positive trend in vegetation productivity or decreasing precipitation. Rising atmospheric CO2 generally caused decreased N leaching (33 % globally), with strongest effects in regions with high productivity and N availability. All drivers combined resulted in a rise of N leaching by 73 % with strongest increases in Europe, eastern North America and South-East Asia, where N deposition rates are highest. Decreases in N leaching were predicted for the Amazon and northern India. We further found that N loss by fire regionally is a large term in the N budget, associated with lower N leaching, particularly in semi-arid biomes. Predicted global N leaching from natural lands rose from 13.6 Tg N yr−1 in 1901–1911 to 18.5 Tg N yr−1 in 1997–2006, accounting for reductions of natural land cover. Ecosystem N status (quantified as the reduction of vegetation productivity due to N limitation) shows a similar positive temporal trend but large spatial variability. Interestingly, this variability is more strongly related to vegetation type than N input. Similarly, the relationship between N status and (relative) N leaching is highly variable due to confounding factors such as soil water fluxes, fire occurrence, and growing season length. Nevertheless, our results suggest that regions with very high N deposition rates are approaching a state of N saturation.

The savanna complex is a highly diverse global biome that occurs within the seasonally dry tropical to sub-tropical equatorial latitudes and are structurally and functionally distinct from grasslands and forests. Savannas are open-canopy environments that encompass a broad demographic continuum, often characterised by a changing dominance between C3-tree and C4-grass vegetation, where frequent environmental disturbances such as fire modulates the balance between ephemeral and perennial life forms. Climate change is projected to result in significant changes to the savanna floristic structure, with increases to woody biomass expected through CO2 fertilisation in mesic savannas and increased tree mortality expected through increased rainfall interannual variability in xeric savannas. The complex interaction between vegetation and climate that occurs in savannas has traditionally challenged terrestrial biosphere models (TBMs), which aim to simulate the interaction between the atmosphere and the land surface to predict responses of vegetation to changing in environmental forcing. In this review, we examine whether TBMs are able to adequately represent savanna fluxes and what implications potential deficiencies may have for climate change projection scenarios that rely on these models. We start by highlighting the defining characteristic traits and behaviours of savannas, how these differ across continents and how this information is (or is not) represented in the structural framework of many TBMs. We highlight three dynamic processes that we believe directly affect the water use and productivity of the savanna system: phenology, root-water access and fire dynamics. Following this, we discuss how these processes are represented in many current-generation TBMs and whether they are suitable for simulating savanna fluxes.Finally, we give an overview of how eddy-covariance observations in combination with other data sources can be used in model benchmarking and intercomparison frameworks to diagnose the performance of TBMs in this environment and formulate road maps for future development. Our investigation reveals that many TBMs systematically misrepresent phenology, the effects of fire and root-water access (if they are considered at all) and that these should be critical areas for future development. Furthermore, such processes must not be static (i.e. prescribed behaviour) but be capable of responding to the changing environmental conditions in order to emulate the dynamic behaviour of savannas. Without such developments, however, TBMs will have limited predictive capability in making the critical projections needed to understand how savannas will respond to future global change.

Reducing greenhouse gas emissions to limit damage to the global economy climate-change-induced and secure the livelihoods of future generations requires ambitious mitigation strategies. The introduction of a global carbon tax on fossil fuels is tested here as a mitigation strategy to reduce atmospheric CO2 concentrations and radiative forcing. Taxation of fossil fuels potentially leads to changed composition of energy sources, including a larger relative contribution from bioenergy. Further, the introduction of a mitigation strategy reduces climate-change-induced damage to the global economy, and thus can indirectly affect consumption patterns and investments in agricultural technologies and yield enhancement. Here we assess the implications of changes in bioenergy demand as well as the indirectly caused changes in consumption and crop yields for global and national cropland area and terrestrial biosphere carbon balance. We apply a novel integrated assessment modelling framework, combining three previously published models (a climate–economy model, a socio-economic land use model and an ecosystem model). We develop reference and mitigation scenarios based on the narratives and key elements of the shared socio-economic pathways (SSPs). Taking emissions from the land use sector into account, we find that the introduction of a global carbon tax on the fossil fuel sector is an effective mitigation strategy only for scenarios with low population development and strong sustainability criteria (SSP1 Taking the green road). For scenarios with high population growth, low technological development and bioenergy production the high demand for cropland causes the terrestrial biosphere to switch from being a carbon sink to a source by the end of the 21st century.

Most northern peatlands developed during the Holocene, sequestering large amounts of carbon in terrestrial ecosystems. However, recent syntheses have highlighted the gaps in our understanding of peatland carbon accumulation. Assessments of the long-term carbon accumulation rate and possible warming-driven changes in these accumulation rates can therefore benefit from process-based modelling studies. We employed an individual-based dynamic global ecosystem model with dynamic peatland and permafrost functionalities and patch-based vegetation dynamics to quantify long-term carbon accumulation rates and to assess the effects of historical and projected climate change on peatland carbon balances across the pan-Arctic region. Our results are broadly consistent with published regional and global carbon accumulation estimates. A majority of modelled peatland sites in Scandinavia, Europe, Russia and central and eastern Canada change from carbon sinks through the Holocene to potential carbon sources in the coming century. In contrast, the carbon sink capacity of modelled sites in Siberia, far eastern Russia, Alaska and western and northern Canada was predicted to increase in the coming century. The greatest changes were evident in eastern Siberia, north-western Canada and in Alaska, where peat production hampered by permafrost and low productivity due the cold climate in these regions in the past was simulated to increase greatly due to warming, a wetter climate and higher CO2 levels by the year 2100. In contrast, our model predicts that sites that are expected to experience reduced precipitation rates and are currently permafrost free will lose more carbon in the future.

Dynamic global vegetation models (DGVMs) are designed for the study of past, present and future vegetation patterns together with associated biogeochemical cycles and climate feedbacks. However, most DGVMs do not yet have detailed representations of permafrost and non-permafrost peatlands, which are an important store of carbon, particularly at high latitudes. We demonstrate a new implementation of peatland dynamics in a customized Arctic version of the LPJ-GUESS DGVM, simulating the long-term evolution of selected northern peatland ecosystems and assessing the effect of changing climate on peatland carbon balance. Our approach employs a dynamic multi-layer soil with representation of freeze–thaw processes and litter inputs from a dynamically varying mixture of the main peatland plant functional types: mosses, shrubs and graminoids. The model was calibrated and tested for a sub-Arctic mire in Stordalen, Sweden, and validated at a temperate bog site in Mer Bleue, Canada. A regional evaluation of simulated carbon fluxes, hydrology and vegetation dynamics encompassed additional locations spread across Scandinavia. Simulated peat accumulation was found to be generally consistent with published data and the model was able to capture reported long-term vegetation dynamics, water table position and carbon fluxes. A series of sensitivity experiments were carried out to investigate the vulnerability of high-latitude peatlands to climate change. We found that the Stordalen mire may be expected to sequester more carbon in the first half of the 21st century due to milder and wetter climate conditions, a longer growing season, and the CO2 fertilization effect, turning into a carbon source after mid-century because of higher decomposition rates in response to warming soils.

A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyse simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models, and compare them with observations from 268 Russian stations. There are large cross-model differences in the simulated differences between near-surface soil and air temperatures (ΔT; 3 to 14 °C), in the sensitivity of soil-to-air temperature (0.13 to 0.96 °C °C−1), and in the relationship between ΔT and snow depth. The observed relationship between ΔT and snow depth can be used as a metric to evaluate the effects of each model's representation of snow insulation, hence guide improvements to the model's conceptual structure and process parameterisations. Models with better performance apply multilayer snow schemes and consider complex snow processes. Some models show poor performance in representing snow insulation due to underestimation of snow depth and/or overestimation of snow conductivity. Generally, models identified as most acceptable with respect to snow insulation simulate reasonable areas of near-surface permafrost (13.19 to 15.77 million km2). However, there is not a simple relationship between the sophistication of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, because several other factors, such as soil depth used in the models, the treatment of soil organic matter content, hydrology and vegetation cover, also affect the simulated permafrost distribution.

Africa has been undergoing significant changes in climate and vegetation in recent decades, and continued changes may be expected over this century. Vegetation cover and composition impose important influences on the regional climate in Africa. Climate-driven changes in vegetation structure and the distribution of forests versus savannah and grassland may feed back to climate via shifts in the surface energy balance, hydrological cycle and resultant effects on surface pressure and larger-scale atmospheric circulation. We used a regional Earth system model incorporating interactive vegetation–atmosphere coupling to investigate the potential role of vegetation-mediated biophysical feedbacks on climate dynamics in Africa in an RCP8.5-based future climate scenario. The model was applied at high resolution (0.44 × 0.44°) for the CORDEX-Africa domain with boundary conditions from the CanESM2 general circulation model. We found that increased tree cover and leaf-area index (LAI) associated with a CO2 and climate-driven increase in net primary productivity, particularly over subtropical savannah areas, not only imposed important local effect on the regional climate by altering surface energy fluxes but also resulted in remote effects over central Africa by modulating the land–ocean temperature contrast, Atlantic Walker circulation and moisture inflow feeding the central African tropical rainforest region with precipitation. The vegetation-mediated feedbacks were in general negative with respect to temperature, dampening the warming trend simulated in the absence of feedbacks, and positive with respect to precipitation, enhancing rainfall reduction over the rainforest areas. Our results highlight the importance of accounting for vegetation–atmosphere interactions in climate projections for tropical and subtropical Africa.

The savanna ecosystem is one of the most dominant and complex terrestrial biomes, deriving from a distinct vegetative surface comprised of co-dominant tree and grass populations. While these two vegetation types co-exist functionally, demographically they are not static but are dynamically changing in response to environmental forces such as annual fire events and rainfall variability. Modelling savanna environments with the current generation of terrestrial biosphere models (TBMs) has presented many problems, particularly describing fire frequency and intensity, phenology, leaf biochemistry of C3 and C4 photosynthesis vegetation, and root-water uptake. In order to better understand why TBMs perform so poorly in savannas, we conducted a model inter-comparison of six TBMs and assessed their performance at simulating latent energy (LE) and gross primary productivity (GPP) for five savanna sites along a rainfall gradient in northern Australia. Performance in predicting LE and GPP was measured using an empirical benchmarking system, which ranks models by their ability to utilise meteorological driving information to predict the fluxes. On average, the TBMs performed as well as a multi-linear regression of the fluxes against solar radiation, temperature and vapour pressure deficit but were outperformed by a more complicated nonlinear response model that also included the leaf area index (LAI). This identified that the TBMs are not fully utilising their input information effectively in determining savanna LE and GPP and highlights that savanna dynamics cannot be calibrated into models and that there are problems in underlying model processes. We identified key weaknesses in a model's ability to simulate savanna fluxes and their seasonal variation, related to the representation of vegetation by the models and root-water uptake. We underline these weaknesses in terms of three critical areas for development. First, prescribed tree-rooting depths must be deep enough, enabling the extraction of deep soil-water stores to maintain photosynthesis and transpiration during the dry season. Second, models must treat grasses as a co-dominant interface for water and carbon exchange rather than a secondary one to trees. Third, models need a dynamic representation of LAI that encompasses the dynamic phenology of savanna vegetation and its response to rainfall interannual variability. We believe that this study is the first to assess how well TBMs simulate savanna ecosystems and that these results will be used to improve the representation of savannas ecosystems in future global climate model studies.

The relative complexity of the mechanisms underlying savanna ecosystem dynamics, in comparison to other biomes such as temperate and tropical forests, challenges the representation of such dynamics in ecosystem and Earth system models. A realistic representation of processes governing carbon allocation and phenology for the two defining elements of savanna vegetation (namely trees and grasses) may be a key to understanding variations in tree–grass partitioning in time and space across the savanna biome worldwide. Here we present a new approach for modelling coupled phenology and carbon allocation, applied to competing tree and grass plant functional types. The approach accounts for a temporal shift between assimilation and growth, mediated by a labile carbohydrate store. This is combined with a method to maximize long-term net primary production (NPP) by optimally partitioning plant growth between fine roots and (leaves + stem). The computational efficiency of the analytic method used here allows it to be uniquely and readily applied at regional scale, as required, for example, within the framework of a global biogeochemical model.We demonstrate the approach by encoding it in a new simple carbon–water cycle model that we call HAVANA (Hydrology and Vegetation-dynamics Algorithm for Northern Australia), coupled to the existing POP (Population Orders Physiology) model for tree demography and disturbance-mediated heterogeneity. HAVANA-POP is calibrated using monthly remotely sensed fraction of absorbed photosynthetically active radiation (fPAR) and eddy-covariance-based estimates of carbon and water fluxes at five tower sites along the North Australian Tropical Transect (NATT), which is characterized by large gradients in rainfall and wildfire disturbance. The calibrated model replicates observed gradients of fPAR, tree leaf area index, basal area, and foliage projective cover along the NATT. The model behaviour emerges from complex feedbacks between the plant physiology and vegetation dynamics, mediated by shifting above- versus below-ground resources, and not from imposed hypotheses about the controls on tree–grass co-existence. Results support the hypothesis that resource limitation is a stronger determinant of tree cover than disturbance in Australian savannas.

Soil temperature (Ts) change is a key indicator of the dynamics of permafrost. On seasonal and interannual timescales, the variability of Ts determines the active-layer depth, which regulates hydrological soil properties and biogeochemical processes. On the multi-decadal scale, increasing Ts not only drives permafrost thaw/retreat but can also trigger and accelerate the decomposition of soil organic carbon. The magnitude of permafrost carbon feedbacks is thus closely linked to the rate of change of soil thermal regimes. In this study, we used nine process-based ecosystem models with permafrost processes, all forced by different observation-based climate forcing during the period 1960–2000, to characterize the warming rate of Ts in permafrost regions. There is a large spread of Ts trends at 20 cm depth across the models, with trend values ranging from 0.010 ± 0.003 to 0.031 ± 0.005 °C yr−1. Most models show smaller increase in Ts with increasing depth. Air temperature (Tsub>a) and longwave downward radiation (LWDR) are the main drivers of Ts trends, but their relative contributions differ amongst the models. Different trends of LWDR used in the forcing of models can explain 61 % of their differences in Ts trends, while trends of Ta only explain 5 % of the differences in Ts trends. Uncertain climate forcing contributes a larger uncertainty in Ts trends (0.021 ± 0.008 °C yr−1, mean ± standard deviation) than the uncertainty of model structure (0.012 ± 0.001 °C yr−1), diagnosed from the range of response between different models, normalized to the same forcing. In addition, the loss rate of near-surface permafrost area, defined as total area where the maximum seasonal active-layer thickness (ALT) is less than 3 m loss rate, is found to be significantly correlated with the magnitude of the trends of Ts at 1 m depth across the models (R = −0.85, P = 0.003), but not with the initial total near-surface permafrost area (R = −0.30, P = 0.438). The sensitivity of the total boreal near-surface permafrost area to Ts at 1 m is estimated to be of −2.80 ± 0.67 million km2 °C−1. Finally, by using two long-term LWDR data sets and relationships between trends of LWDR and Ts across models, we infer an observation-constrained total boreal near-surface permafrost area decrease comprising between 39 ± 14 × 103 and 75 ± 14 × 103 km2 yr−1 from 1960 to 2000. This corresponds to 9–18 % degradation of the current permafrost area.

Children's early accuracy on place value (PV) tasks longitudinally predicts their later multidigit calculation skills. However, another window into children's emerging base-ten concepts is the pattern of errors -- "smart errors" -- they exhibit on these measures. Past research has speculated that these smart errors -- similar to invented spelling -- might reflect children's initial PV understanding that might be important for later learning of multidigit numbers and calculation. The current study examines the development of smart errors on Base-Ten Counting (invented counting errors) and Transcoding (expanded errors) in 279 U.S. kindergartners (M[subscript age] = 5.76 years) and investigated whether the presence of smart errors is associated with (1) higher concurrent levels of PV task accuracy, (2) greater growth in PV understanding over one year, (3) higher levels of multidigit calculation in second grade. Results indicate that the two errors emerged in an overlapping waves pattern, with expanded errors appearing first and waning earlier than invented counting errors. Kindergartners who made invented counting errors but not expanded errors demonstrated the highest overall concurrent PV understanding. Second, kindergartners who made Transcoding expanded errors showed the greatest growth in PV understanding compared to those who exhibited only invented-counting errors. Third, kindergartners who made invented counting errors alone showed stronger multidigit calculation skills in second grade compared to those who made neither error. Thus, these smart errors reflect partial structural knowledge of place value that is a potentially important developmental contributor to learning multidigit number meanings.

Research has shown that ethnic-racial socialization (ERS) predicts education and mental health outcomes for adolescents. However, limited research has evaluated the ERS experiences of Latinx students. The current study examined ERS experiences of Mexican American youth in four focus group interviews that were transcribed and analyzed at both the individual and group level using interpretive phenomenological analysis. Main themes included feeling like an outsider, navigating discrimination, encountering social/emotional difficulties, and achieving a positive identity. Each theme contained two to three subcategories that provide further insight into the Mexican Americans' ERS experiences. Participants reported within-group discrimination, motivation to disprove stereotypes, and infrequent understanding or compassion from adults. Teachers, administrators, counselors, and school psychologists can attend to and seek to promote social connections, implement social-emotional learning interventions, foster resilience, work to dismantle racism, collaborate with parents, and create communities of inclusion.

Traditionally, the exogenous control of gaze by external saliencies and the endogenous control of gaze by knowledge and context have been viewed as competing systems, with late infancy seen as a period of strengthening top-down control over the vagaries of the input. Here we found that one-year-old infants control sustained attention through head movements that increase the visibility of the attended object. Freely moving one-year-old infants (n = 45) wore head-mounted eye trackers and head motion sensors while exploring sets of toys of the same physical size. The visual size of the objects, a well-documented salience, varied naturally with the infant's moment-to-moment posture and head movements. Sustained attention to an object was characterized by the tight control of head movements that created and then stabilized a visual size advantage for the attended object for sustained attention. The findings show collaboration between exogenous and endogenous attentional systems and suggest new hypotheses about the development of sustained visual attention.

In this manuscript, the authors examined the rates of food and housing insecurity experienced by student-veterans enrolled at community colleges in fall 2020. The results of a multi-institutional survey of student-veterans at 113 community colleges suggested that 37.6% of community college student-veterans experienced food insecurity and 52.5% of community college student-veterans experienced housing insecurity. Additionally, 17.8% of community college student-veterans experienced homelessness in the past year. The results also suggest that community college student-veterans who experienced food and housing insecurity had lower grade point averages, higher rates of clinically significant generalized anxiety disorder, and higher rates of clinically significant major depressive disorder. Examples of strategies to support community college student-veterans experiencing food and housing insecurity are included.

A glacial flow model of Smith, Pope and Kohler Glaciers is calibrated by means of control methods against time varying, annually resolved observations of ice height and velocities, covering the period 2002 to 2011. The inversion – termed "transient calibration" – produces an optimal set of time-mean, spatially varying parameters together with a time-evolving state that accounts for the transient nature of observations and the model dynamics. Serving as an optimal initial condition, the estimated state for 2011 is used, with no additional forcing, for predicting grounded ice volume loss and grounding line retreat over the ensuing 30 years. The transiently calibrated model predicts a near-steady loss of grounded ice volume of approximately 21 km3 a−1 over this period, as well as loss of 33 km2 a−1 grounded area. We contrast this prediction with one obtained following a commonly used "snapshot" or steady-state inversion, which does not consider time dependence and assumes all observations to be contemporaneous. Transient calibration is shown to achieve a better fit with observations of thinning and grounding line retreat histories, and yields a quantitatively different projection with respect to ice volume loss and ungrounding. Sensitivity studies suggest large near-future levels of unforced, i.e., committed sea level contribution from these ice streams under reasonable assumptions regarding uncertainties of the unknown parameters.