the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Iceshelf ocean boundary layer dynamics from largeeddy simulations
Carolyn Branecky Begeman
Xylar AsayDavis
Luke Van Roekel
Smallscale turbulent flow below ice shelves is regionally isolated and difficult to measure and simulate. Yet these smallscale processes, which regulate heat and salt transfer between the ocean and ice shelves, can affect sealevel rise by altering the ability of Antarctic ice shelves to “buttress” ice flux to the ocean. In this study, we improve our understanding of turbulence below ice shelves by means of largeeddy simulations at submeter resolution, capturing boundary layer mixing at scales intermediate between laboratory experiments or direct numerical simulations and regional or global ocean circulation models. Our simulations feature the development of an iceshelf ocean boundary layer through dynamic ice melting in a regime with low thermal driving, low iceshelf basal slope, and strong shear driven by the geostrophic flow. We present a preliminary assessment of existing iceshelf basal melt parameterizations adopted in single component or coupled icesheet and ocean models on the basis of a small parameter study. While the parameterized linear relationship between iceshelf melt rate and farfield ocean temperature appears to be robust, we point out a littleconsidered relationship between iceshelf basal slope and melting worthy of further study.
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The largest source of uncertainty in future sealevel rise is the potential loss of ice from the Antarctic Ice Sheet (IPCC, 2014). The rate of grounded ice loss is highly sensitive to the melting of ice shelves, which drain over 80 % of Antarctica’s grounded ice (Reese et al., 2018; Rignot et al., 2013). The iceshelf ocean boundary layer (IOBL) controls iceshelf melting by regulating oceanic heat and salt fluxes to the iceshelf base. Thus, accurate predictions of iceshelf melting depend on representing the turbulent dynamics of the IOBL. This representation is also critical for evaluating the sensitivity of the coupled landice–ocean system to changes in ocean conditions. Of particular concern is the sensitivity of iceshelf melting to increasing seawater temperature at depth, a trend observed along a wide swath of the West Antarctic coastline and a potential trigger for West Antarctic Ice Sheet collapse (Purkey et al., 2018; Ruan et al., 2021; Schmidtko et al., 2014; Wåhlin et al., 2021).
One indication that ocean models do not capture turbulent dynamics in the IOBL is that the simulated thermal driving, the difference between ocean temperature and the local freezing point, and consequently the simulated melt rate differ significantly between ocean models and as resolution is varied within a model (Gwyther et al., 2020). Furthermore, ocean models predict iceshelf melting using parameterizations that neglect the effects of the lateral buoyancy gradient of the IOBL, and often of the vertical buoyancy gradient, on the efficiency of vertical mixing near the boundary. This model deficiency likely biases turbulent fluxes through the IOBL and ocean cavity circulation, which is driven in large part by the buoyant flow of water freshened by iceshelf melting, an overturning circulation also known as the “ice pump” (Webber et al., 2018). A new parameterization of iceshelf melting that accounts for the dynamics of IOBL turbulence is needed to achieve a more physically based, accurate coupling of ice sheets and oceans in climate models (Dinniman et al., 2016; Edwards et al., 2021; Gwyther et al., 2020; Naughten et al., 2018). Supercooling of the IOBL and frazil ice accretion to the iceshelf base are also regionally important processes for cold cavities but are not the focus of this work (GaltonFenzi et al., 2012; Jordan et al., 2015).
IOBLs present unique conditions in the global ocean, involving a stabilizing buoyancy flux from melting ice and a boundary layer that is positively buoyant against a sloping boundary. There is a rich literature on stably stratified boundary layers (typically under constant stabilizing flux boundary conditions), but the dependence of heat, salt, and momentum fluxes on stratification remains a difficult problem, especially for strongly stratified regimes (Zonta and Soldati, 2018). IOBL turbulence has been explored through laboratory experiments, direct numerical simulations, and largeeddy simulations (Middleton et al., 2021; Mondal et al., 2019; Vreugdenhil and Taylor, 2019; McConnochie and Kerr, 2018; Rosevear et al., 2021). However, this body of work has not yet matured to setting a new standard for iceshelf melt parameterization in ocean models. Today, the most commonly used iceshelf melt parameterization is still derived from seaice conditions (i.e., in the absence of a slope), with some parameters tuned for iceshelf settings (Holland and Jenkins, 1999; Jenkins et al., 2010; McPhee et al., 1987). A knowledge gap exists in bridging IOBL dynamics across scales and characterizing the structure of buoyant plumes. Recently, this has been addressed through nonturbulenceresolving 2d or 2.5d models (Cheng et al., 2020; Jenkins, 2016, 2021). Notably, Jenkins (2021) found that vertical mixing in IOBL settings was represented poorly by the commonly used Kprofile parameterization (KPP) in comparison to a higherorder turbulence closure scheme, suggesting that the application of KPP in ocean models may be inappropriate in iceshelf cavities.
In this study, we model turbulent heat, salt, and momentum fluxes through the IOBL using largeeddy simulation (LES). Whereas the ocean models typically used to model subiceshelf circulation lack both the resolution and appropriate parameterizations needed to capture the relevant turbulent scales for boundary layer dynamics, LES captures the dominant energycontaining scales of turbulence and represents smaller, unresolved scales with varying degrees of complexity. An effective parameterization of iceshelf melting is likely to rest on an understanding of how turbulent mixing in the IOBL depends on stratification and shear forcing. We vary farfield ocean temperature and iceshelf slope between model runs and characterize turbulent fluxes and iceshelf melt rates. In this study we focus on the highshear, lowthermaldriving regime. The target of previous LES studies has been on lowshear settings (Middleton et al., 2021; Vreugdenhil and Taylor, 2019). Section 2 describes the LES model, its turbulence closure scheme, and the setup of our simulations. Section 3 presents the results of our simulations, with a focus on comparisons across the sampled range of thermal driving and slope. The discussion is given in two parts: Sect. 4.1 contextualizes the features of our simulated IOBL turbulence and discusses limitations of this study, and Sect. 4.2 focuses on how this study informs parameterizations of iceshelf melting and IOBL turbulence. We provide some closing thoughts in Sect. 5.
2.1 Overview of the LES model
The Parallelized LargeEddy Simulation Model (PALM) was developed at the Institute of Meteorology and Climatology at Leibniz University Hannover, Germany (Raasch and Schröter, 2001), for simulation of atmospheric and ocean boundary layers. For this application to subiceshelf settings, we developed a new version based on PALM v5.0 (Maronga et al., 2015) with added features, including a boundary flux scheme for the ice–ocean interface, rotating the gravity and Coriolis vectors for sloped domains, and a different turbulence closure scheme. Here, we provide a brief overview of PALM with a focus on our additions. For more details we refer readers to Maronga et al. (2015).
The governing equations of PALM are the following:
The momentum terms on the right hand side of Eq. (1) are, in order, advection, Coriolis forcing, imposed geostrophic flow with a geostrophic velocity u_{g}, buoyancy forcing, a correction for divergence in the flow using the pressure perturbation π^{*} (imposing incompressibility), and subgridscale momentum flux. All prognostic variables are considered filtered at the grid scale. This is typically denoted by the overbar, which we omit except for the subgrid turbulent flux terms to emphasize that we only represent averaged effects with a turbulence closure scheme. Double primes denote subgrid fluctuations.
We represent a sloping ice base by rotating the gravity (g) and Coriolis (f) vectors while keeping the domain a rectangular prism as in Vreugdenhil and Taylor (2019). Specifically,
where g_{z} is the magnitude of gravitational acceleration in the geopotential z direction, β is the angle the upslope direction makes with north, and α is the slope angle from horizontal or equivalently the angle between the z axis and g. For the rotated Coriolis vector f, Ω is the rotation rate, and ϕ is the latitude. Unless otherwise specified, quantities are oriented with the simulated grid.
The buoyancy term in Eq. (1) combines the contributions of the alongslope pressure gradient due to the slope of the ice shelf in hydrostatic equilibrium and buoyancy due to changes in density relative to the ambient density of the water column ρ_{a}. We use the nonlinear equation of state from Jackett et al. (2006) to compute densities, and ρ_{a} varies along slope and with depth from the ice interface as a function of the hydrostatic pressure:
where the superscript i denotes the initial state. The reference density ρ_{0} is evaluated in the center of the x–y plane (${\mathit{\rho}}_{\mathrm{0}}\left(z\right)={\mathit{\rho}}_{\mathrm{a}}({x}_{\mathrm{mid}},{y}_{\mathrm{mid}},z)$). We neglect hydrostatic pressure gradients along slope except through ρ_{a} in the buoyancy term. For the maximum slope simulated in this study, the maximum hydrostatic pressure gradient is on the order of 100 Pa m^{−1}. This simplification has the advantage of avoiding pressure discontinuities across the periodic boundary.
The terms on the righthand side of the heat and salt conservation equations (Eqs. 3 and 4) are advection by the resolved flow, turbulent transport by the subgridscale fluctuations, and the source and sink terms Φ_{θ,S}. The source and sink terms are zero in our simulations because we treat heat and salt fluxes due to melting at the boundaries through the subgrid vertical flux term, as discussed in Sect. 2.2.
We employ PALM's implementation of the Piacsek and Williams (1970) secondorder advection scheme for momentum and scalars and a thirdorder Runge–Kutta timestepping scheme. We also use the Temperton (1992) fast Fourier transform algorithm for the pressure solver.
The turbulence closure scheme is anisotropic minimum dissipation (AMD; Rozema et al., 2015) employing the extension to scalars introduced by Abkar et al. (2016). We validated our implementation against the stable atmospheric boundary layer test case published in Abkar and Moin (2017) and Stoll and PortéAgel (2008). We follow the Abkar and Moin (2017) model setup exactly and chose their moderate resolution with 72 × 72 × 72 grid points. Our results are in good agreement with the Abkar and Moin (2017) solution employing another LES model with AMD (boundary layer height differs by <10 %) and other LES models with different turbulence closures (Fig. S1 in our Supplement and their Fig. 1 and references therein). Typical subgridscale momentum and scalar diffusivities in our subiceshelf simulations of stratified turbulence range from 10^{−5} to 10^{−4} m^{2} s^{−1} in the upper twothirds of the domain where damping is not applied (the damping methodology is discussed in Sect. 2.3).
2.2 Boundary conditions for iceshelf melting
Sources and sinks of momentum, as well as of heat and salt due to interactions between the flow and the ice base, are parameterized as subgrid fluxes at the top boundary. The resolved vertical fluxes at the top layer of the model go to zero as w goes to zero according to the noflux boundary condition, and the subgrid fluxes are determined using the scheme described below in place of the AMD scheme at the top layer. PALM is vertically discretized such that the ice boundary (z=0) is located at the edge of the top cell where the vertical velocity component resides, and scalar and horizontal velocity components are located at middepth of the top cell ($z=\frac{\mathrm{1}}{\mathrm{2}}{\mathrm{\Delta}}_{z}$). We denote the interface location with subscript b and the middle of the first cell below the interface with subscript $\frac{\mathrm{1}}{\mathrm{2}}$.
Subgrid momentum fluxes are parameterized according to the law of the wall following a linear stability function for stabilizing buoyancy forcing as in Vreugdenhil and Taylor (2019):
for the horizontal velocity components (i.e., $i=\mathrm{1},\mathrm{2}$). ${\mathit{\zeta}}_{k}={z}_{k}/{L}_{\mathrm{O}}$ is the depth from the boundary scaled by the Monin–Obukhov length, u_{*} is the friction velocity, and z_{0} is the roughness length. The horizontal velocity at the boundary u_{i,b} is always 0. The Monin–Obukhov length L_{O}, computed following McPhee et al. (1987), is a function of both u_{*} and the melt rate. The stability function Ψ_{m} is linear with the scaled depth.
This parameterization assumes that Coriolis forces are not locally dominant in the momentum balance.
Similarly, the subgrid fluxes of scalars are parameterized by
where the exchange coefficients are defined with two terms, one representing turbulent transfer within the turbulent surface layer and one representing molecular transfer within the viscous sublayer following McPhee et al. (1987) (their Eq. 11).
The turbulent flux component follows a shape function consistent with the parameterization of momentum fluxes in Eq. (8):
with analogous stability functions to momentum, Eq. (9),
The coefficients of these stability functions are chosen as in Zhou et al. (2017) and are given in Table S1. The molecular flux components are constant in our simulations following McPhee et al. (1987) and are also given in Table S1. If a portion of the ice face experiences freezing, Γ_{θ} and Γ_{S} are set to a higher value, indicating destabilizing fluxes (Γ_{f}, Table S1).
Taking into account the role of meltwater advection, Eq. (10) is replaced by the following “virtual” freshwater flux form in our implementation (AsayDavis et al., 2016; Jenkins et al., 2001).
The temperature and salinity at the ice–ocean interface, θ_{b} and S_{b}, are unknown. Three equations are used to solve for these quantities and the melt rate m, the socalled threeequation parameterization.
These equations specify that heat and salt are conserved at the ice–ocean interface (Eqs. 16 and 17), and the interface temperature is fixed at the local freezing temperature θ_{f} (Eq. 18). Equation (16) assumes that the conductive heat flux into ice is negligible. ρ_{w} denotes the density of freshwater. The freezing point is calculated using the polynomial function from Jackett et al. (2006).
The PALM implementation applies the fluxes ${\stackrel{\mathrm{\u203e}}{{w}^{\prime \prime}{u}_{i}^{\prime \prime}}}_{b}$, ${\stackrel{\mathrm{\u203e}}{{w}^{\prime \prime}{\mathit{\theta}}^{\prime \prime}}}_{b}$, and ${\stackrel{\mathrm{\u203e}}{{w}^{\prime \prime}{S}^{\prime \prime}}}_{b}$ at the center of the first grid cell from the boundary without interpolation (i.e., ${\stackrel{\mathrm{\u203e}}{{w}^{\prime \prime}{X}^{\prime \prime}}}_{b}={\stackrel{\mathrm{\u203e}}{{w}^{\prime \prime}{X}^{\prime \prime}}}_{\frac{\mathrm{1}}{\mathrm{2}}}$). It is noted by the PALM developers that this error was found to be small, but we have not confirmed this for the subice case. This error would be small if the first ∼10 cm were nearly a constant flux layer; McPhee (1981) hypothesized that the subicesurface layer would be nearly but not exactly a constant flux layer.
2.3 Simulation setup
A schematic of the simulation domain is shown in Fig. 1. A list of parameter choices relevant to our simulations can be found in Table S1. The domain is a 64 m^{3} cube with horizontal resolution of 0.5 m and vertical resolution of 0.25 m. The ice–ocean interface is located on the top boundary of the domain. Largescale horizontal pressure gradients drive the mean flow, which is geostrophic in the far field where buoyancy does not modify the flow. We choose a pressure of 800 dbar at the top of the domain, an intermediate choice given that the depth of iceshelf bottoms ranges over an order of magnitude (roughly −2000 to −200 m). The potentially dynamically relevant differences between conducting these simulations at surface pressure and 800 dbar are that the first derivative of the freezing temperature with respect to salinity is 20 % smaller and the first derivative of density with respect to salinity is about 2 times larger.
We set von Neumann boundary conditions at the top boundary corresponding to the dynamic subgrid momentum and scalar fluxes (Eqs. 8, 14, and 15) as resolved fluxes go to zero at a nopenetration boundary. The roughness length (z_{0} in Eqs. 8 and 12) is chosen such that the equivalent drag coefficient assuming quadratic drag is 0.003, an intermediate value for sea ice and iceshelf bottoms, though poorly constrained (Holland and Jenkins, 1999; Holland and Feltham, 2006; MacAyeal, 1984; Nicholls et al., 2006). Boundary conditions are Dirichlet at the bottom boundary, assigned to the farfield temperature, salinity, and geostrophic velocity. The bottom third of the domain is a sponge layer (Rayleigh damping) within which velocity, temperature, and salinity are relaxed toward their assigned values at the bottom of the domain (Klemp and Lilly, 1978; Maronga et al., 2015). The sponge layer results in negligible vertical fluxes of heat, salt, or momentum across the bottom boundary because scalar and velocity gradients go to 0. The flow is periodic along the x and y dimensions.
We present two sets of simulations which have a base case in common. The base case has low farfield thermal driving of 0.15 ^{∘}C, a slope of 1^{∘}, and vigorous farfield inertial oscillations of 20 cm s^{−1} generated by a 0.03 Pa m^{−1} pressure gradient. These conditions were chosen to favor an energetic regime, for reasons discussed in Sect. 4.1. To examine the relationship between thermal driving and melt rate, as well as turbulent flow characteristics, we vary the farfield thermal driving from 0.15 to 0.6 ^{∘}C in the first set of simulations. The farfield thermal driving, Δθ_{∞}, is defined as the difference between the farfield temperature (θ_{∞}) and the freezing temperature based on the farfield salinity (θ_{f}(S_{∞})). In the second set of simulations, we reduce the slope from 1 to 0.01^{∘}. The ice base always slopes to the east in the positive x dimension of our domain; thus β=90^{∘}. The farfield salinity for all runs is 35 g kg^{−1}. Initially, both temperature and salinity increase with depth over the upper twothirds of the domain. The background salinity stratification dominates the density stratification, with an inverse stability ratio ${R}_{\mathit{\rho}}^{*}$ of 20.
We initiate turbulence over the first 50 min of the simulation with perturbations to the horizontal velocity components on the order of 0.01 m s^{−1}. The simulation duration is 50 h, corresponding to approximately four inertial periods of 13 h. PALM employs adaptive timestepping; timesteps for the simulations presented here range from 0.5 to 2.75 s after 2 h. By the end of our simulations, the timemean kinetic energy of the flow has reached steady state. However, the turbulent intensity for all cases continues to decline, with more pronounced turbulence kinetic energy (TKE) loss at lower slopes (Fig. 2a, d). Unless stated otherwise, the results are presented as averages over the last simulated inertial period and over the domain excluding the sponge layer.
We compute an effective thermal exchange coefficient that differs from that employed by the subgrid scheme to represent the efficiency of heat exchange that may need to be represented to produce accurate melt rates in an ocean model that does not have the vertical resolution or sophisticated turbulence closure that we employ here. This derived thermal exchange coefficient, Γ_{T,der}, can be computed using Eqs. (10) and (16) from the simulated melt rate and ocean properties at any depth below z_{½}, here chosen at −2 m. We substitute the friction velocity computed by Eq. (8) with one computed using a quadratic drag law from the velocity simulated 2 m below the boundary and the applied drag coefficient, consistent with drag implementations in coarseresolution ocean models.
3.1 Overview of the mean simulated state
Melt rates decline over the course of the simulations (Fig. 2), preventing the identification of steadystate melt rates for most runs. This decline is in response to the concomitant increase in stratification during the course of the simulations, which decreases vertical heat fluxes by reducing vertical turbulent fluctuations. We do not continue our simulations beyond four inertial periods in the hope of reaching steadystate conditions because the increase in stratification reduces the turbulent length scales and necessitates higher model resolution than we could computationally afford. Our simulations generally show resolved turbulent fluxes exceeding subgrid turbulent fluxes by at least a factor of 2, but subgrid turbulent fluxes dominate within several meters of the ice base where the stratification is strongest (Fig. S2). Resolved turbulent fluxes are also comparable to subgrid turbulent fluxes late in the simulation of lowslope cases after significant TKE has been lost (≤0.1 ^{∘}C slopes, Fig. S2d). We evaluated the effects of doubling both horizontal and vertical resolutions in a separate simulation with an otherwise identical setup to the base case, which was run for one inertial period after spinup. While a greater portion of the vertical heat fluxes was resolved as expected, the melt rate averaged over one inertial period only increased by 7 %, and differences in the mean state were sufficiently small to justify the use of our standard resolution (Fig. S3).
We present depth profiles of temperature, salinity, and velocity at the end of all simulations (Fig. 3). The timeaveraged farfield velocity shown in Fig. 3c and f removes a periodic signal from inertial oscillations of the geostrophic velocity with a characteristic magnitude of 20 cm s^{−1}. Ekman rotation near the boundary can be seen in all simulations (Fig. 3c, f), but for more strongly sloped runs, buoyancy plays an increasingly strong role in driving the mean flow near the boundary. This effect can be seen most clearly by comparing the upslope component of the flow within several meters of the ice–ocean interface across the slopevarying simulations (Fig. 3f). These mean buoyancy effects increase flow velocities on the order of a few centimeters per second as slope varies from 0.01 to 1^{∘}. Flow velocities near the boundary increase on the order of 10 cm s^{−1} as farfield thermal driving increases from 0.4 to 0.6 ^{∘}C at 1^{∘} slope (Fig. 3c). This velocity increase is attributed to changes in the magnitude of buoyancy forcing near the boundary which is related to differences in melt rates. The vertical momentum flux profiles shown in Fig. 4a and c reveal that flow is accelerated (negative fluxes) over much of the IOBL, with drag dominating only within the first few meters below the boundary.
All simulations show an evolution from the weakly stratified initial conditions to more strongly stratified conditions, particularly within the first 5 m of the ice–ocean interface (Fig. 3b, e). In none of the simulations do we observe a wellmixed boundary layer with respect to scalars. Rather, the simulations show varying degrees of stratification over the first 20 m from the boundary. Stratification within the boundary layer increases with thermal driving (Fig. 3a, b). Thus, the effect on stratification of the increase in meltinduced buoyancy fluxes with thermal driving dominates over the increase in shear induced by the buoyant flow. Conversely, stratification decreases with increasing slope, indicating that the increase in shear induced by the buoyant flow dominates over the increase in meltinduced buoyancy fluxes with slope.
3.2 Turbulent kinetic energy budget
Shear production of turbulence dominates the TKE budget. The evolution of TKE can be described by three source terms and dissipation.
Here, we characterize the evolution of resolved TKE, and primes designate resolved fluctuations. Figure 5b–d show the source terms in this budget for the variable slope simulations; the analogous figure for the thermal driving simulations is Fig. S4 which shows similar patterns. Shear production (F_{shear}) ranges from $\sim {\mathrm{10}}^{\mathrm{9}}$–10^{−8} m^{2} s^{−3} with a maximum at the ice–ocean interface and local maxima in the upslope flow within 5 m of the boundary (Fig. 5a). The increase in TKE throughout the boundary layer as slope increases reflects this shearinduced turbulence (Fig. 5a).
Buoyancy production of turbulence (F_{buoy}) is 1–2 orders of magnitude smaller than shear production: $\sim {\mathrm{10}}^{\mathrm{10}}$–10^{−9} m^{2} s^{−3}. For a sloped ice shelf, the buoyancy term can be broken into two components: the horizontal buoyancy fluxes that increase turbulence when these fluxes are oriented upslope and the vertical buoyancy fluxes that decrease turbulence when ice is melting (Eq. 19). Since our simulations only produce melting, the vertical component is always negative (dashed lines, Fig. 5c). Over the coarse of an inertial oscillation, the horizontal component of buoyancy production is generally positive when the mean flow is oriented upslope and negative when the flow is oriented downslope. Interestingly, the timeaveraged effect of a slope is destruction of turbulence near the boundary and production of turbulence near the base of the boundary layer (dotted lines, Fig. 5c). The net effect of both horizontal and vertical buoyancy components is the destruction of turbulence throughout the IOBL (solid lines, Fig. 5b), with the exception of the base of the IOBL where the horizontal buoyancy component augments entrainment. While these complexities are intriguing from a dynamical perspective, we do not explore them further here since the buoyancy term is not of leading order in the TKE budget.
The transport term (F_{trans}) contains two components: advection of TKE due to pressure fluctuations and turbulent advection of TKE. The former is negligible, reaching maximum values on the order of 10^{−12} m^{2} s^{−3}, while the latter increases turbulence at the boundary with oscillations of decreasing amplitude with distance from the boundary (Fig. 5d).
Dissipation (diss), the remaining term in the TKE budget, can be inferred from the remainder of these terms and the rate of change of TKE. Since we evaluate resolved TKE (e), dissipation here represents the transfer of energy to the subgrid scales; there is no flow of energy from subgrid to resolved scales in the turbulence closure scheme. In these simulations TKE is not in steady state, with an average dissipation rate over the course of a simulation of 𝒪(10^{−11}) m^{2} s^{−3}, as seen in Fig. 2a and d. Dissipation in the IOBL is on the same order as shear production (10^{−9} m^{2} s^{−3}) as all other terms in the TKE budget are small.
3.3 Boundary layer turbulence
To demonstrate the simulated turbulent structures in this regime, we present horizontal and vertical crosssectional snapshots through the domain for the two slope endmembers in Fig. 6. Turbulent structures within the IOBL are consistent with propagating Holmboe shear instabilities under stable stratification (Carpenter et al., 2010). Shear is stronger within the IOBL than at the base of the IOBL due to the concentration of buoyant plume flow near the top of the IOBL. Consequently, the amplitude of these structures increases near the boundary for the more strongly sloped simulations (Fig. 6). The difference in IOBL turbulence with slope is perhaps best seen in the turbulent structures at 1 m from the ice–ocean interface (Fig. 6e, f). The structures become increasingly filamentous (i.e., nearwall streaks, e.g., del Álamo and Jiménez, 2003; Hoyas and Jiménez, 2006) and coherent as slope and thus the velocity of the buoyancydriven current increase. Since the stratification decreases with increasing slope, the ratio of vertical to horizontal velocity variance also increases; velocity fluctuations are less confined to the slopeparallel direction (Fig. S5).
We define the base of the IOBL as the depth where the thermal driving relative to the farfield freezing point is 99 % of the farfield thermal driving. The IOBL depth increases through time, reaching 13–19 m at the end of the simulations. The simulated boundary layer depth increases with the farfield thermal driving (from 13 to 19 m) and with iceshelf basal slope (from 15 to 19 m), reflecting the increase in flow velocities across those parameter changes which drives entrainment into the IOBL. Figure 7 shows the temporal evolution of IOBL depth for a few simulations, with steady IOBL growth at low slopes and punctuated growth at higher slopes corresponding to trends in TKE.
Given the temporal variability in TKE in these simulations, we use a threshold in dissipation rate to characterize the mixing layer depth as being distinct from the mixed layer depth. This criterion has been deployed for stratifying ocean boundary layers (Franks, 2015; Sutherland et al., 2014). We consider the mixing layer as the depth interval over which the horizontally and hourly averaged dissipation rate exceeds 10^{−9} m^{2} s^{−3}. This mixing layer depth shows greater temporal variability than the IOBL depth, as defined by scalar concentration, and drops below the IOBL depth during periods of enhanced entrainment (Fig. 7).
There are also time periods over which the dissipation rate drops below 10^{−9} m^{2} s^{−3} throughout the water column, indicating intermittency in turbulence. We note that these intervals of low dissipation do not correspond to complete loss of TKE nor a shoaling of the IOBL depth as we have defined it (Fig. 7). At higher slopes ($\ge \mathrm{0.5}{}^{\circ}$), this intermittency is interrupted as inertial oscillations enhance upslope IOBL flow, increasing sheardriven mixing. This mixing front begins at the boundary and propagates to the base of the IOBL over a few hours (Fig. 7b). For the less stratified, lowslope cases, the intermittency in turbulence is more frequent as the IOBL flows more slowly and generates less TKE production by shear (Fig. 7a). We discuss this intermittency and its possible implications further in Sect. 4.1.
3.4 Melting and its relation to thermal driving and slope
Average melt rates over the last inertial period range from 0.3 to 2.5 m (Fig. 2c, f), and average Monin–Obukhov length scales are 3–5 m (not shown). The dominant temporal frequency in melt rate is the inertial frequency, and the melt response to those oscillations is highly nonlinear. Maximum melt rates occur when the mean flow is oriented between the upslope direction and the Coriolisfavored direction, and minimum melt rates are roughly 180^{∘} opposed to that. These melt rate fluctuations correspond to fluctuations in TKE which are reflected in the friction velocity shown in Fig. 2b and e. The dominant contribution to turbulence is shear production of TKE, which changes its distribution with depth as the mean flow profile evolves. During highmelt periods, the farfield flow is oriented upslope, and shear production of TKE is concentrated near the boundary. During lowmelt periods, the farfield flow is oriented downslope, and shear production of TKE is concentrated a few meters away from the boundary. Melt rate fluctuations increase in amplitude as thermal driving increases and as slope increases, which we attribute to the increasing importance of buoyancy forcing in driving a nearboundary plume and thus determining the depth distribution of shear. The two simulations associated with the highest thermal driving values, and the highest nearboundary stratification, experience a dramatic reduction in melt rates during the downslope flow period. This coincides with reduced friction velocity (i.e., shear stress) at the ice interface (Fig. 2b) and reduced vertical velocity fluctuations (not shown).
Timeaveraged melt rates depend fairly linearly on farfield thermal driving (Fig. 8a, R^{2}=0.97). This is mostly attributable to a linear relationship between melt rate and interfacial thermal driving as differences in friction velocity and the thermal exchange coefficient are small and do not have a systematic relationship with farfield thermal driving. On the other hand, the derived, timeaveraged thermal exchange coefficient representing the efficiency of heat exchange from −2 m depth to the ice base (Γ_{T,der}) does have a weakly negative relationship with farfield thermal driving (Fig. 8c). This indicates a decrease in the efficiency of heat exchange with increasing nearinterface stratification. The highest thermal driving case shows an anomalously high thermal exchange coefficient over the last inertial period, which features high TKE shown in Fig. 7b. We discuss these derived thermal exchange coefficients further in Sect. 4.2.2.
There is also a linear relationship between melt rate and iceshelf basal slope, with thresholdlike behavior at slopes less than 0.01^{∘}. This linear relationship is due primarily to a linear relationship between friction velocity and melt rate, while there is a smaller ($\sim \mathrm{1}/\mathrm{3}$ size) opposite effect from decreasing interfacial thermal driving with increasing slope (not shown). These differences in friction velocity arise from higher IOBL velocities and turbulence at higher slopes. We observe threshold behavior in melting in the two lowestslope cases, which can be attributed to similar friction velocities arising from similar IOBL velocities and turbulence (Figs. 8d and 3f). This behavior is discussed in more detail in Sect. 4.2.1. The differences in the derived thermal exchange coefficients with varying slopes are on the order of a 40 % change as slope increases from 0.01 to 1^{∘} (Fig. 8d).
3.5 Vertical structure of turbulent fluxes
Vertical heat fluxes are shown in Fig. 4b and e. The vertical heat flux has a maximum at the ice–ocean interface and decreases throughout the IOBL, with small values below the pycnocline. Since the IOBL is fully turbulent (with the exception of some intermittency discussed below), the subgrid diffusivities of momentum, heat, and salt closely resemble one another (Fig. S6). The vertical salt flux profiles are shown in Fig. S7 and have a very similar shape to the vertical heat flux profiles.
The relatively narrow range of conditions simulated here suggests that a depthdependent shape function for scalar fluxes could be formulated. The distance from the interface is scaled by the Ekman depth:
where K_{e} is the mean eddy viscosity in the turbulent boundary layer assuming the total fluxes follow Fick's law:
K_{e} profiles are shown in Fig. S8.
The linear scaling of melt rate with thermal driving suggests a linear scaling of heat flux profiles with thermal driving. We find that this scaling largely collapses the four thermal driving profiles, with notable deviation from this shape for the run that experiences temporal gaps in shear stress during the analysis period (pink curves in Figs. 2a, 4c). The shape of these profiles can be reasonably approximated by a linear decrease in the scaled heat flux with scaled depth over the boundary layer (Fig. 4c). Scalar fluxes decline near the boundary for the 1^{∘} slope cases, a feature that we discuss further in Sect. 4.2.3.
Despite melt rates scaling reasonably well with the basal slope (sin α), the vertical heat flux profile does not, showing a much lower sensitivity to slope ($(\mathrm{sin}\mathit{\alpha}{)}^{\mathrm{1}/\mathrm{4}}$, Fig. 4f). There is strong agreement between the scaled vertical heat flux profiles at different slopes, though the threshold behavior at low slopes noted in melt rates is replicated here. We also find that the Ekman depth is a poor predictor of boundary layer depth. This may be due to the depthvariable shear induced by buoyancy which is not reflected in the depthmean IOBL eddy viscosity used to compute the Ekman depth. We discuss the scaling of vertical fluxes and the possibility of their parameterization in Sect. 4.2.3.
4.1 Understanding IOBL turbulence and the limitations of a LES approach
In the simulations presented here, turbulence declines throughout the course of the simulation, becoming intermittent. The relationship between stable stratification, shear, and the persistence of turbulence remains an open question (Zonta and Soldati, 2018). Thus, it is not possible a priori to determine whether the level of turbulence simulated by this LES model is appropriate for the regime space we have sampled. While we did conduct model validation against a stably stratified atmospheric boundary layer test case (see Sect. 2.2), the degree of stratification at the boundary in that case did not approach that simulated in the subice configuration. Fundamentally, we cannot guarantee that our LES is not overly dissipative such that the TKE generated by the resolved dynamics is lost too quickly relative to realworld subice settings.
Excess dissipation could arise either through the subgrid scheme or the model numerics. We found a rapid loss of turbulence in PALM simulations when a dynamic Smagorinsky turbulence closure was used, which is consistent with previous studies on the limited applicability of the Smagorinsky turbulence closure to strongly stratified flows due to the strong anisotropy of those flows (Flores and Riley, 2011; Jiménez and Cuxart, 2005). This motivated our adoption of the AMD turbulence closure scheme (Abkar et al., 2016). However, we found that the buoyancy term added to this scheme by Abkar and Moin (2017) had an unrealistically high magnitude in the vicinity of the ice base where gradients are large. We removed this term, as it was negligible in the simulations of Vreugdenhil and Taylor (2019) (Catherine Vreugdenhil, personal communication, 2020), but our unrealistic solution for this term suggests that the AMD scheme may not perform optimally at the resolution employed here. Though the resolution of our simulations is significantly lower than that used by Vreugdenhil and Taylor (2019), it enables us to extend the domain from their 2 m to 64 m to allow for the development of a thick IOBL.
Strongly stratified turbulence has been associated with intermittent turbulence (Nieuwstadt, 2005; Wiel et al., 2012), though there are also numerical experiments that fail to produce intermittency even under strongly stable stratification (Arya, 1975; Komori et al., 1983). This is not the first study to find the emergence of intermittent turbulence in stably stratified, subice settings (Vreugdenhil and Taylor, 2019). Donda et al. (2015) have argued that, in strongly stratified flows, the cessation of turbulence is transient provided there are sufficiently large perturbations. This is consistent with our finding that the temporal variability in shear over inertial oscillations provides sufficient perturbations to reinitiate turbulence. Our simulations approach a gradient Richardson number (Ri_{g}) of 0.25, which is considered to be the approximate value at which turbulence neither grows nor decays (Holt et al., 1992; Rohr et al., 1988). Thus, fluctuations in TKE are plausible at the simulated levels of stratification and shear. However, when turbulence is intermittent, as it is in this study, the application of LES may be inappropriate due to its inherent horizontal averaging over laminar and turbulent regions (Stoll and PortéAgel, 2008). Thus, our results should be interpreted with caution.
In this study, we did not attempt to reproduce observed conditions at a particular iceshelf location due to the difficulties of matching unobserved farfield forcings and the exclusion of tides from our simulations. Nonetheless, it appears as though wellmixed boundary layers are seen for a narrower range of conditions in simulations than in observations. The geostrophic flow chosen in these simulations is quite strong at 20 cm s^{−1}, and thermal driving is relatively low such that we expected to produce a wellmixed boundary layer as is observed in melting regions of the Filchner–Ronne Ice Shelf (Nicholls et al., 2001). However, observations to date are insufficient to fully characterize the regime space for a wellmixed IOBL (Malyarenko et al., 2020). The observational picture is quite nuanced with a range of stratification observed even within one ice shelf (Hattermann et al., 2012).
Sheardriven turbulence within the IOBL plays a central role in determining vertical heat fluxes and thus melt rates. In these simulations the destruction of TKE by the stabilizing buoyancy flux is 2 orders of magnitude smaller than shear production of TKE throughout the IOBL. Our finding that shear production of TKE dominates over the buoyancy term is consistent with Davis and Nicholls (2019) who found that shear production was an order of magnitude greater than buoyancy destruction of TKE in the IOBL below Larsen C Ice Shelf.
The flux Richardson number, Ri_{f}, the ratio of buoyancy flux to sheardriven TKE production, provides a measure of mixing efficiency. The simulated Ri_{f} values of 0.05–0.1 are well below the critical Ri_{f} of ∼0.25, indicating that we are in the regime in which mixing efficiency (Ri_{f}) decreases with increasing stratification (Ri_{g}) (Miles, 1961; Howard, 1961; Armenio and Sarkar, 2002; Peltier and Caulfield, 2003). This is consistent with our finding that the derived 2 m depth thermal exchange coefficient decreases for the high thermal driving cases which also achieve stronger IOBL stratification.
Our simulations are certainly missing some sources of TKE present in iceshelf cavities which could modify the IOBL structure and mixing efficiency. These simulations did not include tides, which provide perturbations to the mean velocity that enhance melt rates and entrainment, especially at Filchner–Ronne (Makinson and Nicholls, 1999; Makinson et al., 2011; Mueller et al., 2018). While internal gravity waves arise in LES, in our LES model they may be of smaller amplitude and play a lesser role in mixing than they would in a real iceshelf settings due to the absence of largescale external forcings such as tides and storms, seafloor topography, and possible resonance with the cavity geometry (Gwyther et al., 2020; Mueller et al., 2012; Padman et al., 2018; Robertson, 2013). Enhanced drag at the iceshelf base could increase shear production of TKE; however, under strong stratification, surface roughness elements may suppress turbulence rather than enhance it (Ohya, 2001).
4.2 Representing the IOBL and projecting iceshelf melt rates with ocean models
4.2.1 Insights into melt rate sensitivity to ocean conditions
The decline in IOBL turbulence discussed in Sect. 4.1 results in declining melt rates. Thus, we could not evaluate the relationship between melting and farfield conditions at steady state, which would have offered the most direct path to assessing melt rate sensitivity to ocean conditions. As discussed in Jenkins (2016), achieving steadystate solutions in simulations may require prescribing largescale gradients in temperature. We have not included these largescale gradients in our simulations, but this may be an avenue for future work. However, we believe that our transient solutions do provide some indications of how melt rates and boundary layer properties depend on ocean temperature and iceshelf slopes. One justification for this belief is that the simulated sensitivity of melt rate to ocean temperature remains consistently linear through all inertial periods simulated (Fig. S9). On the other hand, the differently sloped simulations continue to diverge at the end of the simulation as the IOBL accelerates (Fig. 2d–f). Nonetheless we are able to find relationships between the vertical heat flux profiles and ocean temperature and slope that suggest predictability of the mean effects of turbulence despite the transience of these simulations.
The linear relationship we find between local, farfield ocean temperature and melt rates is consistent with some previous studies in iceshelf settings (Holland et al., 2008; Rignot and Jacobs, 2002; Vreugdenhil and Taylor, 2019). A slightly higher exponent of $\mathrm{4}/\mathrm{3}$ is also consistent with our data (R^{2}=0.95), a value reported for the regime in which convective instabilities control melting, while our simulations feature shear instabilities (Kerr and McConnochie, 2015). In contrast, in seaice settings this sensitivity of melt rates to local thermal driving was found to be significantly smaller with an exponent of 0.38 (Ramudu et al., 2018). The relationship between average iceshelf melt rates and the thermal driving for the cavity as a whole can be conceptualized in two components. The first is the relationship between the local thermal driving and melt rate, determined primarily by the local ocean turbulence. The second is the relationship between distant thermal driving (i.e., the water masses entering the iceshelf cavity) and the strength of subiceshelf circulation (Holland et al., 2008). Only the former is addressed by this study. Our simulations do not capture the largescale increase in overturning circulation that accompanies an increase in distant thermal driving. Our simulations partially capture the increase in IOBL velocity due to an increase in thermal driving, but the farfield velocity is unaffected. We note that the often cited quadratic relationship between distant thermal driving and melt rate involves both local turbulent processes and largescale processes (Holland et al., 2008); studies generally find this exponent on distant thermal driving to be between 1.5 and 2 (Favier et al., 2019; Jourdain et al., 2017; Little et al., 2009).
To some extent, this linear relationship between farfield thermal driving and melt rate is embedded in the parameterization of heat fluxes employed at the ice base in these simulations. Specifically, the melt rate is prescribed to have a linear dependence on the local thermal driving, based on the temperature in the first model layer (Eq. 10). However, the level of stratification near the ice base, the buoyant acceleration of the IOBL plume, and the transport of heat from depth to the IOBL all mediate the relationship between farfield thermal driving and melt rates, and yet the dependence of melt rate on farfield thermal driving from these simulations is still fairly linear.
The relationship between melt rate and iceshelf basal slope (m∝(sin α)^{n}) combines two effects: the effect of iceshelf slope on the IOBL's mean velocity profile and the effect of iceshelf slope on IOBL turbulence. In the threeequation parameterization of iceshelf melting, the former has the strongest effect on the friction velocity as derived from the mean flow velocity at a given depth, whereas the latter is represented by the scalar exchange coefficients, with higher values indicating more efficient turbulent transport. We return to the implications of our study for exchange coefficients in Sect. 4.2.2. As noted previously in this section, while our simulations capture some of this IOBL acceleration, they do not reach a steadystate mean velocity profile. Thus, we cannot fully assess the effect of iceshelf slope on the IOBL's mean velocity profile. This effect is addressed by Magorrian and Wells (2016), whose scaling analysis predicted $n=\mathrm{3}/\mathrm{2}$, and Little et al. (2009), who found n=0.94 for the range of slopes considered here. We find that n=1 may be applicable to the sheardominated regime, in contrast to the sensitivity of $n=\mathrm{2}/\mathrm{3}$ found in the convectiondominated regime at higher slopes than those simulated here (5–90^{∘}; McConnochie and Kerr, 2018; Mondal et al., 2019). However, we emphasize that further investigation is needed beyond the small number of simulations presented here to validate our results.
The thresholdlike behavior in melt rates at very low slopes is not predicted by geostrophic balance between Coriolis and buoyancy forcing, which dictates a linear relationship between sin α and IOBL velocity (Jenkins, 2016), nor the scaling analysis of Magorrian and Wells (2016). This threshold behavior could be produced if any additional buoyant acceleration produced by the small increase in slope from 0.01 to 0.1^{∘} increases both shear and dissipation, resulting in a negative feedback on IOBL turbulence. This is not evident in our simulations, which do not show significant differences in TKE budgets between the two runs (Fig. 5). From a dynamical perspective, this may be an interesting target for more highly resolved LES in the nearboundary region. However, this threshold is located at low enough slopes that it is likely not of significance to melt rate parameterization for coarseresolution ocean models, so we do not devote further attention to it here.
4.2.2 Toward nonconstant exchange coefficients in melt parameterization
The thermal exchange coefficient as computed using Eq. (11) at 0.125 m below the ice–ocean interface (the uppermost grid cell) differs from that which would be implemented by coarseresolution models or derived from oceanographic observations, both of which only know ocean properties meters to tens of meters below the ice–ocean interface. To demonstrate the implications of this study for modeling endeavors, we computed the thermal exchange coefficient that yields the simulated melt rate using ocean properties (temperature and velocity) 2 m below the ice–ocean interface. This depth was chosen to capture the faster portions of IOBL flow, representing a bestcase scenario for highresolution ocean models, though this depth choice is somewhat arbitrary.
These derived thermal exchange coefficients are shown in Fig. 8c and d. The derived thermal exchange coefficients are all less than the value of 0.011 derived from observations (Jenkins et al., 2010). There are two main factors that contribute to this result. The first is the choice of the parameterization of fluxes at the ice–ocean interface (i.e., over submeter scales). We chose a stabilitydependent parameterization in which the buoyancy forcing from melting enters through the Monin–Obukhov length (Eq. 9). We believe there is a stronger case for the flux parameterization we implement than for a constant thermal exchange coefficient in light of the success of Monin–Obukhov similarity theory (Monin and Obukhov, 1954; McPhee, 2008), as well as the depth dependence of scalar fluxes in a more highly resolved subice LES (Vreugdenhil and Taylor, 2019). Consequently, Vreugdenhil and Taylor (2019) also found that thermal exchange coefficients were less than the Jenkins et al. (2010) value for all but the lowest thermal driving case. However, we acknowledge that more validation of the subgrid boundary flux parameterizations is needed. The second contributing factor is declining simulated TKE, which reduces thermal exchange coefficients over the course of these simulations (Fig. 8c).
Our results also suggest a modest decline in the thermal exchange coefficient at higher thermal driving. This may lead to a sublinear relationship between thermal driving and melt rate at higher thermal driving values, though this is not evident in our simulations. The decline in exchange coefficient with thermal driving agrees with Vreugdenhil and Taylor (2019), though their sensitivity is greater than that seen in this study (their Fig. 8). As shown in Fig. 8c, this relationship holds during all inertial periods, with an exception during an interval of strong turbulence (see Sect. 3). Due to the limitations of our LES modeling discussed in Sect. 4.1, we cannot recommend a best fit relationship between thermal exchange coefficient and thermal driving. Given the climatic importance of accurately simulating high thermal driving regimes associated with dynamic iceshelf thinning, LES coupled with observational validation across thermal driving regimes may be a fruitful avenue for future work.
We find a linear relationship between the derived thermal exchange coefficient and the slope of the iceshelf base, indicating that mixing is more efficient at higher slopes. This relationship also holds for the derived friction velocity from a quadratic drag law (Fig. 8d). Note that these quadraticdrag friction velocities are greater than the model's parameterization of friction velocity as the former neglects stratification effects, while the latter includes them. This enhanced turbulent efficiency is due to enhanced shear instabilities and is reflected in turbulent diffusivities parameterized by the AMD scheme. Much greater variability in subgrid turbulent diffusivities is seen over the variation in slope than the variation in thermal driving tested here (Fig. S6).
The changes in the thermal exchange coefficient with slope were relatively small, from 0.0045 to 0.007 between 0.1 and 1^{∘} cases. For a coarseresolution simulation, we anticipate that the failure to capture this slope sensitivity will not be a leading source of error in melt projections. Reproducing an accurate friction velocity is likely of a greater concern. Thus, the burden of melt projection accuracy may fall more heavily on parameterizing or resolving buoyant flow than on improving the slope dependence in the parameterization of scalar fluxes (i.e., improving Eq. 10).
These simulations do not reveal whether a similar, linear relationship between friction velocity and thermal exchange coefficient holds when the background flow is varied rather than the slope. LES of seaice melting, for which there is no slope, suggests a sublinear relationship between thermal exchange coefficient and friction velocity (${\mathrm{\Gamma}}_{T}\propto {u}_{*}^{\mathrm{0.5}},\stackrel{\mathrm{\u203e}}{{w}^{\prime}{\mathit{\theta}}^{\prime}}\propto {u}_{*}^{\mathrm{1.5}}$; Ramudu et al., 2018). More studies are required to determine this scaling.
4.2.3 Toward a vertical mixing scheme for the IOBL
There is reason to believe that improving iceshelf basal melt projections using ocean models will require not only an accurate melt parameterization but also an improved vertical mixing scheme. Jenkins (2021) demonstrated significantly different IOBL characteristics when KPP is the turbulence closure scheme in contrast to a low or highorder scheme. The eddy viscosity simulated by our LES model shows better agreement with the viscosity solutions from Jenkins (2021) employing the low and highorder turbulence closure schemes than that employing KPP (compare our Fig. S6 with his Fig. 3). Thus, this study offers additional support for the use of a more sophisticated turbulence closure scheme or a modified KPP scheme in subice settings, such as one based on the depth dependence of vertical turbulent fluxes presented here (Fig. 4).
A complication in melt parameterization of slope effects is the differential ability of ocean models and their vertical grid configurations to capture IOBL flow. We find that this buoyant flow is concentrated in the uppermost 10 m, with peak velocities as close as 3 m from the interface. This flow is unlikely to be resolved by most ocean models, which have typical vertical resolutions near the interface of ∼10 m, though some model configurations are reaching ∼1 m resolutions (Gwyther et al., 2020). If the buoyancydriven boundary current is unresolved in most coarseresolution ocean models, then the friction velocity as computed from a quadratic drag law and the velocity at the first grid cell is likely to underestimate the true friction velocity. For instance, a simulation that lacks this buoyancyinduced current may look more like our negligible (0.01^{∘}) slope simulations than those with a slope and may consequently underestimate melt rates. Thus, more accurate melt rate projections may result from employing a depthdependent parameterization of vertical scalar and momentum fluxes to provide some sensitivity of melt rates to model resolution.
Our simulated depth dependence for vertical scalar fluxes agrees with the shape of the turbulent scalar flux parameterization of McPhee et al. (1987), which features a linear decrease in fluxes with distance from the boundary within the IOBL. The low thermaldriving simulations with slopes of 1^{∘} diverge from this linear behavior within a few meters from the boundary. We attribute this to boundary effects that reduce TKE and eddy viscosity close to the boundary, reminiscent of the constant flux layer predicted by Monin–Obukhov similarity theory (Monin and Obukhov, 1954). It is unclear whether a constant flux layer is absent under some conditions or whether our simulations do not resolve it for those highly stratified cases. Since these differences in eddy viscosity are incorporated into the Ekman depth scaling, the scaled heat flux profiles all show quasilinear slopes (Fig. 4c, f). This linear depth scaling for IOBL fluxes should be explored under a broader range of conditions and at higher resolution to determine whether it is broadly applicable and whether vertical mixing parameterizations need to improve their representation of the constant flux layer in the IOBL.
A few complexities are immediately apparent in pursuing a depthdependent shape function for vertical scalar flux parameterization. One is that a functional form for the eddy diffusivity is also needed to compute the Ekman depth. Another is that any implementation of these depthdependent scalar fluxes necessitates an inequality between the vertical heat and salt fluxes from the top model grid cell and the heat and salt fluxes associated with melting. Thus, the closure of those budgets will likely involve horizontal scalar fluxes as well.
A depthdependent function for momentum is also necessary in iceshelf settings given that strong stratification causes momentum fluxes to significantly deviate from the quadratic drag function typically employed in ocean models. Nearinterface stratification significantly decreases drag (GarcíaVillalba and del Álamo, 2011; McPhee et al., 2008). Tuning the drag coefficient to fit presentday melt rates neglects the functional dependence of drag on melt rate through stratification (as represented by the Monin–Obukhov length). The presence of an iceshelf slope in ocean models that do not fully resolve a buoyant plume further complicates the parameterization of momentum fluxes at the ice–ocean interface. For instance, a depthdependent shape function for momentum fluxes may prescribe negative (downward) momentum fluxes at the top grid cell below ice shelves for typical ocean model resolutions (see Fig. 4a and d at, for example, 5 m depth). In other words, when the buoyant plume is unresolved, the momentum boundary condition may need to accelerate the flow at the boundary (at least in the upslope direction) to produce more accurate sheardriven mixing in the resolved portion of the domain.
In this study we presented a small parameter study of IOBL turbulence as captured by a LES model and chiefly explored the sensitivity of iceshelf melting to ocean temperature and iceshelf basal slope. To our knowledge, this is the first study to explore turbulent effects of low slopes on ice melting at scales spanning tens of meters from the ice base, allowing for IOBL development. These vertical scales allow us to examine the depth dependence of vertical turbulent fluxes, a key component for the development and validation of vertical mixing parameterizations in iceshelf settings. Building on Jenkins (2021), our results suggest an improvement on the standard KPP vertical mixing scheme is needed, but a larger simulation campaign and field validation effort would be required to place such a scheme on strong footing.
The thermal exchange coefficient is a key parameter that determines the sensitivity of iceshelf melting to changing ocean conditions in ocean models. While a constant value is not theoretically supported, it remains the standard choice due to a lack of consensus regarding the appropriate scaling. We concur with a previous study that the thermal exchange coefficient is dependent on thermal driving such that melt rates are less sensitive to changes in ocean temperature (Vreugdenhil and Taylor, 2019). We also suggest that the thermal exchange coefficient should be enhanced at higher slopes to account for shear instabilities that are unresolved by coarseresolution ocean models, with the caveat that our simulations did not reach steady state.
Ultimately, we acknowledge that there is currently no universal guidance for parameterizing subgrid scalar and momentum fluxes near the ice base for ocean models, each of which capture IOBL flow differently depending on resolution, vertical coordinate, numerical representations of the pressure gradient, and likely other aspects of the implementation (Gwyther et al., 2020). Thus, progress toward accurate iceshelf melt projections will require not only theoretical advances in understanding melt rate sensitivity but also advances in numerical modeling to bring resolution (scale) awareness to melt parameterization.
The modified version of PALM used for this study is the LANL contribution to PALM, v1.0.0, available at https://github.com/lanl/palm_lanl (Begeman et al., 2021).
Namelist files required to reproduce the simulations and a subset of simulation data are available at https://doi.org/10.6084/m9.figshare.17900177 (Begeman et al., 2022). The full set of simulation data exceeded memory limits of the nocost repository.
The supplement related to this article is available online at: https://doi.org/10.5194/tc162772022supplement.
All authors conceived and designed the study. CBB conducted and analyzed the simulations with input from XAD and LVR. CBB wrote the manuscript with input from XAD and LVR.
The contact author has declared that neither they nor their coauthors have any competing interests.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Computing resources were provided by the LANL Institutional Computing Program, which is supported by the US DOE National Nuclear Security Administration under contract no. 89233218CNA000001.
This research was supported by the Los Alamos National Laboratory (LANL) through its Center for Space and Earth Science (CSES). CSES is funded by LANL’s Laboratory Directed Research and Development (LDRD) program (project number 20210528CR). This research was also supported by the LDRD program of LANL (project numbers 20210289ER and 20180549ECR). Support for simulation data analysis pertaining to iceshelf melt parameterization and manuscript preparation was provided through the Scientific Discovery through Advanced Computing (SciDAC) program funded by the US Department of Energy (DOE), Office of Science, Advanced Scientific Computing Research and Biological and Environmental Research programs.
This paper was edited by Josefin Ahlkrona and reviewed by two anonymous referees.
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