An empirical MLR for estimating surface layer DIC and a comparative assessment to other gap-filling techniques for ocean carbon time series
<p>Regularized time series of ocean carbon data are necessary for assessing seasonal dynamics, annual budgets, and interannual and climatic variability. There are, however, no standardized methods for filling data gaps and limited evaluation of the impacts on uncertainty in the reconstructed t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-01-01
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| Series: | Biogeosciences |
| Online Access: | https://bg.copernicus.org/articles/19/241/2022/bg-19-241-2022.pdf |
| _version_ | 1798025494519611392 |
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| author | J. M. Vance K. Currie J. Zeldis P. W. Dillingham C. S. Law C. S. Law |
| author_facet | J. M. Vance K. Currie J. Zeldis P. W. Dillingham C. S. Law C. S. Law |
| author_sort | J. M. Vance |
| collection | DOAJ |
| description | <p>Regularized time series of ocean carbon data are necessary for
assessing seasonal dynamics, annual budgets, and interannual and climatic
variability. There are, however, no standardized methods for filling data
gaps and limited evaluation of the impacts on uncertainty in the
reconstructed time series when using various imputation methods. Here we
present an empirical multivariate linear regression (MLR) model to estimate
the concentration of dissolved inorganic carbon (DIC) in the surface ocean,
that can utilize remotely sensed and modeled data to fill data gaps. This
MLR was evaluated against seven other imputation models using data from
seven long-term monitoring sites in a comparative assessment of gap-filling
performance and resulting impacts on variability in the reconstructed time
series. Methods evaluated included three empirical models – MLR, mean
imputation, and multiple imputation by chained equation (MICE) – and five
statistical models – linear, spline, and Stineman interpolation; exponential
weighted moving average; and Kalman filtering with a state space model. Cross
validation was used to determine model error and bias, while a bootstrapping
approach was employed to determine sensitivity to varying data gap lengths.
A series of synthetic gap filters, including 3-month seasonal gaps (spring,
summer, autumn winter), 6-month gaps (centered on summer and winter), and bimonthly (every 2 months) and seasonal (four samples per year) sampling regimes, were applied
to each time series to evaluate the impacts of timing and duration of data
gaps on seasonal structure, annual means, interannual variability, and
long-term trends. All models were fit to time series of monthly mean DIC,
with MLR and MICE models also applied to both measured and modeled
temperature and salinity with remotely sensed chlorophyll. Our MLR estimated
DIC with a mean error of 8.8 <span class="inline-formula">µ</span>mol kg<span class="inline-formula"><sup>−1</sup></span> among five oceanic sites and
20.0 <span class="inline-formula">µ</span>mol kg<span class="inline-formula"><sup>−1</sup></span> for two coastal sites. The MLR performance indicated
reanalysis data, such as GLORYS, can be utilized in the absence of field
measurements without increasing error in DIC estimates. Of the methods
evaluated in this study, empirical models did better than statistical models
in retaining observed seasonal structure but led to greater bias in annual
means, interannual variability, and trends compared to statistical models.
Our MLR proved to be a robust option for imputing data gaps over varied
durations and may be trained with either in situ or modeled data depending
on application. This study indicates that the number and distribution of
data gaps are important factors in selecting a model that optimizes
uncertainty while minimizing bias and subsequently enables robust strategies
for observational sampling.</p> |
| first_indexed | 2024-04-11T18:20:49Z |
| format | Article |
| id | doaj.art-43cdcbb7e8264cb6b1589793ff9c075a |
| institution | Directory Open Access Journal |
| issn | 1726-4170 1726-4189 |
| language | English |
| last_indexed | 2024-04-11T18:20:49Z |
| publishDate | 2022-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Biogeosciences |
| spelling | doaj.art-43cdcbb7e8264cb6b1589793ff9c075a2022-12-22T04:09:47ZengCopernicus PublicationsBiogeosciences1726-41701726-41892022-01-011924126910.5194/bg-19-241-2022An empirical MLR for estimating surface layer DIC and a comparative assessment to other gap-filling techniques for ocean carbon time seriesJ. M. Vance0K. Currie1J. Zeldis2P. W. Dillingham3C. S. Law4C. S. Law5Department of Marine Science, University of Otago, Dunedin, 9016, New ZealandNational Institute of Water and Atmospheric Research – University of Otago Research Centre for Oceanography, Dunedin, 9016, New ZealandNational Institute of Water and Atmospheric Research, Christchurch, 8011, New ZealandCoastal People: Southern Skies Centre of Research Excellence, Department of Mathematics and Statistics, University of Otago, Dunedin, 9016, New ZealandDepartment of Marine Science, University of Otago, Dunedin, 9016, New ZealandNational Institute of Water and Atmospheric Research, Wellington, 6021, New Zealand<p>Regularized time series of ocean carbon data are necessary for assessing seasonal dynamics, annual budgets, and interannual and climatic variability. There are, however, no standardized methods for filling data gaps and limited evaluation of the impacts on uncertainty in the reconstructed time series when using various imputation methods. Here we present an empirical multivariate linear regression (MLR) model to estimate the concentration of dissolved inorganic carbon (DIC) in the surface ocean, that can utilize remotely sensed and modeled data to fill data gaps. This MLR was evaluated against seven other imputation models using data from seven long-term monitoring sites in a comparative assessment of gap-filling performance and resulting impacts on variability in the reconstructed time series. Methods evaluated included three empirical models – MLR, mean imputation, and multiple imputation by chained equation (MICE) – and five statistical models – linear, spline, and Stineman interpolation; exponential weighted moving average; and Kalman filtering with a state space model. Cross validation was used to determine model error and bias, while a bootstrapping approach was employed to determine sensitivity to varying data gap lengths. A series of synthetic gap filters, including 3-month seasonal gaps (spring, summer, autumn winter), 6-month gaps (centered on summer and winter), and bimonthly (every 2 months) and seasonal (four samples per year) sampling regimes, were applied to each time series to evaluate the impacts of timing and duration of data gaps on seasonal structure, annual means, interannual variability, and long-term trends. All models were fit to time series of monthly mean DIC, with MLR and MICE models also applied to both measured and modeled temperature and salinity with remotely sensed chlorophyll. Our MLR estimated DIC with a mean error of 8.8 <span class="inline-formula">µ</span>mol kg<span class="inline-formula"><sup>−1</sup></span> among five oceanic sites and 20.0 <span class="inline-formula">µ</span>mol kg<span class="inline-formula"><sup>−1</sup></span> for two coastal sites. The MLR performance indicated reanalysis data, such as GLORYS, can be utilized in the absence of field measurements without increasing error in DIC estimates. Of the methods evaluated in this study, empirical models did better than statistical models in retaining observed seasonal structure but led to greater bias in annual means, interannual variability, and trends compared to statistical models. Our MLR proved to be a robust option for imputing data gaps over varied durations and may be trained with either in situ or modeled data depending on application. This study indicates that the number and distribution of data gaps are important factors in selecting a model that optimizes uncertainty while minimizing bias and subsequently enables robust strategies for observational sampling.</p>https://bg.copernicus.org/articles/19/241/2022/bg-19-241-2022.pdf |
| spellingShingle | J. M. Vance K. Currie J. Zeldis P. W. Dillingham C. S. Law C. S. Law An empirical MLR for estimating surface layer DIC and a comparative assessment to other gap-filling techniques for ocean carbon time series Biogeosciences |
| title | An empirical MLR for estimating surface layer DIC and a comparative assessment to other gap-filling techniques for ocean carbon time series |
| title_full | An empirical MLR for estimating surface layer DIC and a comparative assessment to other gap-filling techniques for ocean carbon time series |
| title_fullStr | An empirical MLR for estimating surface layer DIC and a comparative assessment to other gap-filling techniques for ocean carbon time series |
| title_full_unstemmed | An empirical MLR for estimating surface layer DIC and a comparative assessment to other gap-filling techniques for ocean carbon time series |
| title_short | An empirical MLR for estimating surface layer DIC and a comparative assessment to other gap-filling techniques for ocean carbon time series |
| title_sort | empirical mlr for estimating surface layer dic and a comparative assessment to other gap filling techniques for ocean carbon time series |
| url | https://bg.copernicus.org/articles/19/241/2022/bg-19-241-2022.pdf |
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