Effects of Wind and Buoyancy on Carbon Dioxide Distribution and Air‐Water Flux of a Stratified Temperate Lake

Abstract

Improved calculations of emissions of greenhouse gases from stratified lakes require understanding the physical processes controlling transport of dissolved gases to the air‐water interface on diel, synoptic, and seasonal time scales. We address this issue during the transition from late summer to autumn cooling in a small temperate lake by combining micrometeorology, physical limnology, and carbon dioxide (CO2) measurements throughout the water column. Over the 26‐day campaign, the lake cooled and emitted CO2 with daily average loss of 23 mmol CO2 m−2 d−1. Over diel cycles, lake surface pCO2 decreased during daytime heating and increased during nighttime cooling, while daytime CO2 fluxes exceeded nighttime fluxes by 35% due to higher daytime wind speeds. We compared the effects of diel and synoptic weather patterns on the CO2 distribution within the lake and lake‐atmosphere CO2 flux. Increases in near‐surface pCO2 scaled with stratification and heat loss which moderated transport of dissolved gases into the mixed layer. When winds were above ~4 m s−1, lake‐scale circulations drove upwelling and downwelling that redistributed heat and carbon dioxide between the northeast and southwest basins. Short‐burst peak CO2 fluxes exceeded 50 mmol m−2 d−1 during windy periods associated with storms. However, the seasonal cooling‐induced transition to persistent deep mixing led to the highest CO2 concentrations in the mixed layer and at the surface and the highest sustained CO2 fluxes (approaching 100 mmol m−2 d−1).

Climate‐Sensitive Controls on Large Spring Emissions of CH4 and CO2 From Northern Lakes

“…Here, κ is the von Kármán constant, and z is operationally defined as 0.15 m (Czikowsky et al, ; Tedford et al, ). We used half‐hourly measurements of the wind speed to compute the waterside friction velocity ( u * w ) following the method and assumptions described in MacIntyre et al ().…”

Drivers of diffusive CH<sub>4</sub> emissions from shallow subarctic lakes on daily to multi-year timescales

“…Progress has been made in understanding how to compute ε and gas transfer rates as a function of wind speed and the heating and cooling at the lake's surface (Tedford et al, 2014). Comparisons between models and other flux estimation methods, such as the eddy covariance technique, illustrate the improved accuracy when computing gas transfer velocities using turbulence-based as opposed to wind-based models (Czikowsky et al, 2018;Heiskanen et al, 2014;Mammarella et al, 2015).…”

“…Arrhenius-type relationships of CH 4 fluxes have emerged from field studies (DelSontro et al, 2018;Natchimuthu et al, 2016;Wik et al, 2014) and across latitudes and aquatic ecosystem types in synthesis reports (Rasilo et al, 2015;Yvon-Durocher et al, 2014). However, the temperature sensitivity is modulated by biogeochemical factors that differ between lake ecosystems, such as nutrient content (Davidson et al, 2018;, methanotrophic activity (Duc et al, 2010;Lofton et al, 2014), predominant emission pathway (DelSontro et al, 2016;Jansen et al, 2019), and warming history (Yvon-Durocher et al, 2017). In lakes, the air-water concentration difference driving the flux (Eq.…”

“…1) is further affected by factors that dissociate production from emission rates. These include biotic factors, such as aerobic and anaerobic methanotrophy, and abiotic factors such as hydrologic inputs of terrestrially produced CH 4 (Miettinen et al, 2015;Paytan et al, 2015) and storage-and-release cycles associated with transient stratification (Czikowsky et al, 2018;Jammet et al, 2017;Vachon et al, 2019). Given these interacting functional dependencies, the magnitude of fluxes has complex patterns of temporal variability.…”

Turbulence and Gas Transfer Velocities in Sheltered Flooded Forests of the Amazon Basin

“…However, with light winds, wind‐driven shear will dominate turbulence production at the air‐water interface with the contribution of wind moderated by the extent of heating (buoyancy flux β > 0) and cooling ( β < 0). Similarity scaling has been verified in a small pond (MacIntyre et al, ), and results using eddy covariance illustrate the accuracy of the similarity scaling for computing emissions and challenges when using eddy covariance at low wind speeds (Czikowsky et al, ). For improved understanding in flooded forests, predictions of ε from similarity scaling for β < 0 and β > 0 require validation with direct measurements of turbulence and calculations of k from the SRM require comparisons with those obtained using chambers.…”