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NARCliM2.0 comprises two Weather Research and Forecasting (WRF) regional climate models (RCMs) downscaling five CMIP6 global climate models contributing to the Coordinated Regional Downscaling Experiment over Australasia at 20 km resolution, and south-east Australia at 4 km convection-permitting resolution. We first describe NARCliM2.0's design, including selecting two, definitive RCMs via testing seventy-eight RCMs using different parameterisations for planetary boundary layer, microphysics, cumulus, radiation, and land surface model (LSM). We then assess NARCliM2.0's skill in simulating the historical climate versus CMIP3-forced NARCliM1.0 and CMIP5-forced NARCliM1.5 RCMs and compare differences in future climate projections. RCMs using the new Noah-MP LSM in WRF with default settings confer substantial improvements in simulating temperature variables versus RCMs using Noah-Unified. Noah-MP confers smaller improvements in simulating precipitation, except for large improvements over Australia's southeast coast. Activating Noah-MP's dynamic vegetation cover and/or runoff options primarily improve simulation of minimum temperature. NARCliM2.0 confers large reductions in maximum temperature bias versus NARCliM1.0 and 1.5 (1.x), with small absolute biases of ~0.5 K over many regions versus over ~2 K for NARCliM1.x. NARCliM2.0 reduces wet biases versus NARCliM1.x by as much as 50 %, but retains dry biases over Australia's north. NARCliM2.0 is biased warmer for minimum temperature versus NARCliM1.5 which is partly inherited from stronger warm biases in CMIP6 versus CMIP5 GCMs. Under shared socioeconomic pathway (SSP)3-7.0, NARCliM2.0 projects ~3 K warming by 2060–79 over inland regions versus ~2.5 K over coastal regions. NARCliM2.0-SSP3-7.0 projects dry futures over most of Australia, except for wet futures over Australia's north and parts of western Australia which are largest in summer. NARCliM2.0-SSP1-2.6 projects dry changes over Australia with only few exceptions. NARCliM2.0 is a valuable resource for assessing climate change impacts on societies and natural systems and informing resilience planning by reducing model biases versus earlier NARCliM generations and providing more up-to-date future climate projections utilising CMIP6. [ABSTRACT FROM AUTHOR]

The diurnal cycle is often poorly reproduced in global climate model (GCM) simulations, particularly in terms of rainfall frequency and amplitude. While improvements in the regional climate model (RCM) with bias‐corrected boundaries have been reported in previous studies, they assumed that diurnal patterns are simulated correctly by the GCM, potentially leading to inaccuracies in the maximum rainfall timing and magnitude within the RCM domain. Here we provide the first examination of improvements to the diurnal cycle, within a RCM domain, achieved through the use of sophisticated bias‐corrected lateral and lower boundary conditions. Results show that the RCMs with bias‐corrected boundaries generally present improvement in capturing both rainfall timing and magnitude, particularly in northern Australia, where a strong diurnal pattern in rainfall is prevalent. We show that correcting systematic sub‐daily multivariate bias in RCM boundaries improves the diurnal rainfall cycle, which is particularly important in regions where short‐term intense precipitation occurs. Plain Language Summary: Global climate models have limitations in simulating precipitation characteristics at sub‐daily time scales, leading to significant bias in capturing rainfall timing and magnitude in regional climate model (RCM) simulations that use global climate model data as input. Previous studies have applied various bias correction approaches to the boundary conditions of RCMs, but they often assume that GCM sub‐daily rainfall patterns are simulated correctly, leading to a significant bias in maximum rainfall timing and magnitude within the domain. This study investigates the consequences of multivariate sub‐daily bias correction on the boundary conditions in order to evaluate the model performance for short‐term precipitation events. The results show that RCMs with bias‐corrected boundary conditions exhibit improvement across Australia, especially in northern Australia, which regularly experiences intense sub‐daily rainfall. Key Points: Regional climate model (RCM) with uncorrected boundaries poorly represented 3‐hourly precipitation with only 20%∼30% agreements compared to observationsSub‐daily bias correction of RCM lateral boundaries improves the sub‐daily representation of fields entering the RCM domainSub‐daily multivariate bias correction on the boundaries resulted in additional improvements, especially in northern Australia [ABSTRACT FROM AUTHOR]

Background We investigated the role of A2B-adenosine receptor in regulating immunosuppressive metabolic stress in the tumor microenvironment. Novel A2B-adenosine receptor antagonist PBF-1129 was tested for antitumor activity in mice and evaluated for safety and immunologic efficacy in a phase I clinical trial of patients with non-small cell lung cancer. Methods The antitumor efficacy of A2B-adenosine receptor antagonists and their impact on the metabolic and immune tumor microenvironment were evaluated in lung, melanoma, colon, breast, and epidermal growth factor receptor–inducible transgenic cancer models. Employing electron paramagnetic resonance, we assessed changes in tumor microenvironment metabolic parameters, including p O2, pH, and inorganic phosphate, during tumor growth and evaluated the immunologic effects of PBF-1129, including its pharmacokinetics, safety, and toxicity, in patients with non-small cell lung cancer. Results Levels of metabolic stress correlated with tumor growth, metastasis, and immunosuppression. Tumor interstitial inorganic phosphate emerged as a correlative and cumulative measure of tumor microenvironment stress and immunosuppression. A2B-adenosine receptor inhibition alleviated metabolic stress, downregulated expression of adenosine-generating ectonucleotidases, increased expression of adenosine deaminase, decreased tumor growth and metastasis, increased interferon γ production, and enhanced the efficacy of antitumor therapies following combination regimens in animal models (anti–programmed cell death 1 protein vs anti–programmed cell death 1 protein plus PBF-1129 treatment hazard ratio = 11.74 [95% confidence interval = 3.35 to 41.13], n = 10, P  < .001, 2-sided F test). In patients with non-small cell lung cancer, PBF-1129 was well tolerated, with no dose-limiting toxicities; demonstrated pharmacologic efficacy; modulated the adenosine generation system; and improved antitumor immunity. Conclusions Data identify A2B-adenosine receptor as a valuable therapeutic target to modify metabolic and immune tumor microenvironment to reduce immunosuppression, enhance the efficacy of immunotherapies, and support clinical application of PBF-1129 in combination therapies. [ABSTRACT FROM AUTHOR]

Improving modeling capacities requires a better understanding of both the physical relationship between the variables and climate models with a higher degree of skill than is currently achieved by Global Climate Models (GCMs). Although Regional Climate Models (RCMs) are commonly used to resolve finer scales, their application is restricted by the inherent systematic biases within the GCM datasets that can be propagated into the RCM simulation through the model input boundaries. Hence, it is advisable to remove the systematic biases in the GCM simulations prior to downscaling, forming improved input boundary conditions for the RCMs. Various mathematical approaches have been formulated to correct such biases. Most of the techniques, however, correct each variable independently leading to physical inconsistencies across the variables in dynamically linked fields. Here, we investigate bias corrections ranging from simple to more complex techniques to correct biases of RCM input boundary conditions. The results show that substantial improvements in model performance are achieved after applying bias correction to the boundaries of RCM. This work identifies that the effectiveness of increasingly sophisticated techniques is able to improve the simulated rainfall characteristics. An RCM with multivariate bias correction, which corrects temporal persistence and inter-variable relationships, better represents extreme events relative to univariate bias correction techniques, which do not account for the physical relationship between the variables. [ABSTRACT FROM AUTHOR]

Increasing levels of photovoltaic (PV) penetration to the electricity grid brings challenges to both design and operation of the grid due to its vulnerability to climate change. A crucial aspect of PV operation is power ramps leading to variability and instability in the grid. With notable large-scale PV deployment planned, including the world's largest planned solar energy infrastructure in Powell Creek Australia, characterising future ramps is crucial for ensuring stable power generation to support large-scale economic development. Using CORDEX-Australasia projections under RCP8.5 and RCP4.5 emission scenarios, future solar ramps across Australia have been characterised up to 2100. Results predict a reduction in ramp magnitude across Australia, with changes in frequency and period length varying with the location. This work highlights the importance of considering future changes in climate when designing large-scale solar farms to ensure the incorporation of frequency control devices and storage plans for a reliable power supply. [ABSTRACT FROM AUTHOR]

Supersymmetry is still a viable explanation for the muon g – 2 anomaly, if the sleptons and electroweak gauginos are O 100 GeV. However, for supersymmetry breaking masses this light, the SUSY flavor and CP-problem are exacerbated. To address this issue, we consider a flavor-safe gauge mediated explanation of the muon g – 2 with additional Higgs soft supersymmetry breaking mass parameters. The setup provides a generic parameter space within minimal gauge mediation. Furthermore, we show that the problematic CP violating phase can be dynamically suppressed. We find that gauge mediation models have large portions of parameter space where the muon g – 2 can be explained at 1 σ level. The interplay between the slepton and the CP-odd Higgs masses also makes the majority of this model's parameter space testable at the LHC through searches for sleptons or additional Higgs bosons. [ABSTRACT FROM AUTHOR]

Supersymmetry is still a viable explanation for the muon g – 2 anomaly, if the sleptons and electroweak gauginos are O 100 GeV. However, for supersymmetry breaking masses this light, the SUSY flavor and CP-problem are exacerbated. To address this issue, we consider a flavor-safe gauge mediated explanation of the muon g – 2 with additional Higgs soft supersymmetry breaking mass parameters. The setup provides a generic parameter space within minimal gauge mediation. Furthermore, we show that the problematic CP violating phase can be dynamically suppressed. We find that gauge mediation models have large portions of parameter space where the muon g – 2 can be explained at 1 σ level. The interplay between the slepton and the CP-odd Higgs masses also makes the majority of this model's parameter space testable at the LHC through searches for sleptons or additional Higgs bosons. [ABSTRACT FROM AUTHOR]

The climate is warming and this is changing some aspects of storms, but we have relatively little knowledge of storm characteristics beyond intensity, which limits our understanding of storms overall. In this study, we apply a cell-tracking algorithm to 20 years of radar data at a mid-latitude coastal-site (Sydney, Australia), to establish a regional precipitation system climatology. The results show that extreme storms in terms of translation-speed, size and rainfall intensity usually occur in the warm season, and are slower and more intense over land between ~ 10 am and ~ 8 pm (AEST), peaking in the afternoon. Precipitation systems are more frequent in the cold season and often initiate over the ocean and move northward, leading to precipitation mostly over the ocean. Using clustering algorithms, we have found five precipitation system types with distinct properties, occurring throughout the year but peaking in different seasons. While overall rainfall statistics don't show any link to climate modes, links do appear for some system types using a multivariate approach. This climatology for a variety of precipitation system characteristics will allow future study of any changes in these characteristics due to climate change. [ABSTRACT FROM AUTHOR]

Short-duration rainfall extremes can cause flash flooding with associated impacts. Previous studies of climate impacts on extreme precipitation have focused mainly on daily rain totals. Subdaily extremes are often generated in small areas that can be missed by gauge networks or satellites and are not resolved by climate models. Here, we show a robust positive trend of at least 20% per decade in subhourly extreme rainfall near Sydney, Australia, over 20 years, despite no evidence of trends at hourly or daily scales. This trend is seen consistently in storms tracked using multiple independent ground radars, is consistent with rain-gauge data, and does not appear to be associated with known natural variations. This finding suggests that subhourly rainfall extremes may be increasing substantially faster than those on more widely reported time scales. [ABSTRACT FROM AUTHOR]

Modes of climate variability can drive significant changes to regional climate affecting extremes such as droughts, floods, and bushfires. The need to forecast these extremes and expected future increases in their intensity and frequency motivates a need to better understand the physical processes that connect climate modes to regional precipitation. Focusing on east Australia, where precipitation is driven by multiple interacting climate modes, this study provides a new perspective into the links between large-scale modes of climate variability and precipitation. Using a Lagrangian backtrajectory approach, we examine how El Niño-Southern Oscillation (ENSO) modifies the supply of evaporative moisture for precipitation, and how this is modulated by the Indian Ocean dipole (IOD) and southern annular mode (SAM). We demonstrate that La Niña modifies large-scale moisture transport together with local thermodynamic changes to facilitate local precipitation generation, whereas below-average precipitation during El Niño stems predominantly from increased regional subsidence. These dynamic-thermodynamic processes were often more pronounced during co-occurring La Niña/negative IOD and El Niño/positive IOD periods. As the SAM is less strongly correlated with ENSO, the impact of cooccurring ENSO and SAM largely depended on the state of ENSO. La Niña-related processes were exacerbated when combined with 1SAM and dampened when combined with 2SAM, and vice versa during El Niño. This new perspective on how interacting climate modes physically influence regional precipitation can help elucidate how model biases affect the simulation of Australian climate, facilitating model improvement and understanding of regional impacts from long-term changes in these modes. [ABSTRACT FROM AUTHOR]

Rationale: The development of an automated platform for the positional analysis of triglycerides (TAGs) based on electrospray ionization tandem mass spectrometry (ESI‐MS/MS) continues to be pursued. This work evaluates the positional sensitivities of the collision‐induced dissociation (CID) spectra of a representative set of XYZ triglycerides using sodium, lithium, and ammonium salts as complexing agents. Methods: A set of triglycerides were synthesized and analyzed via ESI‐MS/MS using an ion trap mass spectrometer. Using three different complexing agents, the product ion spectra of the corresponding precursor ions for twelve XYZ TAGs were collected, where X, Y, and Z represent C16:0, C18:1(c‐9), C18:2(cc‐9,12), and C20:4(cccc‐5,8,11,14) fatty acid chains. These data were then used to prepare ternary plots for four positional isomer systems to evaluate the positional sensitivity differences among the three different complexing agents. Results: The positional sensitivities for each of the four positional isomer systems were robust for the sodium and lithium adducts. The CID data for the sodium and lithium TAGs demonstrated an unfavorable loss of the fatty acid in the center position and showed a higher sensitivity to fatty acid position, when compared with the CID data for ammonium adducts, especially for the arachidonic acid containing triglycerides. Conclusions: The data shows that that the relative abundances of the DAG product ions for the XYZ‐type TAGs when using sodium and lithium complexing agent adducts are sensitive to fatty acid position and are consistent for the diverse array of TAGs studied in this work. This suggests that using sodium or lithium as the complexing agent may be advantageous for the development of an automated platform for the positional analysis of complex TAG mixtures based on ESI‐MS/MS. [ABSTRACT FROM AUTHOR]

We consider proton decay and g μ - 2 in flipped SU(5) GUT models. We first study scenarios in which the soft supersymmetry-breaking parameters are constrained to be universal at some high scale M in above the standard GUT scale where the QCD and electroweak SU(2) couplings unify. In this case the proton lifetime is typically ≳ 10 36 years, too long to be detected in the foreseeable future, and the supersymmetric contribution to g μ - 2 is too small to contribute significantly to resolving the discrepancy between the experimental measurement and data-driven calculations within the Standard Model. However, we identify a region of the constrained flipped SU(5) parameter space with large couplings between the 10- and 5-dimensional GUT Higgs representations where p → e + π 0 decay may be detectable in the Hyper-Kamiokande experiment now under construction, though the contribution to g μ - 2 is still small. A substantial contribution to g μ - 2 is possible, however, if the universality constraints on the soft supersymmetry-breaking masses are relaxed. We find a 'quadrifecta' region where observable proton decay co-exists with a (partial) supersymmetric resolution of the g μ - 2 discrepancy and acceptable values of m h and the relic LSP density. [ABSTRACT FROM AUTHOR]

We analyze the possible magnitude of the supersymmetric contribution to g μ - 2 in a flipped SU(5) GUT model. Unlike other GUT models which are severely constrained by universality relations, in flipped SU(5) the U(1) gaugino mass and the soft supersymmetry-breaking masses of right-handed sleptons are unrelated to the other gaugino, slepton and squark masses. Consequently, the lightest neutralino and the right-handed smuon may be light enough to mitigate the discrepancy between the experimental measurement of g μ - 2 and the Standard Model calculation, in which case they may be detectable at the LHC and/or a 250 GeV e + e - collider, whereas the other gauginos and sfermions are heavy enough to escape detection at the LHC. [ABSTRACT FROM AUTHOR]

A new regional climate projection ensemble has been created for the Australasia region as part of the World Climate Research Programs Coordinated Regional Downscaling Experiment (CORDEX). The CORDEX-Australasia ensemble is the largest regional climate projection ensemble ever created for the region. It is a 20-member ensemble made by 6 regional climate models downscaling 11 global climate models. Overall the ensemble produces a good representation of recent climate. Consistent biases within the ensemble include an underestimation of the diurnal temperature range and an underestimation of precipitation across much of southern Australia. Under a high emissions scenario projected temperature changes by the end of the twenty-first century reach ~ 5 K in the interior of Australia with smaller increases found toward the coast. Projected precipitation changes are towards drying, particularly in the most populated areas of the southwest and southeast of the continent. The projected precipitation change is very seasonal with summer projected to see little change leaning toward an increase. These results provide a foundation enabling future studies of regional climate changes, climate change impacts, and adaptation options for Australia. [ABSTRACT FROM AUTHOR]

Correction of atmospheric variables to remove systematic biases in global climate model (GCM) simulations before downscaling offers a means of improving climate simulation accuracy in climate change impact assessments. Various mathematical approaches have been used to correct the lateral and lower boundary conditions of regional climate models (RCMs). Most of these techniques correct only the magnitude of each variable individually over time without regard to spatial and multivariate bias. Here, we investigate how well an RCM is able to reproduce the dependence of an observed variable based on three aspects: temporal, spatial, and multivariate. Results show that the RCM simulations with univariate bias-corrected GCM boundary conditions perform well in capturing both temporal and spatial dependence. However, all RCM simulations do not show improvement in the representation of dependence between variables, indicating the need for alternatives that correct systematic biases in multivariate dependence in both lateral and lower boundary conditions. Plain Language Summary It is well understood that systematic biases within a global climate model simulation can be passed into the input boundary condition of a regional climate model (RCM). To address this, many bias correction approaches have been used to correct the lateral and lower boundary conditions of RCMs, while assuming that each variable that makes up the lateral or lower boundary is independent. This study investigates the consequences of bias correction to assess whether the dependencies in time, space, and between variables are preserved. Using correlation length, it is shown that there is improvement in spatial and temporal dependence but not in inter-variable dependence which can produce a mismatch in the physical relationships in the simulated outcomes. A more physically consistent approach that considers the relationships between the variables is needed instead of the simplistic univariate correction procedures that have been used to date. [ABSTRACT FROM AUTHOR]

We explore a missing-partner model based on the minimal SU(5) gauge group with 75 , 50 and 50 ¯ Higgs representations, assuming a super-GUT CMSSM scenario in which soft supersymmetry-breaking parameters are universal at some high scale M in above the GUT scale M GUT . We identify regions of parameter space that are consistent with the cosmological dark matter density, the measured Higgs mass and the experimental lower limit on τ (p → K + ν) . These constraints can be satisfied simultaneously along stop coannihilation strips in the super-GUT CMSSM with tan β ∼ 3.5–5 where the input gaugino mass m 1 / 2 ∼ 15–25 TeV, corresponding after strong renormalization by the large GUT Higgs representations between M in and M GUT to m LSP , m t ~ 1 ∼ 2.5–5 TeV and m g ~ ∼ 13–20 TeV, with the light-flavor squarks significantly heavier. We find that τ (p → K + ν) ≲ 3 × 10 34 years throughout the allowed range of parameter space, within the range of the next generation of searches with the JUNO, DUNE and Hyper-Kamiokande experiments. [ABSTRACT FROM AUTHOR]

High-resolution datasets offer the potential to improve our understanding of spatial and temporal precipitation patterns and storm structures. The goal of this study is to evaluate the similarities and differences of object-based storm characteristics as observed using space- or land-based sensors. The Method of Object-based Diagnostic Evaluation (MODE) Time Domain (MTD) is used to identify and track storm objects in two high-resolution merged datasets: the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) final product V06B and gauge-corrected ground-radar-based Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimations. Characteristics associated with landfalling hurricanes were also examined as a separate category of storm. The results reveal that IMERG and MRMS agree reasonably well across many object-based storm characteristics. However, there are some discrepancies that are statistically significant. MRMS storms are more concentrated, with smaller areas and higher peak intensities, which implies higher flash flood risks associated with the storms. On the other hand, IMERG storms can travel longer distances with a higher volume of precipitation, which implies higher risk of riverine flooding. Agreement between the datasets is higher for faster-moving hurricanes in terms of the averaged intensity. Finally, MRMS indicates a higher average precipitation intensity during the hurricane's lifetime. However, in non-hurricanes, the opposite result was observed. This is likely related to MRMS having higher resolution; monitoring the hurricanes from many viewing angles, leading to different signal saturation properties compared to IMERG; and/or the dominance of droplet aggregation effects over evaporation effects at lower altitudes. [ABSTRACT FROM AUTHOR]

The vertical temperature profile in the atmosphere reflects a balance between radiative and convective processes and interactions with the oceanic and land surfaces. Changes in vertical temperature profiles can affect atmospheric stability, which in turn can impact various aspects of weather systems. In this study, we analyzed recent-past trends of temperature over the Australian region using a homogenized monthly upper-air temperature dataset and four reanalysis datasets (NCEP, ERA-Interim, JRA-55 and MERRA). We also used outputs of 12 historical and future regional climate model (RCM) simulations from the NSW/ACT (New South Wales/Australian Capital Territory) Regional Climate Modelling (NARCliM) project and 6 RCM simulations from the CORDEX (Coordinated Regional Downscaling Experiment) Australasian project to investigate projected changes in vertical temperature profiles. The results show that the currently observed positive trend in the troposphere and negative trend in the lower stratosphere will continue in the future with significant warming over the whole troposphere and largest over the middle to upper troposphere. The increasing temperatures are found to be latitude-dependent with clear seasonal variations, and a strong diurnal variation for the near surface layers and upper levels in tropical regions. Changes in the diurnal variability indicate that near surface layers will be less stable in the afternoon leading to conditions favoring convective systems and more stable in the early morning which is favorable for temperature inversions. The largest differences of future changes in temperature between the simulations are associated with the driving GCMs, suggesting that large-scale circulation plays a dominant role in regional atmospheric temperature change. [ABSTRACT FROM AUTHOR]

Regional climate models (RCM) are an important tool for simulating atmospheric information at finer resolutions often of greater relevance to local scale climate change impact assessment studies. The lateral and lower boundary conditions, which form the inputs to the RCM downscaling application, are outputs from the global climate model (GCM). These boundary variables are known to be biased in GCMs, providing the potential to use a statistical approach that corrects these biases before use in downscaling. An array of bias correction techniques have been developed to remove these biases before being used to drive the RCM, but questions remain on their efficacy in terms of the final downscaled output. This study assesses the impact of these bias correction strategies by focussing on how these corrections are translated as one proceeds from the lateral boundaries into the model interior. Of specific interest is the change in the correction from generation of the lateral boundary conditions as well as how correction information moves through the relaxation zone and into the interior of the model. Here we show that bias correction information passing into the regional climate model is limited by interpolations required to generate lateral boundary conditions and dominant outflow wind conditions in the boundaries. This work suggests that these limitations should be addressed in order for bias correction of lateral boundary conditions to robustly influence RCM simulations of climate in the interior of the model domain. [ABSTRACT FROM AUTHOR]

Prescribed burning is used globally to mitigate the risks of wildfires, with severe wildfires increasing in frequency in recent decades. Despite their importance in wildfire management, the nature of future changes to prescribed burn windows under global warming remains uncertain. We use a regional climate projection ensemble to provide a robust spatiotemporal quantification of statistically significant future changes in prescribed burn windows for southeastern Australia. There are significant decreases during months presently used for prescribed burning, that is, in March to May in 2060–2079 versus 1990–2009 across several temperate regions. Conversely, burn windows show widespread significant increases in June to August, that is, months when burns have rarely occurred historically, and also in spring (September–October). Overall, projected changes in temperature and fuel moisture show the most widespread and largest decreases (or increases) in the number of days within their respective ranges suitable for conducting burns. These results support wildfire risk mitigation planning. Plain Language Summary: The frequency of destructive wildfires is increasing in many fire‐prone regions, threatening lives, property, and damaging the environment. To reduce the risk of wildfires occurring, agencies conduct planned, controlled, and preemptive burns of vegetation to reduce the available fuel load. Currently, in southeastern Australia, these "prescribed burns" are conducted during autumn (March–May) and early spring (September) but rarely during winter (June–August). Given climate change, we need to understand how opportunities for conducting these burns might change in future. This is particularly important for forest and woodland areas adjacent to populated urban areas. We use regional climate projection data to assess future changes to the suitability for prescribed burning. We demonstrate that over many regions, windows are projected to decrease during months currently used for conducting burns in 2060–2079 compared to 1990–2009. In contrast, they generally increase in the winter months and further into spring (September–October). This change in seasonality means that these periods could be more suitable for conducting burns in future. Future changes in burn windows are most strongly associated with changes in temperature and vegetation moisture content, though there are many other contributing factors to these changes. These findings are relevant to strategic wildfire prevention and regional risk mitigation. Key Points: Significant decreases in wildfire mitigation windows occur in March–May in 2060–2079 versus 1990–2009 but increase in June–August in 2060–2079Projected temperature and fuel moisture changes have the largest contribution to the change in the wildfire mitigation windowLocations in southeast Australia where changes in wildfire mitigation windows are projected to occur are identified on a monthly basis [ABSTRACT FROM AUTHOR]

We investigate the influence of bias correction of Global Climate Models (GCMs) prior to dynamical downscaling using regional climate models (RCMs), on the change in climate projected. We use 4 GCMs which are bias corrected against ERA-Interim re-analysis as a surrogate truth, and carry out bias corrected and non-bias corrected simulations over the CORDEX Australasia domain using the Weather Research and Forecasting model. Our results show that when considering the effect of bias correction on current and future climate separately, bias correction has a large influence on precipitation and temperature, especially for models which are known to have large biases. However, when considering the change in climate, i.e the Δ change (future minus current), we found that while differences between bias-corrected and non-corrected RCM simulations can be substantial (e.g. more than 1 ∘ C for temperatures) these differences are generally smaller than the models' inter-annual variability. Overall, averaged across all variables, bias corrected boundary conditions produce an overall reduction in the range, standard deviation and mean absolute deviation of the change in climate projected by the 4 models tested, over 61.5%, 62% and 58% of land area, with a larger reduction for precipitation as compared to temperature indices. In addition, we show that changes in the Δ change for DJF tasmax are broadly linked to precipitation changes and consequently soil moisture and surface sensible heat flux and changes in the Δ changefor JJA tasmin are linked to downward longwave heat flux. This study shows that bias correction of GCMs against re-analysis prior to dynamical downscaling can increase our confidence in projected future changes produced by downscaled ensembles. [ABSTRACT FROM AUTHOR]

Coarse resolution global climate models (GCMs) cannot resolve fine-scale drivers of regional climate, which is the scale where climate adaptation decisions are made. Regional climate models (RCMs) generate high-resolution projections by dynamically downscaling GCM outputs. However, evidence of where and when downscaling provides new information about both the current climate (added value, AV) and projected climate change signals, relative to driving data, is lacking. Seasons and locations where CORDEX-Australasia ERA-Interim and GCM-driven RCMs show AV for mean and extreme precipitation and temperature are identified. A new concept is introduced, 'realised added value', that identifies where and when RCMs simultaneously add value in the present climate and project a different climate change signal, thus suggesting plausible improvements in future climate projections by RCMs. ERA-Interim-driven RCMs add value to the simulation of summer-time mean precipitation, especially over northern and eastern Australia. GCM-driven RCMs show AV for precipitation over complex orography in south-eastern Australia during winter and widespread AV for mean and extreme minimum temperature during both seasons, especially over coastal and high-altitude areas. RCM projections of decreased winter rainfall over the Australian Alps and decreased summer rainfall over northern Australia are collocated with notable realised added value. Realised added value averaged across models, variables, seasons and statistics is evident across the majority of Australia and shows where plausible improvements in future climate projections are conferred by RCMs. This assessment of varying RCM capabilities to provide realised added value to GCM projections can be applied globally to inform climate adaptation and model development. [ABSTRACT FROM AUTHOR]

The ability of regional climate models (RCMs) to accurately simulate current and future climate is increasingly important for impact assessment. This is the first evaluation of all reanalysis-driven RCMs within the CORDEX Australasia framework [four configurations of the Weather Forecasting and Research (WRF) model, and single configurations of COSMO-CLM (CCLM) and the Conformal-Cubic Atmospheric Model (CCAM)] to simulate the historical climate of Australia (1981–2010) at 50 km resolution. Simulations of near-surface maximum and minimum temperature and precipitation were compared with gridded observations at annual, seasonal, and daily time scales. The spatial extent, sign, and statistical significance of biases varied markedly between the RCMs. However, all RCMs showed widespread, statistically significant cold biases in maximum temperature which were the largest during winter. This bias exceeded − 5 K for some WRF configurations, and was the lowest for CCLM at ± 2 K. Most WRF configurations and CCAM simulated minimum temperatures more accurately than maximum temperatures, with biases in the range of ± 1.5 K. RCMs overestimated precipitation, especially over Australia's populous eastern seaboard. Strong negative correlations between mean monthly biases in precipitation and maximum temperature suggest that the maximum temperature cold bias is linked to precipitation overestimation. This analysis shows that the CORDEX Australasia ensemble is a valuable dataset for future impact studies, but improving the representation of land surface processes, and subsequently of surface temperatures, will improve RCM performance. The varying RCM capabilities identified here serve as a foundation for the development of future regional climate projections and impact assessments for Australia. [ABSTRACT FROM AUTHOR]

Bias-correction approaches have been widely applied to Global Climate Model (GCM) or Regional Climate Model (RCM) outputs in order to overcome the limitations of climate models in resolving small-scale climate features. Although various software toolkits have been developed to simplify the process for correcting climate model output directly, they were specifically designed to correct surface fields such as precipitation and temperature, often overlooking the physical mechanisms between variables. To address these limitations, this study developed open-source Python software that corrects RCM input boundary variables using reanalysis and raw GCM datasets as inputs. The bias correction technique used is based on a novel approach, Sub-Daily Multivariate Bias Correction (SDMBC), which corrects the inter-variable relationships and distribution of atmospheric variables at a sub-daily time scale. This paper describes the software package, which simplifies the implementation of the bias correction process, and provides a simple example of its application. • The software package described in this study simplifies the implementation of the bias correction process. • It corrects the full atmospheric fields and sea surface temperature of GCM datasets prior to dynamical downscaling. • It aims to remove biases in the RCM input boundary conditions, ultimately improving the accuracy of RCM simulations. • The package performs well in correcting input variables at the sub-daily time scale over the domain. [ABSTRACT FROM AUTHOR]

Pyrocumulonimbus (pyroCb) wildfires cause devastation in many regions globally. Given that fire‐atmosphere coupling is associated with pyroCbs, future changes in coincident high index values of atmospheric instability and dryness (C‐Haines) and near‐surface fire weather are assessed for southeastern Australia using a regional climate projection ensemble. We show that observed pyroCb events occur predominantly on forested, rugged landscapes during extreme C‐Haines conditions, but over a wide range of surface fire weather conditions. Statistically significant increases in the number of days where both C‐Haines and near‐surface fire weather values are conducive to pyroCb development are projected across southeastern Australia, predominantly for November (spring), and less strongly for December (summer) in 2060‐2079 versus 1990‐2009, with future C‐Haines increases linked to increased 850‐hPa dewpoint depression. The increased future occurrence of conditions conducive to pyroCb development and their extension into spring have implications for mitigating these dangerous wildfires and urbanizing fire‐prone landscapes. Plain Language Summary: Pyrocumulonimbus wildfires (pyroCb) are extreme wildfires. They involve a coupling between the fire and the atmosphere, which drives dangerous fire conditions that result in fatalities and considerable damage. Climate change could amplify the conditions associated with pyroCb development. We use high‐resolution regional climate projection data to assess future changes in the risk of pyroCb occurrence for southeastern Australia. We demonstrate that atmospheric instability and dryness is a better indicator of pyroCb development than surface fire weather conditions, with topographic roughness and vegetation type also playing roles. Coincidences of high index values of atmospheric conditions (tropospheric instability and dryness) and surface fire weather conditions (temperature, humidity, wind speed, and precipitation) associated with pyroCb development are projected to increase in frequency predominantly for November (spring) in 2060‐2079 versus 1990‐2009, but less so for summer. The extension of the season conducive to pyroCbs into spring is important because Australian pyroCbs are typically summer phenomena. A change in seasonality has implications for resource allocations by fire agencies. Projected changes in the conditions conducive to pyroCbs are primarily associated with increased dryness at 1.5‐km altitude. These findings are relevant to the mitigation of pyroCbs, and they have implications for continued urban expansion on fire‐prone landscapes. Key Points: Significant increases in the frequency of conditions conducive to pyrocumulonimbus fires occur in November (Spring) 2060‐2079 versus 1990‐2009Atmospheric instability and dryness are better indicators of pyrocumulonimbus fire development than surface fire weatherLocations in southeast Australia where these changes are projected to occur are identified [ABSTRACT FROM AUTHOR]

High-resolution projections of climate change impacts on fire weather conditions in southeast Australia out to 2080 are presented. Fire weather is represented by the McArthur Forest Fire Danger Index (FFDI), calculated from an objectively designed regional climate model ensemble. Changes in annual cumulative FFDI vary widely, from − 337 (− 21%) to + 657 (+ 24%) in coastal areas and − 237 (− 12%) to + 1143 (+ 26%) in inland areas. A similar spread is projected in extreme FFDI values. In coastal regions, the number of prescribed burning days is projected to change from − 11 to + 10 in autumn and − 10 to + 3 in spring. Across the ensemble, the most significant increases in fire weather and decreases in prescribed burn windows are projected to take place in spring. Partial bias correction of FFDI leads to similar projections but with a greater spread, particularly in extreme values. The partially bias-corrected FFDI performs similarly to uncorrected FFDI compared to the observed annual cumulative FFDI (ensemble root mean square error spans 540 to 1583 for uncorrected output and 695 to 1398 for corrected) but is generally worse for FFDI values above 50. This emphasizes the need to consider inter-variable relationships when bias-correcting for complex phenomena such as fire weather. There is considerable uncertainty in the future trajectory of fire weather in southeast Australia, including the potential for less prescribed burning days and substantially greater fire danger in spring. Selecting climate models on the basis of multiple criteria can lead to more informative projections and allow an explicit exploration of uncertainty. [ABSTRACT FROM AUTHOR]

Air pollution has significant impacts on human health. Temperature inversions, especially near surface temperature inversions, can amplify air pollution by preventing convective movements and trapping pollutants close to the ground, thus decreasing air quality and increasing health issues. This effect of temperature inversions implies that trends in their frequency, strength and duration can have important implications for air quality. In this study, we evaluate the ability of three reanalysis-driven high-resolution regional climate model (RCM) simulations to represent near surface inversions at 9 sounding sites in southeast Australia. Then we use outputs of 12 historical and future RCM simulations (each with three time periods: 1990-2009, 2020-2039, and 2060-2079) from the NSW/ACT (New South Wales/Australian Capital Territory) Regional Climate Modelling (NARCliM) project to investigate changes in near surface temperature inversions. The results show that there is a substantial increase in the strength of near surface temperature inversions over southeast Australia which suggests that future inversions may intensify poor air quality events. Near surface inversions and their future changes have clear seasonal and diurnal variations. The largest differences between simulations are associated with the driving GCMs, suggesting that the large-scale circulation plays a dominant role in near surface inversion strengths. [ABSTRACT FROM AUTHOR]

Graphical abstract Abstract Two new cassaine-type diterpenoids, namely erythrofordins D (1) and E (2), sourced from a Cameroon collection of Erythrophleum suaveolens were isolated and assessed for anti-tumor activity. In the NCI-60 cancer cell assay, erythrofordins D (1) and E (2) were found to be cytotoxic in the low micro molar ranges with a mean GI 50 value of 2.45 and 0.71 µM, mean TGI value of 9.77 and 2.29 µM, and a mean LC 50 of 26.92 and 11.48 µM for 1 and 2 respectively. Using the COMPARE algorithm, the new compounds were found to have similar NCI-60 response profiles to the known cardiac glycosides hyrcanoside and strophanthin. In addition, in an assay examining the viability and contractile function in human cardiomyocytes derived from induced pluripotent stem-cells, erythrofordins showed cardiotoxicity effects at concentrations as low as 0.03 µg/mL. [ABSTRACT FROM AUTHOR]

Abstract Accurate quantification of land degradation is a global need, particularly in the world's dryland areas. However, there is a well-documented lack of field data and long-term observational studies for most of these regions. Remotely sensed data offers the only long-term vegetation record that can be used for land degradation assessment at a national, continental or global scale. Both the rainfall and vegetation datasets used for land degradation assessment contain errors and uncertainties, but little work has been done to understand how this may impact results. This study uses the recently developed Time Series Segmented RESidual TREND (TSS-RESTREND) method applied to six rainfall and two vegetation datasets to assess the impact of dataset selection on the estimates of dryland degradation over Australia. Large differences in the data and methods used to produce the precipitation datasets did not significantly impact results with the estimate of average change varying by <4% and a single dataset being sufficient to capture the direction of change in >95% of regions. On the other hand, the vegetation dataset selection had a much greater impact. Calibration errors in the Global Inventory Monitoring and Modeling System Version 3 NDVI (GIMMSv3.0g) dataset caused significant errors in the trends over some of Australia's dryland regions. Though identified over Australia, the problematic calibration in the GIMMSv3.0g dataset may have effected dryland NDVI values globally. These errors have been addressed in the updated GIMMSv3.1g which is strongly recommended for use in future studies. Our analysis suggests that using an ensemble composed of multiple runs performed using different datasets allows for the identification of errors that cannot be detected using only a single run or with the data quality flags of the input datasets. A multi-run ensemble made using different input datasets provides more comprehensive quantification of uncertainty and errors in space and time. [ABSTRACT FROM AUTHOR]

In this study we evaluate the ability of an ensemble of high-resolution Regional Climate Model simulations to represent snow cover characteristics over the Australian Alps and go on to asses future projections of snowpack characteristics. Our results show that the ensemble presents a cold temperature bias and overestimates total precipitation leading to a general overestimation of the snow cover as compared with MODIS satellite data. We then produce a new set of snowpack characteristics by running a temperature based snow melt/accumulation model forced by bias corrected temperature and precipitation fields. While some positive snow cover biases remain, the bias corrected (BC) dataset show large improvements regarding the simulation of total amounts, seasonality and spatial distribution of the snow cover compared with MODIS products. Both the raw and BC datasets are then used to assess future changes in the snowpack characteristics. Both datasets show robust increases in near-surface temperatures and decreases in snowfall that lead to a substantial reduction of the snowpack over the Australian Alps. The snowpack decreases by about 15 and 60% by 2030 and 2070 respectively. While the BC data introduce large differences in the simulation of the present climate snowpack, in relative terms future changes appear to be similar to those obtained using the raw data. Future temperature projections show a clear dependence with elevation through the snow-albedo feedback effect that affects snowpack projections. Uncertainties in future projections of the snowpack are large in both datasets and are mainly dominated by the choice of the lateral boundary conditions. [ABSTRACT FROM AUTHOR]

Global climate model simulations inherently contain multiple biases that, when used as boundary conditions for regional climate models, have the potential to produce poor downscaled simulations. Removing these biases before downscaling can potentially improve regional climate change impact assessment. In particular, reducing the low-frequency variability biases in atmospheric variables as well as modeled rainfall is important for hydrological impact assessment, predominantly for the improved simulation of floods and droughts. The impact of this bias in the lateral boundary conditions driving the dynamical downscaling has not been explored before. Here the use of three approaches for correcting the lateral boundary biases including mean, variance, and modification of sample moments through the use of a nested bias correction (NBC) method that corrects for low-frequency variability bias is investigated. These corrections are implemented at the 6-hourly time scale on the global climate model simulations to drive a regional climate model over the Australian Coordinated Regional Climate Downscaling Experiment (CORDEX) domain. The results show that the most substantial improvement in low-frequency variability after bias correction is obtained from modifying the mean field, with smaller changes attributed to the variance. Explicitly modifying monthly and annual lag-1 autocorrelations through NBC does not substantially improve low-frequency variability attributes of simulated precipitation in the regional model over a simpler mean bias correction. These results raise questions about the nature of bias correction techniques that are required to successfully gain improvement in regional climate model simulations and show that more complicated techniques do not necessarily lead to more skillful simulation. [ABSTRACT FROM AUTHOR]

This study presents future changes in extreme precipitation as projected within the New South Wales and Australian Capital Territory Regional Climate Modelling (NARCliM) project's regional climate ensemble for south-east Australia. Model performance, independence and projected future changes were considered when designing the ensemble. We applied a quantile mapping bias correction to the climate model outputs based on theoretical distribution functions, and the implications of this for the projected precipitation extremes is investigated. Precipitation extremes are quantified using several indices from the Expert Team on Climate Change Detection and Indices set of indices. The bias correction was successful in removing most of the magnitude bias in extreme precipitation but does not correct biases in the length of maximum wet and dry spells. The bias correction also had a relatively small effect on the projected future changes. Across a range of metrics, robust increases in the magnitude of precipitation extreme indices are found. While these increases are often in-line with a continuation of the trends present over the last century, they are not found to be statistically significant within the ensemble as a whole. The length of the maximum consecutive wet spell is projected to remain at present-day levels, while the length of the maximum dry spell is projected to increase into the future. The combination of longer dry spells and increases in extreme precipitation magnitude indicate an important change in the character of the precipitation time series. This could have considerable hydrological implications since changes in the sequencing of events can be just as important as changes in event magnitude for hydrological impacts. [ABSTRACT FROM AUTHOR]

Rationale N-β-Alanyldopamine (NBAD) and N-acetyldopamine (NADA) are catecholamines that are used by insects as sclerotizing precursors to harden their cuticle. They share a common pathway utilizing the same set of sclerotizing enzymes. Yet, cuticles using NBAD are brown, while cuticles using NADA are colorless. To identify the cause of this major unresolved color difference, molecular transformations of NBAD with cuticular enzymes were investigated. Methods Reactions of NBAD and NADA with native cuticle isolated from the wandering stages of Sarcophaga bullata larvae as well as the reactions of NBAD with cuticular sclerotization enzymes - phenoloxidase, quinone isomerase and quinone methide isomerase - were investigated using UV-Vis spectroscopy, high-performance liquid chromatography (HPLC), and mass spectrometry (MS). In addition, the reactivity of enzymatically generated NBAD quinone was investigated by MS. Results Reactions of NBAD with sclerotizing enzymes isolated from Sarcophaga bullata larvae generate colorless products such as N-β-alanylnorepinephrine, N-β-alanylarterenone, dehydro NBAD, the benzodioxan dimers of dehydro NBAD and other minor adducts, the same kind of compounds generated by NADA reaction with cuticular enzymes. However, oxidation of NBAD produces colored quinone adducts, in addition. NADA, which lacks the amino group, did not produce these quinone adducts. Conclusions LC/MS analysis of the reaction mixture of NBAD-cuticular enzyme reactions reveals the novel production of colored quinone adducts that are not possible for NADA. Therefore, our results suggest that the brown coloration of cuticle formed through NBAD crosslinking is likely due to the formation and accumulation of NBAD quinone and its adducts, while NADA quinone adducts tend not to form during NADA crosslinking, producing a nearly colorless cuticle. [ABSTRACT FROM AUTHOR]

Multiple reanalysis datasets currently exist that can provide boundary conditions for dynamic downscaling and simulating local hydro-climatic processes at finer spatial and temporal resolutions. Previous work has suggested that there are two reanalyses alternatives that provide the best lateral boundary conditions for downscaling over southern Africa. This study dynamically downscales these reanalyses (ERA-I and MERRA) over southern Africa to a high resolution (10 km) grid using the WRF model. Simulations cover the period 1981-2010. Multiple observation datasets were used for both surface temperature and precipitation to account for observational uncertainty when assessing results. Generally, temperature is simulated quite well, except over the Namibian coastal plain where the simulations show anomalous warm temperature related to the failure to propagate the influence of the cold Benguela current inland. Precipitation tends to be overestimated in high altitude areas, and most of southern Mozambique. This could be attributed to challenges in handling complex topography and capturing large-scale circulation patterns. While MERRA driven WRF exhibits slightly less bias in temperature especially for La Nina years, ERA-I driven simulations are on average superior in terms of RMSE. When considering multiple variables and metrics, ERA-I is found to produce the best simulation of the climate over the domain. The influence of the regional model appears to be large enough to overcome the small difference in relative errors present in the lateral boundary conditions derived from these two reanalyses. [ABSTRACT FROM AUTHOR]

Dryland degradation is an issue of international significance as dryland regions play a substantial role in global food production. Remotely sensed data provide the only long term, large scale record of changes within dryland ecosystems. The Residual Trend, or RESTREND, method is applied to satellite observations to detect dryland degradation. Whilst effective in most cases, it has been shown that the RESTREND method can fail to identify degraded pixels if the relationship between vegetation and precipitation has broken-down as a result of severe or rapid degradation. This paper presents an extended version of the RESTREND methodology that incorporates the Breaks For Additive Seasonal and Trend method to identify step changes in the time series that are related to significant structural changes in the ecosystem, e.g. land use changes. When applied to Australia, this new methodology, termed Time Series Segmentation and Residual Trend analysis (TSS-RESTREND), was able to detect degradation in 5.25% of pixels compared to only 2.0% for RESTREND alone. This modified methodology was then assessed in two regions with known histories of degradation where it was found to accurately capture both the timing and directionality of ecosystem change. [ABSTRACT FROM AUTHOR]

Preparing for environmental risks requires estimating the frequencies of extreme events, often from data records that are too short to confirm them directly. This requires fitting a statistical distribution to the data. To improve precision, investigators often pool data from neighboring sites into single samples, referred to as "superstations," before fitting. We demonstrate that this technique can introduce unexpected biases in typical situations, using wind and rainfall extremes as case studies. When the combined locations have even small differences in the underlying statistics, the regionalization approach gives a fit that may tend toward the highest levels suggested by any of the individual sites. This bias may be large or small compared to the sampling error, for realistic record lengths, depending on the distribution of the quantity analyzed. The results of this analysis indicate that previous analyses could potentially have overestimated the likelihood of extreme events arising from natural weather variability. Plain Language Summary: We report a previously unknown bias in a common method for estimating how often extremely rare events such as extreme wind bursts or rain events will occur, when return periods are longer than the available data record. The method analyzed is one where an investigator combines data from nearby locations to reduce sampling error. We find by looking at new, high‐resolution data that variations in behavior across sites can sometimes produce biases much larger than the sampling error. The implication is that some observed extreme events are even less likely to have occurred than previously thought, assuming the underlying distribution hasn't changed over the period of observation. Key Points: Grouping data of nearby locations into one larger sample or "superstation" can induce biases at long recurrence intervalsThe superstation fit tends to the highest levels suggested by any of the pooled locationsThe bias may be large or small compared to random uncertainty, depending on the distribution of the extreme event analyzed [ABSTRACT FROM AUTHOR]

Design rainfall is used to analyse and size water infrastructure and is generally derived from historical rainfall records. Given the expected changes in extreme rainfall due to anthropogenic climate change, future hydrologic design based on historical data may not be appropriate. While a number of studies have assessed the impact of climate change on design rainfall using different methods, to date there has been no comprehensive comparison or examination of the implications of alternative methods on future design rainfall estimates. This study compares the nine methods for estimating the design rainfall for the current climate and the potential changes in the future for the Greater Sydney region. A Monte Carlo cross-validation procedure was employed to evaluate the skill of each method in estimating the design rainfall for the current climate. It was found that bias correcting the annual maximum rainfall based on the empirical distribution combined with regional frequency analysis produces the design rainfall closest to the observations. While regional frequency analysis was found to have limited impact on the design rainfall estimation for the current climate, it provides much more spatially coherent patterns of future change and it is recommended that regionalisation be used in all design rainfall impact assessments. Despite the variations between different methods, a 20–35% increase in design rainfall over the coastal region is consistently projected. This will pose significant challenges for existing infrastructure in that area. [ABSTRACT FROM AUTHOR]

We reconsider the minimal SU(5) grand unified theory (GUT) in the context of no-scale supergravity inspired by string compactification scenarios, assuming that the soft supersymmetry-breaking parameters satisfy universality conditions at some input scale Min above the GUT scale MGUT. When setting up such a no-scale super-GUT model, special attention must be paid to avoiding the Scylla of rapid proton decay and the Charybdis of an excessive density of cold dark matter, while also having an acceptable mass for the Higgs boson. We do not find consistent solutions if none of the matter and Higgs fields are assigned to twisted chiral supermultiplets, even in the presence of Giudice-Masiero terms. However, consistent solutions may be found if at least one fiveplet of GUT Higgs fields is assigned to a twisted chiral supermultiplet, with a suitable choice of modular weights. Spin-independent dark matter scattering may be detectable in some of these consistent solutions. [ABSTRACT FROM AUTHOR]

The Northern Colorado Front Range (NCFR) has been in exceedance of the ozone National Ambient Air Quality Standard (NAAQS) since 2004, which has led to much debate over the sources of ozone precursors to the region, as this area is home to both the Denver, CO, metropolitan area and the Denver–Julesburg Basin, which has experienced rapid growth of oil and natural gas (O&NG) operations and associated emissions. Several recent studies have reported elevated levels of atmospheric volatile organic compounds (VOCs) as a result of O&NG emissions and the potential for significant ozone production from these emissions, despite implementation of stricter O&NG VOC emissions regulations in 2008. Approximately 88% of 1-hr elevated ozone events (>75 ppbv) occur during June–August, indicating that elevated ozone levels are driven by regional photochemistry. Analyses of surface ozone and wind observations from two sites, namely, South Boulder and the Boulder Atmospheric Observatory, both near Boulder, CO, show a preponderance of elevated ozone events associated with east-to-west airflow from regions with O&NG operations in the N-ESE, and a relatively minor contribution of transport from the Denver Metropolitan area to the SE-S. Transport from upwind areas associated with abundant O&NG operations accounts for on the order of 65% (mean for both sites) of 1-hr averaged elevated ozone levels, while the Denver urban corridor accounts for 9%. These correlations contribute to mounting evidence that air transport from areas with O&NG operation has a significant impact on ozone and air quality in the NCFR. Implications: This article builds on several previous pieces of research that implied significant contributions from oil and natural gas emissions on ozone production in the Northern Colorado Front Range. By correlating increased ozone events with transport analyses we show that there is a high abundance of transport events with elevated ozone originating from the Denver–Julesburg oil and natural gas basin. These findings will help air quality regulators to better assess contributing sources to ozone production and in directing policies to curb ozone pollution in this region. [ABSTRACT FROM AUTHOR]

Background: Patient mortality is significantly reduced by rapid identification of bacteria from sterile sites. MALDI-TOF can identify bacteria directly from positive blood cultures and multiple sample preparation methods are available. We evaluated three sample preparation methods and two MALDI-TOF score cut-off values. Positive blood culture bottles with organisms present in Gram stains were prospectively analysed by MALDI-TOF. Three lysis reagents (Saponin, SDS, and SepsiTyper lysis bufer) were applied to each positive culture followed by centrifugation, washing and protein extraction steps. Methods were compared using the McNemar test and 16S rDNA sequencing was used to assess discordant results. Results: In 144 monomicrobial cultures, using ≥2.000 as the cut-off value, species level identifications were obtained from 69/144 (48%) samples using Saponin, 86/144 (60%) using SDS, and 91/144 (63%) using SepsiTyper. The difference between SDS and SepsiTyper was not statistically significant (P = 0.228). Differences between Saponin and the other two reagents were significant (P < 0.01). Using ≥1.700 plus top three results matching as the cut-off value, species level identifications were obtained from 100/144 (69%) samples using Saponin, 103/144 (72%) using SDS, and 106/144 (74%) using SepsiTyper and there was no statistical difference between the methods. No true discordances between culture and direct MALDI-TOF identification were observed in monomicrobial cultures. In 32 polymicrobial cultures, MALDI-TOF identified one organism in 34–75% of samples depending on the method. Conclusions: This study demonstrates two inexpensive in-house detergent lysis methods are non-inferior to a commercial kit for analysis of positive blood cultures by direct MALDI-TOF in a clinical diagnostic microbiology laboratory. [ABSTRACT FROM AUTHOR]

Background: Faster identification of bacterial isolates from blood cultures can enable earlier clinical intervention for patients with sepsis. We evaluated the clinical impact of direct identification of micro-organisms from positive blood cultures using MALDI-ToF. Method: Positive blood cultures with organisms seen on Gram stain were included over a four week period. For each patient case, comparison was made between the clinical advice given on day one with only a Gram stain result, and the follow up advice given on day two with the benefit of organism identification. Culture results were then compared with direct MALDI-ToF identification. Results: For 73 of 115 cases (63.5%), direct organism identification was obtained by MALDI-ToF. Of those 73, 70 (95.5%) had a result concordant with that of the plate culture. In 28 of the 115 cases (24.3%) direct MALDI-ToF identification on day one would have had a clear clinical benefit. In 11 cases it would have helped to identify the potential source of bacteraemia. In 11 cases it would have indicated a different antibiotic regimen on day one, with five patients receiving appropriate antibiotics 24 hours earlier. For 14 cases the blood culture isolate could have been designated as unlikely to be clinically significant. Conclusion: We have demonstrated that organism identification on day one of blood culture positivity can have a direct clinical impact. Faster identification using MALDI-ToF assists the clinician in assessing the significance of a blood culture isolate on day one. It can allow earlier appropriate choice of antimicrobial agent, even in the absence of susceptibility testing, and help narrow down the potential source of infection providing a focus for further investigation in a more timely way than conventional techniques alone. [ABSTRACT FROM AUTHOR]

Regional climate modelling studies often begin by downscaling a reanalysis dataset in order to simulate the observed climate, allowing the investigation of regional climate processes and quantification of the errors associated with the regional model. To date choice of reanalysis to perform such downscaling has been made based either on convenience or on performance of the reanalyses within the regional domain for relevant variables such as near-surface air temperature and precipitation. However, the only information passed from the reanalysis to the regional model are the atmospheric temperature, moisture and winds at the location of the boundaries of the regional domain. Here we present a methodology to evaluate reanalyses derived lateral boundary conditions for an example domain over southern Africa using satellite data. This study focusses on atmospheric temperature and moisture which are easily available. Five commonly used global reanalyses (NCEP1, NCEP2, ERA-I, 20CRv2, and MERRA) are evaluated against the Atmospheric Infrared Sounder satellite temperature and relative humidity over boundaries of two domains centred on southern Africa for the years 2003-2012 inclusive. The study reveals that MERRA is the most suitable for climate mean with NCEP1 the next most suitable. For climate variability, ERA-I is the best followed by MERRA. Overall, MERRA is preferred for generating lateral boundary conditions for this domain, followed by ERA-I. While a 'better' LBC specification is not the sole precursor to an improved downscaling outcome, any reduction in uncertainty associated with the specification of LBCs is a step in the right direction. [ABSTRACT FROM AUTHOR]

We present a supersymmetric version of a two-field relaxion model that naturalizes tuned versions of supersymmetry. This arises from a relaxion mechanism that does not depend on QCD dynamics and where the relaxion potential barrier height is controlled by a second axion-like field. During the cosmological evolution, the relaxion rolls with a nonzero value that breaks supersymmetry and scans the soft supersymmetric mass terms. Electroweak symmetry is broken after the soft masses become of order the supersymmetric Higgs mass term and causes the relaxion to stop rolling for superpartner masses up to ∼ 109 GeV. This can explain the tuning in supersymmetric models, including split-SUSY models, while preserving the QCD axion solution to the strong CP problem. Besides predicting two very weakly-coupled axion-like particles, the supersymmetric spectrum may contain an extra Goldstino, which could be a viable dark matter candidate. [ABSTRACT FROM AUTHOR]

In the context of regional downscaling, we study the representation of extreme precipitation in the Weather Research and Forecasting (WRF) model, focusing on a major event that occurred on the 8 of June 2007 along the coast of eastern Australia (abbreviated 'Newy'). This was one of the strongest extra-tropical low-pressure systems off eastern Australia in the last 30 years and was one of several storms comprising a test bed for the WRF ensemble that underpins the regional climate change projections for eastern Australia (New South Wales/Australian Capital Territory Regional Climate Modelling Project, NARCliM). Newy provides an informative case study for examining precipitation extremes as simulated by WRF set up for regional downscaling. Here, simulations from the NARCliM physics ensemble of Newy available at ∼10 km grid spacing are used. Extremes and spatio-temporal characteristics are examined using land-based daily and hourly precipitation totals, with a particular focus on hourly accumulations. Of the different physics schemes assessed, the cumulus and the boundary layer schemes cause the largest differences. Although the Betts-Miller-Janjic cumulus scheme produces better rainfall totals over the entire storm, the Kain-Fritsch cumulus scheme promotes higher and more realistic hourly extreme precipitation totals. Analysis indicates the Kain-Fritsch runs are correlated with larger resolved grid-scale vertical moisture fluxes, which are produced through the influence of parameterized convection on the larger-scale circulation and the subsequent convergence and ascent of moisture. Results show that WRF qualitatively reproduces spatial precipitation patterns during the storm, albeit with some errors in timing. This case study indicates that whilst regional climate simulations of an extreme event such as Newy in WRF may be well represented at daily scales irrespective of the physics scheme used, the representation at hourly scales is likely to be physics scheme dependent. [ABSTRACT FROM AUTHOR]

In minimal supersymmetric SU(5) models, the proton can decay through dimension 5 operators. Since this decay depends directly on the supersymmetric soft masses, it will be constrained by other observables which depend on the soft masses, such as the Higgs mass and the dark matter relic density. In this work, we will examine the upper limit on the proton lifetime in minimal supersymmetric SU(5) with constrained minimal supersymmetric (CMSSM) boundary conditions set at the grand unification scale. We perform a random scan over the variables of the (CMSSM), with of order 106 points, and find that the proton lifetime is within reach of JUNO and Hyper-Kamiokande's experiment. [ABSTRACT FROM AUTHOR]

It is known that the recently reported shift of the W boson mass can be easily explained by an S U (2) L triplet Higgs boson with a zero hypercharge if it obtains a vacuum expectation value (VEV) of O (1) GeV. Surprisingly, the addition of a TeV scale complex triplet Higgs boson to the standard model (SM) leads to a precise unification of the gauge couplings at around 1014 GeV. We consider that it is a consequence of S U (5) grand unification and show a possible potential for the Higgs fields yielding a weak scale complex S U (2) triplet scalar boson. Although it seems the proton decay constraint would doom such a low-scale unification, we show that the constraint can be avoided by introducing vector-like fermions which mix with the SM fermions through mass terms involving the VEV of GUT breaking Higgs field. Importantly, the simplest viable model only requires the addition of one pair of vector-like fermions transforming 10 and 10 ‾. [ABSTRACT FROM AUTHOR]

The "Black Summer" 2019–2020 bushfires covered large areas of eastern Australia, causing substantial health impacts, animal deaths and damage to infrastructure. The smoke-related aerosols produced from these fires can also impact solar energy production by attenuating useable radiation. This study assesses the impacts of high particulate matter (PM) concentrations in photovoltaic (PV) energy production in New South Wales (NSW), from November 2019 to January 2020, during the bushfire season. We use in-situ measurements of commercial rooftop PV systems, pollution data from NSW Air Quality Monitoring Network and the Schools Weather and Air Quality, and satellite information from Himawari-8. We focus the study on clear days to eliminate the effects of clouds and concentrate on the impact of PM alone. We compare the energy generated by rooftop PV systems during clean/polluted conditions with a statistical conditional analysis approach. Results show that most locations exhibit similar behaviour, with PV generation reductions in polluted environments compared to clean conditions. The air quality impacts are stronger in locations near bushfire burning areas. For instance, in Katoomba, mean reductions up to 20% in the generated PV energy are found during high PM episodes. High-speed westerly winds transported the smoke several hundreds of kilometres, influencing the air quality and PV energy generation in most stations across Sydney. On a particular day of high bushfire activity (10 December 2019), we use satellite information to show how smoke caused increases in aerosol levels, increased PM concentrations, attenuated radiation and produced up to 65% reductions in hourly PV energy. • PV energy decreases up to 20% in polluted conditions compared to clean conditions. • Hourly PV reductions from 20% to 65% during a specific day of high bushfire activity. • High-speed westerly winds conditions favoured pollutant advection tens of kilometres. • Himawari-8 AOD is a good predictor of hourly PV energy during clear-sky conditions. [ABSTRACT FROM AUTHOR]

Effective implementation of precision medicine will be enhanced by a thorough understanding of each patient's genetic composition to better treat his or her presenting symptoms or mitigate the onset of disease. This ideally includes the sequence information of a complete genome for each individual. At Partners HealthCare Personalized Medicine, we have developed a clinical process for whole genome sequencing (WGS) with application in both healthy individuals and those with disease. In this manuscript, we will describe our bioinformatics strategy to efficiently process and deliver genomic data to geneticists for clinical interpretation. We describe the handling of data from FASTQ to the final variant list for clinical review for the final report. We will also discuss our methodology for validating this workflow and the cost implications of running WGS. [ABSTRACT FROM AUTHOR]

The climate of the eastern seaboard of Australia is strongly influenced by the passage of low pressure systems over the adjacent Tasman Sea due to their associated precipitation and their potential to develop into extreme weather events. The aim of this study is to quantify differences in the climatology of east coast lows derived from the use of six global reanalyses. The methodology is explicitly designed to identify differences between reanalyses arising from differences in their horizontal resolution and their structure (type of forecast model, assimilation scheme, and the kind and number of observations assimilated). As a basis for comparison, reanalysis climatologies are compared with an observation-based climatology. Results show that reanalyses, specially high-resolution products, lead to very similar climatologies of the frequency, intensity, duration, and size of east coast lows when using spatially smoothed (about 300-km horizontal grid meshes) mean sea level pressure fields as input data. Moreover, at these coarse horizontal scales, monthly, interannual, and spatial variabilities appear to be very similar across the various reanalyses with a generally stronger agreement between winter events compared with summer ones. Results also show that, when looking at cyclones using reanalysis data at their native resolution (approaching 50-km grid spacing for the most recent products), uncertainties related to the frequency, intensity, and size of lows are very large and it is not clear which reanalysis, if any, gives a better description of cyclones. Further work is needed in order to evaluate the usefulness of the finescale information in modern reanalyses and to better understand the sources of their differences. [ABSTRACT FROM AUTHOR]