Data from: Evaluation of different bias correction methods for dynamical downscaled future projections of the California Current Upwelling System
Data files
Nov 20, 2023 version files 46.30 MB
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Figure_01.nc
2.80 MB
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Figure_02.nc
2.77 MB
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Figure_03.nc
3.97 MB
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Figure_04.nc
1.72 MB
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Figure_05.nc
3.93 MB
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Figure_06.nc
164.45 KB
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Figure_07.nc
3.88 MB
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Figure_08.nc
3.23 MB
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Figure_09.nc
3.11 MB
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Figure_10.nc
1.75 MB
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Figure_S1.nc
2.26 MB
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Figure_S2.nc
1.43 MB
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Figure_S3.nc
1.41 MB
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Figure_S4.nc
1.45 MB
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Figure_S5.nc
2.31 MB
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Figure_S6.nc
3.88 MB
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Figure_S7.nc
3.11 MB
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Figure_S8.nc
3.11 MB
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README.md
7.83 KB
Abstract
Biases in global Earth System Models (ESMs) are an important source of errors when used to obtain boundary conditions for regional models. Here we examine historical and future conditions in the California Current System (CCS) using three different methods to force the regional model: (1) interpolation of ESM output to the regional grid with no bias correction; (2) a “seasonally-varying” delta method that obtains a season-dependent mean climate change signal from the ESM for a 30-year future period; and (3) a “time-varying” delta method that includes the interannual variability of the ESM over the 1980–2100 period. To compare these methods, we use a high-resolution (0.1˚) physical-biogeochemical regional model to dynamically downscale an ESM projection under the RCP8.5 emission scenario. Using different downscaling methods, the sign of future changes agrees for most of the physical and ecosystem variables, but the spatial patterns and magnitudes of these changes differ, with the seasonal- and time-varying delta simulations showing more similar changes. Not correcting the ESM forcing leads to amplification of biases in some ecosystem variables as well as misrepresentation of the California Undercurrent and CCS source waters. In the non-bias corrected and time-varying delta simulations, most of the ecosystem variables inherit trends and decadal variability from the ESM, while in the seasonally-varying delta simulation, the future variability reflects the observed historical variability (1980–2010). Our results demonstrate that bias correcting the forcing prior to downscaling improves historical simulations and that the bias correction method may impact the spatial and temporal variability of future projections.
README: Data from: Evaluation of different bias correction methods for dynamical downscaled future projections of the California Current Upwelling System
https://doi.org/10.5061/dryad.t1g1jwt8c
Files List
The data in here reproduce the figures from Pozo Buil et al.: Evaluation of different bias correction methods for dynamical downscaled future projections of the California Current Upwelling System, from Earth and Space Science. The data is listed as figures:
- Figure_01.nc - Spatial annual mean of selected forcing variables for the historical period (1980-2010): Air temperature at 2 m and meridional wind stress at 10 m. Historical monthly mean of the forcing variables averaged from the coast to 100 km offshore.
- Figure_02.nc - Future changes (2070-2100 relative to historical) of air temperature at 2 m and meridional wind stress at 10 m. Seasonal future changes of forcing variables averaged from the coast to 100 km offshore.
- Figure_03.nc - Bias with respect to observations of ecosystem variables: sea surface temperature, mixed layer depth, dissolved oxygen concentration at 150 m, and surface chlorophyll. Historical monthly means of the variables averaged from 100 km to the coast calculated using the original model grid.
- Figure_04.nc - Spatial added value for historical climatological mean of sea surface temperature, mixed layer depth, dissolved oxygen concentration at 150 m, and surface chlorophyll.
- Figure_05.nc - Future changes (2070-2100 relative to historical) for the ecosystem variables: sea surface temperature, mixed layer depth, dissolved oxygen concentration at 150 m, and integrated chlorophyll from 0-50 m. Monthly future changes (2070-2100 relative to historical) averaged from the coast to 100 km offshore
- Figure_06.nc - Time series of annual means and changes with respect to the historical period averaged from the coast to 100 km offshore of sea surface temperature, mixed layer depth, dissolved oxygen concentration at 150 m, and integrated chlorophyll from 0-50 m.
- Figure_07.nc - Ratio between future (2070-2100) and historical (1980-2010) standard deviation for the ecosystem variables: sea surface temperature, mixed layer depth, dissolved oxygen concentration at 150 m, and integrated chlorophyll from 0-50 m.
- Figure_08.nc - Historical and future changes relative to historical (2070-2100 vs. 1980-2010), of seasonal averages of meridional velocity (positive northward) as a function of latitude along the 500 m isobath. Historical and future seasonal averages of potential density as a function of latitude along the 500 m isobath.
- Figure_09.nc - Historical and future changes relative to historical (2070-2100 vs. 1980-2010), of seasonal averages of spiciness as a function of latitude along the 500 m isobath. Historical and future seasonal averages of dissolved oxygen as a function of latitude along the 500 m isobath.
- Figure_10.nc - Historical seasonal averages of meridional velocity and density as a function of latitude along the 500 m isobath form the sensitivity experiments
- Figure_S1.nc - Spatial annual mean of heat fluxes (latent and sensible) for the historical period (1980-2010). Historical monthly mean of heat fluxes averaged from the coast to 100 km offshore.
- Figure_S2.nc - Bias of dissolved oxygen concentration at 150 m with respect to observations.
- Figure_S3.nc - Bias of subsurface concentration at 150 nitrate with respect to observations.
- Figure_S4.nc - Zoomed bias with respect to observations of surface chlorophyll.
- Figure_S5.nc - Historical bias with respect to observations and future changes and variability for nitrate concentration at 150 m.
- Figure_S6.nc - Historical (1980-2010) standard deviation for the ecosystem variables: sea surface temperature, mixed layer depth, dissolved oxygen concentration at 150 m, and integrated chlorophyll from 0-50 m.
- Figure_S7.nc - Historical and future changes relative to historical (2070-2100 vs. 1980-2010), of temperature as a function of latitude along the 500 m isobath. Historical and future seasonal averages of potential density as a function of latitude along the 500 m isobath.
- Figure_S8.nc - Historical and future changes relative to historical (2070-2100 vs. 1980-2010), of salinity as a function of latitude along the 500 m isobath. Historical and future seasonal averages of potential density as a function of latitude along the 500 m isobath.
Sharing/Access information
Data from the downscaled experiments was produced by the authors.
Data to evaluate the downscaling simulations during the historical period was derived from high‐resolution satellite and in situ‐derived data sets:
- For sea surface temperature (SST), we use the Optimum Interpolation SST data set from the National Oceanic and Atmospheric Administration (NOAA OISST v2; Reynolds et al., 2007) at 0.25° from 1982 to 2010.
- For chlorophyll (CHL), we use data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) obtained from the National Aeronautics and Space Administration (NASA) Ocean Color Website (NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group; 2014) at ~0.1° resolution from 2000 to 2010 (original period is 1998-2010).
- Mixed Layer Depth is defined as the shallowest depth at which a difference in temperature, measured from the surface, reaches a threshold of 0.8°C (Kara et al., 2000). We used output from the 30-year regional ocean reanalysis produced by 4D-VAR assimilation of multiple remotely sensed and in situ physical data for the CCS from 1982 to 2010 (Neveu et al., 2016, https://oceanmodeling.ucsc.edu/ccsnrt/#txtAssim).
- For the subsurface oxygen and nitrate concentrations, we use climatological data from the World Ocean Atlas derived from the World Ocean Database (Garcia, 2010a, b), the CSIRO Atlas of Regional Seas (CARS) climatology (Ridgway et al., 2002, Dunn & Ridgway, 2002), and the California Cooperative Oceanic Fisheries Investigations (CalCOFI, https://calcofi.org) and the gridded Newport Hydrographic Line (NHL, Risien et al., 2022, https://zenodo.org/records/5814071).
References:
- Kara, A. B., Rochford, P. A., & Hurlburt, H. E. (2000). An optimal definition for ocean mixed layer depth. Journal of Geophysical Research: Oceans, 105(C7), 16803-16821. https://doi.org/10.1029/2000jc900072
- Neveu, E., Moore, A. M., Edwards, C. A., Fiechter, J., Drake, P., Crawford, W. J., Jacox, M. G., & Nuss, E. (2016). An historical analysis of the California Current circulation using ROMS 4D-Var: System configuration and diagnostics. 99, 133-151. https://doi.org/10.1016/j.ocemod.2015.11.012
- Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., & Schlax, M. G. (2007). Daily High-Resolution-Blended Analyses for Sea Surface Temperature. Journal of Climate, 20(22), 5473-5496. https://doi.org/10.1175/2007jcli1824.1
- Ridgway K.R., J.R. Dunn, and J.L. Wilkin, Ocean interpolation by four-dimensional least squares -Application to the waters around Australia, J. Atmos. Ocean. Tech., Vol 19, No 9, 1357-1375, 2002. https://doi.org/10.1175/1520-0426(2002)019<1357:OIBFDW>2.0.CO;2
- Dunn, J. R., & Ridgway, K. R. (2002, 2002/03/01/). Mapping ocean properties in regions of complex topography. Deep Sea Research Part I: Oceanographic Research Papers, 49(3), 591-604. https://doi.org/https://doi.org/10.1016/S0967-0637(01)00069-3
Methods
Data from the downscaled experiments was produced by the authors.
Data to evaluate the downscaling simulations during the historical period, was derived from high‐resolution satellite and in situ‐derived data sets:
- For sea surface temperature (SST), we use the Optimum Interpolation SST data set from the National Oceanic and Atmospheric Administration (NOAA OISST v2; Reynolds et al., 2007) at 0.25° from 1982 to 2010.
- For chlorophyll (CHL), we use data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) obtained from the National Aeronautics and Space Administration (NASA) Ocean Color Website (NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group; 2014) at ~0.1° resolution from 2000 to 2010 (original period is 1998-2010).
- Mixed Layer Depth is defined as the shallowest depth at which a difference in temperature, measured from the surface, reaches a threshold of 0.8°C (Kara et al., 2000). We used output from the 30-year regional ocean reanalysis produced by 4D-VAR assimilation of multiple remotely sensed and in situ physical data for the CCS from 1982 to 2010 (Neveu et al., 2016, https://oceanmodeling.ucsc.edu/ccsnrt/#txtAssim).
- For the subsurface oxygen and nitrate concentrations, we use climatological data from the World Ocean Atlas derived from the World Ocean Database (Garcia, 2010a, b), the CSIRO Atlas of Regional Seas (CARS) climatology (Ridgway et al., 2002, Dunn & Ridgway, 2002), and the California Cooperative Oceanic Fisheries Investigations (CalCOFI, https://calcofi.org) and the gridded Newport Hydrographic Line (NHL, Risien et al., 2022, https://zenodo.org/records/5814071).
References:
Kara, A. B., Rochford, P. A., & Hurlburt, H. E. (2000). An optimal definition for ocean mixed layer depth. Journal of Geophysical Research: Oceans, 105(C7), 16803-16821. https://doi.org/10.1029/2000jc900072
Neveu, E., Moore, A. M., Edwards, C. A., Fiechter, J., Drake, P., Crawford, W. J., Jacox, M. G., & Nuss, E. (2016). An historical analysis of the California Current circulation using ROMS 4D-Var: System configuration and diagnostics. 99, 133-151. https://doi.org/10.1016/j.ocemod.2015.11.012
Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., & Schlax, M. G. (2007). Daily High-Resolution-Blended Analyses for Sea Surface Temperature. Journal of Climate, 20(22), 5473-5496. https://doi.org/10.1175/2007jcli1824.1
Ridgway K.R., J.R. Dunn, and J.L. Wilkin, Ocean interpolation by four-dimensional least squares -Application to the waters around Australia, J. Atmos. Ocean. Tech., Vol 19, No 9, 1357-1375, 2002. https://doi.org/10.1175/1520-0426(2002)019<1357:OIBFDW>2.0.CO;2
Dunn, J. R., & Ridgway, K. R. (2002, 2002/03/01/). Mapping ocean properties in regions of complex topography. Deep Sea Research Part I: Oceanographic Research Papers, 49(3), 591-604. https://doi.org/https://doi.org/10.1016/S0967-0637(01)00069-3