Constraints on global oceanic emissions of N2O from observations and models

We estimate the global ocean N2O flux to the atmosphere and its confidence interval using a statistical method based on model perturbation simulations and their fit to a database of ΔpN2O (n =  6136). We evaluate two submodels of N2O production. The first submodel splits N2O production into oxic and hypoxic pathways following previous publications. The second submodel explicitly represents the redox transformations of N that lead to N2O production (nitrification and hypoxic denitrification) and N2O consumption (suboxic denitrification), and is presented here for the first time. We perturb both submodels by modifying the key parameters of the N2O cycling pathways (nitrification rates; NH4+ uptake; N2O yields under oxic, hypoxic and suboxic conditions) and determine a set of optimal model parameters by minimisation of a cost function against four databases of N cycle observations. Our estimate of the global oceanic N2O flux resulting from this cost function minimisation derived from observed and model ΔpN2O concentrations is 2.4 ± 0.8 and 2.5 ± 0.8 Tg N yr−1 for the two N2O submodels. These estimates suggest that the currently available observational data of surface ΔpN2O constrain the global N2O flux to a narrower range relative to the large range of results presented in the latest IPCC report

The implications of COP21 for our future climate

Rising CO2 in the atmosphere is the main cause of anthropogenic climate change, and the data shows a clear increase in global temperature of about 1 °C since pre-industrial levels. Changes in climate extremes are also occurring, with observed increases in the frequency of heat waves, in intense precipitation (rainfall and snowfall) in many places, and in sea level and storm surges. A changing climate with rising extremes has associated risks for food production and other health-related impacts. In order to limit climate change well below 2 °C, our carbon emissions must rapidly follow a decreasing trajectory to near zero

Optical characterization of marine phytoplankton assemblages within surface waters of the western Arctic Ocean.

An extensive data set of measurements within the Chukchi and Beaufort Seas is used to characterize the optical properties of seawater associated with different phytoplankton communities. Hierarchical cluster analysis of diagnostic pigment concentrations partitioned stations into four distinct surface phytoplankton communities based on taxonomic composition and average cell size. Concurrent optical measurements of spectral absorption and backscattering coefficients and remote-sensing reflectance were used to characterize the magnitudes and spectral shapes of seawater optical properties associated with each phytoplankton assemblage. The results demonstrate measurable differences among communities in the average spectral shapes of the phytoplankton absorption coefficient. Similar or smaller differences were also observed in the spectral shapes of nonphytoplankton absorption coefficients and the particulate backscattering coefficient. Phytoplankton on average, however, contributed only 25% or less to the total absorption coefficient of seawater. Our analyses indicate that the interplay between the magnitudes and relative contributions of all optically significant constituents generally dampens any influence of varying phytoplankton absorption spectral shapes on the total absorption coefficient, yet there is still a marked discrimination observed in the spectral shape of the ratio of the total backscattering to total absorption coefficient and remote-sensing reflectance among the phytoplankton assemblages. These spectral variations arise mainly from differences in the bio-optical environment in which specific communities were found, as opposed to differences in the spectral shapes of phytoplankton optical properties per se. These results suggest potential approaches for the development of algorithms to assess phytoplankton community composition from measurements of seawater optical properties in western Arctic waters

Modelling of natural convection flows with large temperature differences : a benchmark problem for low Mach number solvers. Part 1, Reference solutions

There are very few reference solutions in the literature on non-Boussinesq natural convection flows. We propose here a test case problem which extends the well-known De Vahl Davis differentially heated square cavity problem to the case of large temperature differences for which the Boussinesq approximation is no longer valid. The paper is split in two parts: in this first part, we propose as yet unpublished reference solutions for cases characterized by a non-dimensional temperature difference of 0.6, (constant property and variable property cases) and (variable property case). These reference solutions were produced after a first international workshop organized by CEA and LIMSI in January 2000, in which the above authors volunteered to produce accurate numerical solutions from which the present reference solutions could be established

Warning signs for stabilizing global CO2 emissions

Carbon dioxide (CO2) emissions from fossil fuels and industry comprise ~90% of all CO2 emissions from human activities. For the last three years, such emissions were stable, despite continuing growth in the global economy. Many positive trends contributed to this unique hiatus, including reduced coal use in China and elsewhere, continuing gains in energy efficiency, and a boom in low-carbon renewables such as wind and solar. However, the temporary hiatus appears to have ended in 2017. For 2017, we project emissions growth of 2.0% (range: 0.8%−3.0%) from 2016 levels (leap-year adjusted), reaching a record 36.8 ± 2 Gt CO2. Economic projections suggest further emissions growth in 2018 is likely. Time is running out on our ability to keep global average temperature increases below 2 °C and, even more immediately, anything close to 1.5 °C

Recent variability of the global ocean carbon sink

We present a new observation-based estimate of the global oceanic carbon dioxide (CO2) sink and its temporal variation on a monthly basis from 1998 through 2011 and at a spatial resolution of 1×1. This sink estimate rests upon a neural network-based mapping of global surface ocean observations of the partial pressure of CO2 (pCO2) from the Surface Ocean CO2 Atlas database. The resulting pCO2 has small biases when evaluated against independent observations in the different ocean basins, but larger randomly distributed differences exist particularly in high latitudes. The seasonal climatology of our neural network-based product agrees overall well with the Takahashi et al. (2009) climatology, although our product produces a stronger seasonal cycle at high latitudes. From our global pCO2 product, we compute a mean net global ocean (excluding the Arctic Ocean and coastal regions) CO2 uptake flux of −1.42 ± 0.53 Pg C yr−1, which is in good agreement with ocean inversion-based estimates. Our data indicate a moderate level of interannual variability in the ocean carbon sink (±0.12 Pg C yr−1, 1𝜎) from 1998 through 2011, mostly originating from the equatorial Pacific Ocean, and associated with the El Nino–Southern Oscillation. Accounting for steady state riverine and Arctic Ocean carbon fluxes our estimate further implies a mean anthropogenic CO2 uptake of −1.99 ± 0.59 Pg C yr−1 over the analysis period. From this estimate plus the most recent estimates for fossil fuel emissions and atmospheric CO2 accumulation, we infer a mean global land sink of −2.82 ± 0.85 Pg C yr−1 over the 1998 through 2011 period with strong interannual variation

Biogeochemical modelling of dissolved oxygen in a changing ocean

Secular decreases in dissolved oxygen concentration have been observed within the tropical oxygen minimum zones (OMZs) and at mid- to high latitudes over the last approximately 50 years. Earth system model projections indicate that a reduction in the oxygen inventory of the global ocean, termed ocean deoxygenation, is a likely consequence of on-going anthropogenic warming. Current models are, however, unable to consistently reproduce the observed trends and variability of recent decades, particularly within the established tropical OMZs. Here, we conduct a series of targeted hindcast model simulations using a state-of-the-art global ocean biogeochemistry model in order to explore and review biases in model distributions of oceanic oxygen. We show that the largest magnitude of uncertainty is entrained into ocean oxygen response patterns due to model parametrization of pCO2-sensitive C : N ratios in carbon fixation and imposed atmospheric forcing data. Inclusion of a pCO2-sensitive C : N ratio drives historical oxygen depletion within the ocean interior due to increased organic carbon export and subsequent remineralization. Atmospheric forcing is shown to influence simulated interannual variability in ocean oxygen, particularly due to differences in imposed variability of wind stress and heat fluxes

A statistical gap-filling method to interpolate global monthly surface ocean carbon dioxide data

We have developed a statistical gap-filling method adapted to the specific coverage and prop-erties of observed fugacity of surface ocean CO2(fCO2). We have used this method to interpolate the Sur-face Ocean CO2Atlas (SOCAT) v2 database on a 2.5832.58 global grid (south of 708N) for 1985–2011 atmonthly resolution. The method combines a spatial interpolation based on a ‘‘radius of influence’’ to deter-mine nearby similar fCO2values with temporal harmonic and cubic spline curve-fitting, and also fits long-term trends and seasonal cycles. Interannual variability is established using deviations of observations fromthe fitted trends and seasonal cycles. An uncertainty is computed for all interpolated values based on thespatial and temporal range of the interpolation. Tests of the method using model data show that it performsas well as or better than previous regional interpolation methods, but in addition it provides a near-globaland interannual coverage

Fingerprint of Climate Change on Southern Ocean Carbon Storage

The Southern Ocean plays a critical role in the uptake, transport, and storage of carbon by the global oceans. It is the ocean's largest sink of CO2, yet it is also among the regions with the lowest storage of anthropogenic carbon. This behavior results from a unique combination of high winds driving the upwelling of deep waters and the subduction and northward transport of surface carbon. Here we isolate the direct effect of increasing anthropogenic CO2 in the atmosphere from the indirect effect of climate variability and climate change on the reorganization of carbon in the Southern Ocean interior using a combination of modeling and observations. We show that the effect of climate variability and climate change on the storage of carbon in the Southern Ocean is nearly as large as the effect of anthropogenic CO2 during the period 1998–2018 compared with the climatology around the year 1995. We identify a distinct climate fingerprint in dissolved inorganic carbon (DIC), with elevated DIC concentration in the ocean at 300–600 m that reinforces the anthropogenic CO2 signal, and reduced DIC concentration in the ocean around 2,000 m that offsets the anthropogenic CO2 signal. The fingerprint is strongest at lower latitudes (30°–55°S). This fingerprint could serve to monitor the highly uncertain evolution of carbon within this critical ocean basin, and better identify its drivers