Search for: All records

This guided, inquiry-based, hands-on lesson uses data from a local monitoring station in Tampa Bay, Florida, to guide students toward understanding how coastal acidification may impact the reproductive success of the Florida stone crab, an important regional fishery. The objectives of the lesson are for students to: (1) determine how pH varies between different habitats, (2) determine how pH can affect the reproductive success of an important commercial fishery, the Florida stone crab, and (3) evaluate whether exposure to variable seawater pH results in greater reproductive success in stone crabs relative to individuals that are not exposed to pH variability. 

The rapid expansion of Earth system model (ESM) data available from the Coupled Model Intercomparison Project Phase 6 (CMIP6) necessitates new methods to evaluate the performance and suitability of ESMs used for hydroclimate applications as these extremely large data volumes complicate stakeholder efforts to use new ESM outputs in updated climate vulnerability and impact assessments. We develop an analysis framework to inform ESM sub‐selection based on process‐oriented considerations and demonstrate its performance for a regional application in the US Pacific Northwest. First, a suite of global and regional metrics is calculated, using multiple historical observation datasets to assess ESM performance. These metrics are then used to rank CMIP6 models, and a culled ensemble of models is selected using a trend‐related diagnostics approach. This culling strategy does not dramatically change climate scenario trend projections in this region, despite retaining only 20% of the CMIP6 ESMs in the final model ensemble. The reliability of the culled trend projection envelope and model response similarity is also assessed using a perfect model framework. The absolute difference in temperature trend projections is reduced relative to the full ensemble compared to the model for each SSP scenario, while precipitation trend errors are largely unaffected. In addition, we find that the spread of the culled ensemble temperature and precipitation trends includes the trend of the “truth” model ∼83%‐92% of the time. This analysis demonstrates a reliable method to reduce ESM ensemble size that can ease use of ESMs for creating and understanding climate vulnerability and impact assessments.

 

Advances in nanotechnology enable the detection of trace molecules from the enhanced Raman signal generated at the surface of plasmonic nanoparticles. We have developed technology to enable super-resolution imaging of plasmonic nanoparticles, where the fluctuations in the surface enhanced Raman scattering (SERS) signal can be analyzed with localization microscopy techniques to provide nanometer spatial resolution of the emitting molecule’s location. Additional work now enables the super-resolved SERS image and the corresponding spectrum to be acquired simultaneously. Here we will discuss how this approach can be applied to provide new insights into biological cells. 

The design of completely synthetic proteins from first principles— de novo protein design—is challenging. This is because, despite recent advances in computational protein–structure prediction and design, we do not understand fully the sequence-to-structure relationships for protein folding, assembly, and stabilization. Antiparallel 4-helix bundles are amongst the most studied scaffolds for de novo protein design. We set out to re-examine this target, and to determine clear sequence-to-structure relationships, or design rules, for the structure. Our aim was to determine a common and robust sequence background for designing multiple de novo 4-helix bundles. In turn, this could be used in chemical and synthetic biology to direct protein–protein interactions and as scaffolds for functional protein design. Our approach starts by analyzing known antiparallel 4-helix coiled-coil structures to deduce design rules. In terms of the heptad repeat, abcdefg — i.e. , the sequence signature of many helical bundles—the key features that we identify are: a = Leu, d = Ile, e = Ala, g = Gln, and the use of complementary charged residues at b and c. Next, we implement these rules in the rational design of synthetic peptides to form antiparallel homo- and heterotetramers. Finally, we use the sequence of the homotetramer to derive in one step a single-chain 4-helix-bundle protein for recombinant production in E. coli . All of the assembled designs are confirmed in aqueous solution using biophysical methods, and ultimately by determining high-resolution X-ray crystal structures. Our route from peptides to proteins provides an understanding of the role of each residue in each design. 

Abstract. Seasonal transitions in Arctic sea ice, such as the melt onset, have been found to be useful metrics for evaluating sea ice in climate models against observations. However, comparisons of melt onset dates between climate models and satellite observations are indirect. Satellite data products of melt onset rely on observed brightness temperatures, while climate models do not currently simulate brightness temperatures, and must therefore define melt onset with other modeled variables. Here we adapt a passive microwave sea ice satellite simulator, the Arctic Ocean Observation Operator (ARC3O), to produce simulated brightness temperatures that can be used to diagnose the timing of the earliest snowmelt in climate models, as we show here using Community Earth System Model version 2 (CESM2) ocean-ice hindcasts. By producing simulated brightness temperatures and earliest snowmelt estimation dates using CESM2 and ARC3O, we facilitate new and previously impossible comparisons between the model and satellite observations by removing the uncertainty that arises due to definition differences. Direct comparisons between the model and satellite data allow us to identify an early bias across large areas of the Arctic at the beginning of the CESM2 ocean-ice hindcast melt season, as well as improve our understanding of the physical processes underlying seasonal changes in brightness temperatures. In particular, the ARC3O allows us to show that satellite algorithm-based melt onset dates likely occur after significant snowmelt has already taken place. 

Abstract The shrinking of Arctic-wide September sea ice extent is often cited as an indicator of modern climate change; however, the timing of seasonal sea ice retreat/advance and the length of the open-water period are often more relevant to stakeholders working at regional and local scales. Here we highlight changes in regional open-water periods at multiple warming thresholds. We show that, in the latest generation of models from the Coupled Model Intercomparison Project (CMIP6), the open-water period lengthens by 63 days on average with 2 °C of global warming above the 1850-1900 average, and by over 90 days in several Arctic seas. Nearly the entire Arctic, including the Transpolar Sea Route, has at least 3 months of open water per year with 3.5 °C warming, and at least 6 months with 5 °C warming. Model bias compared to satellite data suggests that even such dramatic projections may be conservative. 

Abstract. Arctic sea ice experiences a dramatic annual cycle, and seasonal ice loss and growth can be characterized by various metrics: melt onset, breakup, opening, freeze onset, freeze-up, and closing. By evaluating a range of seasonal sea ice metrics, CMIP6 sea ice simulations can be evaluated in more detail than by using traditional metrics alone, such as sea ice area. We show that models capture the observed asymmetry in seasonal sea ice transitions, with spring ice loss taking about 1–2 months longer than fall ice growth. The largest impacts of internal variability are seen in the inflow regions for melt and freeze onset dates, but all metrics show pan-Arctic model spreads exceeding the internal variability range, indicating the contribution of model differences. Through climate model evaluation in the context of both observations and internal variability, we show that biases in seasonal transition dates can compensate for other unrealistic aspects of simulated sea ice. In some models, this leads to September sea ice areas in agreement with observations for the wrong reasons. 

The origins of cheilostome bryozoans and parental care in this group are substantially older than previously thought. 

We provide an assessment of the current and future states of Arctic sea ice simulated by the Community Earth System Model version 2 (CESM2). The CESM2 is the version of the CESM contributed to the sixth phase of the Coupled Model Intercomparison Project (CMIP6). We analyze changes in Arctic sea ice cover in two CESM2 configurations with differing atmospheric components: the CESM2(CAM6) and the CESM2(WACCM6). Over the historical period, the CESM2(CAM6) winter ice thickness distribution is biased thin, which leads to lower summer ice area compared to CESM2(WACCM6) and observations. In both CESM2 configurations, the timing of first ice‐free conditions is insensitive to the choice of CMIP6 future emissions scenario. In fact, the probability of an ice‐free Arctic summer remains low only if global warming stays below 1.5°C, which none of the CMIP6 scenarios achieve. By the end of the 21st century, the CESM2 simulates less ocean heat loss during the fall months compared to its previous version, delaying sea ice formation and leading to ice‐free conditions for up to 8 months under the high emissions scenario. As a result, both CESM2 configurations exhibit an accelerated decline in winter and spring ice area, a behavior that had not been previously seen in CESM simulations. Differences in climate sensitivity and higher levels of atmospheric CO₂by 2100 in the CMIP6 high emissions scenario compared to its CMIP5 analog could explain why this winter ice loss was not previously simulated by the CESM.