A Neural Network‐Based Estimate of the Seasonal to Inter‐Annual Variability of the Lake Superior Carbon Cycle

Lake Superior emits and absorbs CO 2 with significant seasonal and interannual variability, which complicates efforts to constrain its carbon cycle. While it regains atmospheric CO 2 equilibrium on sub‐annual scales, resulting in a sustained rise in observed p CO 2 over the last two decades, significant gaps in observation have prevented examination of variability in its carbon cycle on smaller temporal or spatial scales. We developed a reconstruction of daily mean Lake Superior surface water p CO 2 and CO 2 lake‐air flux with a spatial resolution of 0.02° 0.02° in order to infer trends and drivers of carbon cycling in Lake Superior on seasonal to interannual scales. A feed‐forward neural network was trained and tested on underway p CO 2 measurements spanning ice‐free seasons of 2019–2023, yielding a spatially‐comprehensive product describing inorganic carbon dynamics over a five‐year period. Lake Superior alternated between net annual CO 2 influx and efflux, with values ranging from (influx) to (efflux) and a 5 year mean net annual . This refinement of Lake Superior's carbon budget juxtaposes the lake's large seasonal and interannual variability against a mean net annual balance of carbon sources and sinks, and opens the door to further applications of machine learning reconstruction of lacustrine biogeochemical cycling.