A global inventory of small floating plastic debris

Abstract. Given the large differences between biospheric model estimates of regional carbon exchange, there is a need to understand and reconcile the predicted spatial variability of fluxes across models. This paper presents a set of quantitative tools that can be applied to systematically compare flux estimates despite the inherent differences in model formulation. The presented methods include variogram analysis, variable selection, and geostatistical regression. These methods are evaluated in terms of their ability to assess and identify differences in spatial variability in flux estimates across North America among a small subset of models, as well as differences in the environmental drivers that best explain the spatial variability of predicted fluxes. The examined models are the Simple Biosphere (SiB 3.0), Carnegie Ames Stanford Approach (CASA), and CASA coupled with the Global Fire Emissions Database (CASA GFEDv2), and the analyses are performed on model-predicted net ecosystem exchange, gross primary production, and ecosystem respiration. Variogram analysis reveals consistent seasonal differences in spatial variability among modeled fluxes at a 1° × 1° spatial resolution. However, significant differences are observed in the overall magnitude of the carbon flux spatial variability across models, in both net ecosystem exchange and component fluxes. Results of the variable selection and geostatistical regression analyses suggest fundamental differences between the models in terms of the factors that explain the spatial variability of predicted flux. For example, carbon flux is more strongly correlated with percent land cover in CASA GFEDv2 than in SiB or CASA. Some of the differences in spatial patterns of estimated flux can be linked back to differences in model formulation, and would have been difficult to identify simply by comparing net fluxes between models. Overall, the systematic approach presented here provides a set of tools for comparing predicted grid-scale fluxes across models, a task that has historically been difficult unless standardized forcing data were prescribed, or a detailed sensitivity analysis performed.

In this study, a global isopycnal ocean model (GIM) is described and used for a simulation of variabilities of the global upper ocean during 1992–93. The GIM simulations are compared and validated with both the available observations and simulations with the Geophysical Fluid Dynamics Laboratory Modular Ocean Model (MOM). The observations include sea surface height from TOPEX/Poseidon (T/P), sea surface temperature (SST) from weekly National Centers for Environmental Prediction analysis, and vertical temperature profiles from gridded expandable bathythermographs (XBTs) data. The major differences between the GIM and MOM used in this study are the vertical coordinates, a Kraus–Turner mixed layer, and a tracer-transport velocity associated with an isopycnal-depth diffusion. Otherwise, the two models are formulated in the same parameter space, model configuration, and boundary conditions. The effects of these differences in model formulation on the model simulations are investigated. Due to the difference in the orientation of interior flow and mixing, SST and the thermocline stratification in the eastern equatorial Pacific in GIM are more sensitive to the wind-driven upwelling than they are in MOM. In GIM there is no effective means to transfer heat between the upwelling cold water and the surrounding warm water since subsurface flow and mixing predominantly occur along isopycnic layers. As a result, the SST tends to be cold and the front tends to be sharp compared with the observations in the wind-driven upwelling region. The sharp front could potentially cause numerical instability in GIM. Thus, a large isopycnal-depth diffusivity has to be used to maintain the model stability since the isopycnal-depth diffusion is the most effective way to reduce the steep slope of isopycnals and the strength of the front associated with the cold upwelling in GIM. But the large isopycnal-depth diffusion results in excessive smoothing in the meridional isotherm doming in the equatorial and tropical thermocline. The trade-off between the numerical instability and the excessive isopycnal smoothing points to the necessity of improvement in the isopycnal-depth diffusion. Sea level variabilities during 1992–93 simulated with both GIM and MOM are in good agreement with T/P observations. However, MOM poorly simulates the vertical distribution of the seasonal temperature anomalies in the upper ocean (the baroclinic component of the sea level variability) during 1992–93. Due to the lack of a realistic surface mixed layer, the MOM-simulated temperature profiles have a sharp subsurface gradient, which is not evident in both the GIM simulation and the XBT observation. As a result, the region below the subsurface gradient is almost insulated from the influence of the seasonal temperature variation. The Kraus–Turner mixed layer used in GIM helps to improve the model-simulated seasonal variations of the upper-ocean temperature and the background sea level variability. Implications of deficiencies in both GIM and MOM on the altimetric sea level data assimilation and transient tracer simulations are discussed.

Offshore wind farm installation requires complex planning under uncertainty, and numerous system models have been developed to support scheduling and resource allocation. However, differences in model formulation, solution methods, and level of detail make it difficult to identify the best approaches for a given project. This paper develops a domain-specific taxonomy to compare and select system models for offshore wind installation planning. The taxonomy is grounded in a systematic literature review of 194 studies and adapts general model comparison criteria to four dimensions: application context, model formulation, solution methods, and model features. The taxonomy is applied to six representative offshore scheduling approaches to illustrate its use. Results show that the taxonomy clarifies model scope, strengths, limitations, and trade-offs, providing a structured basis for model selection and revealing opportunities for methodological refinement. While tailored to offshore wind installations, the approach could be transferable to other domains with heterogeneous system models.

Century-scale global near-surface temperature trends in response to rising greenhouse gas concentrations in climate models vary by almost a factor of 2, with greatest intermodel spread in the Arctic region where sea ice is a key climate component. Three factors contribute to the intermodel spread: 1) model formulation, 2) control climate state, and 3) internal climate variability. This study focuses on the influence of Arctic sea ice in the control climate on the intermodel spread in warming, using idealized 1% yr−1 CO2 increase simulations of 33 state-of-the-art global climate models, and combining sea ice–temperature relations on local to large spatial scales. On the Arctic mean scale, the spread in temperature trends is only weakly related to ice volume or area in the control climate, and is probably not dominated by internal variability. This suggests that other processes, such as ocean heat transport and meteorological conditions, play a more important role in the spread of long-term Arctic warming than control sea ice conditions. However, on a local scale, sea ice–warming relations show that in regions with more sea ice, models generally simulate more warming in winter and less warming in summer. The local winter warming is clearly related to control sea ice and universal among models, whereas summer sea ice–warming relations are more diverse, and are probably dominated by differences in model formulation. To obtain a more realistic representation of Arctic warming, it is recommended to simulate control sea ice conditions in climate models so that the spatial pattern is correct.

The mathematical representation of freezing and thawing processes in variably-saturated, non-deformable soils

Abstract. Fram Strait, the deepest gateway to the Arctic Ocean, is strongly influenced by eddy dynamics. Here we analyse the output from two eddy-resolving models (ROMS – Regional Ocean Modeling System; FESOM – Finite-Element Sea-ice Ocean Model) with around 1 km mesh resolution in Fram Strait, with a focus on their representation of eddy properties and dynamics. A comparison with mooring observations shows that both models reasonably simulate hydrography and eddy kinetic energy. Despite differences in model formulation, they show relatively similar eddy properties. The eddies have a mean radius of 4.9 and 5.6 km in ROMS and FESOM, respectively, with slightly more cyclones (ROMS: 54 %, FESOM: 55 %) than anticyclones. The mean lifetime of detected eddies is relatively short in both simulations (ROMS: 10 d, FESOM: 11 d), and the mean travel distance is 35 km in both models. More anticyclones are trapped in deep depressions or move toward deep locations. The two models show comparable spatial patterns of baroclinic and barotropic instability. ROMS has relatively stronger eddy intensity and baroclinic instability, possibly due to its smaller grid size, while FESOM has stronger eddy kinetic energy in the West Spitsbergen Current. Overall, the relatively good agreement between the two models strengthens our confidence in their ability to realistically represent the Fram Strait ocean dynamics and also highlights the need for very high mesh resolution.

Impacts of climate change on methylmercury formation and bioaccumulation in the 21st century ocean

The simulation of weathering, solute distribution or acidification at the catchment scale is predominantly done with either a mass balance or process level model. The former redistributes total elemental concentrations between known points with measured total concentration, but does not explicitly include catchment hydrology. The latter includes compartmental hydrological models and detailed descriptions of spatially averaged chemical reactions. Interestingly, the model applications tend towards hydrologically different watershed structures: mass balance modelling favours bare rock watersheds similar to the Apache Leap Research Site, whereas process level models are applied most often to forested watersheds, among which the Hubbard Brook Experimental Forest is an example. Although constrained either by mineral or water compositions on the one hand, or calibrated against stream or lake water chemistry on the other, both approaches basically fit parameters to the geochemical circumstances of the specific watershed of interest. Limited success is attained if the hydrological conditions remain within the circumstances of the parameter fitting. The principal differences in model formulation and approach to mass balance modelling are discussed. Without advancements in model calibration and rigorous model testing, and the development of methods for optimizing the important reactions and pathways, the transportability of models between watersheds or the simulation of extreme events will continue to be inadequate. © 1997 John Wiley & Sons, Ltd.

We examine whether seven state‐of‐the‐art European regional air quality models provide daily ensembles of predicted ozone maxima that encompass observations. Using tools borrowed from the evaluation of ensemble weather forecasting, we analyze statistics of simulated ensembles of ozone daily maxima over an entire summer season. Although the model ensemble overestimates ozone, the distribution of simulated concentrations is representative of the uncertainty. The spread of simulations is due to random fluctuations resulting from differences in model formulations and input data, but also to the spread between individual model systematic biases. The ensemble average skill increases as the spread decreases. The skill of the ensemble in giving probabilistic predictions of threshold exceedances is also demonstrated. These results allow for optimism about the ability of this ensemble to simulate the uncertainty of the impact of emission control scenarios.

Coupling and comparing a spatially- and temporally-detailed eutrophication model with an ecosystem network model: An initial application to Chesapeake Bay

Amplification of the northern hemisphere seasonal cycle of insolation during the mid‐Holocene causes a northward shift of the main regions of monsoon precipitation over Africa and India in all 18 simulations conducted for the Paleoclimate Modeling Intercomparison Project (PMIP). Differences among simulations are related to differences in model formulation. Despite qualitative agreement with paleoecological estimates of biome shifts, the magnitude of the monsoon increases over northern Africa are underestimated by all the models.

Acid rock drainage (ARD) is a problem of international relevance with substantial environmental and economic implications. Reactive transport modeling has proven a powerful tool for the process-based assessment of metal release and attenuation at ARD sites. Although a variety of models has been used to investigate ARD, a systematic model intercomparison has not been conducted to date. This contribution presents such a model intercomparison involving three synthetic benchmark problems designed to evaluate model results for the most relevant processes at ARD sites. The first benchmark (ARD-B1) focuses on the oxidation of sulfide minerals in an unsaturated tailing impoundment, affected by the ingress of atmospheric oxygen. ARD-B2 extends the first problem to include pH buffering by primary mineral dissolution and secondary mineral precipitation. The third problem (ARD-B3) in addition considers the kinetic and pH-dependent dissolution of silicate minerals under low pH conditions. The set of benchmarks was solved by four reactive transport codes, namely CrunchFlow, Flotran, HP1, and MIN3P. The results comparison focused on spatial profiles of dissolved concentrations, pH and pE, pore gas composition, and mineral assemblages. In addition, results of transient profiles for selected elements and cumulative mass loadings were considered in the intercomparison. Despite substantial differences in model formulations, very good agreement was obtained between the various codes. Residual deviations between the results are analyzed and discussed in terms of their implications for capturing system evolution and long-term mass loading predictions.

Species distribution models (SDMs) are a commonly used tool, which when combined with earth system models (ESMs), can project changes in organismal occurrence, abundance, and phenology under climate change. An often untested assumption of SDMs is that relationships between organisms and the environment are stationary. To evaluate this assumption, we examined whether patterns of distribution among larvae of four small pelagic fishes (Pacific sardine Sardinops sagax, northern anchovy Engraulis mordax, jack mackerel Trachurus symmetricus, chub mackerel Scomber japonicus) in the California Current remained steady across time periods defined by climate regimes, changes in secondary productivity, and breakpoints in time series of spawning stock biomass (SSB). Generalized additive models (GAMs) were constructed separately for each period using temperature, salinity, dissolved oxygen (DO), and mesozooplankton volume as predictors of larval occurrence. We assessed non-stationarity based on changes in six metrics: 1) variables included in SDMs; 2) whether a variable exhibited a linear or non-linear form; 3) rank order of deviance explained by variables; 4) response curve shape; 5) degree of responsiveness of fishes to a variable; 6) range of environmental variables associated with maximum larval occurrence. Across all species and time periods, non-stationarity was ubiquitous, affecting at least one of the six indicators. Rank order of environmental variables, response curve shape, and oceanic conditions associated with peak larval occurrence were the indicators most subject to change. Non-stationarity was most common among regimes defined by changes in fish SSB. The relationships between larvae and DO were somewhat more likely to change across periods, whereas the relationships between fishes and temperature were more stable. Respectively, S. sagax, T. symmetricus, S. japonicus, and E. mordax exhibited non-stationarity across 89%, 67%, 50%, and 50% of indicators. For all species except E. mordax, inter-model variability had a larger impact on projected habitat suitability for larval fishes than differences between two climate change scenarios (SSP1-2.6 and SSP5-8.5), implying that subtle differences in model formulation could have amplified future effects. These results suggest that the widespread non-stationarity in how fishes utilize their environment could hamper our ability to reliably project how species will respond to climatic change.

Combined effects of gravity segregation, viscous fingering and reservoir heterogeneity are examined in particle-tracking simulations of flow in vertical cross sections. Differences in model formulation from previous descriptions are reviewed briefly. The accuracy of the simulation representation of the physical flow mechanisms is tested. For homogeneous cross sections, simulator calculations are presented that illustrate the transition from flow in a single gravity-dominated tongue at low viscous to gravity ratio (Ngr) to flow dominated by viscous fingering at high values of Ngr. Quantitative accuracy of the simulator is tested against the experimental results of Pozzi and Blackwell. Simulation results agree with experimental results over wide ranges of mobility ratio, viscous to gravity ratio, and aspect ratio. Additional simulations also agree well with Stalkup's correlation of breakthrough recovery as a function of mobility ratio and Ngr. The validated simulator is then used to augment Stalkup's correlation with results for additional mobility ratios. In addition, plots are presented of recovery after breakthrough as a function of Ngr for several mobility ratios. The results confirm that in homogeneous porous media, better displacement performance is observed at high viscous to gravity ratio for any mobility ratio. For heterogeneous porous media, however, that conclusion must be qualified. Example simulation results show that for injection of light solvent into a layered reservoir with high permeability low in the reservoir, better displacement efficiency is observed at intermediate values of Ngr than at high or low values. If the high permeability is at the top of the reservoir, high Ngr is still preferred.

Urban air quality has deteriorated in last few decades in the mega cities of both developed and developing countries. Many mathematical models have been widely used as prediction tool for urban air quality management in developed countries. However, applications of these models are limited in developing countries including India due to lack of suffi cient validation studies. In this paper, three state-of-the-art air quality models namely AERMOD, ADMS-Urban and ISCST3 have been used to predict the air quality at an intersection in Delhi city, India, followed by their performance evaluation and sensitive analysis under different meteorological conditions. The models have been run for different climatic conditions, i.e. summer and winter season to predict the concentration of carbon monoxide (CO), nitrogen dioxide (NO 2 ) and PM 2.5 (diameter size less than 2.5 µm). The ISCST3 has performed satisfactorily (d = 0.69) for predicting CO concentrations when compared with AERMOD (d = 0.50) and ADMS-Urban (d = 0.45) for winter period. The ADMS-Urban (d = 0.49) has performed satisfactorily for predicting NO 2 concentration when compared with ISCST3 (d = 0.36) and AERMOD (d = 0.32). The AERMOD, ISCST3 and ADMS-Urban have performed satisfactorily for predicting PM 2.5 concentrations having d values as 0.46, 0.45 and 0.43 respectively. All three models have performed satisfactorily for predicting CO concentrations when wind speed was in the range of 0.5–3 m/s and wind direction in the range 90–180 degrees, i.e. downwind direction. The difference in model’s performance may be due to differences in model formulation and the treatment of terrain features. The causal nature of these Gaussian based models may be one of the reasons for difference in performance of the models, because these are sensitive to