Deducing acidification rates based on short-term time series.

Area of Science:

• Oceanography
• Climate Science
• Statistical Modeling

Background:

• Seawater pH measurements exhibit variability due to different observation durations and sampling distributions.
• Simple linear regression (SLR) is commonly used to determine the rate of temporal change in seawater pH (βpH), referred to as the acidification rate.
• Discrepancies in calculated βpH values from global datasets suggest potential statistical biases.

Purpose of the Study:

• To develop a statistical method to correct for deviations in SLR-determined acidification rates.
• To reconcile varying βpH values from different global time series datasets.
• To establish a more accurate and consistent estimate of the global seawater acidification rate.

Main Methods:

• Utilized simple linear regression (SLR) to analyze seawater pH time series data.
• Expressed the SLR-determined βpH as a linear combination of a constant pH change rate and rates of change in other variables.
• Applied a correction method to account for deviations caused by sampling distributions and observation durations.

Main Results:

• Five global datasets (9-23 years) showed βpH values ranging from 1.61 × 10⁻³ to -2.5 × 10⁻³ pH unit yr⁻¹.
• After applying the correction for sampling deviations, all datasets yielded a consistent acidification rate.
• The corrected acidification rates align with the expected values under air-sea CO2 equilibrium (-1.6 × 10⁻³ to -1.8 × 10⁻³ pH unit yr⁻¹).

Conclusions:

• The proposed statistical method effectively corrects for errors in acidification rate calculations from time series data.
• This method allows for the reconciliation of data from varying observation lengths and sampling strategies.
• Accurate estimation of seawater acidification rates is crucial for understanding and predicting the impacts of climate change.