Abstract The capability to anticipate the exceptionally rapid warming of the Northwest Atlantic Shelf and its evolution over the next decade could enable effective mitigation for coastal communities and marine resources. However, global climate models have struggled to accurately predict this warming due to limited resolution; and past regional downscaling efforts focused on multi‐decadal projections, neglecting predictive skill associated with internal variability. We address these gaps with a high resolution (1/12°) ensemble of dynamically downscaled decadal predictions. The downscaled simulations accurately predicted past oceanic variability at scales relevant to marine resource management, with skill typically exceeding global coarse‐resolution predictions. Over the long term, warming of the Shelf is projected to continue; however, we forecast a temporary warming pause in the next decade. This predicted pause is attributed to internal variability associated with a transient, moderate strengthening of the Atlantic meridional overturning circulation and a southward shift of the Gulf Stream.
Abstract Through a case study of Hurricane Arthur (2014), the Weather Research and Forecasting (WRF) Model and the Finite Volume Coastal Ocean Model (FVCOM) are used to investigate the sensitivity of storm surge forecasts to physics parameterizations and configurations of the initial and boundary conditions in WRF. The turbulence closure scheme in the planetary boundary layer affects the prediction of the storm intensity: the local closure scheme produces lower equivalent potential temperature than the nonlocal closure schemes, leading to significant reductions in the maximum surface wind speed and surge heights. On the other hand, higher-class cloud microphysics schemes overpredict the wind speed, resulting in large overpredictions of storm surge at some coastal locations. Without cumulus parameterization in the outermost domain, both the wind speed and storm surge are grossly underpredicted as a result of large precipitation decreases in the storm center. None of the choices for the WRF physics parameterization schemes significantly affect the prediction of Arthur’s track. Sea surface temperature affects the latent heat release from the ocean surface and thus storm intensity and storm surge predictions. The large-scale atmospheric circulation models provide the initial and boundary conditions for WRF, and influence both the track and intensity predictions, thereby changing the spatial distribution of storm surge along the coastline. These sensitivity analyses underline the need to use an ensemble modeling approach to improve the storm surge forecasts.
Marine heatwaves (MHWs) are warm sea surface temperature (SST) anomalies with substantial ecological and economic consequences. Observations of MHWs are based on relatively short instrumental records, which limit the ability to forecast these events on decadal and longer timescales. Paleoclimate reconstructions can extend the observational record and help to evaluate model performance under near future conditions, but paleo-MHW reconstructions have received little attention, primarily because marine sediments lack the temporal resolution to record short-lived events. Individual foraminifera analysis (IFA) of paleotemperature proxies presents an intriguing opportunity to reconstruct past MHW variability if strong relationships exist between SST distributions and MHW metrics. Here, we describe a method to test this idea by systematically evaluating relationships between MHW metrics and SST distributions that mimic IFA data using a 2000-member linear inverse model (LIM) ensemble. Our approach is adaptable and allows users to define MHWs based on multiple duration and intensity thresholds and to model seasonal biases in five different foraminifera species. It also allows uncertainty in MHW reconstructions to be calculated for a given number of IFA measurements. An example application of our method at 12 north Pacific locations suggests that the cumulative intensity of short-duration, low-intensity MHWs is the strongest target for reconstruction, but that the error on reconstructions will rely heavily on sedimentation rate and the number of foraminifera analyzed. This is evident when a robust transfer function is applied to new core-top oxygen isotope data from 37 individual Globigerina bulloides at a site with typical marine sedimentation rates. In this example application, paleo-MHW reconstructions have large uncertainties that hamper comparisons to observational data. However, additional tests demonstrate that our approach has considerable potential to reconstruct past MHW variability at high sedimentation rate sites where hundreds of foraminifera can be analyzed.
This dataset contains the model data needed to reproduce the figures in the manuscript "Estuarine forecasts at weather to subseasonal time scales" by Ross et al., submitted to Journal of Geophysical Research: Oceans
{variable}_daily.ncThese files provide daily mean hindcast and daily mean, ensemble mean forecast data used in Figures 2, 5, 8, and 9. {variable}_instantaneous.csvThese files provide instantaneous hindcast and ensemble mean reforecast data used in Figures 3 and 7 and in the tables in the supporting information. {variable}_spatial.ncThese files provide daily mean hindcast and daily mean,ensemble mean forecast data at all points in the model grid. These data are used in Figure 6. reproduce_figure5.htmlThis is an HTML version of a Jupyter notebook that shows how to use the provided data to reproduce the results in Figure 5. This example was run with Python 3.6 and the listed modules installed through conda.
Abstract Climate change may increase precipitation, temperatures, and pollution loading and necessitate additional measures and costs to achieve water quality goals. We used two climate change models and the mean of the ensemble of seven climate models (Ensemble Mean), a yield prediction model (Soil and Water Assessment Tool‐Variable Source Area), and a farm economic model to estimate how climate change would affect yields and the costs of reducing nitrogen (N) loading in an agricultural subbasin of the Chesapeake Bay. We estimated costs of meeting water quality goals based on the reduction in farm net returns from limits on N loadings under historical and future climate scenarios. Estimated costs of meeting water quality goals increase under future climate for one of the two climate models and for the Ensemble Mean. Major reasons for increased costs are higher predicted N loading from crops and higher N loading reductions to be achieved under future climate. The farm meets N limits by eliminating wheat and reducing corn and soybean production as well as increased use of best management practices (BMPs) including Conservation Reserve Program. Researchers should analyze effects of climate change on the costs of meeting water quality goals using multiple climate change prediction models and considering possible crop substitutions as well as crop and livestock BMPs. Further research should consider how commodity market reactions to producers’ choices under climate change affect costs of meeting water quality goals.
Abstract. We present the development and evaluation of MOM6-COBALT-NWA12 version 1.0, a 1/12° model of ocean dynamics and biogeochemistry in the Northwest Atlantic Ocean. This model is built using the new regional capabilities in the MOM6 ocean model and is coupled with the COBALT biogeochemical model and SIS2 sea ice model. Our goal was to develop a model to provide information to support living marine resource applications across management time horizons from seasons to decades. To do this, we struck a balance between a broad, coastwide domain to simulate basin-scale variability and capture cross-boundary issues expected under climate change, high enough spatial resolution accurately simulate features like the Gulf Stream separation and advection of water masses through finer-scale coastal features, and the computational economy required to run the long simulations of multiple ensemble members that are needed to quantify prediction uncertainties and produce actionable information. We assess whether MOM6-COBALT-NWA12 is capable of supporting the intended applications by evaluating the model with three categories of metrics: basin-wide indicators of the model's performance, indicators of coastal ecosystem variability and the regional ocean features that drive it, and model run times and computational efficiency. Overall, both the basin-wide and regional ecosystem-relevant indicators are simulated well by the model. Where notable model biases and errors are present in both types of indicators, they are mainly consistent with the challenges of accurately simulating the Gulf Stream separation, path, and variability: for example, the coastal ocean and shelf north of Cape Hatteras is too warm and salty and has minor biogeochemical biases. During model development, we identified a few model parameters that exerted a notable influence on the model solution, including the horizontal viscosity, mixed layer restratification, and tidal self-attraction and loading, which we discuss briefly. The computational performance of the model is adequate to support running numerous long simulations, even with the inclusion of coupled biogeochemistry with 40 additional tracers. Overall, these results show that this first version of a regional MOM6 model for the Northwest Atlantic Ocean is capable of efficiently and accurately simulating historical basin-wide and regional mean conditions and variability, laying the groundwork for future studies to analyze this variability in detail, develop and improve parameterizations and model components to better capture local ocean features, and develop predictions and projections of future conditions to support living marine resource applications across time scales.
Abstract. A historical dataset of river chemistry and discharge is presented for 140 monitoring sites along the United States East Coast, the Gulf of Mexico, and the West Coast from 1950 to 2020. The dataset, referred to here as River Chemistry for the U.S. Coast (RC4USCoast), is mostly derived from the Water Quality Database of the U.S. Geological Survey (USGS), but also includes river discharge from the USGS’s Surface-Water Monthly Statistics for the Nation and the U.S. Army Corps of Engineers. RC4USCoast provides monthly time series as well as long-term averaged monthly climatological patterns for twenty variables including alkalinity and dissolved inorganic carbon concentration. It is mainly intended as a data product for regional ocean biogeochemical models and carbon chemistry studies in the U.S. coastal regions. Here we present the method to derive RC4USCoast and briefly describe the river's carbonate chemistry patterns.
Abstract Secular tidal trends are present in many tide gauge records, but their causes are often unclear. This study examines trends in tides over the last century in the Chesapeake and Delaware Bays. Statistical models show negative M2 amplitude trends at the mouths of both bays, while some upstream locations have insignificant or positive trends. To determine whether sea level rise is responsible for these trends, we include a term for mean sea level in the statistical models and compare the results with predictions from numerical and analytical models. The observed and predicted sensitivities of M2 amplitude and phase to mean sea level are similar, although the numerical model amplitude is less sensitive to sea level. The sensitivity occurs as a result of strengthening and shifting of the amphidromic system in the Chesapeake Bay and decreasing frictional effects and increasing convergence in the Delaware Bay. After accounting for the effect of sea level, significant negative background M2 and S2 amplitude trends are present; these trends may be related to other factors such as dredging, tide gauge errors, or river discharge. Projected changes in tidal amplitudes due to sea level rise over the 21st century are substantial in some areas, but depend significantly on modeling assumptions.
