The assessment of a global marine ecosystem model on the basis of emergent properties and ecosystem function: a case study with ERSEM

Ecosystem models are often assessed using quantitative metrics of absolute ecosystem state, but these model-data comparisons are disproportionately vulnerable to discrepancies in the location of important circulation features. An alternative method is to demonstrate the models capacity to represent ecosystem function; the emergence of a coherent natural relationship in a simulation indicates that the model may have an appropriate representation of the ecosystem functions that lead to the emergent relationship. Furthermore, as emergent properties are large-scale properties of the system, model validation with emergent properties is possible even when there is very little or no appropriate data for the region under study, or when the hydrodynamic component of the model differs significantly from that observed in nature at the same location and time. A selection of published meta-analyses are used to establish the validity of a complex marine ecosystem model and to demonstrate the power of validation with emergent properties. These relationships include the phytoplankton community structure, the ratio of carbon to chlorophyll in phytoplankton and particulate organic matter, the ratio of particulate organic carbon to particulate organic nitrogen and the stoichiometric balance of the ecosystem. These metrics can also inform aspects of the marine ecosystem model not available from traditional quantitative and qualitative methods. For instance, these emergent properties can be used to validate the design decisions of the model, such as the range of phytoplankton functional types and their behaviour, the stoichiometric flexibility with regards to each nutrient, and the choice of fixed or variable carbon to nitrogen ratios.

Assessing the environmental consequences of CO2 leakage from geological CCS: Generating evidence to support environmental risk assessment

At the start of the industrial revolution (circa 1750) the atmospheric concentration of carbon dioxide (CO2) was around 280 ppm. Since that time the burning of fossil fuel, together with other industrial processes such as cement manufacture and changing land use, has increased this value to 400 ppm, for the first time in over 3 million years. With CO2 being a potent greenhouse gas, the consequence of this rise for global temperatures has been dramatic, and not only for air temperatures. Global Sea Surface Temperature (SST) has warmed by 0.4–0.8 °C during the last century, although regional differences are evident (IPCC, 2007). This rise in atmospheric CO2 levels and the resulting global warming to some extent has been ameliorated by the oceanic uptake of around one quarter of the anthropogenic CO2 emissions (Sabine et al., 2004). Initially this was thought to be having little or no impact on ocean chemistry due to the capacity of the ocean’s carbonate buffering system to neutralise the acidity caused when CO2 dissolves in seawater. However, this assumption was challenged by Caldeira and Wickett (2005) who used model predictions to show that the rate at which carbonate buffering can act was far too slow to moderate significant changes to oceanic chemistry over the next few centuries. Their model predicted that since pre-industrial times, ocean surface water pH had fallen by 0.1 pH unit, indicating a 30% increase in the concentration of H+ ions. Their model also showed that the pH of surface waters could fall by up to 0.4 units before 2100, driven by continued and unabated utilisation of fossil fuels. Alongside increasing levels of dissolved CO2 and H+ (reduced pH) an increase in bicarbonate ions together with a decrease in carbonate ions occurs. These chemical changes are now collectively recognised as “ocean acidification”. Concern now stems from the knowledge that concentrations of H+, CO2, bicarbonate and carbonate ions impact upon many important physiological processes vital to maintaining health and function in marine organisms. Additionally, species have evolved under conditions where the carbonate system has remained relatively stable for millions of years, rendering them with potentially reduced capacity to adapt to this rapid change. Evidence suggests that, whilst the impact of ocean acidification is complex, when considered alongside ocean warming the net effect on the health and productivity of the oceans will be detrimental.

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.

An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-abased models

We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll-a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed-layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite-derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low-productivity seasons as well as in sea ice-covered/deep-water regions. Depth-resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption-based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll-a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic-relevant parameters.