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Time series of data corresponding to Evans et al. (2022) "Natural variability and expansion of the nitrogen deficit within the Eastern Tropical North Pacific Oxygen Deficient Zone", containing secondary quality controlled data of 8 cruises in the ETNP ODZ, seven of which on the 110 W line, as well as supplemental sediment core data and CalCOFI oxygen data for comparison. Intermediate data products generated by the code used for this paper are also included, and the code to generate these intermediate products as well as the final outputs has been uploaded onto a separate Zenodo repository, "ETNP_ODZ_time_series_code" at https://doi.org/10.5281/zenodo.6519316. More detailed information is available in the README, but should you have any questions, please reach out to Allan Devol or Natalya Evans.

This dataset includes elemental stoichiometry for Emiliania huxleyi across a range of 12 temperatures from 8.5-28.6C. Global warming will be combined with predicted increases in thermal variability in the future surface ocean, but how temperature dynamics will affect phytoplankton biology and biogeochemistry is largely unknown. Here, we examine the responses of the globally important marine coccolithophore Emiliania huxleyi to thermal variations at two frequencies (1 d and 2 d) at low (18.5 °C) and high (25.5 °C) mean temperatures. Elevated temperature and thermal variation decreased growth, calcification and physiological rates, both individually and interactively. The 1 d thermal variation frequencies were less inhibitory than 2 d variations under high temperatures, indicating that high-frequency thermal fluctuations may reduce heat-induced mortality and mitigate some impacts of extreme high-temperature events. Cellular elemental composition and calcification was significantly affected by both thermal variation treatments relative to each other and to the constant temperature controls. The negative effects of thermal variation on E. huxleyi growth rate and physiology are especially pronounced at high temperatures. These responses of the key marine calcifier E. huxleyi to warmer, more variable temperature regimes have potentially large implications for ocean productivity and marine biogeochemical cycles under a future changing climate.

Flexible butyl interconnection segments are synthetically incorporated into an electronically conductive poly(pyrene methacrylate) homopolymer and its copolymer. The insertion of butyl segment makes the pyrene polymer more flexible, and can better accommodate deformation. This new class of flexible and conductive polymers can be used as a polymer binder and adhesive to facilitate the electrochemical performance of a silicon/graphene composite anode material for lithium ion battery application. They act like a “spring” to maintain the electrode mechanical and electrical integrity. High mass loading and high areal capacity, which are critical design requirements of high energy batteries, have been achieved in the electrodes composed of the novel binders and silicon/graphene composite material. A remarkable area capacity of over 5 mAh/cm2 and volumetric capacity of over 1700 Ah/L have been reached at a high current rate of 333 mA/g.

The rapid development of advanced metering infrastructure provides a new data source—building electrical load profiles with high temporal resolution. Electric load profile characterization can generate useful information to enhance building energy modeling and provide metrics to represent patterns and variability of load profiles. Such characterizations can be used to identify changes to building electricity demand due to operations or faulty equipment and controls. In this study, we proposed a two-path approach to analyze high temporal resolution building electrical load profiles: (1) time-domain analysis and (2) frequency-domain analysis. The commonly adopted time-domain analysis can extract and quantify the distribution of key parameters characterizing load shape such as peak-base load ratio and morning rise time, while a frequency-domain analysis can identify major periodic fluctuations and quantify load variability. We implemented and evaluated both paths using whole-year 15-minute interval smart meter data of 188 commercial office building in Northern California. The results from these two paths are consistent with each other and complementary to represent full dynamics of load profiles. The time- and frequency-domain analyses can be used to enhance building energy modeling by: (1) providing more realistic assumptions about building operation schedules, and (2) validating the simulated electric load profiles using the developed variability metrics against the real building load data.

Agricultural adaptation to climate change is critical for ensuring future food security. Social capital is important for climate change adaptation, but institutions and social networks at multiple scales (e.g., household, community, and institution) have been overlooked in studying agricultural climate change adaptation. We combine data from 13 sites in 11 low-income countries in East Africa, West Africa, and South Asia to explore how multiple scales of social capital relate to household food security outcomes among smallholder farmers. Using social network theory, we define three community organizational social network types (fragmented defined by lack of coordination, brokered defined as having a strong central actor, or shared defined by high coordination) and examine household social capital through group memberships. We find community and household social capital are positively related, with higher household group membership more likely in brokered and shared networks. Household group membership is associated with more than a 10% reduction in average months of food insecurity, an effect moderated by community social network type. In communities with fragmented and shared organizational networks, additional household group memberships is associated with consistent decreases in food insecurity, in some cases up to two months; whereas in brokered networks, reductions in food insecurity are only associated with membership in credit groups. These effects are confirmed by hierarchical random effects models, which control for demographic factors. This suggests that multiple scales of social capital—both within and outside the household—are correlated with household food security. This social capital may both be bridging (across groups) and bonding (within groups) with different implications for how social capital structure affects food security. Efforts to improve food security could recognize the potential for both household and community level social networks and collaboration, which further research can capture by analyzing multiple scales of social capital data.

A new concept, called synchronized Lissajous curve , is proposed to study synchro-waveform measurements that are obtained from waveform measurement units (WMUs) in power distribution systems. Two types of synchronized Lissajous curves are discussed. The first one is created by plotting the difference of two synchronized voltage waveforms versus the difference of two synchronized current waveforms. The second one is created by plotting the difference of two synchronized voltage waveforms versus the difference of the derivatives of two synchronized current waveforms. These two curves carry valuable and complementary information about the state of the power system and the root cause of events and disturbances. We characterize the area , rotational angle , and shape of the introduced synchronized Lissajous curves to study events and disturbances in power distribution feeders.

A new state estimation method is proposed for power distribution networks that suffer from low-observability. The proposed distribution system state estimation (DSSE) method leverages the high reporting rate of only a small number of distribution-level phasor measurement units (D-PMUs), a.k.a., micro-PMUs, to unmask and characterize sparsity among the state variables. The DSSE problem is formulated over differential synchrophasors as an adaptive group sparse recovery problem to track the changes that are made in the states of the system due to the events that are captured in D-PMU measurements. The formulated DSSE is further augmented to use adequate side information on the support of the vector of unknowns that is obtained from the outcome of an event-zone identification analysis prior to solving the DSSE problem. The sufficient conditions for the uniqueness of the obtained sparse recovery solution are derived with respect to the available side information. Moreover, a calibration mechanism is developed to address drifting in the tracking state estimation to enhance robustness.

Atmospheric methane (CH4) is a potent greenhouse gas, and its mole fraction has more than doubled since the preindustrial era. Fossil fuel extraction and use are among the largest anthropogenic sources of CH4 emissions, but the precise magnitude of these contributions is a subject of debate. Carbon-14 in CH4 (14CH4) can be used to distinguish between fossil (14C-free) CH4 emissions and contemporaneous biogenic sources; however, poorly constrained direct 14CH4 emissions from nuclear reactors have complicated this approach since the middle of the 20th century. Moreover, the partitioning of total fossil CH4 emissions (presently 172 to 195 teragrams CH4 per year) between anthropogenic and natural geological sources (such as seeps and mud volcanoes) is under debate; emission inventories suggest that the latter account for about 40 to 60 teragrams CH4 per year. Geological emissions were less than 15.4 teragrams CH4 per year at the end of the Pleistocene, about 11,600 years ago, but that period is an imperfect analogue for present-day emissions owing to the large terrestrial ice sheet cover, lower sea level and extensive permafrost. Here we use preindustrial-era ice core 14CH4 measurements to show that natural geological CH4 emissions to the atmosphere were about 1.6 teragrams CH4 per year, with a maximum of 5.4 teragrams CH4 per year (95 per cent confidence limit)—an order of magnitude lower than the currently used estimates. This result indicates that anthropogenic fossil CH4 emissions are underestimated by about 38 to 58 teragrams CH4 per year, or about 25 to 40 per cent of recent estimates. Our record highlights the human impact on the atmosphere and climate, provides a firm target for inventories of the global CH4 budget, and will help to inform strategies for targeted emission reductions.