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.

In this work, the impacts of spurious numerical salinity mixing ($\mathcal{M}_{num}$) on the larger-scale flow and tracer fields are characterized using idealized simulations. The idealized model is motivated by realistic simulations of the Texas-Louisiana shelf and features oscillatory near-inertial wind forcing. $\mathcal{M}_{num}$ can exceed the physical mixing from the turbulence closure ($\mathcal{M}_{phy}$) in frontal zones and within the mixed layer. This suggests simulated mixing processes in frontal zones may be driven largely by $\mathcal{M}_{num}$. Near-inertial alongshore wind stress amplitude is varied to identify a base case that maximizes the ratio of $\mathcal{M}_{num}$ to $\mathcal{M}_{phy}$. We then we test the sensitivity of the base case with three tracer advection schemes (MPDATA, U3HC4, and HSIMT) and conduct ensemble runs with perturbed bathymetry. Instability growth is evaluated with several analysis methods: volume-integrated eddy kinetic energy ($EKE$) and available potential energy ($APE$), surface and bottom isohaline variability, and alongshore-averaged salinity sections. While all schemes have similar total mixing, HSIMT simulations have over double the volume-integrated $\mathcal{M}_{num}$ and 20\% less $\mathcal{M}_{phy}$ relative to other schemes, which suppresses the release of $APE$ and reduces the $EKE$ by roughly 25\%. HSIMT instabilities are confined shoreward relative to the other schemes. This results in reduced isohaline variability and steeper isopycnals, evidence that enhanced numerical mixing suppresses instability growth.

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.