Lana, Arantxa ... et. al.-- 17 pages, 6 figures, 2 tables, supporting information http://dx.doi.org/10.1029/2010GB003850The potentially significant role of the biogenic trace gas dimethylsulfide (DMS) in determining the Earth's radiation budget makes it necessary to accurately reproduce seawater DMS distribution and quantify its global flux across the sea/air interface. Following a threefold increase of data (from 15,000 to over 47,000) in the global surface ocean DMS database over the last decade, new global monthly climatologies of surface ocean DMS concentration and sea-to-air emission flux are presented as updates of those constructed 10 years ago. Interpolation/extrapolation techniques were applied to project the discrete concentration data onto a first guess field based on Longhurst's biogeographic provinces. Further objective analysis allowed us to obtain the final monthly maps. The new climatology projects DMS concentrations typically in the range of 1–7 nM, with higher levels occurring in the high latitudes, and with a general trend toward increasing concentration in summer. The increased size and distribution of the observations in the DMS database have produced in the new climatology substantially lower DMS concentrations in the polar latitudes and generally higher DMS concentrations in regions that were severely undersampled 10 years ago, such as the southern Indian Ocean. Using the new DMS concentration climatology in conjunction with state-of-the-art parameterizations for the sea/air gas transfer velocity and climatological wind fields, we estimate that 28.1 (17.6–34.4) Tg of sulfur are transferred from the oceans into the atmosphere annually in the form of DMS. This represents a global emission increase of 17% with respect to the equivalent calculation using the previous climatology. This new DMS climatology represents a valuable tool for atmospheric chemistry, climate, and Earth System models.This work was the core of DiMethylSulfide concentrations and emission fluxes in the Global Ocean (DMS‐GO), a joint initiative of the SOLAS Integration Project and the EU projects COST Action 735 and EUR‐OCEANS. Tom Bell, Peter Liss, and SOLAS Project Integration are supported by a U.K. SOLAS Knowledge Transfer grant (NE/E001696/1). Further support was provided by the Spanish Ministry of Science and Innovation through the projects MIMOSA, Malaspina 2010, and a Ph.D. studentship to A.L.Peer reviewe

We consider the relative advantages of two advanced data assimilation systems, 4-D-Var and ensemble Kalman filter (EnKF), currently in use or under consideration for operational implementation.With...

Special section Modeling Transport (Energy) Demand and Policies.-- 14 pages, 2 figures, 2 tables, 6 appendixA global alternative mix to fossil fuels is proposed, based on proven renewable energy technologies that do not use scarce materials. The mix consists of a combination of onshore and offshore wind turbines, concentrating solar power stations, hydroelectricity and wave power devices attached to the offshore turbines. Solar photovoltaic power could contribute to the mix if its dependence on scarce materials is solved. The most adequate deployment areas for the power stations are studied, as well as the required space. Material requirements are studied for the generation, power transport and for some future transport systems. The order of magnitude of copper, aluminium, neodymium, lithium, nickel, zinc and platinum that may be required for the proposed solution is obtained and compared with available reserves. Overall, the proposed global alternative to fossil fuels seems technically feasible. However, lithium, nickel and platinum could become limiting materials for future vehicles fleet if no global recycling systems were implemented and rechargeable zinc-air batteries would not be developed; 60% of the current copper reserves would have to be employed in the implementation of the proposed solution. Altogether, they may become a long-term physical constraint, preventing the continuation of the usual exponential growth of energy consumption. © 2011 Elsevier LtdPeer Reviewe

Soil Moisture and Ocean Salinity (SMOS), launched on 2 November 2009, is the first satellite mission addressing sea surface salinity (SSS) measurement from space. Its unique payload is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), a new two-dimensional interferometer designed by the European Space Agency (ESA) and operating at the L-band frequency. This article presents a summary of SSS retrieval from SMOS observations and shows initial results obtained one year after launch. These results are encouraging, but also indicate that further improvements at various data processing levels are needed and hence are currently under investigation.

This paper aims to present and assess the quality of seven years (2011–2017) of 25 km nine-day Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) objectively analyzed maps in the Arctic and sub-Arctic oceans ( ${50}^{\circ }$ N– ${90}^{\circ }$ N). The SMOS SSS maps presented in this work are an improved version of the preliminary three-year dataset generated and freely distributed by the Barcelona Expert Center. In this new version, a time-dependent bias correction has been applied to mitigate the seasonal bias that affected the previous SSS maps. An extensive database of in situ data (Argo floats and thermosalinograph measurements) has been used for assessing the accuracy of this product. The standard deviation of the difference between the new SMOS SSS maps and Argo SSS ranges from 0.25 and 0.35. The major features of the inter-annual SSS variations observed by the thermosalinographs are also captured by the SMOS SSS maps. However, the validation in some regions of the Arctic Ocean has not been feasible because of the lack of in situ data. In those regions, qualitative comparisons with SSS provided by models and the remotely sensed SSS provided by Aquarius and SMAP have been performed. Despite the differences between SMOS and SMAP, both datasets show consistent SSS variations with respect to the model and the river discharge in situ data, but present a larger dynamic range than that of the model. This result suggests that, in those regions, the use of the remotely sensed SSS may help to improve the models.