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Prediction of future Alaskan lake methane emissions using a small-lake model coupled to a regional climate model
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  • Daniela Hurtado Caicedo,
  • Leon Boegman,
  • Hilmar Hofmann,
  • Aidin Jabbari
Daniela Hurtado Caicedo
Queen's University
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Leon Boegman
Queen's University

Corresponding Author:boegmanl@queensu.ca

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Hilmar Hofmann
University of Konstanz
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Aidin Jabbari
Bedford Institute of Oceanography
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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.
04 Apr 2023Submitted to ESS Open Archive
11 Apr 2023Published in ESS Open Archive