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Silvia Innocenti, Pascal Matte, Vincent Fortin, and Natacha Bernier

Abstract

Reconstructing tidal signals is indispensable for verifying altimetry products, forecasting water levels, and evaluating long-term trends. Uncertainties in the estimated tidal parameters must be carefully assessed to adequately select the relevant tidal constituents and evaluate the accuracy of the reconstructed water levels. Customary harmonic analysis uses ordinary least squares (OLS) regressions for their simplicity. However, the OLS may lead to incorrect estimations of the regression coefficient uncertainty due to the neglect of the residual autocorrelation. This study introduces two residual resamplings (moving-block and semiparametric bootstraps) for estimating the variability of tidal regression parameters and shows that they are powerful methods to assess the effects of regression errors with nontrivial autocorrelation structures. A Monte Carlo experiment compares their performance to four analytical procedures selected from those provided by the RT_Tide, UTide, and NS_Tide packages and the robustfit.m MATLAB function. In the Monte Carlo experiment, an iteratively reweighted least squares (IRLS) regression is used to estimate the tidal parameters for hourly simulations of one-dimensional water levels. Generally, robustfit.m and the considered RT_Tide method overestimate the tidal amplitude variability, while the selected UTide and NS_Tide approaches underestimate it. After some substantial methodological corrections the selected NS_Tide method shows adequate performance. As a result, estimating the regression variance–covariance with the considered RT_Tide, UTide, and NS_Tide methods may lead to the erroneous selection of constituents and underestimation of water level uncertainty, compromising the validity of their results in some applications.

Significance Statement

At many locations, the production of reliable water level predictions for marine navigation, emergency response, and adaptation to extreme weather relies on the precise modeling of tides. However, the complicated interaction between tides, weather, and other climatological processes may generate large uncertainties in tidal predictions. In this study, we investigate how different statistical methods may lead to different quantification of tidal model uncertainty when using data with completely known properties (e.g., knowing the tidal signal, as well as the amount and structure of noise). The main finding is that most commonly used statistical methods may estimate incorrectly the uncertainty in tidal parameters and predictions. This inconsistency is due to some specific simplifying assumptions underlying the analysis and may be reduced using statistical techniques based on data resampling.

Open access
Min-Seop Ahn, Peter J. Gleckler, Jiwoo Lee, Angeline G. Pendergrass, and Christian Jakob

Abstract

Objective performance metrics that measure precipitation variability across time scales from subdaily to interannual are presented and applied to Historical simulations of Coupled Model Intercomparison Project phase 5 and 6 (CMIP5 and CMIP6) models. Three satellite-based precipitation estimates (IMERG, TRMM, and CMORPH) are used as reference data. We apply two independent methods to estimate temporal variability of precipitation and compare the consistency in their results. The first method is derived from power spectra analysis of 3-hourly precipitation, measuring forced variability by solar insolation (diurnal and annual cycles) and internal variability at different time scales (subdaily, synoptic, subseasonal, seasonal, and interannual). The second method is based on time averaging and facilitates estimating the seasonality of subdaily variability. Supporting the robustness of our metric, we find a near equivalence between the results obtained from the two methods when examining simulated-to-observed ratios over large domains (global, tropics, extratropics, land, or ocean). Additionally, we demonstrate that our model evaluation is not very sensitive to the discrepancies between observations. Our results reveal that CMIP5 and CMIP6 models in general overestimate the forced variability while they underestimate the internal variability, especially in the tropical ocean and higher-frequency variability. The underestimation of subdaily variability is consistent across different seasons. The internal variability is overall improved in CMIP6, but remains underestimated, and there is little evidence of improvement in forced variability. Increased horizontal resolution results in some improvement of internal variability at subdaily and synoptic time scales, but not at longer time scales.

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Helga S. Huntley, Maristella Berta, Giovanni Esposito, Annalisa Griffa, Baptiste Mourre, and Luca Centurioni

Abstract

Horizontal velocity gradients of a flow field and the related kinematic properties (KPs) of divergence, vorticity, and strain rate can be estimated from dense drifter deployments, e.g., the spatiotemporal average divergence (and other KPs) over a triangular area defined by three drifters and over a given time interval can be computed from the initial and final areas of said triangle. Unfortunately, this computation can be subject to large errors, especially when the triangle shape is far from equilateral. Therefore, samples with small aspect ratios are generally discarded. Here we derive the thresholds on two shape metrics that optimize the balance between retention of good and removal of bad divergence estimates. The primary tool is a high-resolution regional ocean model simulation, where a baseline for the average divergence can be established, so that actual errors are available. A value of 0.2 for the scaled aspect ratio Λ and a value of 0.86π for the largest interior angle θ are found to be equally effective thresholds, especially at scales of 5 km and below. While discarding samples with low Λ or high θ values necessarily biases the distribution of divergence estimates slightly toward positive values, this bias is small compared to (and in the opposite direction of) the Lagrangian sampling bias due to drifters preferably sampling convergence regions. Errors due to position uncertainty are suppressed by the shape-based subsampling. The subsampling also improves the identification of the areas of extreme divergence or convergence. An application to an observational dataset demonstrates that these model-derived thresholds can be effectively used on actual drifter data.

Significance Statement

Divergence in the ocean indicates how fast floating objects in the ocean spread apart, while convergence (negative divergence) captures how fast they accumulate. Measuring divergence in the ocean, however, remains challenging. One method is to estimate divergence from the trajectories of drifting buoys. This study provides guidance under what circumstances these estimates should be discarded because they are too likely to have large errors. The criteria proposed here are less stringent than some of the ad hoc criteria previously used. This will allow users to retain more of their estimates. We consider how position uncertainty affects the reliability of the divergence estimates. An observational dataset collected in the Mediterranean is used to illustrate an application of these reliability criteria.

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M. Rouault and F. S. Tomety

Abstract

The impact of El Niño–Southern Oscillation (ENSO) on the southern African climate is well documented and provides skill in the seasonal forecast of rainfall, but less is known about the impact of ENSO on the Benguela Current west of southern Africa. There is a significant weak correlation between ENSO and the Benguela Current upwelling sea surface temperature (SST) in austral summer. Correlation is positive for southern Benguela and negative for northern Benguela. A significant correlation exists with up to 8 months lag when ENSO leads. The impact of ENSO is due to weaker-than-normal upwelling favorable southeasterly winds during El Niño in southern Benguela, leading to warmer-than-normal coastal SST. In contrast, during La Niña, stronger-than-normal southeasterly winds lead to cooler-than-normal SST. The opposite effect applies to northern Benguela. The coastal wind change is part of an ENSO large-scale basinwide perturbation in the tropical and South Atlantic. However, non-ENSO-related SST variation in the Benguela upwelling can be as important as ENSO-related SST perturbation, and some ENSO events do not lead to the expected changes. Changes in the Benguela upwelling are linked to changes in the intensity of the trade winds associated with a change of the South Atlantic anticyclone intensity and position. In southern Benguela, changes are also associated with variations in midlatitude low pressure systems and associated upwelling unfavorable westerly winds. La Niñas favor the development of Benguela Niños in Angola and Namibia. This study shows the potential for SST seasonal predictability in the Benguela upwelling due to the leading lag correlation between ENSO and the Benguela upwelling SST.

Open access
Yeonju Choi, Yign Noh, Naoki Hirose, and Hajoon Song

Abstract

The ocean mixed layer model (OMLM) is improved using the large-eddy simulation (LES) and the inverse estimation method. A comparison of OMLM (Noh model) and LES results reveals that underestimation of the turbulent kinetic energy (TKE) flux in the OMLM causes a negative bias of the mixed layer depth (MLD) during convection, when the wind stress is weak or the latitude is high. It is further found that the entrainment layer thickness is underestimated. The effects of alternative approaches of parameterizations in the OMLM, such as nonlocal mixing, length scales, Prandtl number, and TKE flux, are examined with an aim to reduce the bias. Simultaneous optimizations of empirical constants in the various versions of Noh model with different parameterization options are then carried out via an iterative Green’s function approach with LES data as constraining data. An improved OMLM is obtained, which reflects various new features, including the enhanced TKE flux, and the new model is found to improve the performance in all cases, namely, wind-mixing, surface heating, and surface cooling cases. The effect of the OMLM grid resolution on the optimal empirical constants is also investigated.

Significance Statement

This work illustrates a novel approach to improve the parameterization of vertical mixing in the upper ocean, which plays an important role in climate and ocean models. The approach utilizes the data from realistic turbulence simulation, called large-eddy simulation, as proxy observation data for upper ocean turbulence to analyze the parameterization, and the statistical method, called inverse estimation, to obtain the optimized empirical constants used in the parameterization. The same approach can be applied to improve other turbulence parameterization, and the new vertical mixing parameterization can be applied to improve climate and ocean models.

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Gregory Sinnett, Steven R. Ramp, Yiing J. Yang, Ming-Huei Chang, Sen Jan, and Kristen A. Davis

Abstract

Large-amplitude internal solitary wave (ISW) shoaling, breaking, and run-up was tracked continuously by a dense and rapidly sampling array spanning depths from 500 m to shore near Dongsha Atoll in the South China Sea. Incident ISW amplitudes ranged between 78 and 146 m with propagation speeds between 1.40 and 2.38 m s−1. The ratio between wave amplitude and a critical amplitude A 0 controlled breaking type and was related to wave speed cp and depth. Fissioning ISWs generated larger trailing elevation waves when the thermocline was deep and evolved into onshore propagating bores in depths near 100 m. Collapsing ISWs contained significant mixing and little upslope bore propagation. Bores contained significant onshore near-bottom kinetic and potential energy flux and significant offshore rundown and relaxation phases before and after the bore front passage, respectively. Bores on the shallow forereef drove bottom temperature variation in excess of 10°C and near-bottom cross-shore currents in excess of 0.4 m s−1. Bores decelerated upslope, consistent with upslope two-layer gravity current theory, though run-up extent Xr was offshore of the predicted gravity current location. Background stratification affected the bore run-up, with Xr farther offshore when the Korteweg–de Vries nonlinearity coefficient α was negative. Fronts associated with the shoaling local internal tide, but equal in magnitude to the soliton-generated bores, were observed onshore of 20-m depth.

Open access
J. Blunden and T. Boyer
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Qinghua Ding, Axel Schweiger, and Ian Baxter

Abstract

Over the past decades, Arctic climate has exhibited significant changes characterized by strong pan-Arctic warming and a large-scale wind shift trending toward an anticyclonic anomaly centered over Greenland and the Arctic Ocean. Recent work has suggested that this wind change is able to warm the Arctic atmosphere and melt sea ice through dynamically driven warming, moistening, and ice drift effects. However, previous examination of this linkage lacks a capability to fully consider the complex nature of the sea ice response to the wind change. In this study, we perform a more rigorous test of this idea by using a coupled high-resolution modeling framework with observed winds nudged over the Arctic that allows for a comparison of these wind-induced effects with observations and simulated effects forced by anthropogenic forcing. Our nudging simulation can well capture observed variability of atmospheric temperature, sea ice, and the radiation balance during the Arctic summer and appears to simulate around 30% of Arctic warming and sea ice melting over the whole period (1979–2020) and more than 50% over the period 2000–12, which is the fastest Arctic warming decade in the satellite era. In particular, in the summer of 2020, a similar wind pattern reemerged to induce the second-lowest sea ice extent since 1979, suggesting that large-scale wind changes in the Arctic are essential in shaping Arctic climate on interannual and interdecadal time scales and may be critical to determine Arctic climate variability in the coming decades.

Significance Statement

This work conducts a set of new CESM1 nudging simulations to quantify the impact of the observed evolution of large-scale high-latitude atmospheric winds on Arctic climate variability over the past four decades. Variations in climate parameters, including sea ice, radiation, and atmospheric temperatures are well replicated in the model when observed winds are imposed in the Arctic. By investigating simulated sea ice melting processes in the simulation, we illustrate and estimate how large-scale winds in the Arctic help melt sea ice in summer. The nudging method has the potential to make Arctic climate attribution more tangible and to unravel the important physical processes underlying recent abrupt climate change in the Arctic.

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Brandon J. Daub and Neil P. Lareau

Abstract

In this study, we examine variations in boundary layer processes spanning the shallow-to-deep cumulus transition. This is accomplished by differentiating boundary layer properties on the basis of convective outcomes, ranging from shallow to deep, as observed at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma. Doppler lidar, radar, and radiosonde data are combined to determine statistical differences in boundary layer and cloud-layer properties using a large sample (236) of days with a range of convective outcomes: shallow, congestus, and deep convection. In these analyses, the radar characterizes diurnal cloud depth, the lidar quantifies updraft and downdraft properties in the subcloud layer, and daily radiosonde data provide the convective inhibition (CIN). Combined, these data are used to test the hypothesis that deep convection occurs when the strength of the boundary layer turbulence (i.e., TKE) exceeds the strength of the energy barrier (i.e., CIN) at the top of the CBL. Results show that days with deep convective clouds have significantly lower vertical velocity variance and weaker updrafts within the subcloud layer. However, CIN values are also found to be significantly lower on deep convective days, allowing for these weaker updrafts to penetrate the energy barrier and reach the level of free convection. In contrast, shallow convective outcomes occur when the updrafts are strong in an absolute sense but are weak when compared with the strength of the energy barrier. These findings support the use of the CIN/TKE framework in parameterizing convection in coarse resolution models.

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Neil Malan, Moninya Roughan, Geoffrey J. Stanley, Ryan Holmes, and Junde Li

Abstract

Cross-shelf transport plays an important role in the heat, salt, and nutrient budgets of the continental shelf. In this study, we quantify cross-shelf volume transport and explore its dynamics within a high-resolution (2.5–6 km) regional ocean model of the East Australian Current (EAC) System, a western boundary current with a high level of mesoscale eddy activity. We find that the largest time-mean cross-shelf flows (>4 Sv per 100 km; 1 Sv ≡ 106 m3 s−1) occur inshore of the coherent western boundary current, between 26° and 30°S, while the strongest time-varying flows occur in the EAC southern extension, poleward of 32°S, associated with mesoscale eddies. Using a novel diagnostic equation derived from the momentum budget we show that the cross-shelf transport is dominated by the baroclinic and geostrophic component of the velocities, as the EAC jet is relatively free to flow over the variable shelfbreak topography. However, topographic interactions are also important and act through the bottom pressure torque term as a secondary driver of cross-shelf transport. The importance of topographic interaction also increases in shallower water inshore of the coherent jet. Downstream of separation, cross-shelf transport is more time-varying and associated with the interaction of mesoscale eddies with the shelf. The identification of the change in nature and drivers of cross-shelf transport in eddy versus jet dominated regimes may be applicable to understanding cross-shelf transport dynamics in other boundary current systems.

Significance Statement

Cross-shelf transport, i.e., the movement of water from the open ocean on or off the continental shelf, is not reported often as it is difficult to measure and model. We demonstrate a simple but effective method to do this and, using an ocean model, apply it to the East Australian Current System and show what drives it. The results show two distinct regimes, which differ depending on which part of the current system you are in. Our results help to place observations of cross-shelf transport in better context and provide a framework within which to consider the transport of other things such as heat and carbon from the open ocean to the continental shelf.

Open access