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Changes in marine net primary productivity (PP) and export of particulate organic carbon (EP) are projected over the 21st century with four global coupled carbon cycle-climate models. These include representations of marine ecosystems and the carbon cycle of different structure and complexity. All four models show a decrease in global mean PP and EP between 2 and 20% by 2100 relative to preindustrial conditions, for the SRES A2 emission scenario. Two different regimes for productivity changes are consistently identified in all models. The first chain of mechanisms is dominant in the low- and mid-latitude ocean and in the North Atlantic: reduced input of macro-nutrients into the euphotic zone related to enhanced stratification, reduced mixed layer depth, and slowed circulation causes a decrease in macro-nutrient concentrations and in PP and EP. The second regime is projected for parts of the Southern Ocean: an alleviation of light and/or temperature limitation leads to an increase in PP and EP as productivity is fueled by a sustained nutrient input. A region of disagreement among the models is the Arctic, where three models project an increase in PP while one model projects a decrease. Projected changes in seasonal and interannual variability are modest in most regions. Regional model skill metrics are proposed to generate multi-model mean fields that show an improved skill in representing observation-based estimates compared to a simple multi-model average. Model results are compared to recent productivity projections with three different algorithms, usually applied to infer net primary production from satellite observations.

Fires have influenced atmospheric composition and climate since the rise of vascular plants, and satellite data have shown the overall global extent of fires. Our knowledge of historic fire emissions has progressively improved over the past decades due mostly to the development of new proxies and the improvement of fire models. Currently, there is a suite of proxies including sedimentary charcoal records, measurements of fire-emitted trace gases and black carbon stored in ice and firn, and visibility observations. These proxies provide opportunities to extrapolate emission estimates back in time based on satellite data starting in 1997, but each proxy has strengths and weaknesses regarding, for example, the spatial and temporal extents over which they are representative. We developed a new historic biomass burning emissions dataset starting in 1750 that merges the satellite record with several existing proxies and uses the average of six models from the Fire Model Intercomparison Project (FireMIP) protocol to estimate emissions when the available proxies had limited coverage. According to our approach, global biomass burning emissions were relatively constant, with 10-year averages varying between 1.8 and 2.3 Pg C yr−1. Carbon emissions increased only slightly over the full time period and peaked during the 1990s after which they decreased gradually. There is substantial uncertainty in these estimates, and patterns varied depending on choices regarding data representation, especially on regional scales. The observed pattern in fire carbon emissions is for a large part driven by African fires, which accounted for 58 % of global fire carbon emissions. African fire emissions declined since about 1950 due to conversion of savanna to cropland, and this decrease is partially compensated for by increasing emissions in deforestation zones of South America and Asia. These global fire emission estimates are mostly suited for global analyses and will be used in the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations.

SNP arrays are powerful tools for high-resolution studies of the genetic basis of complex traits, facilitating both population genomic and selective breeding research. The European seabass (Dicentrarchus labrax) and the gilthead seabream (Sparus aurata) are the two most important fish species for Mediterranean aquaculture. While selective breeding programmes increasingly underpin stocky supply for this industry, genomic selection is not yet widespread. Genomic selection has major potential to expedite genetic gain, in particular for traits practically impossible to measure on selection candidates, such as disease resistance and fillet yield. The aim of our study was to design a combined-species 60K SNP array for both European seabass and gilthead seabream, and to validate its performance on farmed and wild populations from numerous locations throughout the species range. To achieve this, high coverage Illumina whole genome sequencing of pooled samples was performed for 24 populations of European seabass and 27 populations of gilthead seabream. This resulted in a database of ~20 million SNPs per species, which were then filtered to identify high-quality variants and create the final set for the development of the ‘MedFish’ SNP array. The array was then tested by genotyping a subset of the discovery populations and demonstrated a high conversion rate to functioning polymorphic assays on the array (92% in seabass: 89% in seabream) and repeatability (99.4 - 99.7%). The platform interrogates ~30K markers in each fish species, includes features such as SNPs previously shown to be associated with performance traits, and is enriched for SNPs predicted to alter protein function. The array was demonstrated to be effective at detecting population structure across a wide range of fish populations from diverse geographical origins, and to examine the extent of haplotype sharing among Mediterranean fish farms. Therefore, the MedFish array enables efficient and accurate high-throughput genotyping for genome-wide distributed SNPs on each fish species, and will facilitate stock management, population genomics approaches, and acceleration of selective breeding through genomic selection.

Recent studies have shown the importance of new particle formation (NPF) to global cloud condensation nuclei (CCN) production, as well as to air pollution in megacities. In addition to the necessary presence of low-volatility vapors that can form new aerosol particles, both numerical and observational studies have shown that the dynamics of the planetary boundary layer (BL) plays an important role in NPF. Evidence from field observations suggests that roll vortices might be favorable for inducing NPF in a convective BL. However, direct observations and estimates of the potential importance of this phenomenon to the production of new aerosol particles are lacking. Here we show that rolls frequently induce NPF bursts along the horizontal circulations and that the small clusters and particles originating from these localized bursts grow in size similar to particles typically ascribed to atmospheric NPF that occur almost homogeneously at a regional scale. We outline a method to identify roll-induced NPF from measurements and, based on the collected data, estimate the impact of roll vortices on the overall aerosol particle production due to NPF at a boreal forest site (83 % ± 34 % and 26 % ± 8 % overall enhancement in particle formation for 3 and 10 nm particles, respectively). We conclude that the formation of roll vortices should be taken into account when estimating particle number budgets in the atmospheric BL.

The increase in atmospheric CO2 is a dual threat to the marine environment: from one side it drives climate change, leading to modifications in water temperature, circulation patterns and stratification intensity; on the other side it causes a decrease in marine pH (ocean acidification, or OA) due to the increase in dissolved CO2. Assessing the combined impact of climate change and OA on marine ecosystems is a challenging task. The response of the ecosystem to a single driver can be highly variable and remains still uncertain; additionally the interaction between these can be either synergistic or antagonistic. In this work we use the coupled oceanographic–ecosystem model POLCOMS-ERSEM driven by climate forcing to study the interaction between climate change and OA. We focus in particular on carbonate chemistry, primary and secondary production. The model has been run in three different configurations in order to assess separately the impacts of climate change on net primary production and of OA on the carbonate chemistry, which have been strongly supported by scientific literature, from the impact of biological feedbacks of OA on the ecosystem, whose uncertainty still has to be well constrained. The global mean of the projected decrease of pH at the end of the century is about 0.27 pH units, but the model shows significant interaction among the drivers and high variability in the temporal and spatial response. As a result of this high variability, critical tipping point can be locally and/or temporally reached: e.g. undersaturation with respect to aragonite is projected to occur in the deeper part of the central North Sea during summer. Impacts of climate change and of OA on primary and secondary production may have similar magnitude, compensating in some area and exacerbating in others.

The offline FLEXible PARTicle (FLEXPART) stochastic dispersion model is currently a community model used by many scientists. Here, an alternative FLEXPART model version has been developed and tailored to use with the meteorological output data generated by the CMIP5-version of the Norwegian Earth System Model (NorESM1-M). The atmospheric component of the NorESM1-M is based on the Community Atmosphere Model (CAM4); hence, this FLEXPART version could be widely applicable and it provides a new advanced tool to directly analyse and diagnose atmospheric transport properties of the state-of-the-art climate model NorESM in a reliable way. The adaptation of FLEXPART to NorESM required new routines to read meteorological fields, new post-processing routines to obtain the vertical velocity in the FLEXPART coordinate system, and other changes. These are described in detail in this paper. To validate the model, several tests were performed that offered the possibility to investigate some aspects of offline global dispersion modelling. First, a comprehensive comparison was made between the tracer transport from several point sources around the globe calculated online by the transport scheme embedded in CAM4 and the FLEXPART model applied offline on output data. The comparison allowed investigating several aspects of the transport schemes including the approximation introduced by using an offline dispersion model with the need to transform the vertical coordinate system, the influence on the model results of the sub-grid-scale parameterisations of convection and boundary layer height and the possible advantage entailed in using a numerically non-diffusive Lagrangian particle solver. Subsequently, a comparison between the reference FLEXPART model and the FLEXPART–NorESM/CAM version was performed to compare the well-mixed state of the atmosphere in a 1-year global simulation. The two model versions use different methods to obtain the vertical velocity but no significant difference in the results was found. However, for both model versions there was some degradation in the well-mixed state after 1 year of simulation with the build-up of a bias and an increased scatter. Finally, the capability of the new combined modelling system in producing realistic, backward-in-time transport statistics was evaluated calculating the average footprint over a 5-year period for several measurement locations and by comparing the results with those obtained with the reference FLEXPART model driven by re-analysis fields. This comparison confirmed the effectiveness of the combined modelling system FLEXPART with NorESM in producing realistic transport statistics.

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations.

In this study, Algal Pigment Index 2 (API2) is investigated in Sagres, an area located in the Atlantic off the southwestern Iberian Peninsula. Standard results provided by the MEdium Resolution Image Spectrometer (MERIS) ocean colour sensor were compared with alternative data products, determined through a regional inversion scheme, using both MERIS and in situ remote sensing reflectances (Rrs) as input data. The reference quantity for performance assessment is in situ total chlorophyll a (TChl a) concentration estimated through a phytoplankton absorption coefficient (i.e. equivalent to API2). Additional comparison of data products has also been addressed for TChl a concentration determined by high-performance liquid chromatography. The MERIS matchup analysis revealed a systematic underestimation of TChl a, which was confirmed with an independent comparison of product map analysis. The study demonstrates the importance of regional algorithms for the study area that could complement upcoming standard results of the current Sentinel-3/OLCI space mission.

Snow consists of non-spherical grains of various shapes and sizes. Still, in radiative transfer calculations, snow grains are often treated as spherical. This also applies to the computation of snow albedo in the Snow, Ice, and Aerosol Radiation (SNICAR) model and in the Los Alamos sea ice model, version 4 (CICE4), both of which are employed in the Community Earth System Model and in the Norwegian Earth System Model (NorESM). In this study, we evaluate the effect of snow grain shape on climate simulated by NorESM in a slab ocean configuration of the model. An experiment with spherical snow grains (SPH) is compared with another (NONSPH) in which the snow shortwave single-scattering properties are based on a combination of three non-spherical snow grain shapes optimized using measurements of angular scattering by blowing snow. The key difference between these treatments is that the asymmetry parameter is smaller in the non-spherical case (0.77–0.78 in the visible region) than in the spherical case ( ≈ 0.89). Therefore, for the same effective snow grain size (or equivalently, the same specific projected area), the snow broadband albedo is higher when assuming non-spherical rather than spherical snow grains, typically by 0.02–0.03. Considering the spherical case as the baseline, this results in an instantaneous negative change in net shortwave radiation with a global-mean top-of-the-model value of ca. −0.22 W m−2. Although this global-mean radiative effect is rather modest, the impacts on the climate simulated by NorESM are substantial. The global annual-mean 2 m air temperature in NONSPH is 1.17 K lower than in SPH, with substantially larger differences at high latitudes. The climatic response is amplified by strong snow and sea ice feedbacks. It is further demonstrated that the effect of snow grain shape could be largely offset by adjusting the snow grain size. When assuming non-spherical snow grains with the parameterized grain size increased by ca. 70 %, the climatic differences to the SPH experiment become very small. Finally, the impact of assumed snow grain shape on the radiative effects of absorbing aerosols in snow is discussed.