The ESA Climate Change Initiative: Satellite Data Records for Essential Climate Variables

Observations of Earth from space have been made for over 40 years and have contributed to advances in many aspects of climate science. However, attempts to exploit this wealth of data are often hampered by a lack of homogeneity and continuity and by insufficient understanding of the products and their uncertainties. There is, therefore, a need to reassess and reprocess satellite datasets to maximize their usefulness for climate science. The European Space Agency has responded to this need by establishing the Climate Change Initiative (CCI). The CCI will create new climate data records for (currently) 13 essential climate variables (ECVs) and make these open and easily accessible to all. Each ECV project works closely with users to produce time series from the available satellite observations relevant to users' needs. A climate modeling users' group provides a climate system perspective and a forum to bring the data and modeling communities together. This paper presents the CCI program. It outlines its benefit and presents approaches and challenges for each ECV project, covering clouds, aerosols, ozone, greenhouse gases, sea surface temperature, ocean color, sea level, sea ice, land cover, fire, glaciers, soil moisture, and ice sheets. It also discusses how the CCI approach may contribute to defining and shaping future developments in Earth observation for climate science.

Parameterisation of bivalve functional traits for mechanistic eco-physiological dynamic energy budget (DEB) models

Mechanistic models such as those based on dynamic energy budget (DEB) theory are emergent ecomechanics tools to investigate the extent of fitness in organisms through changes in life history traits as explained by bioenergetic principles. The rapid growth in interest around this approach originates from the mechanistic characteristics of DEB, which are based on a number of rules dictating the use of mass and energy flow through organisms. One apparent bottleneck in DEB applications comes from the estimations of DEB parameters which are based on mathematical and statistical methods (covariation method). The parameterisation process begins with the knowledge of some functional traits of a target organism (e. g. embryo, sexual maturity and ultimate body size, feeding and assimilation rates, maintenance costs), identified from the literature or laboratory experiments. However, considering the prominent role of the mechanistic approach in ecology, the reduction of possible uncertainties is an important objective. We propose a revaluation of the laboratory procedures commonly used in ecological studies to estimate DEB parameters in marine bivalves. Our experimental organism was Brachidontes pharaonis. We supported our proposal with a validation exercise which compared life history traits as obtained by DEBs (implemented with parameters obtained using classical laboratory methods) with the actual set of species traits obtained in the field. Correspondence between the 2 approaches was very high (>95%) with respect to estimating both size and fitness. Our results demonstrate a good agreement between field data and model output for the effect of temperature and food density on age-size curve, maximum body size and total gamete production per life span. The mechanistic approach is a promising method of providing accurate predictions in a world that is under in creasing anthropogenic pressure.

Consistent increase in dimethyl sulphide (DMS) in response to high CO2 in five shipboard bioassays from contrasting NW European waters

The ubiquitous marine trace gas dimethyl sulphide (DMS) comprises the greatest natural source of sulphur to the atmosphere and is a key player in atmospheric chemistry and climate. We explore the short term response of DMS and its algal precursor dimethyl sulphoniopropionate (DMSP) production and cycling to elevated carbon dioxide (CO2) and ocean acidification (OA) in five highly replicated 96 h shipboard bioassay experiments from contrasting sites in NW European shelf waters. In general, the response to OA throughout this region showed little variation, despite encompassing a range of biological and biogeochemical conditions. We observed consistent and marked increases in DMS concentrations relative to ambient controls, and decreases in DMSP concentrations. Quantification of rates of specific DMSP synthesis by phytoplankton and bacterial DMS gross production/consumption suggest algal processes dominated the CO2 response, likely due to a physiological response manifested as increases in direct cellular exudation of DMS and/or DMSP lyase enzyme activities. The variables and rates we report increase our understanding of the processes behind the response to OA. This could provide the opportunity to improve upon mesocosm-derived empirical modelling relationships, and move towards a mechanistic approach for predicting future DMS concentrations.

Quantifying the impact of environmental variables upon catch-per-unit-effort of the Blue Shark Prionace glauca in the Western English Channel

The effect of environmental variables on blue shark Prionace glauca catch per unit effort (CPUE) in a recreational fishery in the western English Channel, between June and September 1998–2011, was quantified using generalized additive models (GAMs). Sea surface temperature (SST) explained 1·4% of GAM deviance, and highest CPUE occurred at 16·7° C, reflecting the optimal thermal preferences of this species. Surface chlorophyll a concentration (CHL) significantly affected CPUE and caused 27·5% of GAM deviance. Additionally, increasing CHL led to rising CPUE, probably due to higher productivity supporting greater prey biomass. The density of shelf-sea tidal mixing fronts explained 5% of GAM deviance, but was non-significant, with increasing front density negatively affecting CPUE. Time-lagged frontal density significantly affected CPUE, however, causing 12·6% of the deviance in a second GAM and displayed a positive correlation. This outcome suggested a delay between the evolution of frontal features and the subsequent accumulation of productivity and attraction of higher trophic level predators, such as P. glauca.

Quantifying the impact of environmental variables upon catch-per-unit-effort of the Blue Shark Prionace glauca in the Western English Channel

The effect of environmental variables on blue shark Prionace glauca catch per unit effort (CPUE) in a recreational fishery in the western English Channel, between June and September 1998–2011, was quantified using generalized additive models (GAMs). Sea surface temperature (SST) explained 1·4% of GAM deviance, and highest CPUE occurred at 16·7° C, reflecting the optimal thermal preferences of this species. Surface chlorophyll a concentration (CHL) significantly affected CPUE and caused 27·5% of GAM deviance. Additionally, increasing CHL led to rising CPUE, probably due to higher productivity supporting greater prey biomass. The density of shelf-sea tidal mixing fronts explained 5% of GAM deviance, but was non-significant, with increasing front density negatively affecting CPUE. Time-lagged frontal density significantly affected CPUE, however, causing 12·6% of the deviance in a second GAM and displayed a positive correlation. This outcome suggested a delay between the evolution of frontal features and the subsequent accumulation of productivity and attraction of higher trophic level predators, such as P. glauca.

Living in warmer, more acidic oceans retards physiological recovery from tidal emersion in the velvet swimming crab, Necora puber

The distribution patterns of many species in the intertidal zone are partly determined by their ability to survive and recover from tidal emersion. During emersion, most crustaceans experience gill collapse, impairing gas exchange. Such collapse generates a state of hypoxemia and a hypercapnia-induced respiratory acidosis, leading to hyperlactaemia and metabolic acidosis. However, how such physiological responses to emersion are modified by prior exposure to elevated CO2 and temperature combinations, indicative of future climate change scenarios, is not known. We therefore investigated key physiological responses of velvet swimming crabs, Necora puber, kept for 14 days at one of four pCO(2)/temperature treatments (400 mu atm/10 degrees C, 1000 mu atm/10 degrees C, 400 mu atm/15 degrees C or 1000 mu atm/15 degrees C) to experimental emersion and recovery. Pre-exposure to elevated pCO(2) and temperature increased pre-emersion bicarbonate ion concentrations [HCO3-], increasing resistance to short periods of emersion (90 min). However, there was still a significant acidosis following 180 min emersion in all treatments. The recovery of extracellular acid-base via the removal of extracellular pCO(2) and lactate after emersion was significantly retarded by exposure to both elevated temperature and pCO(2). If elevated environmental pCO(2) and temperature lead to slower recovery after emersion, then some predominantly subtidal species that also inhabit the low to mid shore, such as N. puber, may have a reduced physiological capacity to retain their presence in the low intertidal zone, ultimately affecting their bathymetric range of distribution, as well as the structure and diversity of intertidal assemblages.

Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey–predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries.