Description: Riverine nutrient export is an important process in marine coastal biogeochemistry and also impacts global marine biology. The nitrogen cycle is a key player here. Internal feedbacks regulate not only nitrogen distribution, but also primary production and thereby oxygen concentrations. Phosphorus is another essential nutrient and interacts with the nitrogen cycle via different feedback mechanisms. After a previous study of the marine nitrogen cycle response to riverine nitrogen supply, we here additionally include phosphorus from river export with different phosphorus burial scenarios and study the impact of phosphorus alone and in combination with nitrogen in a global 3-D ocean biogeochemistry model. Again, we analyse the effects on near coastal and open ocean biogeochemistry. We find that the addition of bio-available riverine phosphorus alone or together with nitrogen affects marine biology on millennial timescales more than riverine nitrogen alone. Biogeochemical feedbacks in the marine nitrogen cycle are strongly influenced by the additional phosphorus. Where bio-available phosphorus is increased by river input, nitrogen concentrations increase as well, except for regions with high denitrification rates. High phosphorus burial rates decrease biological production significantly. Globally, riverine phosphorus leads to elevated primary production rates in the coastal and open oceans.

Description: Spatial predictions of total organic carbon (TOC) concentrations and stocks are crucial for understanding marine sediments’ role as a significant carbon sink in the global carbon cycle. In this study, we present a geospatial prediction of TOC concentrations and stocks at a 5 x 5 arc minute grid scale, using a deep learning model — a novel machine learning approach based on a new compilation of over 22,000 global TOC measurements and a new set of predictors, such as seafloor lithologies, grain size distribution, and an alpha-chlorophyll satellite data. In our study, we compared the predictions and discuss the limitations from various machine learning methods. Our findings reveal that the neural network approach outperforms methods such as k Nearest Neighbors and random forests, which tend to overfit to the training data, especially in highly heterogeneous and complex geological settings. We provide estimates of mean TOC concentrations and total carbon stock in both continental shelves and deep sea settings across various marine regions and oceans. Our model suggests that the upper 10 cm of oceanic sediments harbors approximately 171 Pg of TOC stock and has a mean TOC concentration of 0.68 %. Furthermore, we introduce a standardized methodology for quantifying predictive uncertainty using Monte Carlo dropout and present a map of information gain, that measures the expected increase in model knowledge achieved through in-situ sampling at specific locations which is pivotal for sampling strategy planning.

Description: Nitrous oxide (N2O) is a long-lived potent greenhouse gas and stratospheric ozone-depleting substance, which has been accumulating in the atmosphere since the pre-industrial period. The mole fraction of atmospheric N2O has increased by nearly 25 % from 270 parts per billion (ppb) in 1750 to 336 ppb in 2022, with the fastest annual growth rate since 1980 of more than 1.3 ppb yr-1 in both 2020 and 2021. As a core component of our global greenhouse gas assessments coordinated by the Global Carbon Project (GCP), we present a global N2O budget that incorporates both natural and anthropogenic sources and sinks, and accounts for the interactions between nitrogen additions and the biochemical processes that control N2O emissions. We use Bottom-Up (BU: inventory, statistical extrapolation of flux measurements, process-based land and ocean modelling) and Top-Down (TD: atmospheric measurement-based inversion) approaches. We provide a comprehensive quantification of global N2O sources and sinks in 21 natural and anthropogenic categories in 18 regions between 1980 and 2020. We estimate that total annual anthropogenic N2O emissions increased 40 % (or 1.9 Tg N yr-1) in the past four decades (1980–2020). Direct agricultural emissions in 2020, 3.9 Tg N yr−1 (best estimate) represent the large majority of anthropogenic emissions, followed by other direct anthropogenic sources (including ‘Fossil fuel and industry’, ‘Waste and wastewater’, and ‘Biomass burning’ (2.1 Tg N yr−1), and indirect anthropogenic sources (1.3 Tg N yr−1). For the year 2020, our best estimate of total BU emissions for natural and anthropogenic sources was 18.3 (lower-upper bounds: 10.5–27.0) Tg N yr-1, close to our TD estimate of 17.0 (16.6–17.4) Tg N yr-1. For the period 2010–2019, the annual BU decadal-average emissions for natural plus anthropogenic sources were 18.1 (10.4–25.9) Tg N yr-1 and TD emissions were 17.4 (15.8–19.20 Tg N yr-1. The once top emitter Europe has reduced its emissions since the 1980s by 31 % while those of emerging economies have grown, making China the top emitter since the 2010s. The observed atmospheric N2O concentrations in recent years have exceeded projected levels under all scenarios in the Coupled Model Intercomparison Project Phase 6 (CMIP6), underscoring the urgency to reduce anthropogenic N2O emissions. To evaluate mitigation efforts and contribute to the Global Stocktake of the United Nations Framework Convention on Climate Change, we propose establishing a global network for monitoring and modeling N2O from the surface through the stratosphere. The data presented in this work can be downloaded from https://doi.org/10.18160/RQ8P-2Z4R (Tian et al. 2023).

Description: Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize datasets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (E-FOS) are based on energy statistics and cement production data, while emissions from land-use change (E-LUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (G(ATM)) is computed from the annual changes in concentration. The ocean CO2 sink (S-OCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (S-LAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (B-IM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as +/- 1 sigma. For the first time, an approach is shown to reconcile the difference in our E-LUC estimate with the one from national greenhouse gas inventories, supporting the assessment of collective countries' climate progress. For the year 2020, E-FOS declined by 5.4 % relative to 2019, with fossil emissions at 9.5 +/- 0.5 GtC yr(-1) (9.3 +/- 0.5 GtC yr(-1) when the cement carbonation sink is included), and E-LUC was 0.9 +/- 0.7 GtC yr(-1), for a total anthropogenic CO2 emission of 10.2 +/- 0.8 GtC yr(-1) (37.4 +/- 2.9 GtCO(2)). Also, for 2020, G(ATM) was 5.0 +/- 0.2 GtC yr-1 (2.4 +/- 0.1 ppm yr(-1)), S-OCEAN was 3.0 +/- 0.4 GtC yr(-1), and S-LAND was 2.9 +/- 1 GtC yr(-1), with a B-IM of -0.8 GtC yr(-1). The global atmospheric CO2 concentration averaged over 2020 reached 412.45 +/- 0.1 ppm. Preliminary data for 2021 suggest a rebound in E-FOS relative to 2020 of +4.8 % (4.2 % to 5.4 %) globally. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959-2020, but discrepancies of up to 1 GtC yr(-1) persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and datasets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this dataset (Friedlingstein et al., 2020, 2019; Le Quere et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at (Friedlingstein et al., 2021).

Description: Identification of seismically active fault zones and the definition of sufficiently large respect distances from these faults which enable avoiding the damaged rock zone surrounding the ruptured ground commonly are amongst the first steps to take in the geoscientific evaluation of sites suitable for nuclear waste disposal. In this work we present a GIS-based approach, using the earthquake-epicentre locations from the instrumental earthquake record of South-Korea to identify potentially active fault zones in the country, and compare different strategies for fault zone buffer creation as originally developed for site search in the high seismicity country Japan, and the low-to-moderate seismicity countries Germany and Sweden. In order to characterize the hazard potential of the Korean fault zones, we moreover conducted slip tendency analysis, here for the first time covering the fault zones of the entire Korean Peninsula. For our analyses we used the geo-spatial information from a new version of the Geological map of South-Korea, containing the outlines of 11 rock units, which we simplified to distinguish between 4 different rock types (granites, metamorphic rocks, sedimentary rocks and igneous rocks) and the surface traces of 1,528 fault zones and 6,654 lineaments identified through years of field work and data processing, a rich geo-dataset which we will publish along with this manuscript. Our approach for identification of active fault zones was developed without prior knowledge of already known seismically active fault zones, and as a proof of concept the results later were compared to a map containing already identified active fault zones. The comparison revealed that our approach identified 16 of the 21 known seismically active faults and added 472 previously unknown potentially active faults. The 5 seismically active fault zones which were not identified by our approach are located in the NE- and SW-sectors of the Korean Peninsula, which haven’t seen much recent seismic activity, and thus are not sufficiently well covered by the seismic record. The strike directions of fault zones identified as active are in good agreement with the orientation of the current stress field of the peninsula and slip tendency analysis provided first insights into subsurface geometry such as the dip angles of both active and inactive fault zones. The results of our work are of major importance for the early-stage seismic hazard assessment that has to be conducted in support of the nuclear waste disposal siting in South-Korea. Moreover, the GIS-based methods for identification of active fault zones and buffering of respect areas around fault zone traces presented here, are applicable also elsewhere.

Description: Planktonic food webs were studied contemporaneously in a mesoscale cyclonic (upwelling, ∼ 13 months old) and an anticyclonic (downwelling, ∼ 2 months old) eddy as well as in an uninfluenced background situation in the oligotrophic southeastern Mediterranean Sea (SEMS) during late summer 2018. We show that integrated nutrient concentrations were higher in the cyclone compared to the anticyclone or the background stations by 2–13-fold. Concurrently, Synechococcus and Prochlorococcus were the dominant autotrophs abundance-wise in the oligotrophic anticyclone (∼ 300 × 1010 cells m−2). In the cyclone, functional groups such as dinoflagellates, Prymnesiophyceae and Ochrophyta contributed substantially to the total phytoplankton abundance (∼ 14 × 1010 cells m−2), which was ∼ 65 % lower at the anticyclone and background stations (∼ 5 × 1010 cells m−2). Primary production was highest in the cyclonic eddy (191 ) and 2–5-fold lower outside the eddy area. Heterotrophic prokaryotic cell-specific activity was highest in the cyclone (∼ 10 ), while the least productive cells were found in the anticyclone (4 ). Total zooplankton biomass in the upper 300 m was 10-fold higher in the cyclone compared with the anticyclone or background stations (1337 vs. 112–133 mg C m−2, respectively). Copepod diversity was much higher in the cyclone (44 species), compared to the anticyclone (6 small-size species). Our results highlight that cyclonic and anticyclonic eddies show significantly different community structure and food-web dynamics in oligotrophic environments, with cyclones representing productive oases in the marine desert of the SEMS.

Description: In engineering, machines are typically built after a careful conception and design process: All components of a system, their roles and the interaction between them is well understood, and often even digital models of the system exist before the actual hardware is built. This enables simulations and even feedback loops between the real-world system and a digital model, leading to a digital twin that allows better testing, prediction and understanding of complex effects. On the contrary, in Earth sciences, and particularly in ocean sciences, models exist only for certain aspects of the real world, of certain processes and of some interactions and dependencies between different “components” of the ocean. These individual models cover large temporal (seconds to millions of years) and spatial (millimetres to thousands of kilometres) scales, a variety of field data underpin them, and their results are represented in many different ways. A key to enabling digital twins in the oceans is fusion at different levels, in particular, fusion of data sources and modalities, fusion over different scales and fusion of differing representations. We outline these challenges and exemplify different envisioned digital twins employed in the oceans involving remote sensing, underwater photogrammetry and computer vision, focusing on optical aspects of the digital twinning process. In particular, we look at the holistic sensing scenarios of optical properties in coastal waters as well as seafloor dynamics at volcanic slopes and discuss road blockers for digital twins as well as potential solutions to increase and widen the use of digital twins.

Description: Plastic debris is the most common and exponentially increasing human pollutant in the world's ocean. The distribution and impact of plastic in the Pacific and Atlantic oceans have been the subject of many publications but not so the Indian Ocean (IO). Some of the IO rim countries have the highest population densities globally and mismanagement of plastic waste is of concern in many of these rim states. Some of the most plastic-polluted rivers empty into the IO, with all this suggesting that the IO receives a tremendous amount of plastic debris each year. However, the concentration, distribution, and impacts of plastics in the IO are poorly understood as the region is under-sampled compared to other oceans. In this review, we discuss sources and sinks, which are specific to the IO. We also discuss unique atmospheric, oceanographic, and topographic features of the IO that control plastic distribution, such as reversing wind directions due to the monsoon, fronts, and upwelling regions. We identify hotspots of possible plastic accumulation in the IO, which differ between the two hemispheres. In the southern IO, plastics accumulate in a garbage patch in the subtropical gyre. However, this garbage patch is not well defined, and plastics may leak into the southern Atlantic or the Pacific Ocean. There is no subtropical gyre and associated garbage in the northern IO due to the presence of landmasses. Instead, the majority of buoyant plastics most likely end up on coastlines. Finally, we identify the vast knowledge gaps concerning plastics in the IO and point to the most pressing topics for future investigation.

Description: The incorporation of water isotopologues into the hydrology of general circulation models (GCMs) facilitates the comparison between modeled and measured proxy data in paleoclimate archives. However, the variability and drivers of measured and modeled water isotopologues, as well as the diversity of their representation in different models, are not well constrained. Improving our understanding of this variability in past and present climates will help to better constrain future climate change projections and decrease their range of uncertainty. Speleothems are a precisely datable terrestrial paleoclimate archives and provide well-preserved (semi-)continuous multivariate isotope time series in the lower latitudes and mid-latitudes and are therefore well suited to assess climate and isotope variability on decadal and longer timescales. However, the relationships of speleothem oxygen and carbon isotopes to climate variables are influenced by site-specific parameters, and their comparison to GCMs is not always straightforward. Here we compare speleothem oxygen and carbon isotopic signatures from the Speleothem Isotopes Synthesis and Analysis database version 2 (SISALv2) to the output of five different water-isotope-enabled GCMs (ECHAM5-wiso, GISS-E2-R, iCESM, iHadCM3, and isoGSM) over the last millennium (850–1850 CE). We systematically evaluate differences and commonalities between the standardized model simulation outputs. The goal is to distinguish climatic drivers of variability for modeled isotopes and compare them to those of measured isotopes. We find strong regional differences in the oxygen isotope signatures between models that can partly be attributed to differences in modeled surface temperature. At low latitudes, precipitation amount is the dominant driver for stable water isotope variability; however, at cave locations the agreement between modeled temperature variability is higher than for precipitation variability. While modeled isotopic signatures at cave locations exhibited extreme events coinciding with changes in volcanic and solar forcing, such fingerprints are not apparent in the speleothem isotopes. This may be attributed to the lower temporal resolution of speleothem records compared to the events that are to be detected. Using spectral analysis, we can show that all models underestimate decadal and longer variability compared to speleothems (albeit to varying extents). We found that no model excels in all analyzed comparisons, although some perform better than the others in either mean or variability. Therefore, we advise a multi-model approach whenever comparing proxy data to modeled data. Considering karst and cave internal processes, e.g., through isotope-enabled karst models, may alter the variability in speleothem isotopes and play an important role in determining the most appropriate model. By exploring new ways of analyzing the relationship between the oxygen and carbon isotopes, their variability, and co-variability across timescales, we provide methods that may serve as a baseline for future studies with different models using, e.g., different isotopes, different climate archives, or different time periods.