ORCID Profile
0000-0003-2179-7656
Current Organisations
Utrecht University
,
Universiteit Utrecht
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Publisher: Wiley
Date: 30-09-2021
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-2725
Abstract: Mode waters are defined as thick, weakly stratified layers with homogeneous properties. They have the ability to store these properties, such as heat, carbon and nutrients, and exchange these with the surface or atmosphere during outcropping events or with other layers via mixing processes. Eighteen Degree Water (EDW) is the subtropical mode water of the western North Atlantic. Its yearly outcropping events in late winter makes it an important regulator of ocean heat, nutrients and carbon in the North Atlantic on annual timescales.Previous studies have given insight into the formation and destruction of Eighteen Degree Water. These have largely focused on physical aspects such as EDW formation rates. Due to the importance of EDW formation in setting the biogeochemical environment in the North Atlantic, it is instructive to investigate how biogeochemical tracers are altered along EDW formation routes. This study investigates in particular how dissolved inorganic carbon (DIC) is altered along ocean water parcel trajectories as EDW is formed. To do so, we compute Lagrangian trajectories of subducted EDW backwards in time using a coupled hydrodynamic and biogeochemical model. By s ling biogeochemical tracer values along Lagrangian pathways, we construct timeseries which we use to map the dominant locations at which DIC concentrations are altered in space and time to identify the Lagrangian fingerprint of DIC in Eighteen Degree Water.
Publisher: Wiley
Date: 23-12-2021
Publisher: Copernicus GmbH
Date: 27-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-5705
Abstract: & & & & & span& When faced with a question, scientists and scholars are trained to& search& academic and informal& literature to find& the answer.& But where& can& the public& find reliable& answers to& questions about the climate crisis?& After all,& the climate crisis& is a topic about which our understanding& rapidly& evolves& across a wide array of disciplines.& The validity& and reliability& of& offered& information is difficult to& assess& for non-specialists, while scientific consensus is& sometimes deliberately undermined& in popular articles. Moreover,& civil& questions about& the& climate& crisis& can be very specific, pragmatic or locally applicable, so not all answers can be found on popular sources& that& commonly& rovide only the theoretical principles or general background.& This raises the question how we can connect citizens with climate-related questions to understandable scientific expert& & /span& & span& knowledge& /span& & span& .& /span& & & & / & & & & & & span& KlimaatHelpdesk.org& is meant to become the go-to place in the Netherlands for& citizens& with climate-related questions. It is a unique,& independent,& and accessible c& /span& & span& ommunications& /span& & span& & latform that connects the& ublic& with scientists and experts, run by a volunteer group& of students and academics. People who ask& a& question on this platform will receive& a& eer-reviewed answer to their question from a network of affiliated scholars and experts. KlimaatHelpdesk& archives& the& & /span& & span& question and& & /span& & span& corresponding& & /span& & span& answer& /span& & span& & on the website and thereby& rovides an& expanding,& easily accessible source of reliable and evidence-based information. Since the official launch in November 2020, more than& & enthusiastic experts& have& answered and/or reviewed& over& & questions& in a variety of disciplines:& from& meteorology, oceanography and biology to psychology,& law,& and philosophy.& /span& & & & / & & & & & & span& KlimaatHelpdesk& also serves as& a platform for students and young academics to get involved in science outreach and public engagement, and for scientists to explain their& research& to a targeted audience.& While& KlimaatHelpdesk& is further& expanding its& reach in& the Netherlands,& & /span& & span& we also work to make& the& latform portable to other& countries and disciplines. We are& eager and& ready to share our gained experience& with the wider& Science Communication, Engagement & Outreach& community.& /span& & & & / &
Publisher: Copernicus GmbH
Date: 26-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-926
Abstract: & & Mesoscale eddies play a major role in ocean ventilation by stirring ocean tracers, such as carbon, along sloping surfaces of neutral buoyancy. To capture the effects of these turbulent eddies, coarse resolution ocean models resort to tracer diffusion parameterizations that take into account neutral surface slopes. Likewise, when studying tracer pathways in a Lagrangian framework, the effect of eddy dispersion needs to be parameterized when coarse models are used.& & & & Dispersion in Lagrangian simulations is traditionally parameterized by random walks, equivalent to diffusion in Eulerian models. Beyond random walks, there is a hierarchy of stochastic parameterizations, where stochastic perturbations are added to Lagrangian particle velocities, accelerations, or hyper-accelerations. These parameterizations are referred to as the 1& sup& st& /sup& , 2& sup& nd& /sup& and 3& sup& rd& /sup& order & #8216 Markov models& #8217 (Markov-N& em& )& /em& respectively. Most previous studies investigate these parameterizations in two-dimensional setups, often restricted to the ocean surface. The few studies that investigated Lagrangian dispersion parameterizations on three-dimensional neutral surfaces have focused only on random walk (Markov-0) dispersion.& & & & Here, we present a three-dimensional isoneutral formulation of the Markov-1 model. We also implement an anisotropic, shear-dependent formulation of Lagrangian random walk dispersion, originally formulated as a Eulerian diffusion parameterization by Le Sommer et al (2011). Random walk dispersion and Markov-1 are compared using an idealized setup as well as more realistic coarse and coarsened (50 km) ocean model output. While random walk dispersion and Markov-1 produce similar particle distributions over time, Markov-1 yields more realistic Lagrangian trajectories and leads to a smaller spurious dianeutral flux.& &
Publisher: American Geophysical Union (AGU)
Date: 02-2022
DOI: 10.1029/2021MS002850
Abstract: To capture the effects of mesoscale turbulent eddies, coarse‐resolution Eulerian ocean models resort to tracer diffusion parameterizations. Likewise, the effect of eddy dispersion needs to be parameterized when computing Lagrangian pathways using coarse flow fields. Dispersion in Lagrangian simulations is traditionally parameterized by random walks, equivalent to diffusion in Eulerian models. Beyond random walks, there is a hierarchy of stochastic parameterizations, where stochastic perturbations are added to Lagrangian particle velocities, accelerations, or hyper‐accelerations. These parameterizations are referred to as the first, second and third order “Markov models” (Markov‐N), respectively. Most previous studies investigate these parameterizations in two‐dimensional setups, often restricted to the ocean surface. On the other hand, the few studies that investigated Lagrangian dispersion parameterizations in three dimensions, where dispersion is largely restricted to neutrally buoyant surfaces, have focused only on random walk (Markov‐0) dispersion. Here, we present a three‐dimensional isoneutral formulation of the Markov‐1 model. We also implement an anisotropic, shear‐dependent formulation of random walk dispersion, originally formulated as a Eulerian diffusion parameterization. Random walk dispersion and Markov‐1 are compared using an idealized setup as well as more realistic coarse and coarsened (50 km) ocean model output. While random walk dispersion and Markov‐1 produce similar particle distributions over time when using our ocean model output, Markov‐1 yields Lagrangian trajectories that better resemble trajectories from eddy‐resolving simulations. Markov‐1 also yields a smaller spurious dianeutral flux.
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-1033
Abstract: & & Lagrangian simulations contribute to the study and comprehension of particulate-matter transport, its dissolution and dispersion in the oceans. Parcels is an open-source, Python-based module for Lagrangian ocean simulations. It is a known tool in the oceanographic community that has been applied to a variety of case studies, such as the tracing of microplastics, the backtracking of ocean floor plankton, and the migration of fish. In this module, particles are advected over time according to a selected flow field, where those particles can represent particulate-matter, biota or other objects with physical, hydrodynamic or biogeochemical properties. In this contribution, we present the substantial extensions of Parcels with respect to usability, physics modelling aspects of particle advection, and computational aspects of versatile, scalable and efficient simulations.& & & & Specifically, a suite of simple, concise notebook tutorials are tailored to novice user, covering step-by-step simulation setup instructions, whereas self-contained special-issue tutorials address advanced- and proficient user requirements. The considerable expansion of supported OGCM flow field input formats (e.g. MITgcm, POP and MOM5, among others) is a major interest in Parcels v2.2 for our steadily-growing user base.& & & & The new version further integrates previously-published physics methods into practical lagrangian particle simulations. As such, we implement an analytical advection scheme in addition to existing Runge-Kutta advection schemes. Furthermore, two-dimensional advection-diffusion is upgraded with the Milstein stochastic integration scheme and improved documentation. Those capabilities enable a more consistent modelling of diffusion- and uncertainty-dominated fluid transport processes.& & & & The case studies performed with previous versions indicate increased computational demands. Simulations are run over long decadal time scales as well as over day-periods with sub-second temporal increments, involving multiple basins and global scenarios, while also modelling increasingly complex particle processes. Overall, our developments respond to the big-data requirements of modern oceanographic studies, which include the aspects of (i) high record volume (i.e. large number of particles), (ii) high dimensionality in multi-variate records, (iii) high spatial resolution, (iv) high temporal resolution, (v) high scenario (i.e. case study) variability and (vi) the prevention of numerical error accumulation over long simulation time scales.& & & & The novel features of Parcels v2.2 are illustrated on distinct case studies within our contribution, in order to connect the technical features to their impact on particulate-matter ocean transport studies.& &
Publisher: American Geophysical Union (AGU)
Date: 2021
DOI: 10.1029/2020JC016416
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-201
Abstract: & & & span& To identify barriers to transport in a fluid domain, community detection algorithms from network science have been used to ide the domain into clusters that are sparsely connected with each other. In a previous application to the closed domain of the Mediterranean Sea, communities detected by the & em& Infomap& /em& algorithm have barriers that often coincide with well-known oceanographic features. We apply this clustering method to the surface of the Arctic and subarctic oceans and thereby show that it can also be applied to open domains. First, we construct a Lagrangian flow network by simulating the exchange of Lagrangian particles between different bins in an icosahedral-hexagonal grid. Then, & em& Infomap & /em& is applied to identify groups of well-connected bins. The resolved transport barriers include naturally occurring structures, such as the major currents. As expected, clusters in the Arctic are affected by seasonal and annual variations in sea-ice concentration. An important caveat of community detection algorithms is that many different isions into clusters may qualify as good solutions. Moreover, while certain cluster boundaries lie consistently at the same location between different good solutions, other boundary locations vary significantly, making it difficult to assess the physical meaning of a single solution. We therefore consider an ensemble of solutions to find persistent boundaries, trends and correlations with surface velocities and sea-ice cover.& /span& & / &
No related grants have been discovered for Daan Reijnders.