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Worldwide, warming ocean temperatures have contributed to extreme harmful algal bloom events and shifts in phytoplankton species composition. In 2016 in the Gulf of Maine (GOM), an unprecedented Pseudo-nitzschia bloom led to the first domoic-acid induced shellfishery closures in the region. Potential links between climate change, warming temperatures, and the GOM Pseudo-nitzschia assemblage, however, remain unexplored. In this study, a global climate change projection previously downscaled to 7-km resolution for the Northwest Atlantic was further refined with a 1–3-km resolution simulation of the GOM to investigate the effects of climate change on HAB dynamics. A 25-year time slice of projected conditions at the end of the 21(st) century (2073–2097) was compared to a 25-year hindcast of contemporary ocean conditions (1994–2018) and analyzed for changes to GOM inflows, transport, and Pseudo-nitzschia australis growth potential. On average, climate change is predicted to lead to increased temperatures, decreased salinity, and increased stratification in the GOM, with the largest changes occurring in the late summer. Inflows from the Scotian Shelf are projected to increase, and alongshore transport in the Eastern Maine Coastal Current is projected to intensify. Increasing ocean temperatures will likely make P. australis growth conditions less favorable in the southern and western GOM but improve P. australis growth conditions in the eastern GOM, including a later growing season in the fall, and a longer growing season in the spring. Combined, these changes suggest that P. australis blooms in the eastern GOM could intensify in the 21(st) century, and that the overall Pseudo-nitzschia species assemblage might shift to warmer-adapted species such as P. plurisecta or other Pseudo-nitzschia species that may be introduced.

As a key biogeochemical pathway in the marine nitrogen cycle, nitrification (ammonia oxidation and nitrite oxidation) converts the most reduced form of nitrogen – ammonium/ammonia (NH4+/ NH3) into the oxidized species nitrite (NO2−) and nitrate (NO3−). In the ocean, these processes are mainly performed by ammonia-oxidizing archaea (AOA) and bacteria (AOB), and nitrite-oxidizing bacteria (NOB). By transforming nitrogen speciation and providing substrates for nitrogen removal, nitrification affects microbial community structure, marine productivity (including chemoautotrophic carbon fixation) and the production of a powerful greenhouse gas, nitrous oxide (N2O). Nitrification is hypothesized to be regulated by temperature, oxygen, light, substrate concentration, substrate flux, pH, and other environmental factors. Although the number of field observations from various oceanic regions has increased considerably over the last few decades, a global synthesis is lacking, and understanding how environmental factors control nitrification remains elusive. Therefore, we have compiled a database of nitrification rates and nitrifier abundance in the global ocean from published literature and unpublished datasets. This database includes 2393 and 1006 measurements of ammonia oxidation and nitrite oxidation rates, and 2187 and 631 quantifications of ammonia oxidizers and nitrite oxidizers, respectively. This community effort confirms and enhances our understanding of the spatial distribution of nitrification and nitrifiers, and their corresponding drivers such as the important role of substrate concentration in controlling nitrification rates and nitrifier abundance. Some conundrums are also revealed including the inconsistent observations of light limitation and high rates of nitrite oxidation reported from anoxic waters. This database can be used to constrain the distribution of marine nitrification, to evaluate and improve biogeochemical models of nitrification, and to quantify the impact of nitrification on ecosystem functions like marine productivity and N2O production. This database additionally sets a baseline for comparison with future observations and guides future exploration (e.g., measurements in the poorly sampled regions such as the Indian Ocean; method comparison/standardization). The database is publicly available at Zenodo repository: https://doi.org/10.5281/zenodo.7942922 (Tang et al., 2023).

The ability to anticipate marine habitat responses to climate variability has high socioeconomic value. Here we quantify interannual-to-decadal predictability of habitat shifts by combining trait-based aerobic habitat constraints with a suite of initialized retrospective Earth System Model forecasts, for diverse marine species in the North American Large Marine Ecosystems. We find that aerobic habitat viability, defined by joint constraints of temperature and oxygen on organismal energy balance, is potentially predictable in the upper-600 m of the ocean, showing a substantial improvement over a simple persistence forecast. The skillful multiyear predictability is dominated in most regions by the oxygen component, yielding habitat predictions with longer lead time than previously estimated based on temperature alone. Notable predictability differences exist among species differing in temperature sensitivity of hypoxia vulnerability, especially along the northeast coast. This tool will be critical in predicting marine habitat shifts in face of a changing climate.

This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, www.isimip.org) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

Fish community biomass is generally thought to decline with increasing temperature due to higher metabolic losses resulting in less efficient energy transfer in warm-water food webs. However, whether these metabolic predictions explain observed macroecological patterns in fish community biomass is virtually unknown. Here we test these predictions by examining the variation in demersal fish biomass across 21 productive shelf regions using high-resolution monitoring data from the North Atlantic and Northeast Pacific. We find that biomass per km2 varies 40-fold across regions and is highest in cold waters and areas with low fishing exploitation. We find no evidence that temperature change has impacted biomass within marine regions over time. Yet, the cross-regional patterns suggest that long-term impacts of warming will be negative on biomass. These results provide an empirical basis for predicting future changes in fish community biomass and its associated services for human wellbeing i.e., food provisioning, under global warming.

Ocean alkalinity is critical to the uptake of atmospheric carbon in surface waters and provides buffering capacity towards associated acidification. However, unlike dissolved inorganic carbon (DIC), alkalinity is not directly impacted by anthropogenic carbon emissions. Within the context of projections of future ocean carbon uptake and potential ecosystem impacts, especially through Coupled Model Intercomparison Projects (CMIPs), the representation of alkalinity and the main driver of its distribution in the ocean interior, the calcium carbonate cycle, have often been overlooked. Here we track the changes from CMIP5 to CMIP6 with respect to the Earth system model (ESM) representation of alkalinity and the carbonate pump which depletes the surface ocean in alkalinity through biological production of calcium carbonate, and releases it at depth through export and dissolution. We report a significant improvement in the representation of alkalinity in CMIP6 ESMs relative to those in CMIP5. This improvement can be explained in part by an increase in calcium carbonate (CaCO3) production for some ESMs, which redistributes alkalinity at the surface and strengthens its vertical gradient in the water column. We were able to constrain a PIC export estimate of 51–70 Tmol yr-1 at 100 m for the ESMs to match the observed vertical gradient of alkalinity. Biases in the vertical profile of DIC have also significantly decreased, especially with the enhancement of the carbonate pump, but the representation of the saturation horizons has slightly worsened in contrast. Reviewing the representation of the CaCO3 cycle across CMIP5/6, we find a substantial range of parameterizations. While all biogeochemical models currently represent pelagic calcification, they do so implicitly, and they do not represent benthic calcification. In addition, most models simulate marine calcite but not aragonite. In CMIP6 certain model groups have increased the complexity of simulated CaCO3 production, sinking, dissolution and sedimentation. However, this is insufficient to explain the overall improvement in the alkalinity representation, which is therefore likely a result of improved marine biogeochemistry model tuning or ad hoc parameterizations. We find differences in the way ocean alkalinity is initialized that lead to offsets of up to 1 % in the global alkalinity inventory of certain models. These initialization biases should be addressed in future CMIPs by adopting accurate unit conversions. Although modelers aim to balance the global alkalinity budget in ESMs in order to limit drift in ocean carbon uptake under preindustrial conditions, varying assumptions in the closure of the budget have the potential to influence projections of future carbon uptake. For instance, in many models, carbonate production, dissolution and burial are independent of the seawater saturation state, and when considered, the range of sensitivities is substantial. As such, the future impact of ocean acidification on the carbonate pump, and in turn ocean carbon uptake, is potentially underestimated in current ESMs and insufficiently constrained.

We test whether skillful 35-day probabilistic forecasts of estuarine sea surface temperature (SST) are possible and whether these forecasts could potentially be used to reduce the economic damages associated with extreme SST events. Using an ensemble of 35-day retrospective forecasts of atmospheric temperature and a simple model that predicts daily mean SST from past SST and forecast atmospheric temperature, we create an equivalent ensemble of retrospective SST forecasts. We compare these SST forecasts with reference forecasts of climatology and damped persistence and find that the SST forecasts are skillful for up to two weeks in the summer. Then, we post-process the forecasts using nonhomogeneous Gaussian regression and assess whether the resulting calibrated probabilistic forecasts are more accurate than the probability implied by the raw model ensemble. Finally, we use an idealized framework to assess whether these probabilistic forecasts can valuably inform decisions to take protective action to mitigate the effects of extreme temperatures and heatwaves. We find that the probabilistic forecasts provide value relative to a naive climatological forecast for 1-2 weeks of lead time, and the value is particularly high in cases where the cost of protection is small relative to the preventable losses suffered when a heatwave occurs. In most cases, the calibrated probabilistic forecasts are also more valuable than deterministic forecasts based on the ensemble mean and naive probabilistic forecasts based on damped persistence. Probabilistic SST forecasts could provide substantial value if applied to adaptively manage the rapid impacts of extreme SSTs, including managing the risks of catch-and-release mortality in fish and Vibrio bacteria in oysters.

Climate-related shifts in marine mammal range and distribution have been observed in some populations; however, the nature and magnitude of future responses are uncertain in novel environments projected under climate change. This poses a challenge for agencies charged with management and conservation of these species. Specialized diets, restricted ranges, or reliance on specific substrates or sites (e.g., for pupping) make many marine mammal populations particularly vulnerable to climate change. High-latitude, predominantly ice-obligate, species have experienced some of the largest changes in habitat and distribution and these are expected to continue. Efforts to predict and project marine mammal distributions to date have emphasized data-driven statistical habitat models. These have proven successful for short time-scale (e.g., seasonal) management activities, but confidence that such relationships will hold for multi-decade projections and novel environments is limited. Recent advances in mechanistic modeling of marine mammals (i.e., models that rely on robust physiological and ecological principles expected to hold under climate change) may address this limitation. The success of such approaches rests on continued advances in marine mammal ecology, behavior, and physiology together with improved regional climate projections. The broad scope of this challenge suggests initial priorities be placed on vulnerable species or populations (those already experiencing declines or projected to undergo ecological shifts resulting from climate changes that are consistent across climate projections) and species or populations for which ample data already exist (with the hope that these may inform climate change sensitivities in less well observed species or populations elsewhere). The sustained monitoring networks, novel observations, and modeling advances required to more confidently project marine mammal distributions in a changing climate will ultimately benefit management decisions across time-scales, further promoting the resilience of marine mammal populations.

We use a novel, high-resolution global climate model (GFDL-ESM2.6) to investigate the influence of warm and cold El Niño/Southern Oscillation (ENSO) events on the physics and biogeochemistry of the California Current System (CalCS). We focus on the effect of ENSO on variations in the O2 concentration and the pH of the coastal waters of the CalCS. An assessment of the CalCS response to six El Niño and seven La Niña events in ESM2.6 reveals significant variations in the response between events. However, these variations overlay a consistent physical and biogeochemical (O2 and pH) response in the composite mean. Focusing on the mean response, our results demonstrate that O2 and pH are affected rather differently in the euphotic zone above ~100 m. The strongest O2 response reaches up to several 100 km offshore, whereas the pH signal occurs only within a ~100 km-wide band along the coast. By splitting the changes in O2 and pH into individual physical and biogeochemical components that are affected by ENSO variability, we found that O2 variability in the surface ocean is primarily driven by changes in surface temperature that affect the O2 solubility. In contrast, surface pH changes are predominantly driven by changes in dissolved inorganic carbon (DIC), which in turn is affected by upwelling, explaining the confined nature of the pH signal close to the coast. Below ~100 m, we find conditions with anomalously low O2 and pH, and by extension also anomalously low aragonite saturation, during La Niña. This result is consistent with findings from previous studies and highlights the stress that the CalCS ecosystem could periodically undergo in addition to impacts due to climate change.