Ice-edge blooms are significant features of Arctic primary production, yet have received relatively little attention. Here we combine satellite ocean colour and sea-ice data in a pan-Arctic study. ...Ice-edge blooms occur in all seasonally ice-covered areas and from spring to late summer, being observed in 77–89% of locations for which adequate data exist, and usually peaking within 20 days of ice retreat. They sometimes form long belts along the ice-edge (greater than 100 km), although smaller structures were also found. The bloom peak is on average more than 1 mg m−3, with major blooms more than 10 mg m−3, and is usually located close to the ice-edge, though not always. Some propagate behind the receding ice-edge over hundreds of kilometres and over several months, while others remain stationary. The strong connection between ice retreat and productivity suggests that the ongoing changes in Arctic sea-ice may have a significant impact on higher trophic levels and local fish stocks.
Natural‐abundance stable isotope ratios provide a wealth of ecological information relating to food web structure, trophic level, and location. The correct interpretation of stable isotope data ...requires an understanding of spatial and temporal variation in the isotopic compositions at the base of the food web. In marine pelagic environments, accurate interpretation of stable isotope data is hampered by a lack of reliable, spatio‐temporally distributed measurements of baseline isotopic compositions. In this study, we present a relatively simple, process‐based carbon isotope model that predicts the spatio‐temporal distributions of the carbon isotope composition of phytoplankton (here expressed as δ13CPLK) across the global ocean at one degree and monthly resolution. The model is driven by output from a coupled physics‐biogeochemistry model, NEMO‐MEDUSA, and operates offline; it could also be coupled to alternative underlying ocean model systems. Model validation is challenged by the same lack of spatio‐temporally explicit data that motivates model development, but predictions from our model successfully reproduce major spatial patterns in carbon isotope values observed in zooplankton, and are consistent with simulations from alternative models. Model predictions represent an initial hypothesis of spatial and temporal variation in carbon isotopic baselines in ocean areas where a few data are currently available, and provide the best currently available tool to estimate spatial and temporal variation in baseline isotopic compositions at ocean basin to global scales.
MEDUSA-1.0 (Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification) was developed as an "intermediate complexity" plankton ecosystem model to study the biogeochemical ...response, and especially that of the so-called "biological pump", to anthropogenically driven change in the World Ocean (Yool et al., 2011). The base currency in this model was nitrogen from which fluxes of organic carbon, including export to the deep ocean, were calculated by invoking fixed C:N ratios in phytoplankton, zooplankton and detritus. However, due to anthropogenic activity, the atmospheric concentration of carbon dioxide (CO sub(2)) has significantly increased above its natural, inter-glacial background. As such, simulating and predicting the carbon cycle in the ocean in its entirety, including ventilation of CO sub(2) with the atmosphere and the resulting impact of ocean acidification on marine ecosystems, requires that both organic and inorganic carbon be afforded a more complete representation in the model specification. Here, we introduce MEDUSA-2.0, an expanded successor model which includes additional state variables for dissolved inorganic carbon, alkalinity, dissolved oxygen and detritus carbon (permitting variable C:N in exported organic matter), as well as a simple benthic formulation and extended parameterizations of phytoplankton growth, calcification and detritus remineralisation. A full description of MEDUSA-2.0, including its additional functionality, is provided and a multi-decadal spin-up simulation (1860-2005) is performed. The biogeochemical performance of the model is evaluated using a diverse range of observational data, and MEDUSA-2.0 is assessed relative to comparable models using output from the Coupled Model Intercomparison Project (CMIP5).
The seasonal cycle (i.e. phenology) of oceanic primary production (PP) is expected to change in response to climate warming. Here, we use output from 6 global biogeochemical models to examine the ...response in the seasonal amplitude of PP and timing of peak PP to the IPCC AR5 warming scenario. We also investigate whether trends in PP phenology may be more rapidly detectable than trends in annual mean PP. The seasonal amplitude of PP decreases by an average of 1-2% per year by 2100 in most biomes, with the exception of the Arctic which sees an increase of ~1% per year. This is accompanied by an advance in the timing of peak PP by ~0.5-1 months by 2100 over much of the globe, and particularly pronounced in the Arctic. These changes are driven by an increase in seasonal amplitude of sea surface temperature (where the maxima get hotter faster than the minima) and a decrease in the seasonal amplitude of the mixed layer depth and surface nitrate concentration. Our results indicate a transformation of currently strongly seasonal (bloom forming) regions, typically found at high latitudes, into weakly seasonal (non-bloom) regions, characteristic of contemporary subtropical conditions. On average, 36 yr of data are needed to detect a climate-change-driven trend in the seasonal amplitude of PP, compared to 32 yr for mean annual PP. Monthly resolution model output is found to be inadequate for resolving phenological changes. We conclude that analysis of phytoplankton seasonality is not necessarily a shortcut to detecting climate change impacts on ocean productivity.
One of the most characteristic features in ocean productivity is the North Atlantic spring bloom. Responding to seasonal increases in irradiance and stratification, surface phytopopulations rise ...significantly, a pattern that visibly tracks poleward into summer. While blooms also occur in the Arctic Ocean, they are constrained by the sea‐ice and strong vertical stratification that characterize this region. However, Arctic sea‐ice is currently declining, and forecasts suggest this may lead to completely ice‐free summers by the mid‐21st century. Such change may open the Arctic up to Atlantic‐style spring blooms, and do so at the same time as Atlantic productivity is threatened by climate change‐driven ocean stratification. Here we use low and high‐resolution instances of a coupled ocean‐biogeochemistry model, NEMO‐MEDUSA, to investigate productivity. Drivers of present‐day patterns are identified, and changes in these across a climate change scenario (IPCC RCP 8.5) are analyzed. We find a globally significant decline in North Atlantic productivity (> −20%) by 2100, and a correspondingly significant rise in the Arctic (> +50%). However, rather than the future Arctic coming to resemble the current Atlantic, both regions are instead transitioning to a common, low nutrient regime. The North Pacific provides a counterexample where nutrients remain high and productivity increases with elevated temperature. These responses to climate change in the Atlantic and Arctic are common between model resolutions, suggesting an independence from resolution for key impacts. However, some responses, such as those in the North Pacific, differ between the simulations, suggesting the reverse and supporting the drive to more fine‐scale resolutions.
Key Points:
Across 21st century, N. Atlantic productivity significantly declines while increasing in the Arctic
Despite disparate climate change responses, regions share underlying convergence in nutrient status
Results broadly consistent between different model resolutions though they differ in subbasin detail
The Ocean Model Intercomparison Project (OMIP) focuses on the physics and biogeochemistry of the ocean component of Earth system models participating in the sixth phase of the Coupled Model ...Intercomparison Project (CMIP6). OMIP aims to provide standard protocols and diagnostics for ocean models, while offering a forum to promote their common assessment and improvement. It also offers to compare solutions of the same ocean models when forced with reanalysis data (OMIP simulations) vs. when integrated within fully coupled Earth system models (CMIP6). Here we detail simulation protocols and diagnostics for OMIP's biogeochemical and inert chemical tracers. These passive-tracer simulations will be coupled to ocean circulation models, initialized with observational data or output from a model spin-up, and forced by repeating the 1948-2009 surface fluxes of heat, fresh water, and momentum. These so-called OMIP-BGC simulations include three inert chemical tracers (CFC-11, CFC-12, SF subscript 6) and biogeochemical tracers (e.g., dissolved inorganic carbon, carbon isotopes, alkalinity, nutrients, and oxygen). Modelers will use their preferred prognostic BGC model but should follow common guidelines for gas exchange and carbonate chemistry. Simulations include both natural and total carbon tracers. The required forced simulation (omip1) will be initialized with gridded observational climatologies. An optional forced simulation (omip1-spunup) will be initialized instead with BGC fields from a long model spin-up, preferably for 2000 years or more, and forced by repeating the same 62-year meteorological forcing. That optional run will also include abiotic tracers of total dissolved inorganic carbon and radiocarbon, CTabio and 14CTabio, to assess deep-ocean ventilation and distinguish the role of physics vs. biology. These simulations will be forced by observed atmospheric histories of the three inert gases and CO2 as well as carbon isotope ratios of CO2. OMIP-BGC simulation protocols are founded on those from previous phases of the Ocean Carbon-Cycle Model Intercomparison Project. They have been merged and updated to reflect improvements concerning gas exchange, carbonate chemistry, and new data for initial conditions and atmospheric gas histories. Code is provided to facilitate their implementation.
Until recently, the Arctic Basin was generally considered to be a low productivity area and was afforded little attention in global- or even basin-scale ecosystem modelling studies. Due to ...anthropogenic climate change however, the sea ice cover of the Arctic Ocean is undergoing an unexpectedly fast retreat, exposing increasingly large areas of the basin to sunlight. As indicated by existing Arctic phenomena such as ice-edge blooms, this decline in sea-ice is liable to encourage pronounced growth of phytoplankton in summer and poses pressing questions concerning the future of Arctic ecosystems. It thus provides a strong impetus to modelling of this region. The Arctic Ocean is an area where plankton productivity is heavily influenced by physical factors. As these factors are strongly responding to climate change, we analyse here the results from simulations of the 1/4° resolution global ocean NEMO (Nucleus for European Modelling of the Ocean) model coupled with the MEDUSA (Model for Ecosystem Dynamics, carbon Utilisation, Sequestration and Acidification) biogeochemical model, with a particular focus on the Arctic basin. Simulated productivity is consistent with the limited observations for the Arctic, with significant production occurring both under the sea-ice and at the thermocline, locations that are difficult to sample in the field. Results also indicate that a substantial fraction of the variability in Arctic primary production can be explained by two key physical factors: (i) the maximum penetration of winter mixing, which determines the amount of nutrients available for summer primary production, and (ii) short-wave radiation at the ocean surface, which controls the magnitude of phytoplankton blooms. A strong empirical correlation was found in the model output between primary production and these two factors, highlighting the importance of physical processes in the Arctic Ocean.
We have extended the 3-D ocean based "Grid ENabled Integrated Earth system model" (GENIE-1) to help understand the role of ocean biogeochemistry and marine sediments in the long-term (~100 to 100 000 ...year) regulation of atmospheric CO2, and the importance of feedbacks between CO2 and climate. Here we describe the ocean carbon cycle, which in its first incarnation is based around a simple single nutrient (phosphate) control on biological productivity. The addition of calcium carbonate preservation in deep-sea sediments and its role in regulating atmospheric CO2 is presented elsewhere (Ridgwell and Hargreaves, 2007). We have calibrated the model parameters controlling ocean carbon cycling in GENIE-1 by assimilating 3-D observational datasets of phosphate and alkalinity using an ensemble Kalman filter method. The calibrated (mean) model predicts a global export production of particulate organic carbon (POC) of 8.9 PgC yr−1, and reproduces the main features of dissolved oxygen distributions in the ocean. For estimating biogenic calcium carbonate (CaCO3) production, we have devised a parameterization in which the CaCO3:POC export ratio is related directly to ambient saturation state. Calibrated global CaCO3 export production (1.2 PgC yr-1) is close to recent marine carbonate budget estimates. The GENIE-1 Earth system model is capable of simulating a wide variety of dissolved and isotopic species of relevance to the study of modern global biogeochemical cycles as well as past global environmental changes recorded in paleoceanographic proxies. Importantly, even with 12 active biogeochemical tracers in the ocean and including the calculation of feedbacks between atmospheric CO2 and climate, we achieve better than 1000 years per (2.4 GHz) CPU hour on a desktop PC. The GENIE-1 model thus provides a viable alternative to box and zonally-averaged models for studying global biogeochemical cycling over all but the very longest (>1 000 000 year) time-scales.
Most future projections forecast significant and ongoing climate change during the 21st century, but with the severity of impacts dependent on efforts to restrain or reorganise human activity to ...limit carbon dioxide (CO2) emissions. A major sink for atmospheric CO2, and a key source of biological resources, the World Ocean is widely anticipated to undergo profound physical and – via ocean acidification – chemical changes as direct and indirect results of these emissions. Given strong biophysical coupling, the marine biota is also expected to experience strong changes in response to this anthropogenic forcing. Here we examine the large-scale response of ocean biogeochemistry to climate and acidification impacts during the 21st century for Representative Concentration Pathways (RCPs) 2.6 and 8.5 using an intermediate complexity global ecosystem model, MEDUSA-2.0. The primary impact of future change lies in stratification-led declines in the availability of key nutrients in surface waters, which in turn leads to a global decrease (1990s vs. 2090s) in ocean productivity (−6.3%). This impact has knock-on consequences for the abundance of the low trophic level biogeochemical actors modelled by MEDUSA-2.0 (−5.8%), and these would be expected to similarly impact higher trophic level elements such as fisheries. Related impacts are found in the flux of organic material to seafloor communities (−40.7% at 1000 m), and in the volume of ocean suboxic zones (+12.5%). A sensitivity analysis removing an acidification feedback on calcification finds that change in this process significantly impacts benthic communities, suggesting that a~better understanding of the OA-sensitivity of calcifying organisms, and their role in ballasting sinking organic carbon, may significantly improve forecasting of these ecosystems. For all processes, there is geographical variability in change – for instance, productivity declines −21% in the Atlantic and increases +59% in the Arctic – and changes are much more pronounced under RCP 8.5 than the RCP 2.6 scenario.
Ocean biogeochemistry (OBGC) models span a wide variety of complexities, including highly simplified nutrient-restoring schemes, nutrient-phytoplankton-zooplankton-detritus (NPZD) models that crudely ...represent the marine biota, models that represent a broader trophic structure by grouping organisms as plankton functional types (PFTs) based on their biogeochemical role (dynamic green ocean models) and ecosystem models that group organisms by ecological function and trait. OBGC models are now integral components of Earth system models (ESMs), but they compete for computing resources with higher resolution dynamical setups and with other components such as atmospheric chemistry and terrestrial vegetation schemes. As such, the choice of OBGC in ESMs needs to balance model complexity and realism alongside relative computing cost. Here we present an intercomparison of six OBGC models that were candidates for implementation within the next UK Earth system model (UKESM1). The models cover a large range of biological complexity (from 7 to 57 tracers) but all include representations of at least the nitrogen, carbon, alkalinity and oxygen cycles. Each OBGC model was coupled to the ocean general circulation model Nucleus for European Modelling of the Ocean (NEMO) and results from physically identical hindcast simulations were compared. Model skill was evaluated for biogeochemical metrics of global-scale bulk properties using conventional statistical techniques. The computing cost of each model was also measured in standardised tests run at two resource levels. No model is shown to consistently outperform all other models across all metrics. Nonetheless, the simpler models are broadly closer to observations across a number of fields and thus offer a high-efficiency option for ESMs that prioritise high-resolution climate dynamics. However, simpler models provide limited insight into more complex marine biogeochemical processes and ecosystem pathways, and a parallel approach of low-resolution climate dynamics and high-complexity biogeochemistry is desirable in order to provide additional insights into biogeochemistry-climate interactions.
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