
==== Front
Data Brief
Data Brief
Data in Brief
2352-3409
Elsevier

S2352-3409(24)00810-2
10.1016/j.dib.2024.110846
110846
Data Article
Downscaled and bias-corrected bioclimatic dataset for the Mediterranean Sea (2005–2099)
Fianchini Marco mfianchini@ogs.it
abc⁎
Solidoro Cosimo a
Canu Donata a
a National Institute of Oceanography and Applied Geophysics - OGS, Via Beirut 2, 34151 Trieste - Trst, Italy
b Department of Life Sciences, University of Trieste, 34127, Trieste, Italy
c Department of Environmental Life Sciences, University of Udine, 33100, Udine, Italy
⁎ Corresponding author at: National Institute of Oceanography and Applied Geophysics - OGS, Via Beirut 2, 34151 Trieste - Trst, Italy. mfianchini@ogs.it
28 8 2024
12 2024
28 8 2024
57 11084619 7 2024
8 8 2024
12 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
This dataset provides annual statistical descriptors (mean, minimum, maximum, range and standard deviation) of key biogeochemical and physical variables for the Mediterranean Sea. It covers the period 2005–2099 under a high emissions scenario (CMIP5-RCP8.5), with a spatial resolution of 1/24 degree (∼4 km²). The CMIP5-RCP8.5 scenario considers the current trajectory of increasing greenhouse gas emissions and population growth to the end of the century with nominal policies to reduce emissions. Variables include temperature, salinity, pH, water velocity, nutrients (NO3, PO4, NH4), dissolved inorganic carbon, oxygen, and net primary production. Data are available for both surface and at bathymetry level. The original projections were generated using OGSTM-BFM and MFS16 models at daily time and 1/16 degree grid resolution. These were downscaled to 1/24 degree resolution and bias corrected by quantile delta mapping using CMEMS reanalysis products from 2005 to 2020. The dataset is provided in a user-friendly format so that it is accessible for various ecological and environmental modelling applications.

Keywords

Biogeochemical model
RCP8.5
Nutrients
Climate change
Bioclimatic variables
==== Body
pmcSpecifications TableSubject	Ecological Modelling.	
Specific subject area	Mediterranean Sea Physics and Biogeochemistry annual statistical descriptors for the 21th century in the light of RCP8.5 scenario.	
Type of data	Processed, netcdf format.	
Data collection	All the processing was performed in Python 3.12.3 using the packages xarray, dask scipy, numpy, pandas, cmethods, copernicusmarine, ddsapi, on a High Performance Computing system (Galileo100, Cineca-Italy) using a total of 24 cores and 256GB RAM. Surface and bottom Reanalysis data at monthly resolution were obtained by directly downloading them through copernicusmarine API service (A detailed method to obtain bathymetry level data is provided at https://help.marine.copernicus.eu/en/articles/6049335-how-to-retrieve-a-variable-at-bathymetry-level-in-python). Future projected data for RCP8.5 scenario at daily resolution were downloaded through ddsapi, averaged to monthly data and homogenised in terms of dimensions and variables names. Trilinear interpolation was performed to downscale projected data to Reanalysis spatial resolution and missing values were filled using Nearest Neighbour method, in a conservative approach. The same procedure used to extract bathymetry level variables as for Reanalysis data was used. Quantile Delta Mapping algorithm was applied to minimise distributional biases between projections and reanalysis time-series data using the 2005–2020 overlapping period as control.	
Data source location	Raw data are stored and freely available (Creative Commons Attribution 4.0 International (CC BY 4.0) license) at:
CMCC DDS API:
https://dds.cmcc.it/#/dataset/medsea-cmip5-projections-biogeochemistry/
https://dds.cmcc.it/#/dataset/medsea-cmip5-projections-physics/
E.U. Copernicus Marine Service Information:
https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1
https://doi.org/10.25423/cmcc/medsea_multiyear_bgc_006_008_medbfm3	
Data accessibility	Data are publicly available at:
Repository name: 2005–2099 High resolution bioclimatic variables for the surface and bottom of the Mediterranean Sea
Data identification number: 10.5281/zenodo.12780160
Direct URL to data: https://zenodo.org/doi/10.5281/zenodo.12780160
Download requires no registration or any other procedure.	
Related research article	none.	

1 Value of the Data

• The data provide an extended set of annual projected key bioclimatic variables for the surface and bottom of the Mediterranean Sea for the 21th century at high spatial resolution.

• Bioclimatic variables at regional scale can be useful for a variety of ecological and environmental research applications, ranging from environmental management and risk assessment to biogeographic and species distribution modelling studies.

• The data follows the World Geodetic System standard (WGS84) and are ready-to-use.

• The OGSTM-BFM model provides accurate simulations of plankton productivity and biogeochemical cycles for the Mediterranean Sea.

2 Background

The Mediterranean Sea is characterised by its unique marine ecosystems and significant economic activities, including fishing and tourism. It has complex biogeochemical dynamics influenced by unique oceanographic and climatic conditions. Climate change is exacerbating these dynamics and affecting the availability and distribution of nutrients. The 1/16° resolution Mediterranean Forecasting System and the OGSTM-BFM model provide detailed insights into the biogeochemical present and future state of the basin under different emission scenarios [1,2]. However, the complexity and scale of the data hinder their widespread, practical application. This dataset addresses these challenges by providing annual statistical descriptors of key climate change indicators and nutrients. The dataset is obtained by harmonising, downscaling and bias-correcting the model simulations outputs [1]. The decision to focus on the RCP8.5 scenario is because it can be considered a fairly realistic scenario compared to RCP4.5 [3]. The result is a ready-to-use dataset containing variables for the surface and bathymetric levels [4]. With improved accessibility and interpretability, this derived dataset represents an optimised resource for biologists, ecological modellers and policy makers involved in marine conservation and climate resilience planning.

3 Data Description

The dataset is composed of 95 files in a single folder. Names are structured as ‘{statistical_indicator}_{varname}_{layer}.nc’ (e.g. mean_thetao_bottom.nc). Note that for Net Primary Production, there are layers reporting the vertical integration of 0–220 m depth layers named ‘{statistical_indicator}_{nppv}_integrated.nc’. See Table 1 for a complete list of files and variables.Table 1 Description of the dataset files. Variable names, units, statistics and file names describe the content of the repository.

Table 1Long name	Variable name	Unit	Statistic	Filenames		
Temperature	thetao	°C	min	max_thetao_surface.nc	max_thetao_bottom.nc	
			max	range_thetao_surface.nc	range_thetao_bottom.nc	
			range	std.dev_thetao_surface.nc	std.dev_thetao_bottom.nc	
			std.dev	mean_thetao_surface.nc	mean_thetao_bottom.nc	
			mean	min_thetao_surface.nc	min_thetao_bottom.nc	
Salinity	so	PSU	min	min_so_surface.nc	min_so_bottom.nc	
			max	max_so_surface.nc	max_so_bottom.nc	
			range	range_so_surface.nc	range_so_bottom.nc	
			std.dev	std.dev_so_surface.nc	std.dev_so_bottom.nc	
			mean	mean_so_surface.nc	mean_so_bottom.nc	
Water velocity	Wv	m/s	min	min_WV_surface.nc	min_WV_bottom.nc	
			max	max_WV_surface.nc	max_WV_bottom.nc	
			range	range_WV_surface.nc	range_WV_bottom.nc	
			std.dev	std.dev_WV_surface.nc	std.dev_WV_bottom.nc	
			mean	mean_WV_surface.nc	mean_WV_bottom.nc	
pH	ph	.	min	min_ph_surface.nc	min_ph_bottom.nc	
			max	max_ph_surface.nc	max_ph_bottom.nc	
			range	range_ph_surface.nc	range_ph_bottom.nc	
			std.dev	std.dev_ph_surface.nc	std.dev_ph_bottom.nc	
			mean	mean_ph_surface.nc	mean_ph_bottom.nc	
Ammonium	nh4	[mmol m-3]	min	min_NH4_surface.nc	min_NH4_bottom.nc	
			max	max_NH4_surface.nc	max_NH4_bottom.nc	
			range	range_NH4_surface.nc	range_NH4_bottom.nc	
			std.dev	std.dev_NH4_surface.nc	std.dev_NH4_bottom.nc	
			mean	mean_NH4_surface.nc	mean_NH4_bottom.nc	
Nitrate	no3	[mmol m-3]	min	min_NO3_surface.nc	min_NO3_bottom.nc	
			max	max_NO3_surface.nc	max_NO3_bottom.nc	
			range	range_NO3_surface.nc	range_NO3_bottom.nc	
			std.dev	std.dev_NO3_surface.nc	std.dev_NO3_bottom.nc	
			mean	mean_NO3_surface.nc	mean_NO3_bottom.nc	
Phosphate	po4	[mmol m-3]	min	min_PO4_surface.nc	min_PO4_bottom.nc	
			max	max_PO4_surface.nc	max_PO4_bottom.nc	
			range	range_PO4_surface.nc	range_PO4_bottom.nc	
			std.dev	std.dev_PO4_surface.nc	std.dev_PO4_bottom.nc	
			mean	mean_PO4_surface.nc	mean_PO4_bottom.nc	
Dissolved Oxygen	o2	[mmol m-3]	min	min_o2_surface.nc	min_o2_bottom.nc	
			max	max_o2_surface.nc	max_o2_bottom.nc	
			range	range_o2_surface.nc	range_o2_bottom.nc	
			std.dev	std.dev_o2_surface.nc	std.dev_o2_bottom.nc	
			mean	mean_o2_surface.nc	mean_o2_bottom.nc	
Net Primary Production	nppv	[mg m-2 day-1]	min	min_nppv_integrated.nc		
			max	max_nppv_integrated.nc		
			range	range_nppv_integrated.nc		
			std.dev	std.dev_nppv_integrated.nc		
			mean	mean_nppv_integrated.nc		
Dissolved Inorganic Carbon	dissic	[mol m-3]	min	min_DIC_surface.nc	min_DIC_bottom.nc	
			max	max_DIC_surface.nc	max_DIC_bottom.nc	
			range	range_DIC_surface.nc	range_DIC_bottom.nc	
			std.dev	std.dev_DIC_surface.nc	std.dev_DIC_bottom.nc	
			mean	mean_DIC_surface.nc	mean_DIC_bottom.nc	

Each file covers the Mediterranean Sea (BBOX: lon.min = −6.00; lon.max = 36.28; lat.min = 30.21; lat.max = 45.97, CRS = EPSG:4326) for each year from 2005 to 2099. See Fig. 1 for an example.Fig. 1 Projection of the mean surface temperature for the Mediterranean Sea in January 2050.

Fig. 1

4 Experimental Design, Materials and Methods

4.1 Computational environment

Processing was performed on Galileo100 HPC system (Cineca-Italy) using 24 cores and 256GB RAM using Python language. Dependencies are: copernicusmarine, ddsapi, xarray, dask, pandas, numpy, scipy, matplotlib, cmethods.

4.2 Materials

The dataset was created using data from two primary sources: the European Union's Copernicus Marine Service Information (CMEMS) and the Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC). The specific products used were:1. Mediterranean Sea Biogeochemistry Reanalysis [5]. E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS).

2. Mediterranean Sea Physical Reanalysis [6]. E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS).

3. MEDSEA CMIP5 Projections Physics [7]. CMCC Data Delivery System (DDS).

4. MEDSEA CMIP5 Projections Biogeochemistry [1]. CMCC Data Delivery System (DDS).

4.3 The future of the Mediterranean in the light of the RCP 8.5 scenario

Simulations following the RCP8.5 scenario predict significant changes in the Mediterranean by the end of the 21st century:1. Increased water temperature and stratification, reduction in vertical mixing and nutrient upwelling.

2. Reduced availability of phosphorus and nitrogen in the upper layers, limiting primary productivity and phytoplankton populations. The eastern Mediterranean could develop into oligotrophic conditions.

3. Reduced oxygen solubility and increased acidification due to higher CO2 uptake, affecting marine life, especially calcifying organisms.

4. Altered ecosystem composition and functionality with possible northward migration of species adapted to warm conditions and decline of less adaptable species.

5. Variable regional impacts, with potentially greater changes in the eastern Mediterranean compared to the western part.

6. Fluctuations in salinity due to changes in precipitation, river flows and evaporation rates, affecting water density and circulation patterns.

4.4 GIS modelling

All data processing was done using the native coordinate system World Geodetic System 1984 (WGS84) (EPSG:4326) to transform the regular 1/16° grid of the modelled data into the regular 1/24° grid of the reanalysis data. The distances are calculated “on the fly” using the Haversine formula. This approach minimises the risk of introducing integration and numerical errors associated with the planar projection and preserves the latitude/longitude trends as produced by the model simulations.

4.5 Experimental design and methods

Daily raw RCP8.5 data from the OGSTM-BFM model for the period 2005–2100 with a grid resolution of 1/16 degree were processed to obtain bias-corrected statistical descriptors with a grid resolution of 1/24 degree and an annual time scale. Data processing included the following steps (package.function in brackets):1. Monthly aggregation: the daily data were averaged monthly to reduce the data size and conform to the CMEMS reanalysis format.

2. Spatial downscaling: The data were downscaled to 1/24 degree using trilinear interpolation (scipy.interpolate.interpn) and masked (numpy.where) to exactly match the spatial grid of the CMEMS reanalysis.

3. Imputation of missing values: Missing values in the downscaled data set were filled using a nearest neighbour algorithm (xarray.Dataset.ffill), following a conservative approach. This ensured that the resulting dataset had the same number of values and the same spatial structure as the CMEMS reanalysis dataset while limiting extrapolation artifacts.

4. Bias correction: A Quantile Delta Mapping algorithm (cmethods.adjust, method = quantile_delta_mapping, quantiles = 10,000) was used to align projections to the trends in the CMEMS reanalysis time series [8]. The overlapping 2005–2020 period was used as a reference to fit a transfer function that maps the model's distribution to the reanalysis distribution while maintaining the relative changes projected by the model.

5. Calculation of statistical descriptors: Bias-corrected data were aggregated annually and five statistical indices were calculated: Minimum(numpy.min), Maximum(numpy.max), Mean(numpy.mean), Range(numpy.range) and Standard Deviation(numpy.std). The data were exported as netcdf files.

An example/template script for reproducing the entire process, starting with the download of the raw data, can be found in the Supplementary Material.

Limitations

It is important to recognise that all model projections are subject to uncertainties. These can come from various sources, such as: Model structure, model parameterisation, uncertainties in initial conditions, uncertainties in boundary conditions and forcing functions (scenarios), numerical approximations. Usually the uncertainties in the initialisation dominate on a shorter time scale, the uncertainties in the scenarios are generally more important on a longer time scale, while the uncertainty in the choice of model parameterisation is always important. However, the results of a systematic global sensitivity analysis on the parameter uncertainties in a complex marine ecosystem model show that while the details of the time course of many variables are sensitive to the choice of parameter values, the integrated indicators are much more robust.

In this case, the importance of the atmospheric forcing and boundary conditions (i.e. the scenarios) should be emphasised, which can strongly influence the dynamics of the Mediterranean Sea, including vertical mixing, and thus the nutrient distribution and the dynamics of the lower trophic levels. Another point to consider is the spatial horizontal and vertical discretisation of the model. The OGSTM-BFM model focuses on resolving the dynamics of the low trophic levels in the euphotic zone at high resolution, while the representation of the processes in deep waters and near the bottom is less detailed. A final consideration concerns the downscaling and bias correction processes, which adjust and harmonise the dataset to the trends observed in the reanalysis, but may also introduce artefacts and smooth environmental signals. Considering all these factors, the dataset is well suited for studies at regional and sub-regional scales, but should be used with caution for applications at scales below grid resolution.

Ethics Statement

The authors have read the ethical requirements for publication in Data in Brief and comply with them. They confirm that the present work does not include human subjects, animal experiments or data from social media platforms.

CRediT authorship contribution statement

Marco Fianchini: Conceptualization, Methodology, Investigation, Data curation, Visualization, Writing – original draft, Writing – review & editing. Cosimo Solidoro: Conceptualization, Methodology, Validation, Writing – review & editing. Donata Canu: Conceptualization, Methodology, Validation, Writing – review & editing.

Data Availability

2005–2099 High resolution bioclimatic variables for the surface and bottom of the Mediterranean Sea. (Original data) (Zenodo).

Acknowledgements

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. We thank the National Biodiversity Future Center (NBFC) for the opportunity and Stefano Piani for useful guidance during the coding phase.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
==== Refs
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