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The ocean has absorbed ~one third of the excess atmospheric carbon dioxide (CO2) released since the Industrial Revolution. When the ocean absorbs excess CO2, a series of chemical reactions occur that result in a reduction in seawater pH, a process called ocean acidification. The excess atmospheric CO2 is also resulting in warmer seawater temperatures. These stressors pose a threat to marine organisms, especially during earlier life stages (i.e., larvae). The larvae of species like the Florida stone crab (Menippe mercenaria) are free swimming, allowing a population to disperse and recruit into new habitats. After release, stone crab larvae undergo vertical swimming excursions in response to abiotic stimuli (gravity, light, pressure) allowing them to control their depth. Typically, newly hatched larvae respond to abiotic cues that would promote a shallower depth distribution, where surface currents can transport them offshore to complete development. As larvae develop offshore, they become less sensitive to certain abiotic stimuli, which promotes a deeper depth distribution that may expose them to variable current speeds, thus influencing the direction of advection (horizontal movement). Environmental stressors like ocean acidification and elevated seawater temperatures may also impact the larvae’s natural response to these abiotic stimuli throughout ontogeny (development). Changes in their natural swimming behavior due to climate stressors could, therefore, influence the transport and dispersal of the species. This guided-inquiry lesson challenges introductory marine biology and oceanography students to determine how future ocean pH and temperature projections could impact the swimming behavior of Florida stone crab larvae. 

Abstract Anthropogenic activities like habitat degradation, excess nutrient runoff, and sewage outfalls can decrease seawater pH in coastal environments. Coastal waters can also experience frequent fluctuations in seawater pH due to biological activity (i.e., photosynthesis and respiration). Commercially important species like the Florida stone crab, Menippe mercenaria (Say, 1818), inhabit coastal waters and experience fluctuations in seawater pH on both diurnal and seasonal scales. Organisms exposed to reductions in seawater pH may have difficulty sensing chemical cues due to physiological changes and the associated metabolic stress of compensating for a more acidic environment. Here we determined the foraging activity of the Florida stone crab when exposed to reduced pH conditions (control pH 7.8, reduced pH 7.6). The impacts of reduced pH on foraging activity were determined by monitoring activity time, stress, predation attempts, and handling time when crabs were exposed to lower seawater pH for 12 hrs. Crabs exposed to reduced pH conditions experienced elevated stress levels and reduced activity than crabs in the control pH treatment. These results suggest that exposure to more extreme pH conditions may limit the foraging activity of stone crabs. 

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. 

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. 

Earliest snowmelt estimation dates calculated for the year 2003 are provided using sea ice brightness temperatures from AMSR-E (Cavalieri et al., 2014) and DMSP SSM/I-SSMIS (Meier et al., 2019), as well as simulated sea ice brightness temperatures from the CESM2 JRA-55 (Danabasoglu et al., 2020; Kobayashi et al., 2015; Tsujino et al., 2018), which were created using the Arctic Ocean Observation Operator (ARC3O; Burgard et al, 2020a,b). Scripts and README files are provided for preparing the model data to act as input to ARC3O. 

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. 

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. 

This dataset includes annual, gridded Arctic sea ice seasonal transition metrics (dates and periods) for fifteen Coupled Model Intercomparison Project version 6 (CMIP6) models and the Community Earth System Model version 1.1 (CESM1.1) Large Ensemble (CESM LE) (Kay, et al., 2015). Seasonal transition dates include melt onset, opening, break-up, freeze onset, freeze-up and closing. Seasonal transition periods include the melt period, the seasonal loss-of-ice period, the freeze period, the seasonal gain-of-ice period, the melt season, the open water period and the outer ice-free period. Data are provided for one ensemble member of the following models: Australian Community Climate and Earth System Simulator CM2 (ACCESS-CM2), Beijing Climate Center Climate System Model 2 MR (BCC-CSM2-MR), Beijing Climate Center Earth System Model 1 (BCC-ESM1), Community Earth System Model 2 (CESM2), Community Earth System Model 2 FV2 (CESM2-FV2), Community Earth System Model 2 Whole Atmosphere Community Climate Model (CESM2-WACCM), Community Earth System Model 2 Whole Atmosphere Community Climate Model FV2 (CESM2-WACCM-FV2), Centre National de Recherches Météorologiques ESM 2-1 (CNRM-ESM2-1), Centre National de Recherches Météorologiques CM 6-1 (CNRM-CM6-1), EC-Earth3, Meteorological Research Institute Earth System Model 2-0 (MRI-ESM2-0), Norwegian Earth System Model 2 LM (NorESM2-LM) and Norwegian Earth System Model 2 MM (NorESM2-MM). Data are provided for 40 members of the Community Earth System Model Large Ensemble (CESM LE), 35 members of Canadian Earth System Model 5 (CanESM5) and 30 members of Institut Pierre Simon Laplace CM6A LR (IPSL-CM6A-LR). The data is stored in netcdf format, and includes metadata in the netcdf files. The raw CMIP6 and CESM LE model output that these transition metrics are calculated from are publicly available at https://esgf-node.llnl.gov/projects/cmip6/ and https://www.earthsystemgrid.org/ respectively. This dataset was created to evaluate climate model projections of Arctic sea ice using seasonal transition metrics in the context of both observations and internal variability. It is used in the article Smith, Jahn, Wang (2020), Seasonal transition dates can reveal biases in Arctic sea ice simulations, The Cryosphere, in press. The discussion paper with a link to the final paper can be found at https://doi.org/10.5194/tc-2020-81. This work was conducted at the University of Colorado Boulder from 2019-2020. 

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.