Global sea-to-air flux climatology for bromoform, dibromomethane and methyl iodide

Volatile halogenated organic compounds containing bromine and iodine, which are naturally produced in the ocean, are involved in ozone depletion in both the troposphere and stratosphere. Three prominent compounds transporting large amounts of marine halogens into the atmosphere are bromoform (CHBr3), dibromomethane (CH2Br2) and methyl iodide (CH3I). The input of marine halogens to the stratosphere has been estimated from observations and modelling studies using low-resolution oceanic emission scenarios derived from top-down approaches. In order to improve emission inventory estimates, we calculate data-based high resolution global sea-to-air flux estimates of these compounds from surface observations within the HalOcAt (Halocarbons in the Ocean and Atmosphere) database (https://halocat.geomar.de/). Global maps of marine and atmospheric surface concentrations are derived from the data which are divided into coastal, shelf and open ocean regions. Considering physical and biogeochemical characteristics of ocean and atmosphere, the open ocean water and atmosphere data are classified into 21 regions. The available data are interpolated onto a 1 degrees x 1 degrees grid while missing grid values are interpolated with latitudinal and longitudinal dependent regression techniques reflecting the compounds' distributions. With the generated surface concentration climatologies for the ocean and atmosphere, global sea-to-air concentration gradients and sea-to-air fluxes are calculated. Based on these calculations we estimate a total global flux of 1.5/2.5 Gmol Br yr(-1) for CHBr3, 0.78/0.98 Gmol Br yr(-1) for CH2Br2 and 1.24/1.45 Gmol Br yr(-1) for CH3I (robust fit/ordinary least squares regression techniques). Contrary to recent studies, negative fluxes occur in each sea-to-air flux climatology, mainly in the Arctic and Antarctic regions. "Hot spots" for global polybromomethane emissions are located in the equatorial region, whereas methyl iodide emissions are enhanced in the subtropical gyre regions. Inter-annual and seasonal variation is contained within our flux calculations for all three compounds. Compared to earlier studies, our global fluxes are at the lower end of estimates, especially for bromoform. An under-representation of coastal emissions and of extreme events in our estimate might explain the mismatch between our bottom-up emission estimate and top-down approaches.

Global assessment of ocean carbon export by combining satellite observations and food-web models

The export of organic carbon from the surface ocean by sinking particles is an important, yet highly uncertain, component of the global carbon cycle. Here we introduce a mechanistic assessment of the global ocean carbon export using satellite observations, including determinations of net primary production and the slope of the particle size spectrum, to drive a food-web model that estimates the production of sinking zooplankton feces and algal aggregates comprising the sinking particle flux at the base of the euphotic zone. The synthesis of observations and models reveals fundamentally different and ecologically consistent regional-scale patterns in export and export efficiency not found in previous global carbon export assessments. The model reproduces regional-scale particle export field observations and predicts a climatological mean global carbon export from the euphotic zone of ~6 Pg C yr−1. Global export estimates show small variation (typically < 10%) to factor of 2 changes in model parameter values. The model is also robust to the choices of the satellite data products used and enables interannual changes to be quantified. The present synthesis of observations and models provides a path for quantifying the ocean's biological pump.

Satellite estimates of net community production indicate predominance of net autotrophy in the Atlantic Ocean

There is ongoing debate as to whether the oligotrophic ocean is predominantly net autotrophic and acts as a CO2 sink, or net heterotrophic and therefore acts as a CO2 source to the atmosphere. This quantification is challenging, both spatially and temporally, due to the sparseness of measurements. There has been a concerted effort to derive accurate estimates of phytoplankton photosynthesis and primary production from satellite data to fill these gaps; however there have been few satellite estimates of net community production (NCP). In this paper, we compare a number of empirical approaches to estimate NCP from satellite data with in vitro measurements of changes in dissolved O2 concentration at 295 stations in the N and S Atlantic Ocean (including the Antarctic), Greenland and Mediterranean Seas. Algorithms based on power laws between NCP and particulate organic carbon production (POC) derived from 14C uptake tend to overestimate NCP at negative values and underestimate at positive values. An algorithm that includes sea surface temperature (SST) in the power function of NCP and 14C POC has the lowest bias and root-mean square error compared with in vitro measured NCP and is the most accurate algorithm for the Atlantic Ocean. Nearly a 13 year time series of NCP was generated using this algorithm with SeaWiFS data to assess changes over time in different regions and in relation to climate variability. The North Atlantic subtropical and tropical Gyres (NATL) were predominantly net autotrophic from 1998 to 2010 except for boreal autumn/winter, suggesting that the northern hemisphere has remained a net sink for CO2 during this period. The South Atlantic subtropical Gyre (SATL) fluctuated from being net autotrophic in austral spring-summer, to net heterotrophic in austral autumn–winter. Recent decadal trends suggest that the SATL is becoming more of a CO2 source. Over the Atlantic basin, the percentage of satellite pixels with negative NCP was 27%, with the largest contributions from the NATL and SATL during boreal and austral autumn–winter, respectively. Variations in NCP in the northern and southern hemispheres were correlated with climate indices. Negative correlations between NCP and the multivariate ENSO index (MEI) occurred in the SATL, which explained up to 60% of the variability in NCP. Similarly there was a negative correlation between NCP and the North Atlantic Oscillation (NAO) in the Southern Sub-Tropical Convergence Zone (SSTC),which explained 90% of the variability. There were also positive correlations with NAO in the Canary Current Coastal Upwelling (CNRY) and Western Tropical Atlantic (WTRA)which explained 80% and 60% of the variability in each province, respectively. MEI and NAO seem to play a role in modifying phases of net autotrophy and heterotrophy in the Atlantic Ocean.

Metocean Comparisons of Jason-2 and AltiKa—A Method to Develop a New Wind Speed Algorithm

As well as range, the AltiKa altimeter provides estimates of wave height, Hs and normalized backscatter, s0, that need to be assessed prior to statistics based on them being included in climate databases. An analysis of crossovers with the Jason-2 altimeter shows AltiKa Hs values to be biased high by only »0.05m, with a standard deviation (s.d.) of »0.1m for seven-point averages. AltiKa’s s 0 values are 2.5–3 dB less than those from Jason-2, with a s.d. of »0.3 dB, with these relatively large mismatches to be expected as AltiKa measures a different part of the spectrum of sea surface roughness. A new wind speed algorithm is developed through matchinghistogram of s0 values to that for Jason-2 wind speeds. The algorithm is robust to the use of short durations of data, with a consistency at roughly the 0.1 m/s level. Incorporation of Hs as a secondary input reduces the assessed error at crossovers from 0.82 m/s to 0.71 m/s. A comparison across all altimeter frequencies used to date demonstrates that the lowest wind speeds preferentially develop the shortest scales of roughness.

Uncertainty estimates of remote sensing reflectance derived from comparison of ocean color satellite data sets

Assigning uncertainty to ocean-color satellite products is a requirement to allow informed use of these data. Here, uncertainty estimates are derived using the comparison on a 12th-degree grid of coincident daily records of the remote-sensing reflectance RRS obtained with the same processing chain from three satellite missions, MERIS, MODIS and SeaWiFS. The approach is spatially resolved and produces σ, the part of the RRS uncertainty budget associated with random effects. The global average of σ decreases with wavelength from approximately 0.7– 0.9 10−3 sr−1 at 412 nm to 0.05–0.1 10−3 sr−1 at the red band, with uncertainties on σ evaluated as 20–30% between 412 and 555 nm, and 30–40% at 670 nm. The distribution of σ shows a restricted spatial variability and small variations with season, which makes the multi-annual global distribution of σ an estimate applicable to all retrievals of the considered missions. The comparison of σ with other uncertainty estimates derived from field data or with the support of algorithms provides a consistent picture. When translated in relative terms, and assuming a relatively low bias, the distribution of σ suggests that the objective of a 5% uncertainty is fulfilled between 412 and 490 nm for oligotrophic waters (chlorophyll-a concentration below 0.1 mg m−3). This study also provides comparison statistics. Spectrally, the mean absolute relative difference between RRS from different missions shows a characteristic U-shape with both ends at blue and red wavelengths inversely related to the amplitude of RRS. On average and for the considered data sets, SeaWiFS RRS tend to be slightly higher than MODIS RRS, which in turn appear higher than MERIS RRS. Biases between mission-specific RRS may exhibit a seasonal dependence, particularly in the subtropical belt.