Using Satellite Observations to Evaluate Model Microphysical Representation of Arctic Mixed-Phase Clouds

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Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Radiative transfer in the earth-atmosphere-space system

The global climate is determined by the exchange of energy between the earth, atmosphere, and space. Arriving from the sun, solar radiation is transformed by processes of absorption, scattering, and emission as it enters, interacts with, and eventually leaves the planet. Occurring dominantly through radiative and convective mechanisms, the upward motion of thermal energy gives the at- mosphere its structure and determines surface temperatures. Understanding radiative transfer and its dependence on atomic spectra gives insight on why minute changes in the composition of the atmosphere can have drastic effects on climate through the greenhouse effect

Assessing Clouds Using Satellite Observations Through Three Generations of Global Atmosphere Models

Abstract Clouds are parameterized in climate models using quantities on the model grid‐scale to approximate the cloud cover and impact on radiation. Because of the complexity of processes involved with clouds, these parameterizations are one of the key challenges in climate modeling. Differences in parameterizations of clouds are among the main contributors to the spread in climate sensitivity across models. In this work, the clouds in three generations of an atmosphere model lineage are evaluated against satellite observations. Satellite simulators are used within the model to provide an appropriate comparison with individual satellite products. In some respects, especially the top‐of‐atmosphere cloud radiative effect, the models show generational improvements. The most recent generation, represented by two distinct branches of development, exhibits some regional regressions in the cloud representation; in particular the southern ocean shows a positive bias in cloud cover. The two branches of model development show how choices during model development, both structural and parametric, lead to different cloud climatologies. Several evaluation strategies are used to quantify the spatial errors in terms of the large‐scale circulation and the cloud structure. The Earth mover's distance is proposed as a useful error metric for the passive satellite data products that provide cloud‐top pressure‐optical depth histograms. The cloud errors identified here may contribute to the high climate sensitivity in the Community Earth System Model, version 2 and in the Energy Exascale Earth System Model, version 1

Using Satellite Observations to Evaluate Model Microphysical Representation of Arctic Mixed-Phase Clouds

Mixed-phase clouds play an important role in determining Arctic warming, but are parametrized in models and difficult to constrain with observations. We use two satellite-derived cloud phase metrics to investigate the vertical structure of Arctic clouds in two global climate models that use the Community Atmosphere Model version 6 (CAM6) atmospheric component. We report a model error limiting ice nucleation, produce a set of Arctic-constrained model runs by adjusting model microphysical variables to match the cloud phase metrics, and evaluate cloud feedbacks for all simulations. Models in this small ensemble uniformly overestimate total cloud fraction in the summer, but have variable representation of cloud fraction and phase in the winter and spring. By relating modeled cloud phase metrics and changes in low-level liquid cloud amount under warming to longwave cloud feedback, we show that mixed-phase processes mediate the Arctic climate by modifying how wintertime and springtime clouds respond to warming

On the Links Between Ice Nucleation, Cloud Phase, and Climate Sensitivity in CESM2

Abstract Ice nucleation in mixed‐phase clouds has been identified as a critical factor in projections of future climate. Here we explore how this process influences climate sensitivity using the Community Earth System Model 2 (CESM2). We find that ice nucleation affects simulated cloud feedbacks over most regions and levels of the troposphere, not just extratropical low clouds. However, with present‐day global mean cloud phase adjusted to replicate satellite retrievals, similar total cloud feedback is attained whether ice nucleation is simulated as aerosol‐sensitive, insensitive, or absent. These model experiments all result in a strongly positive total cloud feedback, as in the default CESM2. A microphysics update from CESM1 to CESM2 had substantially weakened ice nucleation, due partly to a model issue. Our findings indicate that this update reduced global cloud phase bias, with CESM2's high climate sensitivity reflecting improved mixed‐phase cloud representation

Variation In The Number Of Hibernating Cave Myotis (Myotis Velifer) In Western Oklahoma And Northwest Texas Caves Prior To The Arrival Of White-nose Syndrome

We report on variation in abundance of hibernating cave myotis (Myotis velifer) prior to the arrival of White-nose Syndrome. The report is based on cave surveys and literature reports in gypsum caves of western Oklahoma and the Texas panhandle. From 1988 to 2017, 34,625–195,234 M. velifer were estimated to occur in the hibernacula surveyed. This report provides important preexposure baseline data on population sizes in the region and will help researchers quantify the impact as the disease spreads into new environs. The population estimates will also serve as a data set for other research. Other species of bats encountered in the surveys were Townsend\u27s big-eared bat (Corynorhinus townsendii), the tricolored bat (Perimyotis subflavus), and the big brown bat (Eptesicus fuscus). The changes in population sizes at the various hibernacula might be owing to a variety of factors, including annual variations in hibernacula microclimates, structural changes in hibernacula, changes in distribution patterns, ability of surveyors to locate and count bats, alterations of the surrounding habitats near hibernacula, or other unknown factors