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Constraining an Ocean Model Under Getz Ice Shelf, Antarctica, Using A Gravity‐Derived Bathymetry

Constraining an Ocean Model Under Getz Ice Shelf, Antarctica, Using A Gravity‐Derived Bathymetry

We compare the ocean properties simulated by the 3D ocean model along the front of GIS with in situ obser- vations from summer 2007 (Jacobs et al., 2013). These conditions will be referred to as the “thermal forcing” of the ice shelf. The three experiments capture the presence of warm water (e.g., >2 K above the in situ freez- ing temperature) at the ice shelf front, consistent with the 2007 survey (Figures S3 –S6). Salinities of >34.5 psu are associated with either mCDW or CDW (see Section 1). East of Dean Island, Cases I –II repro- duce the observed temperatures reasonably well, while Case III is ∼0.5 K too warm. West of Dean Island, the simulated thermocline in Cases I and II is too shallow by ∼100 m compared with the results of Jacobs et al. (2013) and too shallow by ∼200 m in Case III (see Section 2.2 for the bias origin). The model thus overesti- mates the temperature west of Dean Island in all cases, especially in Case III. For salinity, the model exhibits a positive bias of 0.1 psu in the eastern part of the ice shelf (all three cases), increasing to 0.2 psu in the wes- tern ice shelf (and 0.3 psu in Case III). The same biases (shallow thermocline and positive salinity bias) are apparent in an additional model ‐data comparison that uses the original casts of the 2007 cruise (Figure S7). The surveys of 2000 and 2007 (Jacobs et al., 2013) revealed two locations where warm water consistently enters the ice shelf cavity: the Siple ‐Dean opening (see Assmann et al., 2019) and the Grant‐West opening. These two in flows are strongly apparent in Cases I and II (dark blue shades, Figures S4e and S5e). Case III stands apart from the other cases, as the in flow across the Grant‐West opening is accompanied by an outflow of similar magnitude (Figure S6e) because the channel between the continent and Grant Island is not resolved.
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A coupled atmosphere-ocean model of intermediate complexity for climate change study

A coupled atmosphere-ocean model of intermediate complexity for climate change study

Therefore, a systematic study of possible climate changes requires carrying out a significant number of long-term simulations, which in turn requires the use of computationally efficient models. A number of coupled atmosphere-ocean models of intermediate complexity have been developed in recent years (Stoker, 1992; Petuchov et al., 1999; Wiebe and Weaver, 1999). In this paper we document a model, which consists of an ocean model with simplified geometry coupled to a two-dimensional atmospheric model (Sokolov and Stone, 1998). The atmospheric component of the model was developed from the GISS general circulation model (GCM) (Hansen et al., 1983). It solves the primitive equations as an initial value problem and includes parameterizations for all the main physical processes. Therefore it can simulate atmospheric circulation and its response to external forcing in a more realistic way then the energy-balance models typically used in coupled models of intermediate complexity. At the same time, it is efficient enough computationally to be used in climate change studies that require carrying out multiple long-term climate simulations (Prinn et al., 1999).
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Assimilation of simulated satellite altimetric data and ARGO temperature data into a double-gyre NEMO ocean model

Assimilation of simulated satellite altimetric data and ARGO temperature data into a double-gyre NEMO ocean model

In this work, several experiments, assimilating the simulated altimetry and temperature observations into a double-gyre NEMO ocean model, are performed with objective to investigate the impact of different assimilation setups, including changing the observation distribution, the ensemble size and the localisation scale, on the quality of the analysis. The double-gyre NEMO ocean model corresponds to an idealized configuration of the NEMO model: a square and 5000-meter deep flat bottom ocean at mid latitudes (the so called square-box or SQB configuration). The main physical parameters governing the dominant characteristics of the flow are the initial stratification, the wind stress, the bottom friction and the lateral mixing parameterization. The domain extends from 24N to 44N, over 30° in longitude (60W - 30W) with 11 vertical levels between 152 m and 4613 m in depth. The minimum horizontal resolution of the model is 1/4°.
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Coupling of the ocean model NEMO to the regional climate model MAR over Greenland

Coupling of the ocean model NEMO to the regional climate model MAR over Greenland

Coupling of the ocean model NEMO to the regional climate model MAR over Greenland -work in progess- Imke Sievers (isievers@uliege.be), Xavier Fettweis Laboratoire de Climatologie et Topoclimatologie, Quartier Village 4, Clos Mercator 3, B11, 4000 Liège, Belgique

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Data assimilation experiments using diffusive back-and-forth nudging for the NEMO ocean model

Data assimilation experiments using diffusive back-and-forth nudging for the NEMO ocean model

Abstract. The diffusive back-and-forth nudging (DBFN) is an easy-to-implement iterative data assimilation method based on the well-known nudging method. It consists of a se- quence of forward and backward model integrations, within a given time window, both of them using a feedback term to the observations. Therefore, in the DBFN, the nudging asymptotic behaviour is translated into an infinite number of iterations within a bounded time domain. In this method, the backward integration is carried out thanks to what is called backward model, which is basically the forward model with reversed time step sign. To maintain numeral stability, the diffusion terms also have their sign reversed, giving a dif- fusive character to the algorithm. In this article the DBFN performance to control a primitive equation ocean model is investigated. In this kind of model non-resolved scales are modelled by diffusion operators which dissipate energy that cascade from large to small scales. Thus, in this article, the DBFN approximations and their consequences for the data assimilation system set-up are analysed. Our main result is that the DBFN may provide results which are comparable to those produced by a 4Dvar implementation with a much sim- pler implementation and a shorter CPU time for convergence. The conducted sensitivity tests show that the 4Dvar profits of long assimilation windows to propagate surface informa- tion downwards, and that for the DBFN, it is worth using short assimilation windows to reduce the impact of diffusion- induced errors. Moreover, the DBFN is less sensitive to the first guess than the 4Dvar.
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Coupling an atmospheric model to an ocean model to study air-ice-ocean interactions in Antarctica: challenges and applications

Coupling an atmospheric model to an ocean model to study air-ice-ocean interactions in Antarctica: challenges and applications

 One of the two grid has to fully cover the other one  Combine data from forcing sets and coupled models. Technical challenges of a coupling[r]

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Application of the probabilistic collocation method for an uncertainty analysis of a simple ocean model

Application of the probabilistic collocation method for an uncertainty analysis of a simple ocean model

where τ n is the thermal relaxation time and Te n is the surface equilibrium temperature that would be attained if there were no oceanic heat transport. In this model, a further simplification is the specification of a fixed salinity flux H s . The model performs an Euler integration of Eqs. (20)– (25) over a long time period (4000 years) to reach a stable equilibrium state from initial values. In models of thermohaline circulation such as this one, there exist multiple stable equilibria. For many parameter sets there exists both a high-latitude sinking equilibrium and a low-latitude sinking equilibrium. This model and its more complex versions were built to explore the stability of these equilibria, and the transitions between equilibria that result from perturbations
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On the effects of circulation, sediment resuspension and biological incorporation by diatoms in an ocean model of aluminium*

On the effects of circulation, sediment resuspension and biological incorporation by diatoms in an ocean model of aluminium*

, (10) where o is the observed and m the modelled [Al diss ] , weight- ing with model layer thickness 1z k of layer k ∈ {1 . . . 30} for every station j ∈ {1 . . . 60}. The l signifies the vertical weighting modification of the standard RMSD. This is done to compensate for the over-representation of data points near the ocean surface. Furthermore, for each sensitivity simula- tion we calculate the significance of the change in the RMSD compared with the corresponding reference simulation. This is determined by means of a Monte Carlo simulation on the reference simulation for which a subsample of 400 has been randomly selected from the original set of 1800 data–model points. They are the pairs of observations and model data, both on the model grid. This is done 50 000 times and from this the 2σ confidence interval is calculated (the mean ± two times the standard deviation). Suppose that we wish to sim- ulate q, and assume q is in steady state. For each model Y resulting in q Y ( x), the average RMSD of the Monte Carlo simulation of q Y ( x) must be outside the 2σ confidence range of the RMSD distribution of q X ( x) to say that Y is a signifi- cant improvement or worsening compared to X.
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A coupled atmosphere-ocean model of thermohaline circulation, including wind-driven gyre circulation with an analytical solution

A coupled atmosphere-ocean model of thermohaline circulation, including wind-driven gyre circulation with an analytical solution

In addition, the increased strength of negative feedbacks between atmospheric transports and the temperature gradient destabilizes the thermohaline circulation slightly; in[r]

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Simulating dimethylsulphide seasonality with the Dynamic Green Ocean Model PlankTOM5

Simulating dimethylsulphide seasonality with the Dynamic Green Ocean Model PlankTOM5

[ 5 ] The construction of global mechanistic models may shed light on the underlying mechanisms of DMS produc- tion in complex marine food webs. Current global prognostic DMS models are based on state ‐of‐the‐art multiplankton functional type models (Dynamic Green Ocean Models (DGOMs) [Le Quéré et al., 2005]), and represent the DMS cycle with up to 3 sulphurous tracers (DMS, DMSPp and/or DMSPd) [Bopp et al., 2008; Six and Maier‐Reimer, 2006; Chu et al., 2003]. Most prognostic models represent surface DMS patterns reasonably well in the mid‐ and high latitudes, where DMS and chlorophyll are tightly coupled, but some are unable to simulate the observed decoupling of DMS and chlorophyll ‐a in the low latitudes [e.g., Six and Maier‐Reimer, 2006]: Between ca. 40°N and 40°S, DMS concentrations are maximal during the summer, when chlorophyll concentrations are at their lowest [Vallina et al., 2006]. In contrast, DMS concentrations are low during the spring bloom, when chlorophyll values are maximal in this area. This behavior was called the “summer paradox” [Simó and Pedrós ‐Alió, 1999; Toole et al., 2003]. A mathematical analysis of the system of equations describing the evolution of DMS in one prognostic model revealed that DMS dynamics were “slaved” to the dynamics of the ecosystem model [Cropp et al., 2004]. Given that the model equations analysed by Cropp et al. [2004] are fairly similar to other equations used in current DMS model, this finding is likely to be representative for the characteristics of other DMS models. The authors conclude that decoupling between DMS and chlorophyll is difficult to achieve without an external forcing, a finding exploited, e.g., by Lefèvre et al. [2002]. Consequently, some authors opt for the inclusion of DMS source and sink terms with an explicit dependence on environmental conditions such as light, in order to achieve the observed decoupling [Vallina et al., 2008].
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Assessing ocean-model sensitivity to wind forcing uncertainties

Assessing ocean-model sensitivity to wind forcing uncertainties

The rather weak sensitivity of surface temperature to perturbations in air-sea heat and momentum fluxes is due to the fact that the initial state from which the ensemble has been run, co[r]

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Model constraints on the anthropogenic carbon budget of the Arctic Ocean

Model constraints on the anthropogenic carbon budget of the Arctic Ocean

Vertically, all three model configurations have the same discretization, where the full-depth water column is divided into 46 levels whose thicknesses vary from 6 m (top level) to 249 m (level 45), but the latter can reach up to 498 m, be- ing extended into level 46 as a function of the bathymetry (partial steps). For its bathymetry, the ocean model relies on the 2 min bathymetry file ETOPO2 from the National Geo- physical Data Center, which is based on satellite-derived data (Smith and Sandwell, 1997) except for the highest latitudes: the International Bathymetric Chart of the Arctic Ocean (IB- CAO) data are used in the Arctic (Jakobsson et al., 2000) and Antarctic Bedrock Mapping (BEDMAP) bathymetric data are used for the Southern Ocean south of 72 ◦ S (Lythe and Vaughan, 2001). To interpolate the bathymetry on the model grid, the median of all data points in one model grid cell was computed. NEMO uses the partial-step approach for the model to better match the observed topography. In this ap- proach, the bathymetry of the model is not tied directly to the bottom edge of the deepest ocean grid level, which varies with latitude and longitude; rather, the deepest ocean grid level for each column of grid cells is partially filled in to better match the observed ocean bathymetry (Barnier et al., 2006).
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Variability of the ocean heat content during the last millennium – an assessment with the ECHO-g Model

Variability of the ocean heat content during the last millennium – an assessment with the ECHO-g Model

Received: 30 July 2012 – Published in Clim. Past Discuss.: 30 August 2012 Revised: 5 February 2013 – Accepted: 7 February 2013 – Published: 4 March 2013 Abstract. Studies addressing climate variability during the last millennium generally focus on variables with a direct in- fluence on climate variability, like the fast thermal response to varying radiative forcing, or the large-scale changes in at- mospheric dynamics (e.g. North Atlantic Oscillation). The ocean responds to these variations by slowly integrating in depth the upper heat flux changes, thus producing a delayed influence on ocean heat content (OHC) that can later im- pact low frequency SST (sea surface temperature) variability through reemergence processes. In this study, both the ex- ternally and internally driven variations of the OHC during the last millennium are investigated using a set of fully cou- pled simulations with the ECHO-G (coupled climate model ECHAMA4 and ocean model HOPE-G) atmosphere–ocean general circulation model (AOGCM). When compared to ob- servations for the last 55 yr, the model tends to overestimate the global trends and underestimate the decadal OHC vari- ability. Extending the analysis back to the last one thousand years, the main impact of the radiative forcing is an OHC increase at high latitudes, explained to some extent by a re- duction in cloud cover and the subsequent increase of short- wave radiation at the surface. This OHC response is domi- nated by the effect of volcanism in the preindustrial era, and by the fast increase of GHGs during the last 150 yr. Like- wise, salient impacts from internal climate variability are ob- served at regional scales. For instance, upper temperature in the equatorial Pacific is controlled by ENSO (El Ni˜no South- ern Oscillation) variability from interannual to multidecadal
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Assessment of sub-shelf melting parameterisations using the ocean-ice-sheet coupled model NEMO(v3.6)-Elmer/Ice(v8.3)

Assessment of sub-shelf melting parameterisations using the ocean-ice-sheet coupled model NEMO(v3.6)-Elmer/Ice(v8.3)

tic realistic ocean–ice-sheet systems in order to improve sea level projections. 6 Conclusions We compared a wide variety of sub-shelf melting parame- terisations depending on oceanic properties to an ensemble of ocean–ice-sheet coupled simulations, using a new cou- pled model combining the ocean model NEMO and the ice- sheet model Elmer/Ice. Among the complex parameterisa- tions that we assessed, representing melting through a 2-D emulation of a 1-D plume model gives good results for cold conditions (e.g. in the Ronne-Filchner cavity) but underes- timates the melt rates and sea level contribution for warm conditions (e.g. in Pine Island glacier cavity). Given the high degree of complexity in the physics represented in the plume model, it is possible that calibrating more parameters could improve the validity of the scaling across multiple ice-shelf sizes. More work may also improve the way to extend the 1-D plume model to a realistic ice draft. The box parame- terisation representing the vertical overturning in the cavity gives results relatively close to the coupled simulations, es- pecially when used with five boxes. We showed that a lin- ear parameterisation of thermal forcing is not able to rep- resent ocean-induced melting beneath an ice shelf. Instead, a quadratic parameterisation of thermal forcing gives much better results, which are even improved for a local and non- local approach, as opposed to a fully local approach. Studies aiming at projecting the future contribution of Antarctica to sea level should take care about the choice of the melting pa- rameterisation before providing predictions. We recommend validating the chosen parameterisation with regard to ocean– ice-sheet model coupled simulations within the specific en- vironmental conditions and ice physics, although our results have to be taken carefully, until assessment based upon other models are produced.
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Poorly ventilated deep ocean at the Last Glacial Maximum inferred from carbon isotopes: A data-model comparison study

Poorly ventilated deep ocean at the Last Glacial Maximum inferred from carbon isotopes: A data-model comparison study

2 variations. We find that the mean ocean 𝛿 13 C change can be explained by a 378 ± 88 Gt C (2 𝜎) smaller LGM terrestrial carbon reservoir compared to the Holocene. Critically, in this model, differences in the oceanic 𝛿 13 C spatial pattern can only be reconciled with a LGM ocean circulation state characterized by a weak (10–15 Sv) and relatively shallow (2000–2500 m) North Atlantic Deep Water cell, reduced Antarctic Bottom Water transport (≤10 Sv globally integrated), and relatively weak (6–8 Sv) and shallow (1000–1500 m) North Pacific Intermediate Water formation. This oceanic circulation state is corroborated by results from the isotope-enabled Bern3D ocean model and further confirmed by high LGM ventilation ages in the deep ocean, particularly in the deep South Atlantic and South Pacific. This suggests a poorly ventilated glacial deep ocean which would have facilitated the sequestration of carbon lost from the terrestrial biosphere and atmosphere.
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Baroclinic Adjustment in an Atmosphere–Ocean Thermally Coupled Model: The Role of the Boundary Layer Processes

Baroclinic Adjustment in an Atmosphere–Ocean Thermally Coupled Model: The Role of the Boundary Layer Processes

In the atmosphere–ocean thermally coupled model used in this study, heat transport by oceanic dynamics is represented with a precalculated Q flux. The variation of the oceanic heat transport caused by the boundary layer processes is neglected. Although, as reviewed by Frankignoul (1985), surface heat fluxes are the dominant heating for the SST variation especially in fall and winter, the slab ocean model is a good assumption only as long as the variation time scale of the oceanic heat transport is much longer than the other surface energy fluxes. For decadal–interdecadal climate variability of the midlatitude coupled system, horizontal heat transport by upper-ocean currents plays an important role in the SST variation, in which the change in surface wind stress can excite the oceanic Rossby waves (Jin 1997; Neelin and Weng 1999; Schneider et al. 2002) and result in the slow adjustment of the oceanic gyre circulation (Jin 1997; Seager et al. 2001; Lee et al. 2008). The meridional overturning circula- tion in the ocean, which also contributes to the decadal– interdecadal climate variability, especially in the North Atlantic, is associated with the surface heat and momen- tum fluxes as well (Delworth and Greatbatch 2000; Dong and Sutton 2005). When taking into account these oceanic dynamics, how the boundary layer processes influence the decadal–interdecadal variability of the coupled system could be another interesting topic. In addition, the at- mospheric model used in this study is a dry process model. The effect of condensational heating, combined with the radiative forcing, is simply considered by a Newtonian cooling, in which the moist physics are not included. Given that condensational heating is an important com- ponent in the midlatitude circulation (Trenberth and Stepaniak 2003), the influence of the moist dynamics on the baroclinic adjustment needs further studies. The role of the boundary layer processes, especially the surface evaporating cooling, in the baroclinic eddy equilibration needs further investigated in a moist model.
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Holocene North Atlantic Overturning in an atmosphere-ocean-sea ice model compared to proxy-based reconstructions

Holocene North Atlantic Overturning in an atmosphere-ocean-sea ice model compared to proxy-based reconstructions

The ocean-sea ice component is CLIO3 [Goosse and Fichefet, 1999], consisting of a free-surface ocean general circulation model with a horizontal resolution of 3°x3° latitude-longitude and 20 vertical levels. Smaller scale processes not explicitly resolved by the model are parameterized. Vertical mixing and convection are para- meterized via an approximation of the turbulent kinetic energy, calculating viscosity and diffusivity, and an additional convective adjustment scheme, which increases vertical diffusivity in a statically unstable water column. To further improve the representation of dense water flows, Campin and Goosse [1999] included a parameterization of downslope currents within the grid box resolution. These approximations for subgrid ocean processes require numerous parameters of which the isopycnal mixing is one of the most crucial [Stone, 2004]. The resolution of the model has implications on the amount of details being resolved by the bathymetry in the model compared to the real ocean. For example the Greenland-Scotland Ridge is uniformly about 1200 m deep compared to 400 –800 m in the real ocean with canyons and trenches changing on scales far below what the model resolution does permit. The ocean model is coupled to a sea ice component [Fichefet and Maqueda, 1997, 1999] employing a three-layer dynamic-thermodynamic model. The atmo- spheric component is ECBILT [Opsteegh et al., 1998], a spectral T21, three-level quasi-geostrophic model coupled to a land-surface model that contains a bucket-type hydrological model for soil moisture and runoff. Cloud cover is prescribed according to observed present-day climatology [Rossow, 1996]. The dynamical vegetation model, VECODE [Brovkin et al., 2002], simulates two plant types (trees and grasses) and desert as a dummy type. In our simulations we prescribed the ice sheets manually.
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Global model simulations of the impact of ocean-going ships on aerosols, clouds, and the radiation budget

Global model simulations of the impact of ocean-going ships on aerosols, clouds, and the radiation budget

to −0.60 W/m 2 . The regions affected are in particular the Northeastern Pacific off the west coast of North America, the Northeastern Atlantic, and the Atlantic off the west coast of Southern Africa. These regions are characterized by fre- quent occurrence of low marine clouds and coinciding high ship traffic density. The model results show that the impact of shipping is mostly confined to liquid water clouds. Ice clouds are hardly influenced. This is related to the fact that liquid water clouds are the dominant cloud type in the regions and altitude range (<1.5 km) predominantly affected by ship- ping. The additional cloud condensation nuclei from ship- ping increase the cloud droplet number concentration of the marine clouds, whereas the simulated liquid water content is only slightly changed. This results in a decrease in the cloud droplet effective radii, thereby increasing the reflectivity of the marine clouds and thus enhancing the shortwave cloud forcing. Sensitivity studies using pre-industrial emissions suggest that shipping contributes between 17% and 39% of the total anthropogenic indirect aerosol effect. This large contribution is related to the larger albedo changes by clouds over dark oceans than over land, and to the fact that ship emissions are released in regions with frequent occurrence of low clouds, which are highly susceptible to the enhanced aerosol number concentration in an otherwise clean marine environment. This results in a much higher response for ship- ping than for continental anthropogenic aerosol sources of the same source strength.
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Impact of circulation on export production, dissolved organic matter, and dissolved oxygen in the ocean: Results from Phase II of the Ocean Carbon-cycle Model Intercomparison Project (OCMIP-2)

Impact of circulation on export production, dissolved organic matter, and dissolved oxygen in the ocean: Results from Phase II of the Ocean Carbon-cycle Model Intercomparison Project (OCMIP-2)

3.3. Dissolved Organic Matter [ 20 ] Comparison of model DOP with observations is not straightforward because of the assumption of a C:P ratio close to the standard Redfield ratio in the production and decomposition of dissolved organic matter (DOM) in the OCMIP-2 models, which is in clear violation of observa- tions that show strongly and systematically non-Redfield behavior of DOM. Karl and Bjo¨rkman [2002] synthesized historical DOP measurements and found decreases in open-ocean DOP from the surface to 300 m of less than 0.1 mmol L 1 , when averaged over large areas or long time periods. Corresponding DOC decreases are typically 30 mmol L 1 or more [Hansell, 2002]. Thus semilable DOM has a C:P ratio of at least 300, far exceeding the standard Redfield ratio. Because the OCMIP-2 models were designed to capture export of carbon, and used fairly standard Redfield ratios (C:P = 117), model DOP is actually more of an analogue for bulk DOM (see Appendix A) as opposed to the DOP pool itself. We have therefore chosen to make comparisons with DOC observations. This also has the advantage of comparing with a larger database of higher-quality observations. The choice of conventional Redfield ratios in model DOM cycling undoubtedly produ- ces unrealistic artifacts, which are discussed in section 4.
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What is ocean deoxygenation?

What is ocean deoxygenation?

Cultural services are an understudied area in ecosystem services in general. Spiritual, therapeutic, and aesthetic values remain difficult to quantify. One area of deoxygenation effects on cultural services is related to fisheries and is when deoxygenation, often in tandem with other stressors, affects traditional harvests of fish or shellfish used by indigenous peoples for spiritual purposes (Lynn et al., 2013). The potential for loss of valuable cultural services is illustrated by a recent bioeconomic analysis for the Chesapeake Bay. The loss was illustrated by showing the economic benefits that would result from improving water quality from today’s conditions. The focus was on how management actions would lead to improved water quality that would result in increased recreational activities. Massey et al. (2017) roughly estimated that if the management targets on water quality were realized the benefits (in dollars) of improved water quality (including, but not limited to, higher oxygen) attributed to recreational use outside of fishing can be on the order of hundreds of millions of dollars annually. While this analysis is not a straightforward assessment of the impact of deoxygenation effects on cultural services, the results suggest that such assessments should be pursued. The negative effects of deoxygenation on key regulating and supporting services continue to be areas of intense investigation. Alterations of benthic diversity and community structure (regulating services) due to low oxygen have been well documented (Diaz & Rosenberg, 2001; Levin et al., 2009). Another likely pathway is the loss of coral and seagrass cover due to low oxygen decreasing erosion control and water purification. How low oxygen affects the supporting services aspects of ecosystem services in terms of changes to biogeochemical cycling and the magnitude and food web pathways of productivity in coastal and open ocean ecosystems is becoming clearer but still further studies are required before generalizations can be made (Shepherd et al., 2017).
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