National Oceanic and Atmospheric Administration

Downscaled precipitation projections were created using the Intermediate Complexity Atmospheric Research (ICAR) model over the western United States to increase the physical realism in orographic precipitation changes. End-of-century simulations from eight models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) were downscaled with ICAR and compared to the widely utilized statistically downscaled dataset, localized constructed analogs (LOCAs), to understand where and why projections of cool-season (September–May) precipitation differed. ICAR and LOCA precipitation projections were similar, but their sign differed in hydrologically relevant regions likely due to ICAR’s simulation of microphysics and mesoscale dynamics with high-resolution topography (6 km). In the Pacific Northwest, cool-season precipitation projections from ICAR showed an increase on the windward side of the Cascades and no significant change within the lee. This difference between the windward and leeward side was attributed to reduced zonal wind speeds, allowing more time for microphysical processes within ICAR. This contrast is enhanced by rain’s faster fall speed compared to snow, limiting transport into the lee. Meanwhile, LOCA projected an increase in precipitation across the Cascades. In the Upper Colorado River basin, LOCA projected an increase in precipitation in high elevation regions (>3000 m), but ICAR projected no significant change or a decrease in precipitation. High elevation differences were most evident in the spring and fall and were also attributed to a snow-to-rain transition and dynamical processes that impacted orographic enhancement within ICAR. Idealized, controlled studies are needed to better isolate individual processes, but these results underscore the importance of including microphysics and mesoscale dynamics within regional-scale precipitation projections. Significance Statement A set of global climate model simulations was downscaled using an atmospheric model that contains key physical equations, referred to as Intermediate Complexity Atmospheric Research (ICAR). ICAR was used to examine projected changes in end-of-century cool-season precipitation over mountains in the western United States. Precipitation projections from ICAR were similar to projections that used statistical relationships to downscale climate projections. However, projections differed between ICAR and statistically downscaled datasets in whether they increased, decreased, or stayed the same in specific, hydrologically relevant regions such as the eastern Cascades and high elevation areas of the Upper Colorado River basin. These differences were attributed to the simulation of physical processes in ICAR. The results highlight the importance of kilometer-scale atmospheric processes in regional climate projections.

Genetic methods have become an essential component of ecological investigation and conservation planning for fish and wildlife. Among these methods is the use of genetic marker data to identify individuals to populations, or stocks, of origin. More recently, methods that involve genetic pedigree reconstruction to identify relationships between individuals within populations have also become common. We present here a novel set of multiallelic microhaplotype genetic markers for Chinook salmon, which provide excellent resolution for population discrimination and relationship identification from a rapidly and economically assayed panel of markers. We show how this set of genetic markers assayed by sequencing 204 amplicons, in tandem with a reference dataset of 1636 individual samples from 17 populations, provides definitive power to identify all known lineages of Chinook salmon in California. The inclusion of genetic loci that have known associations with phenotype and that were identified as outliers in examination of whole‐genome sequence data allows resolution of stocks that are not highly genetically differentiated but are phenotypically distinct and managed as such. This same set of multiallelic genetic markers has ample variation to accurately identify parent‐offspring and full‐sibling pairs in all California populations, including the genetically depauperate winter‐run lineage. Validation of this marker panel in coastal salmon populations not previously studied with modern genetic methods also reveals novel biological insights, including the presence of a single copy of a haplotype for a phenotype that has not been documented in that part of the species range, and a clear signal of mixed ancestry for a salmon population that is on the geographic margins of the primary evolutionary lineages present in California.

Ecosystem‐based fisheries management strives to account for species interactions and ecosystem processes in natural resource management and conservation. In this context, ecosystem‐wide caps on total fishery catches have been proposed as one tool to manage multispecies fisheries with an ecosystem approach. However, determining effective ecosystem caps is complicated because fish stock production is influenced by environmental conditions, species interactions, and fishing. Consequently, the implementation of ecosystem caps in fisheries management frameworks remains uncommon. We investigated whether ecosystem caps should account for climate variability and for predator–prey dynamics to achieve management objectives in complex marine ecosystems. We considered the example of the Gulf of Alaska (United States), a North Pacific large marine ecosystem where annual groundfish catches are managed using an “optimum yield” ecosystem cap of 800,000 t. We simulated multispecies yield of the 12 most abundant and commercially valuable groundfish stocks under selected climate and fishing scenarios using an end‐to‐end marine ecosystem model (Atlantis), which accounts for predator–prey and ecosystem dynamics. We found that total groundfish yield was never projected to exceed the 800,000 mt optimum yield cap across scenarios and fishing mortalities. Projected climate change led to decreased groundfish yield, and predation from the underexploited groundfish predator arrowtooth flounder (Atheresthes stomias) led to foregone catches. Groundfish removals had negative indirect effects on groundfish predators, despite total yield never exceeding the optimum yield cap, highlighting that an ineffective cap may not protect non‐target species. These results suggest that the optimum yield cap currently used in the Gulf of Alaska may be too high to constrain groundfish catches under future climate change and low exploitation rates of predators. We propose that ecosystem caps should be reviewed when environmental conditions, stock productivity, or species interactions change.

In 2019, a red macroalgal species, Chondria tumulosa , was discovered overgrowing native coral and algal species and changing the benthic communities of Manawai (Pearl and Hermes Atoll) in Papahānaumokuākea Marine National Monument (PMNM). The main objective of this study was to assess the spatial distribution of C. tumulosa across the forereef and backreef of Manawai using satellite remote sensing. WorldView‐2 and ‐3 commercial high‐resolution satellite images were obtained for a 12‐year period from 2010 to 2021, from which, time‐series animations were created. Previous studies reported that C. tumulosa appeared as distinctively dark features in satellite imagery with the first evidence of C. tumulosa in 2015. Thus, the animations were visually inspected to identify dark patches that became visible around the time of discovery and persisted in subsequent years. Field survey data of C. tumulosa cover collected in 2019–2021 were used to gain confidence in the identification of dark patches. Using those dark features as a reference and a support vector machine, the latest high‐resolution satellite images from the 12‐year period were classified into a map of distinctive dark patches suspected to be C. tumulosa with an average overall accuracy of 78%. Accuracy assessments of the classification results of C. tumulosa based on field survey data collected in 2019–2021 resulted in an overall accuracy of 79%. This study leverages the use of remote sensing to map a newly discovered alga in a remote area in the hopes of providing managers with a methodology to further monitor the species for long‐term management.

Large‐scale biodiversity assessments and conservation applications require integrated and up‐to‐date datasets across regions. In the oceans, monitoring is fragmented, which affects knowledge exchange and usage. Among existing monitoring programs, scientific bottom‐trawl surveys (SBTS) are long‐term, rich, and well‐maintained data sources at the scale of each sampled region, but these data are under‐utilized in biodiversity applications, especially across regions. This is hampered by the lack of an international community and database maintained through time. To address this, we created FISHGLOB, an infrastructure gathering SBTS and experts. In 5 years, we developed an integrated database of SBTS and a consortium gathering more than 100 experts and users. Here, we are sharing the project history, achievements, challenges, and outlooks. In particular, we reflect on the infrastructure‐building social and technical processes which will guide the development of similar infrastructures. The FISHGLOB project takes ocean monitoring one step forward in working as a unified community across disciplines and regions of the world.

Background Climate change is impacting the distribution and movement of mobile marine organisms globally. Statistical species distribution models are commonly used to explain past patterns and anticipate future shifts. However, purely correlative models can fail under novel environmental conditions, or omit key mechanistic processes driving species habitat use. Methods Here, we used a unique combination of laboratory measurements, field observations, and environmental predictors to investigate spatial variability in energetic seascapes for juvenile North Pacific albacore tuna (Thunnus alalunga). This species undertakes some of the longest migrations of any finfish, but their susceptibility to climate-driven habitat changes is poorly understood. We first built a framework based on Generalized Additive Models to understand mechanisms of energy gain and loss in albacore, and how these are linked to ocean conditions. We then applied the framework to projections from an ensemble of earth system models to quantify changes in thermal and foraging habitats between historical (1971–2000) and future (2071–2100) time periods. Results We show how albacore move seasonally between feeding grounds in the California Current System and the offshore North Pacific, foraging most successfully in spring and summer. The thermal corridors used for migration largely coincide with minimum metabolic costs of movement. Future warming may result in loss of favorable thermal habitat in the sub-tropics and a reduction in total habitat area, but allow increased access to productive and energetically favorable sub-arctic ecosystems. Importantly, while thermal considerations suggest a loss in habitat area, forage considerations suggest that these losses may be offset by more energetically favorable conditions in the habitat that remains. In addition, the energetic favorability of coastal foraging areas may increase in future, with decreasing suitability of offshore foraging grounds. Our results clearly show the importance of moving beyond temperature when considering climate change impacts on marine species and their movement ecology. Conclusions Considering energetic seascapes adds essential mechanistic underpinning to projections of habitat gain and loss, particularly for highly migratory animals. Overall, improved understanding of mechanisms driving migration behavior, physiological constraints, and behavioral plasticity is required to better anticipate how climate change will impact pelagic marine ecosystems.

We present a new experimental facility to investigate the nucleation and growth of liquid droplets and ice particles under controlled conditions and characterize processes relevant to cloud microphysics: the rapid expansion aerosol chamber (REACh). REACh is an intermediate size chamber (∼0.14 m³) combining the principle of an expansion chamber with the ability to probe the influence of turbulent flows. Water droplet heterogeneous nucleation onto seeding aerosols is achieved via a sudden pressure drop accompanied by a temperature drop, which can cause humid air to condense into a cloud of droplets under appropriate thermodynamic conditions. REACh features tight control and monitoring of the initial saturation ratio of water vapor, identity and concentration of seeding aerosol particles, temperature, pressure, and air flow mixing, together with high speed real-time measurements of aerosol and droplet size and number. Here, we demonstrate that the minimum temperature reached during each expansion can be reasonably described by the thermodynamics of dry or moist adiabats for a range of initial relative humidities. The size and number of droplets formed and the overall lifetime of the cloud are characterized as a function of the aerosol concentration and initial water vapor saturation ratio. The total droplet concentration scales linearly with the seeding aerosol concentration, suggesting that all injected aerosol particles serve as condensation nuclei. While the total number of droplets formed increases with aerosol concentration, the mean droplet size decreases with the concentration of seeding aerosols as a result of competition for the available water vapor. Theoretical considerations provide a quantitative prediction for the mean droplet size over a range of conditions. The high repetition rate of experiments that we can perform with the REACh facility will permit extensive characterization of aerosol processes, including droplet and ice nucleation onset and growth, and the importance of turbulence fluctuations. We will leverage the capabilities of this facility to explore a wide range of physical parameters encompassing regimes relevant to cloud microphysics.

The El Niño-Southern Oscillation (ENSO) is a dominant driver of seasonal-interannual climate variability and has been linked to record-setting extremes such as marine heatwaves (MHWs). However, quantifying the effects of ENSO on MHW characteristics remains a challenge due to data limitations. Here, we use an ensemble of tropical Pacific “Pacemaker” simulations with a fully-coupled Earth System Model as a testbed for assessing the skill of four empirical methods aimed at isolating ENSO’s contribution to monthly SST anomalies including MHW extremes. We then apply the most skillful method to the observational record to determine ENSO’s impact on the spatial coverage, intensity and duration of MHWs since 1960 (after removing the background warming trend). We find that the El Niño of 2023-2024 contributed to about half of the global coverage of record-setting MHWs, with the tropical Indian and tropical Atlantic Oceans being most clearly impacted. Our results shed light on the critical role ENSO plays in driving the most severe MHW conditions in the historical record.

Lower tropospheric enhanced gravity wave drag parameterization has been operational in the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) since the late 1990s. The operational version of the scheme, as of January 2024 (GFS version 16), is based on the study by Kim and Arakawa, along with the addition of flow blocking. In contrast, an alternative subgrid orographic parameterization (SOP) algorithm rooted in the Kim and Arakawa scheme has been developed and operationalized in the Korean Integrated Model (KIM). The KIM's SOP scheme (hereafter referred to as the KSOP scheme) was implemented in the GFS model and evaluated by comparing it with the SOP scheme of GFS version 16 (hereafter referred to as the GSOP scheme). In a comparative evaluation of the magnitudes of gravity wave and flow-blocking drag, it is evident that these two components play a significant role in the KSOP scheme, whereas the flow-blocking component is prevalent in the GSOP scheme. The 500 hPa anomaly correlation coefficient (ACC) forecast skill for medium-range forecasts is significantly improved in the Northern Hemisphere during both boreal summer and winter when the KSOP scheme is introduced, while slight degradation is observed in the Southern Hemisphere. The percentage improvements of the 500 hPa ACC scores in the Northern Hemisphere at 120 hours for winter and summer are 140% and 35%, respectively. The precipitation forecasting skills in the CONUS region have also improved. Furthermore, accurate representation of the subgrid orography data is crucial for the success of the SOP scheme.

Methane is an important greenhouse gas¹ and its atmospheric concentration has almost tripled since pre-industrial times2, 3–4. Atmospheric methane mixing ratios vary seasonally, with the seasonal cycle amplitude (SCA) having decreased in northern high latitudes and increased in the subtropics and tropics since the 1980s5,6. These opposing SCA trends can help understanding of long-term changes in the global methane budget, as methane emissions and sinks have opposing effects on the SCA⁵. However, trends in the methane SCA have not yet been explored in detail5,6. Here we use a suite of atmospheric transport model simulations and attribute the observed trends in the seasonal amplitude of methane to changes in emissions and the atmospheric sink from reaction with the hydroxyl radical (OH). We find that the decreasing amplitude in the northern high latitudes is mainly caused by an increase in natural emissions (such as wetlands) owing to a warmer climate, adding evidence to previous studies suggesting a positive climate feedback7, 8–9. In contrast, the enhanced methane amplitude in the subtropics and tropics is mainly attributed to strengthened OH oxidation. Our results provide independent evidence for an increase in tropospheric OH concentration10,11 of 10 ± 1% since 1984, which together with an increasing atmospheric methane concentration suggests a 21 ± 1% increase in the atmospheric methane sink.

Passive Microwave Imagers (PMWIs) aboard meteorological satellites have been instrumental in advancing the understanding of Earth’s atmospheric and surface processes, providing invaluable data for weather forecasting, climate monitoring, and environmental research. This review examines the relevance, applications, and benefits of PMWI data, focusing on their practical use and benefits to society rather than the specific techniques or algorithms involved in data processing. Specifically, it assesses the impact of PMWI data on Tropical Cyclone (TC) intensity and structure, global precipitation and extreme events, flood prediction, the effectiveness of tropical storm and hurricane watches, fire severity and carbon emissions, weather forecasting, and drought mitigation. Additionally, it highlights the importance of PMWIs in hydrometeorological and real-time applications, emphasizing their current usage and potential for improvement. Key recommendations from users include expanding satellite networks for more frequent global coverage, reducing data latency, and enhancing resolution to improve forecasting accuracy. Despite the notable benefits, challenges remain, such as a lack of direct research linking PMWI data to broader societal outcomes, the time-intensive process of correlating PMWI use with measurable societal impacts, and the indirect links between PMWI and improved weather forecasting and disaster management. This study provides insights into the effectiveness and limitations of PMWI data, stressing the importance of continued research and development to maximize their contribution to disaster preparedness, climate resilience, and global weather forecasting.

This study assesses the representation of the observed relationship between Atlantic tropical cyclones (TCs) and the Madden-Julian Oscillation (MJO) across nine models participating in CMIP6-HighResMIP. Most models struggle to faithfully reproduce the observed impacts of the MJO on Atlantic TCs, with the primary issue being the underestimated TC activity over the Atlantic main development region (MDR). The negative biases in genesis frequency within the MDR can be further attributed to weaker-than-observed African easterly wave (AEW) activity south of ∼12°N. Errors in the diabatic heating profile within the Atlantic intertropical convergence zone lead to insufficient potential vorticity production in the lower troposphere and constrain the amplification of AEWs. In addition to the biased TC climatology, the eastward-propagating power of the MJO is consistently underestimated across all models. Nevertheless, in models with a higher eastward-to-westward power ratio, the simulated MJO demonstrates a stronger capacity to modulate sub-seasonal TC activity. Models with relatively realistic eastward propagation of the MJO also exhibit greater variance in tropical intra-seasonal convection. Stronger contrasts in convective heating over the North Atlantic between phases 2-3 and phases 6-7 drive larger fluctuations in MDR shear and AEW activity over the Gulf of Mexico and West Africa, resulting in a more pronounced TC response to the MJO. Overall, our findings suggest that improved MDR TC climatology and MJO propagation are essential for models to accurately capture the observed modulations of Atlantic TCs by the MJO.

Plain Language Summary Over 30 years ago, a group of space scientists set out to coordinate efforts toward the creation of a community‐wide numerical modeling resource. This led to the formation of the Geospace Environment Modeling program, and one of the regular activities of this program is the instigation of “community challenges.” These challenges typically select a particular geospace activity interval or a physical process and then rally the research community to participate in the analysis of this phenomenon. The practice has led to substantial new knowledge of Earth's space environment and significant advancements in numerical modeling capabilities of this region. Here, we describe the history of these community challenges, highlight the lessons learned, and collect the best practices that maximize participation and optimize scientific return.

Solid structures (buildings and topography) act as obstacles and significantly influence the wind flow. Because of their importance, faithfully representing the geometry of structures in numerical predictions is critical to modeling accurate wind fields. A higher‐order geometry representation (the cut‐cell method) is incorporated in the mass‐consistent wind model, Quick Environmental System (QES)‐Winds. To represent the differences between a stair‐step and the cut‐cell method, an urban case study (the Oklahoma City JU2003 experiments) and a complex terrain case (from the MATERHORN campaign) are modeled in QES‐Winds. Comparison between the simulation results with the stair‐step and cut‐cell methods and the measured data for sensors close to walls and buildings showed that the sensitivity of the cut‐cell method to changes in resolution is less than the stair‐step method. Another way to improve the effects of solid geometries on the flow is to correct the velocity gradient near the surface. QES‐Winds solves a conservation of mass equation and not a conservation of momentum equation. This means that QES‐Winds overestimates velocity gradients near the surface which leads to higher rates of scalar transport. The near‐surface parameterization is designed to correct the tangential near‐surface velocity component using the logarithmic assumption. Results, including the near‐wall parameterization, are evaluated with data from the Granite Mountain case (the MATERHORN campaign), which indicates that the parameterization slightly improves the performance of the model for cells near the surface. The new geometry representation and near‐wall parameterization added to a mass‐consistent platform, enhances the model's ability to simulate the effects of solid geometries on wind fields.

Background Compound extreme weather events are severe weather conditions that can jointly magnify human health risks beyond any single event alone. Drought and heatwaves are extreme weather conditions associated with adverse health, but their combined impact is poorly understood. Methods We designed a case–crossover study to estimate heatwave-associated mortality stratified by drought conditions in 183,725 US Veteran patients (2016–2021) with chronic obstructive pulmonary disease (COPD). A conditional logistic regression with distributed lag models was applied. Droughts were categorized into binary and categorical metrics, and we further explored the timing of heatwaves as a risk factor. Results Our results indicate that drought amplifies heatwaves with hotter temperatures and longer durations during drought conditions, and the percentage of mortality attributable to heatwaves during drought was 7.41% (95% confidence interval [CI]: 2.91, 12.28) compared with 2.91% (95% CI: 0.00, 4.76) for heatwaves during nondrought conditions. Heatwaves that occurred during drought conditions in the late warm season had a larger association with mortality compared with late-season heatwaves during nondrought conditions, 7.41% (95% CI: 1.96, 13.04) of mortality events and 0.99% (95% CI: −1.01, 3.85) of mortality events attributable to these exposures, respectively. Conclusion Compound drought and heatwave events trend toward increased mortality risk among patients with COPD and present a growing human health threat under climate change. Existing heat warnings and vulnerability maps may include drought conditions to better capture heat-related public health risks.

Bivalves enhance microhabitat complexity and improve water clarity in coastal ecosystems. Ocean warming (OW) and acidification (OA), pose a significant threat to bivalves in shallow continental shelf environments where stressors can be amplified and uncoupled. This study investigated global change effects on Chama macerophylla, a widespread clam in the Gulf of Mexico. Laboratory experiments assessed physiology and shell mineralogy of C. macerophylla exposed to different levels of OW, OA, and combined stressors (OWA). Temperature and carbonate chemistry from collection sites confirm ambient (control) treatments used in experiments were commonly observed in the field. Clam oxygen consumption increased with OW and, initially, with OA. After 30 days, clams within moderate and extreme OA lowered consumption. In contrast, clam oxygen consumption declined in OWA treatments. Net calcification was only affected by OA with higher calcification in the extreme treatment than in moderate. Meat weight relative to shell weight (condition) was negatively affected by OW in the extreme treatment. Shell accretion, clearance rates, and mineralogy were unaffected by OW, OA, and OWA. This is the first report of a bimineralic shell for this species. Results highlight resilience of clam survivorship to stressors. OW appears to increase metabolism and drive declines in clam condition (meat: shell weight). OWA may have a greater impact on C. macerophylla than single stressors, particularly if reduced oxygen consumption is sustained. This research underscores the need to understand long-term stress on bivalves. Future research should examine both size-age relationships with global stressors and the role of acclimation to prolonged stress.