Climate change indicators dataset for coastal locations of the European Atlantic area

Over time, considerable changes in the earth's climate have always occurred due to a wide variety of natural processes. During the last century, these natural changes have all been accelerated by global warming, which has been driven by human activities. Climate change leads to wide variations in environmental variables such as temperature, relative humidity, carbon dioxide, etc. These changes could adversely affect the performance, serviceability, and safety of infrastructure assets. The challenge, therefore, is to not only understand the effect of extreme events and their links to climate change, but also to obtain data that could be used for assessing long-term gradual effects affecting infrastructure assets. In this paper is presented a climate indicators database that was collected and provided in an excel format. This database could be used for assessing the durability, vulnerability, and cost-effectiveness of adaptation measures for coastal infrastructure assets. The database contains information for specific coastal locations placed in five European countries: Caxias (Portugal), Saint Nazaire (France), Vigo (Spain), Brighton (UK), Dublin and Cork (Ireland). The database includes atmospheric, and oceanic indicators, as well as and the flow of rivers. It covers a time series of up to 2100 with various representative concentration pathways and climate models.


a b s t r a c t
Over time, considerable changes in the earth's climate have always occurred due to a wide variety of natural processes. During the last century, these natural changes have all been accelerated by global warming, which has been driven by human activities. Climate change leads to wide variations in environmental variables such as temperature, relative humidity, carbon dioxide, etc. These changes could adversely affect the performance, serviceability, and safety of infrastructure assets. The challenge, therefore, is to not only understand the effect of extreme events and their links to climate change, but also to obtain data that could be used for assessing long-term gradual effects affecting infrastructure assets. In this paper is presented a climate indicators database that was collected and provided in an excel format. This database could be used for assessing the durability, vulnerability, and cost-effectiveness of adaptation measures for coastal infrastructure assets. The database contains information for specific coastal locations placed in five European countries: Caxias (Portugal), Saint Nazaire (France), Vigo (Spain), Brighton (UK), Dublin and Cork (Ireland). The database includes atmospheric, and oceanic indicators, as well as and the flow of rivers. It covers a time series of up to 2100 with various representative concentration pathways and climate models.

Value of the Data
• The database consists of atmospheric and oceanic datasets that provide an objective basis for understanding and predicting future evolutions, and related effects for the built environment, of atmospheric, and oceanic variables or river flow in specific locations of the European Atlantic area. • This database was mainly proposed for research institutions and enterprises interested in estimating the durability, consequences, vulnerability, and cost-effectiveness of adaptation measures for infrastructure assets and buildings in specific locations of European Atlantic regions [1 , 2] . However, other sectors could also take advantage of this database. • The database is provided in an excel format that could be used easily. Information from specific locations, variables, climate change scenarios, and models is available for assessing climate change effects on infrastructure assets and formulating adaptation strategies. Comprehensive lifetime assessment models considering time-variant climatic inputs could be used towards these aims. Moreover, it is valuable to develop stochastic predicting models.

Data Description
The database includes atmospheric, oceanic and river flow datasets. The climate indicators availability in the database is described in Tables 1-3 . The climate change indicators database comprises a variety of climate models, in which each model has specific driving methods to generate the data e.g., ensemble, data source, Institution, Jet stream, influence, aerosols, forcing, initial state of run, etc. The availability of different models provides one idea of the levels of uncertainty of the model that is useful for consequence analysis or lifecycle assessment. The climate change indicators database aims to cover the historical period and several climate change scenarios, as follows: RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 [3 , 4] .
The resolution of the climate models varies as shown in Table 4 , resulting in differences in the regions covered by each model. The atmospheric dataset is obtained from high-resolution regional models and provides representative information for the locations of interest. However, the oceanic dataset should be validated as it is obtained from low-resolution global climate models. The resolution of the river flow could cover several rivers in the region. Therefore, some posttreatment processing of the data is required to determine the river flow for a specific river.
The selected geographical locations are located alongside the European Atlantic Ocean area ( Table 5 ). The extraction of the database is based on the nearest point to the model's coordinates. The region covered in each model is provided by the resolution distribution worksheets available in the document D.SIRMA-WP4-3.2-RD . A sample of this file is presented in Table 5 .
The Model description Excel Worksheets D.SIRMA-WP4-3.1-MD describes the Dataset Metadata for each climate model, provides the data source, and information for each climate model. Table 6 represents a sample of these Excel Worksheets, in which the indicated forcing factors are defined in the Excel Worksheet D.SIRMA-WP4-2.1-VFD .

Experimental Design, Materials and Methods
The main conditions for collecting data are summarized as follows. We focused on coastal infrastructure located in the European Atlantic Area which is the region of study of the SIRMA project. The database in intended to be used for evaluating the effects and proposing adaptation solutions for infrastructure assets and buildings. Therefore, the conditions to select and extract parameters were defined based on the most common durability and vulnerably issues affecting these assets, e.g., corrosion, chloride ingress into concrete, extreme winds, erosion, sea-level rise, etc.
The choice of a climate model with a very large resolution could lead to wrong lifetime or vulnerability infrastructure assessments. Therefore, we considered models and resolutions that are representative of different infrastructure assets. Historical data and predictions for several climate change scenarios are also considered lifetime or vulnerability assessments for existing and new infrastructure under past and future weather conditions. The inclusion of several climate models in the database allows to consider the model uncertainty for the infrastructure assessments.
The information was extracted from three main databases: World Climate Research Program, Coupled Model Inter-comparison Project 5, Copernicus Climate Change Service, Coordinated Regional Climate Downscaling Experiment. However, the information given in these databases is not easy to use for researchers and companies. Therefore, this database provides a friendly website application that allows to download data in Excel format depending on the requirements of the user.
This database was extracted and prepared by writing and using a numerical script using R software. Those scripts are capable of: 1-Extracting variables for a region determined by the model's resolution. 2-Describing each model dataset Metadata to distinguish the measurement variation of a common variable (e.g., experiment, aim, frequency, start date, institution, project, product, source, forcing, ensemble, Version, driving model).

Ethics Statement
This database did not involve the use of human subjects, animal experiments, nor data collected from social media platforms.