A rapid transition from ice covered CO2–rich waters to a biologically mediated CO2 sink in the eastern Weddell Gyre

Circumpolar Deep Water (CDW), locally called Warm Deep Water (WDW), enters the Weddell Gyre in the southeast, roughly at 25° E to 30° E. In December 2002 and January 2003 we studied the effect of entrainment of WDW on the fugacity of carbon dioxide (fCO2) and dissolved inorganic carbon (DIC) in Weddell Sea surface waters. Ultimately the fCO2 difference across the sea surface drives air-sea fluxes of CO2. Deep CTD sections and surface transects of fCO2 were made along the Prime Meridian, a northwest-southeast section, and along 17° E to 23° E during cruise ANT XX/2 on FS Polarstern. Upward movement and entrainment of WDW into the winter mixed layer had significantly increased DIC and fCO2 below the sea ice along 0° W and 17° E to 23° E, notably in the southern Weddell Gyre. Nonetheless, the ice cover largely prevented outgassing of CO2 to the atmosphere. During and upon melting of the ice, biological activity rapidly reduced surface water fCO2 by up to 100 µatm, thus creating a sink for atmospheric CO2. Despite the tendency of the surfacing WDW to cause CO2 supersaturation, the Weddell Gyre may well be a CO2 sink on an annual basis due to this effective mechanism involving ice cover and ensuing biological fCO2 reduction. Dissolution of calcium carbonate (CaCO3) in melting sea ice may play a minor role in this rapid reduction of surface water fCO2

Global surface-ocean pCO2 and sea–air CO2 flux variability from an observation-driven ocean mixed-layer scheme

A temporally and spatially resolved estimate of the global surface-ocean CO₂ partial pressure field and the sea–air CO₂ flux is presented, obtained by fitting a simple data-driven diagnostic model of ocean mixed-layer biogeochemistry to surface-ocean CO₂ partial pressure data from the SOCAT v1.5 database. Results include seasonal, interannual, and short-term (daily) variations. In most regions, estimated seasonality is well constrained from the data, and compares well to the widely used monthly climatology by Takahashi et al. (2009). Comparison to independent data tentatively supports the slightly higher seasonal variations in our estimates in some areas. We also fitted the diagnostic model to atmospheric CO₂ data. The results of this are less robust, but in those areas where atmospheric signals are not strongly influenced by land flux variability, their seasonality is nevertheless consistent with the results based on surface-ocean data. From a comparison with an independent seasonal climatology of surface-ocean nutrient concentration, the diagnostic model is shown to capture relevant surface-ocean biogeochemical processes reasonably well. Estimated interannual variations will be presented and discussed in a companion paper

Uncertainties in eddy covariance air–sea CO<sub>2</sub> flux measurements and implications for gas transfer velocity parameterisations

Air–sea carbon dioxide (CO2) flux is often indi�rectly estimated by the bulk method using the air–sea difference in CO2 fugacity (1f CO2) and a parameterisation of the gas transfer velocity (K). Direct flux measurements by eddy covariance (EC) provide an independent reference for bulk flux estimates and are often used to study processes that drive K. However, inherent uncertainties in EC air–sea CO2 flux measurements from ships have not been well quantified and may confound analyses of K. This paper evaluates the uncertainties in EC CO2 fluxes from four cruises. Fluxes were measured with two state-of-the-art closed-path CO2 analysers on two ships. The mean bias in the EC CO2 flux is low, but the random error is relatively large over short timescales. The uncertainty (1 standard deviation) in hourly averaged EC air–sea CO2 fluxes (cruise mean) ranges from 1.4 to 3.2 mmolm−2 d−1. This corresponds to a relative uncertainty of ∼ 20 % during two Arctic cruises that observed large CO2 flux magnitude. The relative uncertainty was greater (∼ 50 %) when the CO2 flux magnitude was small during two Atlantic cruises. Random uncertainty in the EC CO2 flux is mostly caused by sampling error. Instrument noise is relatively unimportant. Random uncertainty in EC CO2 fluxes can be reduced by averaging for longer. However, averaging for too long will result in the inclusion of more natural variability. Auto-covariance analysis of CO2 fluxes suggests that the optimal timescale for averaging EC CO2 flux measurements ranges from 1 to 3 h, which increases the mean signal-to-noise ratio of the four cruises to higher than 3. Applying an appropriate averaging timescale and suitable 1f CO2 threshold (20 µatm) to EC flux data enables an optimal analysis of K

Near‐Surface Stratification Due to Ice Melt Biases Arctic Air‐Sea CO 2 Flux Estimates

Air-sea carbon dioxide (CO2) flux is generally estimated by the bulk method using upper ocean CO2 fugacity measurements. In the summertime Arctic, sea-ice melt results in stratification within the upper ocean (top ∼10 m), which can bias bulk CO2 flux estimates when the seawater CO2 fugacity is taken from a ship's seawater inlet at ∼6 m depth (fCO2w_bulk). Direct flux measurements by eddy covariance are unaffected by near-surface stratification. We use eddy covariance CO2 flux measurements to infer sea surface CO2 fugacity (fCO2w_surface) in the Arctic Ocean. In sea-ice melt regions, fCO2w_surface values are consistently lower than fCO2w_bulk by an average of 39 μatm. Lower fCO2w_surface can be partially accounted for by fresher (≥27%) and colder (17%) melt waters. A back-of-the-envelope calculation shows that neglecting the summertime sea-ice melt could lead to a 6%–17% underestimate of the annual Arctic Ocean CO2 uptake

Strengthening seasonal marine CO2 variations due to increasing atmospheric CO2

The increase of atmospheric CO2 (ref. 1) has been predicted to impact the seasonal cycle of inorganic carbon in the global ocean2,3, yet the observational evidence to verify this prediction has been missing. Here, using an observation-based product of the oceanic partial pressure of CO2 (pCO2) covering the past 34 years, we find that the winter-to-summer difference of the pCO2 has increased on average by 2.2 ± 0.4 μatm per decade from 1982 to 2015 poleward of 10° latitude. This is largely in agreement with the trend expected from thermodynamic considerations. Most of the increase stems from the seasonality of the drivers acting on an increasing oceanic pCO2 caused by the uptake of anthropogenic CO2 from the atmosphere. In the high latitudes, the concurrent ocean-acidification-induced changes in the buffer capacity of the ocean enhance this effect. This strengthening of the seasonal winter-to-summer difference pushes the global ocean towards critical thresholds earlier, inducing stress to ocean ecosystems and fisheries4. Our study provides observational evidence for this strengthening seasonal difference in the oceanic carbon cycle on a global scale, illustrating the inevitable consequences of anthropogenic CO2 emissions

Estimating the monthly pCO2 distribution in the north Atlantic using a self-organizing neural network

Here we present monthly, basin-wide maps of the partial pressure of carbon dioxide (pCO2) for the North Atlantic on a 1° latitude by 1° longitude grid for years 2004 through 2006 inclusive. The maps have been computed using a neural network technique which reconstructs the non-linear relationships between three biogeochemical parameters and marine pCO 2. A self organizing map (SOM) neural network has been trained using 389 000 triplets of the SeaWiFS-MODIS chlorophyll-a concentration, the NCEP/NCAR reanalysis sea surface temperature, and the FOAM mixed layer depth. The trained SOM was labelled with 137 000 underway pCO2 measurements collected in situ during 2004, 2005 and 2006 in the North Atlantic, spanning the range of 208 to 437 µatm. The root mean square error (RMSE) of the neural network fit to the data is 11.6 µatm, which equals to just above 3 per cent of an average pCO2 value in the in situ dataset. The seasonal pCO2 cycle as well as estimates of the interannual variability in the major biogeochemical provinces are presented and discussed. High resolution combined with basin-wide coverage makes the maps a useful tool for several applications such as the monitoring of basin-wide air-sea CO2 fluxes or improvement of seasonal and interannual marine CO2 cycles in future model predictions. The method itself is a valuable alternative to traditional statistical modelling techniques used in geosciences

Temperature dependence of CO2-enhanced primary production in the European Arctic Ocean

The Arctic Ocean is warming at two to three times the global rate1 and is perceived to be a bellwether for ocean acidification2, 3. Increased CO2 concentrations are expected to have a fertilization effect on marine autotrophs4, and higher temperatures should lead to increased rates of planktonic primary production5. Yet, simultaneous assessment of warming and increased CO2 on primary production in the Arctic has not been conducted. Here we test the expectation that CO2-enhanced gross primary production (GPP) may be temperature dependent, using data from several oceanographic cruises and experiments from both spring and summer in the European sector of the Arctic Ocean. Results confirm that CO2 enhances GPP (by a factor of up to ten) over a range of 145–2,099 μatm; however, the greatest effects are observed only at lower temperatures and are constrained by nutrient and light availability to the spring period. The temperature dependence of CO2-enhanced primary production has significant implications for metabolic balance in a warmer, CO2-enriched Arctic Ocean in the future. In particular, it indicates that a twofold increase in primary production during the spring is likely in the Arctic

Perspectives and Integration in SOLAS Science

Why a chapter on Perspectives and Integration in SOLAS Science in this book? SOLAS science by its nature deals with interactions that occur: across a wide spectrum of time and space scales, involve gases and particles, between the ocean and the atmosphere, across many disciplines including chemistry, biology, optics, physics, mathematics, computing, socio-economics and consequently interactions between many different scientists and across scientific generations. This chapter provides a guide through the remarkable diversity of cross-cutting approaches and tools in the gigantic puzzle of the SOLAS realm. Here we overview the existing prime components of atmospheric and oceanic observing systems, with the acquisition of ocean–atmosphere observables either from in situ or from satellites, the rich hierarchy of models to test our knowledge of Earth System functioning, and the tremendous efforts accomplished over the last decade within the COST Action 735 and SOLAS Integration project frameworks to understand, as best we can, the current physical and biogeochemical state of the atmosphere and ocean commons. A few SOLAS integrative studies illustrate the full meaning of interactions, paving the way for even tighter connections between thematic fields. Ultimately, SOLAS research will also develop with an enhanced consideration of societal demand while preserving fundamental research coherency. The exchange of energy, gases and particles across the air-sea interface is controlled by a variety of biological, chemical and physical processes that operate across broad spatial and temporal scales. These processes influence the composition, biogeochemical and chemical properties of both the oceanic and atmospheric boundary layers and ultimately shape the Earth system response to climate and environmental change, as detailed in the previous four chapters. In this cross-cutting chapter we present some of the SOLAS achievements over the last decade in terms of integration, upscaling observational information from process-oriented studies and expeditionary research with key tools such as remote sensing and modelling. Here we do not pretend to encompass the entire legacy of SOLAS efforts but rather offer a selective view of some of the major integrative SOLAS studies that combined available pieces of the immense jigsaw puzzle. These include, for instance, COST efforts to build up global climatologies of SOLAS relevant parameters such as dimethyl sulphide, interconnection between volcanic ash and ecosystem response in the eastern subarctic North Pacific, optimal strategy to derive basin-scale CO2 uptake with good precision, or significant reduction of the uncertainties in sea-salt aerosol source functions. Predicting the future trajectory of Earth’s climate and habitability is the main task ahead. Some possible routes for the SOLAS scientific community to reach this overarching goal conclude the chapter

Phytoplankton responses to marine climate change – an introduction

Phytoplankton are one of the key players in the ocean and contribute approximately 50% to global primary production. They serve as the basis for marine food webs, drive chemical composition of the global atmosphere and thereby climate. Seasonal environmental changes and nutrient availability naturally influence phytoplankton species composition. Since the industrial era, anthropogenic climatic influences have increased noticeably – also within the ocean. Our changing climate, however, affects the composition of phytoplankton species composition on a long-term basis and requires the organisms to adapt to this changing environment, influencing micronutrient bioavailability and other biogeochemical parameters. At the same time, phytoplankton themselves can influence the climate with their responses to environmental changes. Due to its key role, phytoplankton has been of interest in marine sciences for quite some time and there are several methodical approaches implemented in oceanographic sciences. There are ongoing attempts to improve predictions and to close gaps in the understanding of this sensitive ecological system and its responses