National Oceanic and Atmospheric Administration

Harmful algal blooms caused by toxin‐producing species of the diatom genus Pseudo‐nitzschia have been linked to anomalously warm ocean conditions in the Northern California Current System. This study compares summertime concentrations of Pseudo‐nitzschia spp. and the toxin they produce, domoic acid, during a marine heatwave year (2019) and a climatologically neutral year (2021). An Imaging FlowCytobot was installed on a fishery survey vessel alongside environmental sensors to continuously sample phytoplankton and oceanographic parameters. This was paired with targeted manual sample collections for nutrients, chlorophyll, and domoic acid. Accumulations of Pseudo‐nitzschia spp. were associated with upwelling zones and established hotspot regions: the Juan de Fuca Eddy, Heceta Bank, and Trinidad Head. Overall, however, Pseudo‐nitzschia spp. and domoic acid concentrations were low during both summers and appear to have been limited by nitrate. Nutrient availability may therefore modulate the response of Pseudo‐nitzschia spp. to warm anomalies. Comparison of these results with 2015, another marine heatwave year but one that produced record concentrations of Pseudo‐nitzschia spp. and domoic acid, suggests that the timing of marine heatwave conditions in the nearshore relative to seasonal upwelling plays a key role in determining whether a Pseudo‐nitzschia spp. harmful algal bloom will occur.

Social information is predicted to enhance the quality of animals’ migratory decisions in dynamic ecosystems, but the relative benefits of social information in the long-range movements of marine megafauna are unknown. In particular, whether and how migrants use nonlocal information gained through social communication at the large spatial scale of oceanic ecosystems remains unclear. Here we test hypotheses about the cues underlying timing of blue whales’ breeding migration in the Northeast Pacific via individual-based models parameterized by empirical behavioral data. Comparing emergent patterns from individual-based models to individual and population-level empirical metrics of migration timing, we find that individual whales likely rely on both personal and social sources of information about forage availability in deciding when to depart from their vast and dynamic foraging habitat and initiate breeding migration. Empirical patterns of migratory phenology can only be reproduced by models in which individuals use long-distance social information about conspecifics’ behavioral state, which is known to be encoded in the patterning of their widely propagating songs. Further, social communication improves pre-migration seasonal foraging performance by over 60% relative to asocial movement mechanisms. Our results suggest that long-range communication enhances the perceptual ranges of migrating whales beyond that of any individual, resulting in increased foraging performance and more collective migration timing. These findings indicate the value of nonlocal social information in an oceanic migrant and suggest the importance of long-distance acoustic communication in the collective migration of wide-ranging marine megafauna.

In the face of a changing climate, the understanding, predictions, and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long‐term adaptation strategies, and guide mitigation approaches. Increasingly complex socio‐economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities. These include the unrealized potential for very high‐resolution modeling of global‐to‐local Earth system processes across timescales, reduction of model biases, enhanced integration of human systems and the Earth Systems, better quantification of predictability and uncertainties; expedited science‐to‐service pathways, and co‐production of actionable information with stakeholders. Enabling technological opportunities include exascale computing, advanced data storage, novel observations and powerful data analytics, including artificial intelligence and machine learning. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally‐funded U.S. modeling groups outline here perspectives on a six‐pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support next generation climate services.

Extreme precipitation events are projected to increase in frequency across much of the land‐surface as the global climate warms, but such projections have typically relied on coarse‐resolution (100–250 km) general circulation models (GCMs). The ensemble of HighResMIP GCMs presents an opportunity to evaluate how a more finely resolved atmosphere and land‐surface might enhance the fidelity of the simulated contribution of large‐magnitude storms to total precipitation, particularly across topographically complex terrain. Here, the simulation of large‐storm dominance, that is, the number of wettest days to reach half of the total annual precipitation, is quantified across the western United States (WUS) using four GCMs within the HighResMIP ensemble and their coarse resolution counterparts. Historical GCM simulations (1950–2014) are evaluated against a baseline generated from station‐observed daily precipitation (4,803 GHCN‐D stations) and from three gridded, observationally based precipitation data sets that are coarsened to match the resolution of the GCMs. All coarse‐resolution simulations produce less large‐storm dominance than in observations across the WUS. For two of the four GCMs, bias in the median large‐storm dominance is reduced in the HighResMIP simulation, decreasing by as much as 62% in the intermountain west region. However, the other GCMs show little change or even an increase (+28%) in bias of median large‐storm dominance across multiple sub‐regions. The spread in differences with resolution amongst GCMs suggests that, in addition to resolution, model structure and parameterization of precipitation generating processes also contribute to bias in simulated large‐storm dominance.

The effects of turbidity and sedimentation stress on early life stages of corals are poorly understood, particularly in Atlantic species. Dredging operations, beach nourishment, and other coastal construction activities can increase sedimentation and turbidity in nearby coral reef habitats and have the potential to negatively affect coral larval development and metamorphosis, reducing sexual reproduction success. In this study, we investigated the performance of larvae of the threatened Caribbean coral species Orbicella faveolata exposed to suspended sediments collected from a reef site in southeast Florida recently impacted by dredging (Port of Miami), and compared it to the performance of larvae exposed to sediments collected from the offshore, natal reef of the parent colonies. In a laboratory experiment, we tested whether low and high doses of each of these sediment types affected the survival, settlement, and respiration of coral larvae compared to a no-sediment control treatment. In addition, we analyzed the sediments used in the experiments with 16S rRNA gene amplicon sequencing to assess differences in the microbial communities present in the Port versus Reef sediments, and their potential impact on coral performance. Overall, only O. faveolata larvae exposed to the high-dose Port sediment treatment had significantly lower survival rates compared to the control treatment, suggesting an initial tolerance to elevated suspended sediments. However, significantly lower settlement rates were observed in both Port treatments (low- and high-dose) compared to the control treatment one week after exposure, suggesting strong latent effects. Sediments collected near the Port also contained different microbial communities than Reef sediments, and higher relative abundances of the bacteria Desulfobacterales, which has been associated with coral disease. We hypothesize that differences in microbial communities between the two sediments may be a contributing factor in explaining the observed differences in larval performance. Together, these results suggest that the settlement success and survival of O. faveolata larvae are more readily compromised by encountering port inlet sediments compared to reef sediments, with potentially important consequences for the recruitment success of this species in affected areas.

The El Niño–Southern Oscillation (ENSO) provides most of the global seasonal climate forecast skill1–3, yet, quantifying the sources of skilful predictions is a long-standing challenge4–7. Different sources of predictability affect ENSO evolution, leading to distinct global effects. Artificial intelligence forecasts offer promising advancements but linking their skill to specific physical processes is not yet possible8–10, limiting our understanding of the dynamics underpinning the advancements. Here we show that an extended nonlinear recharge oscillator (XRO) model shows skilful ENSO forecasts at lead times up to 16–18 months, better than global climate models and comparable to the most skilful artificial intelligence forecasts. The XRO parsimoniously incorporates the core ENSO dynamics and ENSO’s seasonally modulated interactions with other modes of variability in the global oceans. The intrinsic enhancement of ENSO’s long-range forecast skill is traceable to the initial conditions of other climate modes by means of their memory and interactions with ENSO and is quantifiable in terms of these modes’ contributions to ENSO amplitude. Reforecasts using the XRO trained on climate model output show that reduced biases in both model ENSO dynamics and in climate mode interactions can lead to more skilful ENSO forecasts. The XRO framework’s holistic treatment of ENSO’s global multi-timescale interactions highlights promising targets for improving ENSO simulations and forecasts.

Satellite retrievals of carbon monoxide (CO) are routinely assimilated in atmospheric chemistry models to improve air quality forecasts, produce reanalyzes and to estimate emissions. This study applies the quantile‐conserving ensemble filter framework, a novel assimilation algorithm that can deal with non‐Gaussian and modestly nonlinear distributions. Instead of assuming normal distributions like the Ensemble Adjustments Kalman Filter (EAKF), we now apply a bounded normal rank histogram (BNRH) distribution for the prior. The goal is to efficiently estimate bounded quantities such as CO atmospheric mixing ratios and emission fluxes while maintaining the good performance achieved by the EAKF. We contrast assimilating meteorological and MOPITT (Measurement of Pollution in the Troposphere) observations for May 2018. We evaluate the results with the fourth deployment of the NASA Atmospheric Tomography Mission (ATom‐4) airborne field campaign. We also compare simulations with CO tropospheric columns from the network for the detection of atmospheric composition change and surface in‐situ observations from NOAA carbon cycle greenhouse gases. While the differences remain small, the BNRH approach clearly works better than the EAKF in comparison to all observation data sets.

Advances in tagging technologies are expanding opportunities to estimate survival of fish and wildlife populations. Yet, capture and handling effects could impact survival outcomes and bias inference about natural mortality processes. We developed a multistage time-to-event model that can partition the survival process into sequential phases that reflect the tagged animal experience, including handling and release mortality, post-release recovery mortality, and subsequently, natural mortality. We demonstrate performance of multistage survival models through simulation testing and through fish and bird telemetry case studies. Models are implemented in a Bayesian framework and can accommodate left, right, and interval censorship events. Our results indicate that accurate survival estimates can be achieved with reasonable sample sizes (n≈100+)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n\approx 100+)$$\end{document} and that multimodel inference can inform hypotheses about the configuration and length of survival stages needed to adequately describe mortality processes for tracked specimens. While we focus on survival estimation for tagged fish and wildlife populations, multistage time-to-event models could be used to understand other phenomena of interest such as migration, reproduction, or disease events across a range of taxa including plants and insects.

The vertical accuracy of elevation data in coastal environments is critical because small variations in elevation can affect an area's exposure to waves, tides, and storm-related flooding. Elevation data contractors typically quantify the vertical accuracy of lidar-derived digital elevation models (DEMs) on a per-project basis to gauge whether the datasets meet quality and accuracy standards. Here, we collated over 5200 contractor elevation checkpoints along the Atlantic and Gulf of Mexico coasts of the United States that were collected for project-level analyses produced for assessing DEMs acquired for the U.S. Geological Survey's Three-Dimensional Elevation Program. We used land cover data to quantify non-vegetated vertical accuracy and vegetated vertical accuracy statistics (overall and by point spacing bins) and assessed elevation error by land cover class. We found the non-vegetated vertical accuracy had an overall root mean square error of 6.9 cm and vegetated areas had a 95th percentile vertical error of 22.3 cm. Point spacing was generally positively correlated to elevation accuracy, but sample size limited the ability to interpret results from accuracy by land cover, particularly in wetlands. Based on the specific questions a researcher may be asking, use of literature or fieldwork could assist with enhancing error statistics in underrepresented classes.

The temperature range of Earth's open‐ocean waters is roughly 0–30°C, yet our understanding of the seawater carbon dioxide (CO2) system is largely derived from analyses conducted within a narrow temperature range (e.g., laboratory temperature of 20°C or 25°C). Herein, we address two aspects of open‐ocean CO2‐system measurements and modeling: (1) a highly precise spectrophotometric technique is used to determine bicarbonate dissociation constants (K2) in seawater at temperatures as low as 3°C and (2) a cruise dataset uniquely including total scale pH measurements at two temperatures is used for CO2‐system internal consistency comparisons at 12°C and 25°C. Our pK2 parameterization (where pK = −log K) is applicable for broad ranges of salinity (20 ≤ SP ≤ 40) and temperature (3°C ≤ t ≤ 35°C). Our CO2‐system internal consistency evaluation (comparison of measured and calculated CO2‐system parameters) utilized data obtained during NOAA's 2021 West Coast Ocean Acidification Cruise: total alkalinity (TA), total dissolved inorganic carbon (DIC), pH measured at 25°C, and pH measured at 12°C (n = 265). Results demonstrate that, relative to calculations utilizing the TA, DIC pair, agreement between measured and calculated parameters is improved when either TA or DIC is paired with pH measurements at either temperature. Calculations of CO2 fugacity (fCO2) and aragonite saturation state (Ωar) using pH measurements made at 25°C or 12°C (paired with either TA or DIC) are statistically indistinguishable. Results also suggest that the temperature dependence of current CO2‐system dissociation constants need further refinement.

Aerosol‐cloud‐precipitation interactions are a leading source of uncertainty in estimating climate sensitivity. Remote marine boundary layers where accumulation mode (∼100–400 nm diameter) aerosol concentrations are relatively low are very susceptible to aerosol changes. These regions also experience heightened Aitken mode aerosol (∼10–100 nm) concentrations associated with ocean biology. Aitken aerosols may significantly influence cloud properties and evolution by replenishing cloud condensation nuclei and droplet number lost through precipitation (i.e., Aitken buffering). We use a large‐eddy simulation with an Aitken‐mode enabled microphysics scheme to examine the role of Aitken buffering in a mid‐latitude decoupled boundary layer cloud regime observed on 15 July 2017 during the Aerosol and Cloud Experiments in the Eastern North Atlantic flight campaign: cumulus rising into stratocumulus under elevated Aitken concentrations (∼100–200 mg⁻¹). In situ measurements are used to constrain and evaluate this case study. Our simulation accurately captures observed aerosol‐cloud‐precipitation interactions and reveals time‐evolving processes driving regime development and evolution. Aitken activation into the accumulation mode in the cumulus layer provides a reservoir for turbulence and convection to carry accumulation aerosols into the drizzling stratocumulus layer above. Further Aitken activation occurs aloft in the stratocumulus layer. Together, these activation events buffer this cloud regime against precipitation removal, reducing cloud break‐up and associated increases in heterogeneity. We examine cloud evolution sensitivity to initial aerosol conditions. With halved accumulation number, Aitken aerosols restore accumulation concentrations, maintain droplet number similar to original values, and prevent cloud break‐up. Without Aitken aerosols, precipitation‐driven cloud break‐up occurs rapidly. In this regime, Aitken buffering sustains brighter, more homogeneous clouds for longer.

In the past decade, two large marine heatwaves (MHWs) formed in the northeast Pacific near Ocean Station Papa (OSP), one of the oldest oceanic time series stations. Physical, biogeochemical, and biological parameters observed at OSP from 2013 to 2020 are used to assess ocean response and potential impacts on marine life from the 2019 northeast Pacific MHW. The 2019 MHW reached peak surface and subsurface temperature anomalies in the summertime and had both coastal, impacting fisheries, and offshore consequences that could potentially affect multiple trophic levels in the Gulf of Alaska. In the Gulf of Alaska, the 2019 MHW was preceded by calm and stratified upper ocean conditions, which preconditioned the enhanced surface warming in late spring and early summer. The MHW coincided with lower dissolved inorganic carbon and higher pH of surface waters relative to the 2013–2020 period. A spike in the summertime chlorophyll followed by a decrease in surface macronutrients suggests increased productivity in the well‐lit stratified upper ocean during summer 2019. More blue whale calls were recorded at OSP in 2019 compared to the prior year. This study shows how the utility of long‐term, continuous oceanographic data sets and analysis with an interdisciplinary lens is necessary to understand the potential impact of MHWs on marine ecosystems.

Aerosol optical depth (AOD) is a vital parameter in atmospheric research. Using observations of the Visible Infrared Imaging Radiometer Suite (VIIRS), onboard Suomi National Polar‐orbiting Partnership (Suomi‐NPP) and NOAA‐20 satellites, National Oceanic and Atmospheric Administration (NOAA) produces near‐real time AOD product with high pixel resolution (750 m), wide swath width (3,040 km), and a 16‐day repeat cycle. Here we report the evaluation of the NOAA/VIIRS AOD using a comprehensive aerosol data set, derived from a global‐scale, multi‐seasonal airborne mission, the NASA Atmospheric Tomography Mission (ATom). This data set includes rich physical and chemical information, such as size distributions, chemical compositions, optical properties, and hygroscopicities of major aerosol types, including dust, sea salt, smoke, internally mixed sulfate/nitrate/organics particles (non‐smoke), black carbon, etc. Globally, VIIRS AOD (Suomi‐NPP and NOAA‐20) shows good agreement with the ATom AOD in the moderate to high AOD range (>0.3), with respect to measurement uncertainties (orthogonal distance regression fitting slope: 1.5 ± 0.2 for Suomi‐NPP and 1.6 ± 0.5 for NOAA‐20; correlation coefficient: 0.85 for Suomi‐NPP and 0.73 for NOAA‐20). There is a persistent bias in the low AOD range (<0.3) on the order of 0.03, likely reflecting systematic errors on VIIRS and/or the ATom AOD product. Ångström exponent reported by VIIRS shows excellent agreement with ATom results within expected uncertainties. Given the unique insights revealed by the ATom AOD and aerosol property data set, it is desirable to have ATom‐like comprehensive payloads in future airborne satellite validation programs.

Objective This study examines the productivity and technical efficiency (TE) of diving operations that target queen conches Aliger gigas in the Commonwealth of Puerto Rico, the largest producer of queen conches in the United States. Currently, there is a proposal to list queen conch as threatened under the Endangered Species Act (ESA). Methods We use stochastic production frontier methods to investigate the relationship between catch and fishing inputs and the technical performance of diving operations. Result Our results show that the fleet could increase its catches, on average, by 30% (and, thus, increase its income) by using existing fishing inputs and technology more efficiently. We find that the potential to expand catches was slightly higher from increasing the crew size than from extending the length of the fishing trip. The study also finds considerable heterogeneity across coastal regions and operation sizes. Overall, operations on the east and west coasts and those having three or more crew members were more efficient. Operations that use a single gear and specialize on few species (revenue concentration) were associated with higher levels of TE. We also find that diving operations exhibit decreasing returns to scale. Conclusion The potential ESA listing of the queen conch poses a dilemma because increasing the efficiency of the fleet may continue to compromise the sustainability of the resource. While a threatened designation does not necessarily result in additional trade or harvest restrictions, further actions may be advisable given the many threats, such as overutilization, habitat loss, coastal pollution, and disruptive environmental change that queen conch populations face. Our model suggests that reducing the size of the crew and/or the length of the trip may increase efficiency, but these restrictions may not be advisable on safety grounds. Thus, management agencies may want to reassess existing trip limits and the length of the closed season and explore the use of closed areas.

The recent rise of ‘omics and other molecular research technologies alongside improved techniques for tissue preservation have broadened the scope of marine mammal research. Collecting biological samples from wild marine mammals is both logistically challenging and expensive. To enhance the power of marine mammal research, great effort has been made in both the field and the laboratory to ensure the scientific integrity of samples from collection through processing, supporting the long‐term use of precious samples across a broad range of studies. However, identifying the best methods of sample preservation can be challenging, especially as this technological toolkit continues to evolve and expand. Standardizing best practices could maximize the scientific value of biological samples, foster multi‐institutional collaborative efforts across fields, and improve the quality of individual studies by removing potential sources of error from the collection, handling, and preservation processes. With these aims in mind, we summarize relevant literature, share current expert knowledge, and suggest best practices for sample collection and preservation. This manuscript is intended as a reference resource for scientists interested in exploring collaborative studies and preserving samples in a suitable manner for a broad spectrum of analyses, emphasizing support for ‘omics technologies.

The Antarctic Peninsula marine ecosystem is highly productive, with large populations of commercially and ecologically important species including Antarctic krill Euphausia superba , Adélie penguins Pygoscelis adeliae , and crabeater seals Lobodon carcinophagus . The ecology of the peninsula is rapidly changing due to accelerating climate change and fishing pressure. Systematic ecosystem surveys have focused on austral spring and summer, leaving an information gap on winter ecosystem dynamics. Using data from 5 consecutive ecosystem surveys, we quantified the composition and distribution of winter predator communities and investigated the physical and biological influences on community structure. Seabirds and marine mammals clustered into 3 communities: an ice-associated community represented by Adélie penguins and crabeater seals; a diverse marginal ice zone community dominated by fur seals and several species of seabirds including 3 petrels, kelp gulls Larus dominicanus , and Antarctic terns Sterna vittata ; and an open water community consisting of southern fulmars Fulmarus glacialoides and 4 species of petrels. These communities were distributed along an environmental gradient ranging from ice-covered, cold, saline water to ice-free, warmer, and fresher water with greater chlorophyll concentrations. Predator communities were also associated with different communities of macrozooplankton: ice-associated predators with an extremely diverse assemblage of typically mesopelagic zooplankton; marginal ice zone predators with a community of large-bodied euphausiids ( E. superba, E. crystallorophias ); and open water predators with a community of small-bodied euphausiids ( Thysanoessa macrura ). Our synthesis of integrated winter predator and macrozooplankton communities relative to sea-ice concentration provides reference points for future ecosystem assessments within this rapidly changing region.

Ocean warming due to climate change can affect the metabolism, performance, and survival of ectothermic marine species. On the US Northeast continental shelf (US NES), waters are warming faster than the global average, leading to elevated mean temperatures and an increased risk of marine heatwave exposure in the region. Thus, it is critical to understand the effects of warming on the region’s living marine resources. Here, we quantified the acute temperature sensitivity of metabolic traits to evaluate their role as possible drivers of acute thermal tolerance and viable habitat in the spiny dogfish shark Squalus acanthias on the US NES. From 10-23°C, the standard metabolic rate increased more rapidly than the maximum metabolic rate, resulting in a reduction in factorial aerobic scope at warmer temperatures. However, the oxygen supply capacity increased with temperature in proportion to maximum metabolic rate, and neither metric declined at the warmest temperatures, suggesting oxygen supply capacity does not limit performance within the tested range. Although behavioral observations revealed overt thermal stress via loss of equilibrium at ≥20°C and estimated lethal temperature at ∼24°C, sharks retained the ability to regulate their resting metabolic rate, achieve maximum activity, and peak absolute aerobic scope at warm temperatures. Results suggest that factors other than oxygen supply or aerobic scope are constraining thermal tolerance in S. acanthias and support the notion that aerobic scope cannot be universally applied to determine optimal or viable metabolic habitat.

Multiple studies in a range of taxa have found links between structural variants and the development of ecologically important traits. Such variants are becoming easier to find due, in large part, to the increase in the amount of genome-wide sequence data in nonmodel organisms. The salmonids (salmon, trout, and charr) are a taxonomic group with abundant genome-wide datasets due to their importance in aquaculture, fisheries, and variation in multiple ecologically important life-history traits. Previous research on rainbow trout (Oncorhynchus mykiss) has documented a large pericentric (∼55 Mb) chromosomal inversion (CI) on chromosome 5 (Omy05) and a second smaller (∼14 Mb) chromosome inversion on Omy20. While the Omy05 inversion appears to be associated with multiple adaptive traits, the inversion on Omy20 has received far less attention. In this study, we re-analyze RAD-seq and amplicon data from several populations of rainbow trout (O. mykiss) to better document the structure and geographic distribution of variation in the Omy20 CI. Moreover, we utilize phylogenomic techniques to characterize both the age- and the protein-coding gene content of the Omy20 CI. We find that the age of the Omy20 inversion dates to the early stages of O. mykiss speciation and predates the Omy05 inversion by ∼450,000 years. The 2 CIs differ further in terms of the frequency of the homokaryotypes. While both forms of the Omy05 CI are found across the eastern Pacific, the ancestral version of the Omy20 CI is restricted to the southern portion of the species range in California. Furthermore, the Omy20 inverted haplotype is comparable in genetic diversity to the ancestral form, whereas derived CIs typically show substantially reduced genetic diversity. These data contribute to our understanding of the age and distribution of a large CI in rainbow trout and provide a framework for researchers looking to document CIs in other nonmodel species.