Abstract Phytoplankton phenology and the length of the growing season have implications that cascade through trophic levels and ultimately impact the global carbon flux to the seafloor. Coupled hydrodynamic‐ecosystem models must accurately predict timing and duration of phytoplankton blooms in order to predict the impact of environmental change on ecosystem dynamics. Meteorological conditions, such as solar irradiance, air temperature, and wind speed are known to strongly impact the timing of phytoplankton blooms. Here, we investigate the impact of degrading the temporal resolution of meteorological forcing (wind, surface pressure, air, and dew point temperatures) from 1–24 hr using a 1‐D coupled hydrodynamic‐ecosystem model at two contrasting shelf‐sea sites: one coastal intermediately stratified site (L4) and one offshore site with constant summer stratification (CCS). Higher temporal resolutions of meteorological forcing resulted in greater wind stress acting on the sea surface increasing water column turbulent kinetic energy. Consequently, the water column was stratified for a smaller proportion of the year, producing a delayed onset of the spring phytoplankton bloom by up to 6 days, often earlier cessation of the autumn bloom, and shortened growing season of up to 23 days. Despite opposing trends in gross primary production between sites, a weakened microbial loop occurred with higher meteorological resolution due to reduced dissolved organic carbon production by phytoplankton caused by differences in resource limitation: light at CCS and nitrate at L4. Caution should be taken when comparing model runs with differing meteorological forcing resolutions. Recalibration of hydrodynamic‐ecosystem models may be required if meteorological resolution is upgraded.
The design of efficient monitoring programmes required for the assurance of offshore geological storage requires an understanding of the variability and heterogeneity of marine carbonate chemistry. In the absence of sufficient observational data and for extrapolation both spatially and seasonally, models have a significant role to play. In this study a previously evaluated hydrodynamic-biogeochemical model is used to characterise carbonate chemistry, in particular pH heterogeneity in the vicinity of the sea floor. Using three contrasting regions, the seasonal and short term variability are analysed and criteria that could be considered as indicators of anomalous carbonate chemistry identified. These criteria are then tested by imposing a number of randomised DIC perturbations on the model data, representing a comprehensive range of leakage scenarios. In conclusion optimal criteria and general rules for developing monitoring strategies are identified. Detection criteria will be site specific and vary seasonally and monitoring may be more efficient at periods of low dynamics. Analysis suggests that by using high frequency, sub-hourly monitoring anomalies as small as 0.01 of a pH unit or less may be successfully discriminated from natural variability – thereby allowing detection of small leaks or at distance from a leakage source. Conversely assurance of no leakage would be profound. Detection at deeper sites is likely to be more efficient than at shallow sites where the near bed system is closely coupled to surface processes. Although this study is based on North Sea target sites for geological storage, the model and the general conclusions are relevant to the majority of offshore storage sites lying on the continental shelf.
Dataset of model hindcast and climate projection data from a NEMO-ERSEM simulation of the 7km-resolution Atlantic Margin Model (AMM7). Model description and data are presented in Wakelin, S. L., Y. Artioli, J. T. Holt, M. Butenschön, and J. Blackford (2020), Controls on near-bed oxygen concentration on the Northwest European Continental Shelf under a potential future climate scenario, Progress in Oceanography, 102400. doi: https://doi.org/10.1016/j.pocean.2020.102400. Coupled NEMO-ERSEM model simulations are used to study temperature, salinity and near-bed oxygen concentrations on the northwest European Continental Shelf (NWES). Data are from a hindcast (1980 to 2007) and a climate projection (1980 to 2099) under the RCP8.5 climate emissions scenario. The climate projection (1980 to 2099) under the RCP8.5 climate emissions scenario is described as experiment E1 in Holt, J., J. Polton, J. Huthnance, S. Wakelin, E. O'Dea, J. Harle, A. Yool, Y. Artioli, J. Blackford, J. Siddorn, and M. Inall (2018), Climate-Driven Change in the North Atlantic and Arctic Oceans Can Greatly Reduce the Circulation of the North Sea, Geophysical Research Letters, 45(21), 11,827-811,836. doi: 10.1029/2018gl078878. The dataset consists of Hindcast simulation data AMM7_hindcast_3D_S_1980_2007.nc - monthly mean salinity fields. AMM7_hindcast_3D_T_1980_2007.nc - monthly mean temperature fields. AMM7_hindcast_near_bed_O2o_1980_2007.nc - near-bed oxygen concentrations on the NWES. Climate projection data AMM7_RCP8_5_3D_S_1980_2099.nc - monthly mean salinity fields. AMM7_RCP8_5_3D_T_1980_2099.nc - monthly mean temperature fields. AMM7_RCP8_5_3D_U_1980_2099.nc - monthly mean eastwards currents. AMM7_RCP8_5_3D_V_1980_2099.nc - monthly mean northwards currents. AMM7_RCP8_5_near_bed_1980_2099.nc - monthly mean near-bed oxygen concentrations and near-bed bacterial respiration on the NWES. AMM7_RCP8_5_netPP_1980_2099.nc - monthly mean depth integrated net primary production.
Carbon capture with offshore storage may take place at various geographical locations, characterized by diverse physical and biogeochemical properties and dynamics of the overlying water. In order to ensure storage integrity, baseline conditions must be carefully assessed for each potential storage area, which will allow design and deployment of optimal monitoring and sampling programs and establish appropriate site-specific criteria for anomaly detection, to allow timely reaction and necessary remedial measures.Within this paper, we assess applicability of using outputs of coupled hydrodynamic-biogeochemical models for the selection of appropriate variables to describe baseline variability and, consequently, strategies for the following monitoring. Via application of multivariate linear regression we identify combinations of modelled variables that best predict variability in pCO2 at a location corresponding to the potential storage site at Goldeneye Field in the Central North Sea. Although some variable pairs better predict pCO2 variability, we focus on a combination of oxygen saturation and silicate, as variables that can potentially be frequently and accurately monitored over long periods. In this work we employ highly simplified leakage scenarios to highlight the accuracy of baseline characterization and implications for establishment of thresholds for anomaly detection in highly dynamic marine environments. We conclude that hydrodynamic-biogeochemical models are invaluable tools for informing cost-effective monitoring strategies regarding the optimal number and combination of parameters surveyed and for establishing appropriate anomaly criteria for each potential storage location.
This repository contains configuration files for running GOTM-FABM-ERSEM at stations L4 and CCS to produce results presented in the manuscript "Sensitivity of shelf sea marine ecosystems to meteorological forcing" in addition to meteorology files for running the sensitivity analysis presented in the manuscript. Ncfiles containing model results for all scenarios presented in the manuscript are also included within the zip files for both stations GOTM code is freely available from: https://github.com/gotm-model/code FABM code is freely available from: https://github.com/fabm-model/fabm.git ERSEM code is freely available from: https://www.pml.ac.uk/Modelling_at_PML/Access_Code Instructions for compiling GOTM-FABM-ERSEM can be found in the ERSEM git repository after registering for the code using the link above. Versions/commits for the model code used to create results presented in this manuscript are: GOTM: commit 38e5d5b77adc7b3b5364aed7d7e4921b04b1781f FABM: commit 69da88c87ec59a51d1e2143c1f76111526ed6498 ERSEM: Version 19.04
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