Methane emissions from lakes will increase with climate warming. However, CH4 these emissions are not presently in the surface schemes of Global Climate Models (GCMs). Because climate projections depend on future atmospheric CH4 concentrations, a positive feedback loop is not simulated. To address this issue, a one-dimensional model was developed to simulate future CH4 diffusive and ebullitive fluxes from four Alaskan lakes. The model was hindcast for validation (1976-2005) and forecast for prediction (2071-2100) with one-way coupling to raw meteorological data from the CanESM2 ensemble GCM. Three climate warming scenarios (RCPs 2.6, 4.5 and 8.5) simulated bottom water to warm by up to 2.24{degree sign}C, increasing the CH4 flux from the lakes by 38 - 129%. However, RCP 2.6 and 4.5 led to stabilized temperatures and CH4 emissions by 2100, at levels of 0.63 - 1.21{degree sign}C and 38 - 67%, respectively, above the 1976-2005 averages. The CH4 diffusion parameterization was transferable between the four lakes; however, different ebullition parameterizations were required for the two deeper lakes (~6-7 m mean depth) versus the two shallower lakes (~1-3 m mean depth). Relative to using observed meteorological forcing, which had a cold bias (-0.15 to -0.63 {degree sign}C) and RMSE of 0.38 to 0.90 {degree sign}C, the GCM-forced models had a warm bias (+0.96 to +3.13{degree sign}C) and marginally higher RMSE (1.03 to 3.50{degree sign}C) compared to observations. The results support continued efforts to couple CH4 lake-emission models to GCMs without downscaling meteorological data, allowing feedback between CH4 dynamics and future climates to be modelled.

Parameterizations for bottom shear stress are required to predict sediment resuspension from field observations and within numerical models that do not resolve flow within the viscous sublayer. This study assessed three observation-based bottom shear stress (τb) parameterizations, including (1) the sum of surface wave stress and mean current (quadratic) stress (τb= τw +τc); (2) the log-law (τb= τL); and (3) the turbulent kinetic energy (τb= τTKE); using two years of observations from a large shallow lake. For this system, the parameterization τb= τw +τc was sufficient to qualitatively predict resuspension, since bottom currents and surface wave orbitals were the two major processes found to resuspend bottom sediments. However, the τL and τTKE parameterizations also captured the development of a nepheloid layer within the hypolimnion associated with high-frequency internal waves. Reynolds-averaged Navier-Stokes (RANS) equation models parameterize τb as the summation of modeled current-induced bottom stress (τc,m) and modelled surface wave-induced bottom stress (τw,m). The performance of different parameterizations for τc,m and τw,m in RANS models was assessed against the observations. The optimal parameterizations yielded root-mean-square errors of 0.031 and 0.025 Pa, respectively, when τc,m, and τw,m were set using a constant canonical drag coefficient. A RANS-based τL parameterization was developed; however, the grid-averaged modelled dissipation did not always match local observations, leading to O(10) errors in prediction of bottom stress. Turbulence-based parameterizations should be further developed for application to flows with mean shear-free boundary turbulence.