This study develops a surrogate-based method to assess the uncertainty within a convective permitting integrated modeling system of the Great Lakes region, arising from interacting physics parameterizations across the lake, atmosphere, and land surface. Perturbed physics ensembles of the model during the 2018 summer are used to train a neural network surrogate model to predict lake surface temperature (LST) and near-surface air temperature (T2m). Average physics uncertainties are determined to be 1.5°C for LST and T2m over land, and 1.9°C for T2m over lake, but these have significant spatiotemporal variations. We find that atmospheric physics parameterizations are the dominant sources of uncertainty for both LST and T2m, and there is a substantial atmosphere-lake physics interaction component. LST and T2m over the lake are more uncertain in the deeper northern lakes, particularly during the rapid warming phase that occurs in late spring/early summer. The LST uncertainty increases with sensitivity to the lake model’s surface wind stress scheme. T2m over land is more uncertain over forested areas in the north, where it is most sensitive to the land surface model, than the more agricultural land in the south, where it is most sensitive to the atmospheric planetary boundary and surface layer scheme. Uncertainty also increases in the southwest during multiday temperature declines with higher sensitivity to the land surface model. Last, we show that the deduced physics uncertainty of T2m is statistically smaller than a regional warming perturbation exceeding 0.5°C.

There are large uncertainties in our future projections of climate change at the regional scale, with spatial variabilities not resolved adequately by coarse-grained Earth System Models (ESMs). In this study, we use pseudo global warming simulations driven by end of the century upper end RCP (Representative Concentration Pathway) 8.5 projections from 11 state-of-the-art ESMs to examine changes in summer heat stress extremes using physiologically relevant heat stress metrics (heat index and wet bulb globe temperature) over the Great Lakes Region (GLR). These simulations, generated from a cloud-resolving model, are at a fine spatiotemporal resolution to detect heterogeneities relevant for human heat exposure. These downscaled climate projections are combined with gridded future population estimates to isolate population versus warming contributions to population-adjusted heat stress in this region. Our results show that a significant portion of summer will be dominated by critical outdoor heat stress levels within GLR for this scenario. Additionally, regions with higher heat stress generally have disproportionately higher population densities. Humidity change generates positive feedback on future heat stress, generally amplifying heat stress (by 24.2% to 79.5%) compared to changing air temperature alone, with the degree of control of humidity depending on the heat stress metric used. The uncertainty of the results for future heat stress are quantified based on multiple ESMs and heat stress metrics used in this study. Overall, our study shows the importance of dynamically resolving heat stress at population-relevant scales to get more accurate estimates of future heat risk in the region.

This study showcases a global, heterogeneously coupled total water level system wherein salinity and temperature outputs from a coarse-resolution ($\sim$12 km) ocean general circulation model are used to calculate density-driven terms within a global, high-resolution ($\sim$2.5 km) depth-averaged total water level model. We demonstrate that the inclusion of baroclinic forcing in the barotropic model requires careful treatment of the internal wave drag term in order to maintain the fidelity of tidal results from the purely barotropic model. By accurately capturing the internal tide dissipation within the coupled system, the resulting heterogeneously coupled model has deep-ocean tidal errors of 2.27 cm, outperforming global, depth-resolving ocean models in representing global tides. Moreover, global median root mean square errors as compared to observations of total water levels, 30-day sea levels, and non-tidal residuals improve by 1.86, 2.55, and 0.36 cm respectively. The drastic improvement in model performance highlights the importance of including density-driven effects within global hydrodynamic models and will help to improve the results of both hindcasts and forecasts in modeling extreme and nuisance flooding. With only an 11\% increase in computational time as compared to the fully barotropic total water level model, this efficient approach paves the way for high resolution coastal water level and flood models to be used directly alongside climate models, improving operational forecasting of total water levels.

To understand the future summer precipitation changes over the Great Lakes Region (GLR), we perform an ensemble of regional climate simulations through the Pseudo-Global Warming (PGW) approach. We found that different types of convective precipitation respond to the PGW signal differently. Isolated deep convection (IDC), which is usually concentrated in the southern domain, shows an increase in precipitation to the north of the GLR. Mesoscale convective systems (MCSs), which are usually concentrated upstream of the GLR, shows a shift to the downstream region with increased precipitation. Thermodynamic variables such as convective available potential energy (CAPE) and convective inhibition energy (CIN) are found to be increased in almost the entire studied domain, providing a potential environment more (less) favorable for stronger (weaker) convection systems. Meanwhile, changes in lifting condensation level (LCL) and level of free convection (LFC) show a strong correlation with variations in convective precipitation, underscoring the significance of these thermodynamic factors in controlling precipitation over the domain. Results show that decreased LCL and LCF over places where convective precipitation is increased, is mainly contributed by the atmospheric moisture increase. In response to the prescribed warming perturbation, MCSs show more frequent occurrences downstream, while localized IDCs show more intense rain rate, longer duration, and larger rainfall area.

Coastal interfaces blend processes dominated by upland region hydrology and ocean hydrodynamics (tides, winds, waves, baroclinic fluctuations, among others). These areas tend to be vulnerable to flooding, a matter of concern considering that around 40% of the world’s population lives within 100 km of the ocean. Specifically, The US East and Gulf of Mexico Coasts are heavily affected by extratropical storms every year with catastrophic consequences. Models that integrate the dynamics of both oceans and river networks are needed in order to better improve flood forecast systems in coastal areas. Due to their spatial and temporal scale differences, traditional models solve river and ocean hydrodynamics independently. As a first step toward unifying coastal interface modeling, we designed an ADCIRC-based model that uses unstructured, highly variable-sized triangular meshes that can accurately represent both ocean basins and inland river networks. This meshing technique allows for incorporating features that control the dynamics of the nearshore area, such as barrier islands, jetties, and dredged channels. We analyze how mesh design impacts water level estimations in the deep ocean as well as inland rivers. Accuracy in the deep ocean is sensitive primarily to bathymetry in areas with high energy dissipation, whereas water level prediction within river networks depends on both bathymetry and resolution. While a minimum resolution in the order of a hundred meters is enough to accurately predict water level for most rivers with tidal influence, smaller tributaries require resolutions down to tens of meters. Future research will use these findings to build precipitation and rainfall-runoff into the model for a more comprehensive understanding of the coastal interface hydrodynamics.

This study investigates changes and uncertainties to cool-season (November-March) storm tides along the U.S. northeast coast in the 21st century under the high RCP8.5 emission scenario compared to late 20th century. A high-fidelity (50-m coastal resolution) hydrodynamic storm tide model is forced with three dynamically-downscaled regional climate models (RCMs) over three decadal periods (historical, mid-21st century and late-21st century) to project future changes in peak storm tide elevations at coastal counties in the region. While there is no absolute consensus on future changes to storm tides, for any one future decade two out of the three RCMs project an increase at counties along the Hudson River, Delaware River and northern Chesapeake Bay due to more intense cyclones that track inland of these locations leading to favorable surge generating conditions. The same RCMs also project a decrease at counties facing the open ocean in the mid-Atlantic Bight as cyclone densities just offshore of the coastline decrease, particularly by late-century. The larger tidal range in northern areas leads to significant uncertainty due to the arbitrary relationship between the local tidal stage and when a surge event occurs, which affects both the magnitude and sign of the projected changes. This tide-surge timing is less important in the Chesapeake Bay and unimportant in Albemarle Sound and Pamlico Sound. Similar to other recent studies, we highlight that sea level rise is likely to be more critical than storm climatology for future changes to the cool-season coastal flooding potential.

The mechanisms and geographic locations of tidal dissipation in barotropic tidal models is examined using a global, unstructured, finite element model. From simulated velocities and depths, the total dissipation within the global model is estimated. This study examines the effect that altering bathymetry can have on global tides. The Ronne ice shelf and Hudson Bay are identified as a highly sensitive region to bathymetric specification. We examine where dissipation occur and find that high boundary layer dissipation regions are very limited in geographic extent while internal tide dissipation regions are more distributed. By varying coefficients used in the parameterizations of both boundary layer and internal tide dissipation, regions that are highly sensitive to perturbations are identified. Particularly sensitive regions are used in a simple optimization technique to improve both global and local tidal results. Bottom friction coefficients are high in energetic flow regions, across the arctic ocean, and across deep ocean island chains such as the Aleutian and Ryuku Islands. Global errors of the best solution in the $M_2$ are 3.10 \si{cm} overall, 1.94 \si{cm} in areas deeper than 1000 \si{m}, and 7.74 \si{cm} in areas shallower than 1000 \si{m}. In addition to improvements in tidal amplitude, the total dissipation is estimated and compared to astronomical estimates. Greater understanding of the geographical distribution of regions which are sensitive to friction allows for a more efficient approach to optimizing tidal models.