Is there gender discrimination in named professorships? An econometric analysis of economics departments in the US South

Metrics based on streamflow and/or climate variables are used in water management for monitoring and evaluating available resources. To reflect future change in the hydrological regime, metrics are estimated using climate change information from Global Climate Models or from stochastic time series representing future climates. Whilst often simple to calculate, many metrics implicitly represent complex physical process. We evaluate the scientific validity of metrics used in a climate change context, demonstrating their use to reflect aspects of timing, magnitude, extreme values, variability, duration, state, system services and performance. We raise awareness about the following generic issues: • formulation: metrics often assume stationarity of the input data, which is invalid under climate change; and do not always consider potential changes to seasonality and the relevance of the temporal window used for analysis; • climate change input data: how well are the physical processes relevant to the metric represented in the climate change input data; what is the impact of bias correction on relevant spatial and temporal scale dependencies and relevant intervariable dependencies; how realistic are the data in representing sequencing of events and natural variability in large ocean-atmosphere systems; • decision making context: are rules and values that frame the decision-making process likely to remain constant or change in a future world. If critical climate or hydrological processes are not well represented by the metric constituents, these indices can be misleading about plausible future change. However, knowledge of how to construct a robust metric can safeguard against misleading interpretations about future change

Satellites reveal Earth's seasonally shifting dust emission sources

Establishing mineral dust impacts on Earth's systems requires numerical models of the dust cycle. Differences between dust optical depth (DOD) measurements and modelling the cycle of dust emission, atmospheric transport, and deposition of dust indicate large model uncertainty due partially to unrealistic model assumptions about dust emission frequency. Calibrating dust cycle models to DOD measurements typically in North Africa, are routinely used to reduce dust model magnitude. This calibration forces modelled dust emissions to match atmospheric DOD but may hide the correct magnitude and frequency of dust emission events at source, compensating biases in other modelled processes of the dust cycle. Therefore, it is essential to improve physically based dust emission modules. Here we use a global collation of satellite observations from previous studies of dust emission point source (DPS) dichotomous frequency data. We show that these DPS data have little-to-no relation with MODIS DOD frequency. We calibrate the albedo-based dust emission model using the frequency distribution of those DPS data. The global dust emission uncertainty constrained by DPS data (±3.8 kg m−2 y−1) provides a benchmark for dust emission model development. Our calibrated model results reveal much less global dust emission (29.1 ± 14.9 Tg y−1) than previous estimates, and show seasonally shifting dust emission predominance within and between hemispheres, as opposed to a persistent North African dust emission primacy widely interpreted from DOD measurements. Earth's largest dust emissions, proceed seasonally from East Asian deserts in boreal spring, to Middle Eastern and North African deserts in boreal summer and then Australian shrublands in boreal autumn-winter. This new analysis of dust emissions, from global sources of varying geochemical properties, have far-reaching implications for current and future dust-climate effects. For more reliable coupled representation of dust-climate projections, our findings suggest the need to re-evaluate dust cycle modelling and benefit from the albedo-based parameterisation.</p