ALBERT

Description: In this study we look beyond the previously studied effects of oceanic CO2 injections on atmospheric and oceanic reservoirs and also account for carbon cycle and climate feedbacks between the atmosphere and the terrestrial biosphere. Considering these additional feedbacks is important since backfluxes from the terrestrial biosphere to the atmosphere in response to reducing atmospheric CO2 can further offset the targeted reduction. To quantify these dynamics we use an Earth system model of intermediate complexity to simulate direct injection of CO2 into the deep ocean as a means of emissions mitigation during a high CO2 emission scenario. In three sets of experiments with different injection depths, we simulate a 100-year injection period of a total of 70 GtC and follow global carbon cycle dynamics over another 900 years. In additional parameter perturbation runs, we varied the default terrestrial photosynthesis CO2 fertilization parameterization by ±50 % in order to test the sensitivity of this uncertain carbon cycle feedback to the targeted atmospheric carbon reduction through direct CO2 injections. Simulated seawater chemistry changes and marine carbon storage effectiveness are similar to previous studies. As expected, by the end of the injection period avoided emissions fall short of the targeted 70 GtC by 16–30 % as a result of carbon cycle feedbacks and backfluxes in both land and ocean reservoirs. The target emissions reduction in the parameter perturbation simulations is about 0.2 and 2 % more at the end of the injection period and about 9 % less to 1 % more at the end of the simulations when compared to the unperturbed injection runs. An unexpected feature is the effect of the model's internal variability of deep-water formation in the Southern Ocean, which, in some model runs, causes additional oceanic carbon uptake after injection termination relative to a control run without injection and therefore with slightly different atmospheric CO2 and climate. These results of a model that has very low internal climate variability illustrate that the attribution of carbon fluxes and accounting for injected CO2 may be very challenging in the real climate system with its much larger internal variability.

Description: Tectonics play a significant role in shaping the morphology of submarine canyons, which form essential links in source-to-sink (S2S) systems. It is difficult, however, to investigate the resulting morphodynamics over the long term. For this purpose, we propose a novel experimental approach that can generate submarine canyons and hanging-wall fans on continuously evolving active faults. We utilize morphometric analysis and morphodynamic models to understand the response of these systems to fault slip rate (Vr) and inflow discharge (Q). Our research reveals several key findings. Firstly, the fault slip rate controls the merging speed of submarine canyons and hanging-wall fans, which in turn affects their quantity and spacing. Additionally, the long profile shapes of submarine canyons and hanging-wall fans can be decoupled into a gravity-dominated breaching process and an underflow-dominated diffusion process, which can be described using a constant-slope relationship and a morphodynamic diffusion model, respectively. Furthermore, both experimental and simulated submarine canyon–hanging-wall fan long profiles exhibit strong self-similarity, indicating that the long profiles are scale independent. The Hack's scaling relationship established through morphometric analyses serves as an important link between different scales in S2S systems, bridging laboratory-scale data to field-scale data and submarine-to-terrestrial relationships. Lastly, for deep-water sedimentary systems, we propose an empirical formula to estimate fan volume using canyon length, and the data from 26 worldwide S2S systems utilized for comparison show a strong agreement. Our geomorphic experiments provide a novel perspective for better understanding of the influence of tectonics on deep-water sedimentary processes. The scaling relationships and empirical formulas we have established aim to assist in estimating volume information that is difficult to obtain during long-term landscape evolution processes.

Description: Numerical models are a powerful tool for investigating the dynamic processes in the interior of the Earth and other planets, but the reliability and predictive power of these discretized models depends on the numerical method as well as an accurate representation of material properties in space and time. In the specific context of geodynamic models, particle methods have been applied extensively because of their suitability for advection-dominated processes and have been used in applications such as tracking the composition of solid rock and melt in the Earth's mantle, fluids in lithospheric- and crustal-scale models, light elements in the liquid core, and deformation properties like accumulated finite strain or mineral grain size, along with many applications outside the Earth sciences.There have been significant benchmarking efforts to measure the accuracy and convergence behavior of particle methods, but these efforts have largely been limited to instantaneous solutions, or time-dependent models without analytical solutions. As a consequence, there is little understanding about the interplay of particle advection errors and errors introduced in the solution of the underlying transient, nonlinear flow equations. To address these limitations, we present two new dynamic benchmarks for transient Stokes flow with analytical solutions that allow us to quantify the accuracy of various advection methods in nonlinear flow. We use these benchmarks to measure the accuracy of our particle algorithm as implemented in the ASPECT geodynamic modeling software against commonly employed field methods and analytical solutions. In particular, we quantify if an algorithm that is higher-order accurate in time will allow for better overall model accuracy and verify that our algorithm reaches its intended optimal convergence rate. We then document that the observed increased accuracy of higher-order algorithms matters for geodynamic applications with an example of modeling small-scale convection underneath an oceanic plate and show that the predicted place and time of onset of small-scale convection depends significantly on the chosen particle advection method.Descriptions and implementations of our benchmarks are openly available and can be used to verify other advection algorithms. The availability of accurate, scalable, and efficient particle methods as part of the widely used open-source code ASPECT will allow geodynamicists to investigate complex time-dependent geodynamic processes such as elastic deformation, anisotropic fabric development, melt generation and migration, and grain damage.

Description: The distribution of water masses, and the ventilation rates of these, are of significance to the thermohaline circulation and biogeochemistry of the world oceans. The distribution of the main water masses in the Atlantic Ocean is published in a companion study (Liu and Tanhua, 2021), their ages and ventilation time-scales are reported here by using observations of the transient tracers, CFC-12 and SF6. Two different definitions of water mass ages are presented; the mean-age representing an average age of a water mass, and the mode-age that better represents the advective time-scale. In general, ages increase with pressure and along the pathway of a water mass. The central waters in the upper layer obtain the mean-ages of up to ~100 years and the mode-ages of up to ~30 years. In the intermediate layer, the Antarctic Intermediate Water (AAIW) and the Mediterranean Water (MW) show gradients of water mass ages in the meridional and zonal direction respectively. The AAIW obtains the highest mean-age of ~300 years and mode-age of ~80 years at 30° N, while the MW shows the highest mean-age of ~400 years and mode-age of ~100 years in the equator region. As the dominant water mass in the deep and overflow layer, the North Atlantic Deep Water (NADW) from high northern latitudes obtains the highest mean-age of ~600 years and mode-age of ~100 years in the Antarctic Circumpolar Current (ACC) region at 50° S. In the bottom layer, the Antarctic Bottom Water (AABW) from the Weddell Sea obtains the highest mean-age of ~600 years and mode-age of ~100 years in the equator. As the continuation of AABW, the Northeast Atlantic Bottom Water (NEABW) obtains the highest mean-age of ~800 years and mode-age of ~120 years at 50° N. The mode-age increases with the transport distance from formation area, accompanied by significant differences between the eastern and western basins. The mode-age is used to calculate the oxygen utilization rate (OUR) with apparent oxygen utilization (AOU) during the active transport in water masses. The western basin exhibits lower mode-age with higher oxygen (low AOU) due to the better ventilation. The OUR shows similar distribution to dissolved oxygen (DO), indicating higher oxidation rate in the high oxygen region.

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: Ocean alkalinity enhancement (OAE) is a negative emissions technology (NET) that shows significant potential for climate change mitigation. By increasing the bicarbonate ion concentration in ocean water, OAE could enhance long-term carbon storage and mitigate ocean acidification. However, the side effects and/or potential co-benefits of OAE on natural planktonic communities remain poorly understood. To address this knowledge gap, a mesocosm experiment was conducted in the oligotrophic waters of Gran Canaria. A CO2-equilibrated total alkalinity (TA) gradient was employed in increments of 300 µmol L−1, ranging from ∼ 2400 to ∼ 4800 µmol L−1. This study represents the first attempt to evaluate the potential impacts of OAE on planktonic communities under natural conditions. The results show that net community production (NCP), gross production (GP), community respiration (CR) rates, and the metabolic balance (GP:CR) did not exhibit a linear response to the whole alkalinity gradient. Instead, significant polynomial and linear regression models were observed for all rates up to ΔTA 1800 µmol L−1, in relation to the dissolved inorganic carbon (DIC) concentrations. Notably, the ΔTA 1500 and 1800 µmol L−1 treatments showed peaks in NCP shifting from a heterotrophic to an autotrophic state, with NCP values of 4 and 8 µmol O2 kg−1 d−1, respectively. These peaks and the optimum curve were also reflected in the nanoplankton abundance, size-fractionated chlorophyll a, and 14C uptake data. Furthermore, abiotic precipitation occurred in the highest treatment after day 21, but no impact on the measured parameters was detected. Overall, a damaging effect of CO2-equilibrated OAE in the range applied here on phytoplankton primary production, community metabolism, and composition could not be inferred. In fact, a potential co-benefit to OAE was observed in the form of the positive curvilinear response to the DIC gradient up to the ΔTA 1800 treatment. Further experimental research at this scale is key to gain a better understanding of the short- and long-term effects of OAE on planktonic communities.

Description: Nitric oxide (NO) is an intermediate of various microbial nitrogen cycle processes and the open ocean and coastal areas are generally a source of NO in the atmosphere. However, our knowledge about its distribution and the main production processes in coastal areas and estuaries is rudimentary at best. To this end, dissolved NO concentrations were measured for the first time in surface waters along the lower Elbe Estuary and Hamburg Port area in July 2021. The discrete surface water samples were analyzed using a chemiluminescence detection method. The NO concentrations ranged from below the limit of detection (9.1 × 10−12 mol L−1) to 17.7 × 10−12 mol L−1, averaging at 12.5 × 10−12 mol L−1 and were supersaturated in the surface layer of both the lower Elbe Estuary and the Hamburg Port area, indicating that the study site was a source of NO to the atmosphere during the study period. On the basis of a comprehensive comparison of NO concentrations with parallel nutrient, oxygen, and nitrous oxide concentration measurements, we conclude that the observed distribution of dissolved NO was most likely resulting from microbial nitrogen transformation processes, particularly nitrification in the coastal-brackish and limnic zones of the lower Elbe Estuary and nitrifier-denitrification in the Hamburg Port area.

Description: The addition of carbonate minerals to seawater through an artificial ocean alkalinity enhancement (OAE) process increases the concentrations of hydroxide, bicarbonate, and carbonate ions. This leads to changes in the pH and the buffering capacity of the seawater. Consequently, OAE could have relevant effects on marine organisms and in the speciation and concentration of trace metals that are essential for their physiology. During September and October 2021, a mesocosm experiment was carried out in the coastal waters of Gran Canaria (Spain), consisting on the controlled variation of total alkalinity (TA). Different concentrations of carbonate salts (NaHCO3 and Na2CO3) previously homogenized were added to each mesocosm to achieve an alkalinity gradient between Δ0 to Δ2400 µmol L−1. The lowest point of the gradient was 2400 µmol kg−1, being the natural alkalinity of the medium, and the highest point was 4800 µmol kg−1. Iron (Fe) speciation was monitored during this experiment to analyse total dissolved iron (TdFe, unfiltered samples), dissolved iron (dFe, filtered through a 0.2 µm pore size filter), soluble iron (sFe, filtered through a 0.02 µm pore size filter), dissolved labile iron (dFe′), iron-binding ligands (LFe), and their conditional stability constants () because of change due to OAE and the experimental conditions in each mesocosm. Observed iron concentrations were within the expected range for coastal waters, with no significant increases due to OAE. However, there were variations in Fe size fractionation during the experiment. This could potentially be due to chemical changes caused by OAE, but such an effect is masked by the stronger biological interactions. In terms of size fractionation, sFe was below 1.0 nmol L−1, dFe concentrations were within 0.5–4.0 nmol L−1, and TdFe was within 1.5–7.5 nmol L−1. Our results show that over 99 % of Fe was complexed, mainly by L1 and L2 ligands with ranging between 10.92 ± 0.11 and 12.68 ± 0.32, with LFe ranging from 1.51 ± 0.18 to 12.3 ± 1.8 nmol L−1. Our data on iron size fractionation, concentration, and iron-binding ligands substantiate that the introduction of sodium salts in this mesocosm experiment did not modify iron dynamics. As a consequence, phytoplankton remained unaffected by alterations in this crucial element.

Description: Phytoplankton form the base of the marine food web by transforming CO2 into organic carbon via photosynthesis. Despite the importance of phytoplankton for marine ecosystems and global carbon cycling, projections of phytoplankton biomass in response to climate change differ strongly across Earth system models, illustrating uncertainty in our understanding of the underlying processes. Differences are especially large in the Southern Ocean, a region that is notoriously difficult to represent in models. Here, we argue that total (depth-integrated) phytoplankton biomass in the Southern Ocean is projected to largely remain unchanged under climate change by the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble because of a shifting balance of bottom-up and top-down processes driven by a shoaling mixed-layer depth. A shallower mixed layer is projected on average to improve growth conditions, consequently weaken bottom-up control, and confine phytoplankton closer to the surface. An increase in the phytoplankton concentration promotes zooplankton grazing efficiency, thus intensifying top-down control. However, large differences across the model ensemble exist, with some models simulating a decrease in surface phytoplankton concentrations. To reduce uncertainties in projections of surface phytoplankton concentrations, we employ an emergent constraint approach using the observed sensitivity of surface chlorophyll concentration, taken as an observable proxy for phytoplankton, to seasonal changes in the mixed-layer depth as an indicator for future changes in surface phytoplankton concentrations. The emergent constraint reduces uncertainties in surface phytoplankton concentration projections by around one-third and increases confidence that surface phytoplankton concentrations will indeed rise due to shoaling mixed layers under global warming, thus favouring intensified top-down control. Overall, our results suggest that while changes in bottom-up conditions stimulate enhanced growth, intensified top-down control opposes an increase in phytoplankton and becomes increasingly important for the phytoplankton response to climate change in the Southern Ocean.

Description: Events of extreme precipitation pose a hazard to many parts of Europe but are typically not well represented in climate models. Here, we evaluate daily extreme precipitation over Europe during 1982–2019 in observations (GPCC), reanalysis (ERA5) and a set of atmosphere-only simulations at low- (100 km), medium- (50 km) and high- (25 km) horizontal resolution with identical vertical resolutions using OpenIFS (version 43r3). We find that both OpenIFS simulations and reanalysis underestimate the rates of extreme precipitation compared to observations. The biases are largest for the lowest resolution (100 km) and decrease with increasing horizontal resolution (50 and 25 km) simulations in all seasons. The sensitivity to horizontal resolution is particularly high in mountain regions (such as the Alps, Scandinavia, Iberian Peninsula), likely linked to the sensitivity of vertical velocity to the representation of topography. The sensitivity of precipitation to model resolution increases dramatically with increasing percentiles, with modest biases in the 70th–80thpercentile range and large biases above the 99th percentile range. We also find that precipitation above the 99th percentile mostly consists of large-scale precipitation (~80 %) in winter, while in summer it is mostly large-scale precipitation in Northern Europe (~70 %) and convective precipitation in Southern Europe (~70 %). Compared to ERA5, the OpenIFS overestimates large-scale precipitation extremes in winter, but underestimates in summer. The discrepancy between OpenIFS and ERA5 decreases with increasing horizontal resolutions. We also examine the sensitivity of extreme precipitation to model time step and find that the convective contribution to extreme precipitation is more sensitive to the model time step than the horizontal resolution. This is likely due to the sensitivity of convective activity to model time step. On the other hand, the large-scale contribution to extreme precipitation is more sensitive to horizontal resolution than the model time step, which may be due to sharper fronts and steeper topography at higher horizontal resolution.