Explore the vast range of titles available.

The seas of Southeast Asia are home to some of the world’s most diverse ecosystems and resources that support the livelihoods of millions of people. Climate change will bring temperature changes, acidification and other environmental change, with uncertain consequences for human and natural systems, but there has been little regional-scale climate modelling of the marine ecosystem. We present initial dynamically downscaled projections using a biogeochemical model suitable for coastal and shelf seas. A coupled physical-biogeochemical model with a resolution of 0.1° (approximately 11 km) was used to create projections of future environmental conditions under moderate (RCP4.5) and high (RCP8.5) greenhouse gas scenarios. Changes for different parts of the region are presented, including four sensitive coastal sites of key importance for biodiversity and sustainable development: UNESCO Biosphere Reserves at Cu Lao Cham-Hoi An in Vietnam, Palawan in the Philippines and Taka Bonerate-Kepulauan Selayar in Indonesia, and coastal waters of Sabah, Malaysia, which include several marine parks. The projections show a sea that is warming by 1.1 to 2.9°C through the 21st century, with dissolved oxygen decreasing by 5 to 13 mmol m -3 and changes in many other environmental variables. The changes reach all parts of the water column and many places are projected to experience conditions well outside the range seen at the start of the century. The resulting damage to coral reefs and altered species distribution would have consequences for biodiversity, the livelihoods of small-scale fishers and the food security of coastal communities. Further work using a range of global models and regional models with different biogeochemical components is needed to provide confidence levels, and we suggest some ways forward. Projections of this type serve as a key tool for communities and policymakers as they plan how they will adapt to the challenge of climate change.

The Machine learning (ML) revolution is becoming established in oceanographic research, but its applications to emulate marine biogeochemical models are still rare. We pioneer a novel application of machine learning to emulate a highly complex physical-biogeochemical model to predict marine oxygen in the shelf-sea environment. The emulators are developed with intention of supporting future digital twins for two key stakeholder applications: (i) prediction of hypoxia for aquaculture and fisheries, (ii) extrapolation of oxygen from marine observations. We identify the key drivers behind oxygen concentrations and determine the constrains on observational data for a skilled prediction of marine oxygen across the whole water column. Through this we demonstrate that ML models can be very useful in informing observation measurement arrays. We compare the performance of multiple different ML models, discuss the benefits of the used approaches and identify outstanding issues, such as limitations imposed by the spatio-temporal resolution of the training/validation data.

Marine biogeochemical (BGC) models are highly uncertain in their parameterization. The value of the BGC parameters are poorly known and lead to large uncertainties in the model outputs. This study focuses on the uncertainty quantification of model fields and parameters within a one-dimensional (1-D) ocean BGC model applying ensemble data assimilation. We applied an ensemble Kalman filter provided by the Parallel Data Assimilation Framework (PDAF) into a 1-D vertical configuration of the BGC model Regulated Ecosystem Model 2 (REcoM2) at two BGC time-series stations: the Bermuda Atlantic Time-series Study (BATS) and the Dynamique des Flux Atmosphériques en Méditerranée (DYFAMED). We assimilated 5-day satellite chlorophyll-a (chl-a) concentration and monthly in situ net primary production (NPP) data for 3 years to jointly estimate 10 preselected key BGC parameters and the model state. The estimated set of parameters resulted in improvements in the model prediction up to 66% for the surface chl-a and 56% for NPP. Results show that assimilating satellite chl-a concentration data alone degraded the prediction of NPP. Simultaneous assimilation of the satellite chl-a data and in situ NPP data improved both surface chl-a and NPP simulations. We found that correlations between parameters preclude estimating parameters independently. Co-dependencies between parameters also indicate that there is not a unique set of optimal parameters. Incorporation of proper uncertainty estimation in BGC predictions, therefore, requires ensemble simulations with varying parameter values.

The global climate response to solar radiation absorbed by phytoplankton is investigated by performing multi-century simulations with a coupled ocean–atmosphere-biogeochemistry model. The absorption of solar radiation by phytoplankton increases radiative heating in the near-surface ocean and raises sea surface temperature (SST) by overall ~0.5°C. The resulting increase in evaporation enhances specific atmospheric humidity by 2–5%, thereby increasing the Earth’s greenhouse effect and the atmospheric temperatures. The Hadley Cell exhibits a weakening and poleward expansion, therefore reducing cloudiness at subtropical-middle latitudes and increasing it at tropical latitudes except near the Equator. Higher SST at polar latitudes reduces sea ice cover and albedo, thereby increasing the high-latitude ocean absorption of solar radiation. Changes in the atmospheric baroclinicity cause a poleward intensification of mid-latitude westerly winds in both hemispheres. As a result, the North Atlantic Ocean meridional overturning circulation extends more northward, and the equatorward Ekman transport is enhanced in the Southern Ocean. The combination of local and dynamical processes decreases upper-ocean heat content in the Tropics and in the subpolar Southern Ocean, and increases it at middle latitudes. This study highlights the relevance of coupled ocean–atmosphere processes in the global climate response to phytoplankton solar absorption. Given that simulated impacts of phytoplankton on physical climate are within the range of natural climate variability, this study suggests the importance of phytoplankton as an internal constituent of the Earth’s climate and its potential role in participating in its long-term climate adjustments.

The Arabian Gulf (also known as Persian Gulf, hereafter Gulf) is a shallow semi-enclosed subtropical sea known for its extreme physical environment. Recent observations suggest a decline in oxygen concentrations in the Gulf over the past few decades accompanied by an expansion of seasonal near-bottom hypoxia. Here, we reconstruct the evolution of dissolved oxygen in the Gulf from 1982 through 2010 and explore its controlling factors. To this end, we use an eddy-resolving hindcast simulation forced with winds and heat and freshwater fluxes from an atmospheric reanalysis. We show that seasonal near-bottom hypoxia (O 2 < 60 mmol m -3 ) emerges in the deeper part of the Gulf over summer and peaks in autumn in response to enhanced vertical stratification inhibiting mixing and O 2 replenishment at depth. We also find a significant deoxygenation in the Gulf over the study period, with the Gulf O 2 content dropping by nearly 1% per decade and near-bottom O 2 decreasing by between 10 and 30 mmol m -3 in the deeper part of the Gulf between the early 1980s and the late 2000s. These changes result in the horizontal expansion of seasonal bottom hypoxia with the hypoxia-prone seafloor area increasing from less than 20,000 km 2 in the 1980s to around 30,000 km 2 in the 2000s. The expansion of hypoxia is also accompanied by a lengthening of the hypoxic season with hypoxia emerging locally 1 to 2 months earlier in the late 2000s relative to the early 1980s. Furthermore, declining near-bottom O 2 levels result in the expansion of suboxic conditions (O 2 < 4 mmol m -3 ) and the emergence and amplification of denitrification there. An analysis of the Gulf oxygen budget demonstrates that deoxygenation is essentially caused by reduced oxygen solubility near the surface and enhanced respiration near the bottom. While reduced solubility results from the warming of the Gulf waters, enhanced respiration is mostly driven by an increased supply of nutrients imported from the Arabian Sea due to the weakening of winter Shamal winds over the study period. Our findings suggest that recent changes in local climate are not only altering the Gulf physical environment but are also having a strong impact on the Gulf biogeochemistry with profound potential implications for the ecosystems and the fisheries of the region.

This paper investigates the impact of a regional climate change scenario on an ecosystem model of the northern Adriatic Sea. The study was performed by applying a biogeochemical biomass-based ecosystem model (ERSEM: European Regional Seas Ecosystem Model) coupled with a 1-dimensional version of a commonly used hydrodynamical model (POM: Princeton Ocean Model). The model response was studied by performing 2 simulations: one with atmospheric forcing functions for present-day climate conditions ('Control', 1970–1999), and one for the future climate after a doubling of the CO₂ concentration ('Scenario', 2060–2089). Time-series of meteorological forcing functions were obtained from a greenhouse gas experiment with the high-resolution version of the ECHAM4 atmospheric general circulation model with a horizontal resolution of about 100 km. Present-day simulation results were compared to the available historical observations and showed a satisfactory agreement with the main seasonal cycles of hydrodynamical variables and nutrients in the limits of the 1-dimensional representation. Under the Scenario conditions, the model predicted an overall enhancement of the water-column stratification on an annual basis, with stronger intensification during the summer periods. The diffusion of oxygen and nutrients between surface and bottom layers is reduced, and the transfer of organic matter through the food web is shifted towards the smaller components of the microbial web. The model gives indications of an increase in the specific phytoplankton uptake rates of inorganic carbon in the Scenario, but the photosynthesized carbon ends up in the DOC pool because of the reduced supply of nutrients. The mean annual concentration of DOC is thus higher in the Scenario, with possible negative consequences on the water quality. Additional experiments show that the response of primary producers is not directly linked to the increase in ambient temperature. The structure of the food web modulates the interaction of phytoplankton with the abiotic conditions. The results give indications of a possible non-linear response of the biogeochemical cycles to the climate change warming scenario and confirm the feasibility of using the downscaling technique to couple large-scale climate models with comprehensive regional ecosystem models

PISCES-v2 (Pelagic Interactions Scheme for Carbon and Ecosystem Studies volume 2) is a biogeochemical model which simulates the lower trophic levels of marine ecosystems (phytoplankton, microzooplankton and mesozooplankton) and the biogeochemical cycles of carbon and of the main nutrients (P, N, Fe, and Si). The model is intended to be used for both regional and global configurations at high or low spatial resolutions as well as for short-term (seasonal, interannual) and long-term (climate change, paleoceanography) analyses. There are 24 prognostic variables (tracers) including two phytoplankton compartments (diatoms and nanophytoplankton), two zooplankton size classes (microzooplankton and mesozooplankton) and a description of the carbonate chemistry. Formulations in PISCES-v2 are based on a mixed Monod–quota formalism. On the one hand, stoichiometry of C / N / P is fixed and growth rate of phytoplankton is limited by the external availability in N, P and Si. On the other hand, the iron and silicon quotas are variable and the growth rate of phytoplankton is limited by the internal availability in Fe. Various parameterizations can be activated in PISCES-v2, setting, for instance, the complexity of iron chemistry or the description of particulate organic materials. So far, PISCES-v2 has been coupled to the Nucleus for European Modelling of the Ocean (NEMO) and Regional Ocean Modeling System (ROMS) systems. A full description of PISCES-v2 and of its optional functionalities is provided here. The results of a quasi-steady-state simulation are presented and evaluated against diverse observational and satellite-derived data. Finally, some of the new functionalities of PISCES-v2 are tested in a series of sensitivity experiments.

The decline of dissolved oxygen in the oceans could be detrimental to marine life and biogeochemical cycles. However, predicting future oxygen availability with models that mainly focus on temporal and spatial large-scale mean values could lead to incorrect predictions. Marine ecosystems are strongly influenced by short temporal- and small spatial-scale oxygen fluctuations. Large-scale modelling neglects fluctuations, which include the pervasive occurrence of high oxygen supersaturation on a daily time scale in productive ecosystems such as coral reefs, seagrass meadows and mangrove forests and the spatial heterogeneity in oxygen availability at microclimatic scales. In these temporal and spatial micro-environments, oxygen fluctuations control biogeochemical cycles and alter community responses to, for example, heat stress and hypoxia. Robust projections on the impact of predicted ocean and coastal deoxygenation require a better understanding of the dynamics of the dissolved oxygen coupled with scaled-down projections of oxygen fluctuations at small relevant scales for marine biogeochemical processes and communities. Overall, the study of the true oxygen dynamics in marine productive habitats can provide crucial insights into the feedback mechanisms between climate change and marine ecosystems and can help to develop effective management and conservation strategies.The impact of dissolved oxygen fluctuations on marine ecosystems requires consideration of appropriate temporal and spatial scales.

High-quality climate predictions are crucial for understanding the impacts of different greenhouse gas emission scenarios and for mitigating and adapting to the resulting climatic changes. Bonan and Doney review advances in Earth system models that include the terrestrial and marine biosphere. Such models capture interactions between physical and biological aspects of the Earth system. This provides insight into climate impacts of societal importance, such as altered crop yields, wildfire risk, and water availability. Further research is needed to better understand model uncertainties, some of which may be unavoidable, and to better translate observations into abstract model representations. Science , this issue p. eaam8328 Many global change stresses on terrestrial and marine ecosystems affect not only ecosystem services that are essential to humankind, but also the trajectory of future climate by altering energy and mass exchanges with the atmosphere. Earth system models, which simulate terrestrial and marine ecosystems and biogeochemical cycles, offer a common framework for ecological research related to climate processes; analyses of vulnerability, impacts, and adaptation; and climate change mitigation. They provide an opportunity to move beyond physical descriptors of atmospheric and oceanic states to societally relevant quantities such as wildfire risk, habitat loss, water availability, and crop, fishery, and timber yields. To achieve this, the science of climate prediction must be extended to a more multifaceted Earth system prediction that includes the biosphere and its resources.

Plastic pollution is one of the most pressing environmental and social issues of the 21st century. Recent work has highlighted the atmosphere’s role in transporting microplastics to remote locations [S. Allen et al., Nat. Geosci. 12, 339 (2019) and J. Brahney, M. Hallerud, E. Heim, M. Hahnenberger, S. Sukumaran, Science 368, 1257–1260 (2020)]. Here, we use in situ observations of microplastic deposition combined with an atmospheric transport model and optimal estimation techniques to test hypotheses of the most likely sources of atmospheric plastic. Results suggest that atmospheric microplastics in the western United States are primarily derived from secondary reemission sources including roads (84%), the ocean (11%), and agricultural soil dust (5%). Using our best estimate of plastic sources and modeled transport pathways, most continents were net importers of plastics from the marine environment, underscoring the cumulative role of legacy pollution in the atmospheric burden of plastic. This effort uses high-resolution spatial and temporal deposition data along with several hypothesized emission sources to constrain atmospheric plastic. Akin to global biogeochemical cycles, plastics now spiral around the globe with distinct atmospheric, oceanic, cryospheric, and terrestrial residence times. Though advancements have been made in the manufacture of biodegradable polymers, our data suggest that extant nonbiodegradable polymers will continue to cycle through the earth’s systems. Due to limited observations and understanding of the source processes, there remain large uncertainties in the transport, deposition, and source attribution of microplastics. Thus, we prioritize future research directions for understanding the plastic cycle.