Principal Investigators


At a Glance

Breaking waves induce bubbles of air in the upper part of the ocean that enhance air-sea gas exchange. This research utilizes new theory, data, and model simulations to quantify the contribution of bubbles to the air-sea carbon dioxide (CO2) flux in a global context. Our results indicate that bubbles contributed roughly 40% of the total air-sea CO2 flux over the timeframe 1982–2015, indicating they play a critical role in this important process.

 


Research Highlight

A major component of understanding how the Earth’s systems will respond to increasing greenhouse gas emissions is knowing where emitted compounds reside in the system. It is estimated that approximately 20–30% of the CO2 emitted in a given year is transferred from the atmosphere into the ocean (IPCC, 2014; Le Quére et al., 2018). This has important implications for the atmosphere, since it mitigates the greenhouse warming effect, and also for the ocean, where increased carbonic acid concentrations are lowering the ocean’s pH to the detriment of marine ecosystems.

The current generation of Earth System Models that predict the evolution of Earth’s climate system use empirical parameterization to estimate the air-sea CO2 flux (Fco2), which are represented as the product of a gas transfer velocity (kw), a gas solubility (K0), and the partial pressure difference of CO2 (ΔpCO2):

Fco2 = kwK0ΔpCO2

The ΔpCO2 component of the problem is resolved by the model (or can be derived historically, e.g., Landschützer et al., 2014) and K0 is well-constrained. However, models or empirical methods are needed to estimate kw, which is highly uncertain. The most common empirical relationships use the gas diffusivity and the wind-speed as the sole input to determine kw (e.g., Wanninkhof, 2014). This reflects the fact that the relevant processes to air-sea gas transfer (turbulence, micro-breaking, and bubbles produced by breaking waves) are highly correlated with the local wind speed. However, bubbles in particular are also strongly dependent on variability in the local surface wave-field that dictates the tendency for wave breaking, which is only partially correlated with the local wind speed. In recent years, several methods have been developed to estimate kw through decomposition into bubble and non- bubble components, where the bubble component is then estimated using additional information from the surface waves.

For this research, the team employed the recent ocean surface gas transfer model DM18 (Deike & Melville, 2018), historical time-series, and model simulations of the global wind (Kobayashi et al., 2015; Tsujino et al., 2018) and wave fields (using wave model WAVEWATCH-III) to estimate FCO2 and thus to better understand the importance of bubbles and waves in a global context. Figure 4.1 shows global maps of the yearly mean flux of CO2 computed from this method and averaged for the time period 1982–2015. This method can isolate the contribution of bubbles (right panel) from the total flux (left panel). This comparison shows that the bubbles are not the majority control on air-sea CO2 flux, but can still provide a sizeable contribution to the total flux.

CO2 Flux Map
Figure 4.1. The yearly mean CO2 flux predicted by the DM18 model for the total flux (left panel) and the component of the flux due to bubbles (right panel). By convention, positive is an upward flux (out of the ocean).

 

To look further into the importance of waves and bubbles in air-sea CO2 flux, the team looked at the time-series of the globally integrated CO2 flux, also decomposed into its bubble and non-bubble fractions (Figure 4.2). Here it can be seen clearly that the bubble contribution to the total flux is much less variable than the total flux, and can contribute between 40 and 55% of the total flux (with a mean flux of roughly 40% over the time-series). The previous estimate of the contribution of bubbles to air-sea CO2 flux was 30% (Woolf, 1997), which is revised up here based on the new data and theory. This result implies that a more advanced understanding of the role of bubbles in air-sea gas flux may improve understanding of air- sea CO2 exchange. Future research efforts toward improved and better constrained wave-state dependent kw models offer physically appealing methods that may improve CO2 flux estimates on global scales.

CO2 flux panel and Bubble Flux map
Figure 4.2. Left panel: Time series of the 12-month running mean flux of CO2 out of the ocean, where the black line is the DM18 estimate and the green line is the DM18 estimate for bubbles alone. The shaded region encompasses yearly minimum and maximum values. Right panel: The ratio of the bubble flux to the total flux for monthly means (gray markers) and 12-month running mean (black lines).

 


References

Deike, L., and W.K. Melville, 2018. Gas transfer by breaking waves. Geophysical Re- search Letters, 45(19): 10482–10492. doi.org/10.1029/2018GL078758.

IPCC, 2014. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp.

Kobayashi, S. et al., 2015. The JRA-55 reanalysis: general specifications and basic characteristics. J. Meteorolog. Soc. Jpn., 93(1): 5–48. doi.org/10.2151/jmsj.2015-001.

Landschützer, P., N. Gruber, D.C.E. Bakker, and U. Schuster, 2014. Recent variability of the global ocean carbon sink. Global Biogeochem. Cycles, 28(9): 927–949. doi.org/10.1002/2014GB004853.

Le Quére, C. et al., 2018. Carbon Budget 2018. Earth Syst. Sci. Data, 10(4): 2141–2194. doi.org/10.5194/ essd-10-2141-2018.

Reichl, B.G. and L. Deike. Contribution of Bubbles to Carbon Dioxide Flux from Global Wave Simulation. In preparation.

Tsujino, H. et al., 2018. JRA-55 based surface dataset for driving ocean—sea-ice models (JRA55-do). Ocean Modell. 130: 79–139. doi.org/10.1016/j.ocemod.2018.07.002.

Wanninkhof, R., 2014. Relationship between wind speed and gas exchange over the ocean revisited. Liminology and Oceanography Methods, 12(6): 351–362. doi.org/10.4319/lom.2014.12.351.