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

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Abstract

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 the 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 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, oceanic, and river flow indicators. It covers a time series of up to 2100 with various representative concentration pathways and climate models.
Data in Brief 43 (2022) 108339
Contents lists available at ScienceDirect
Data in Brief
journal homepage: www.elsevier.com/locate/dib
Data Article
Climate change indicators dataset for coastal
locations of the European Atlantic area
Bassel Habeeb
a , b
, Emilio Bastidas-Arteaga
b ,
a
Institute for Research in Civil and Mechanical Engineering (GeM UMR CNRS 6183), University of Nantes, France
b
Laboratory of Engineering Sciences for Environment (LaSIE UMR CNRS 7356), La Rochelle University, France
a r t i c l e i n f o
Article history:
Received 25 April 2022
Revised 25 May 2022
Accepted 26 May 2022
Available online 1 June 2022
Dataset link: Climate change indicators
database (Reference data)
Keywo rds:
Climate indicators database
Atmospheric indicators
Oceanic indicators
Climate change
Atlantic area
Coastal infrastructure assets
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 hu-
man activities. Climate change leads to wide variations in en-
vironmental variables such as temperature, relative humid-
ity, carbon dioxide, etc. These changes could adversely af-
fect the performance, serviceability, and safety of infrastruc-
ture assets. The challenge, therefore, is to not only under-
stand the effect of extreme events and their links to climate
change, but also to obtain data that could be used for assess-
ing 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 infras-
tructure assets. The database contains information for spe-
cific coastal locations placed in five European countries: Cax-
ias (Portugal), Saint Nazaire (France), Vigo (Spain), Brighton
(UK), Dublin and Cork (Ireland). The database includes atmo-
spheric, and oceanic indicators, as well as and the flow of
rivers. It covers a time series of up to 2100 with various rep-
resentative concentration pathways and climate models.
Corresponding author.
E-mail address: ebastida@univ-lr.fr (E. Bastidas-Arteaga).
Social media: @BastidasArteaga (E. Bastidas-Arteaga)
https://doi.org/10.1016/j.dib.2022.108339
2352-3409/© 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license
( http://creativecommons.org/licenses/by/4.0/ )
2 B. Habeeb and E. Bastidas-Arteaga / Data in Brief 43 (2022) 108339
©2022 The Authors. Published by Elsevier Inc.
This is an open access article under the CC BY license
( http://creativecommons.org/licenses/by/4.0/ )
Specifications Tabl e
Subject Civil and Structural Engineering
Specific subject area Atmospheric and oceanic climate variables and river flow for infrastructure
vulnerability assessment for several locations in the European Atlantic area
Type of data Tabl es
How data were acquired Data was extracted from: Wo rld Climate Research Program, Coupled Model
Inter-comparison Project 5, Copernicus Climate Change Service, Coordinated
Regional Climate Downscaling Experiment
Data format Filtered
Description of data collection The main conditions for collecting data were: (1) to select coastal locations in
the European Atlantic Area with different weather conditions; (2) to choose
models and resolutions that are appropriate for lifetime and vulnerability
infrastructure assessment;
(3) to select climate parameters affecting the
durability and safety of infrastructures in coastal areas; (4) to provide
information in an excel format that could be easily used by researchers and
companies; (5) to deliver historical data and predictions for several climate
change scenarios extracted from several climate models.
Data source location First location of interest:
City/Town/Region: Saint Nazaire/Loire-Atlantique/Pays de la Loire.
Country: France.
Latitude and longitude: 47.31 °N, 2.17 °W
Second location of interest:
City/Town/Region: Vigo/Galicia/Pontevedra.
Country: Spain.
Latitude and longitude: 42.19 °N, 8.78 °W
Third location of interest:
City/Town/Region: Caxias/Oeiras /Lisbon.
Country: Portugal.
Latitude and longitude: 38.65
°N, 9.3 °W
Fourth location of interest:
City/Town/Region: Brighton/East Sussex/South East England.
Country: United Kingdom.
Latitude and longitude : 50.85 °N, 0.11 °W
Fifth location of interest:
City/Town/Region: Dublin/Leinster/Eastern Ireland.
Country: Ireland.
Latitude and longitude: 53.29 °N, 6.3 °W
Sixth location of interest:
City/Town/Region: Cork/Munster/South West Ireland.
Country: Ireland.
Latitude and longitude: 51.87 °N, 8.54 °W
Primary data sources and models:
Regional atmospheric climate models: (RCA4, HIRAM5, WRF381P, CCLM4–8–17,
RACMO22E, REMO2015)
Global oceanic climate models: (CANESM2, MOHC
HadGEM2,
NIMR-KMAHadGEM2-AO, ERA5)
River flow regional model: (IMPACT2C)
Data accessibility Direct link to the dataset:
https://sirma- project.eu/dissemination/climate- change- indicators- database/
B. Habeeb and E. Bastidas-Arteaga / Data in Brief 43 (2022) 108339 3
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 At-
lantic area.
This database was mainly proposed for research institutions and enterprises interested in es-
timating the durability, consequences, vulnerability, and cost-effectiveness of adaptation mea-
sures 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 spe-
cific locations, variables, climate change scenarios, and models is available for assessing cli-
mate change effects on infrastructure assets and formulating adaptation strategies. Compre-
hensive lifetime assessment models considering time-variant climatic inputs could be used
towards these aims. Moreover, it is valuable to develop stochastic predicting models.
1. 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 cli-
mate 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 re-
gional 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 post-
treatment 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 coordi-
nates. 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 Work sheets D.SIRMA- WP4- 3.1- MD describes the Dataset Meta-
data 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 .
4 B. Habeeb and E. Bastidas-Arteaga / Data in Brief 43 (2022) 108339
Tabl e 1
Oceanic dataset availability.
Sea Surface
Temperature
Sea Surface
Salinity
Sea Wate r X
Velocity
Sea Wate r Y
Velocity
Sea Wate r
Pressure at
Sea Wate r
Surface
Significant
height of
combined
wind waves
and swell
Mean wave
period
Sea Surface
Height
Above Geoid
Model Start period End period Projections
Celsius
Degree PSU m/s m/s Decibar m sec m
CANESM2 Jan. 1850 Dec. 2100 RCP 2.6 RCP
4.5 RCP 8.5
+ + + + +
MOHC
HadGEM2 Dec. 1859 Dec. 2099 RCP 2.6 RCP
4.5 RCP 6.0
RCP 8.5
+ + +
NIMR-KMA
HadGEM2-AO
Jan. 1860 Dec. 2100 RCP 2.6 RCP
4.5 RCP 6.0
RCP 8.5
+ + + + +
ERA5 Jan. 1979 Dec. 2099 No
projections
+ +
B. Habeeb and E. Bastidas-Arteaga / Data in Brief 43 (2022) 108339 5
Tabl e 2
Atmospheric dataset availability.
Near Surface
Air
Temperature
Near Surface
Relative
Humidity Precipitation
Daily Mean
Near-Surface
Wind Speed
Model Start period End period Projections
Celsius
Degree % kg m
-2
s
-1 m/s
RCA4 1/01/1971 31/12/210 0 RC P 2.6 RCP
4.5 RCP 8.5
+ + + +
HIRHAM5 26/12/1950 27/06/2098 RCP 4.5 RCP
8.5
+ + + +
WRF381P 01/01/ 1951 30/11/2099 RCP 8.5 +
CCLM4–8–17 01/01/1951 31/ 12/2100 RCP 4.5 RCP
8.5
+
RACMO22E 26/12/1950 27/06/2098 RCP 2.6 RCP
4.5 RCP 8.5
+ + + +
REMO2015 12/01/1951 11 / 01 / 2 10 1 RCP 8.5 + + + +
Tabl e 3
River dataset availability.
Model Start period End period Projections River Flow
m
3
/s
IMPACT2C 1/01/1979 31/12/ 2100 RCP 2.6 RCP 4.5 RCP 8.5 +
Tabl e 4
Models’ resolution.
Approx. resolution (km)
Dataset Model Latitude Longitude
Ocean CANESM2 103 .2 156.1
MOHC
HadGEM2 111.0 111 .0
NIMR-KMAHadGEM2-AO 111.0 111. 0
ERA5 55.5 55.5
Atmosphere RCA4 12.2 12 .2
HIRHAM5 12.2 12.2
WRF381P 12.2 12. 2
CCLM4–8–17 12.2 12. 2
RACMO22E 12.2 12.2
REMO2015 12.2 12. 2
River IMPACT2C 55.5 55.5
Tabl e 5
Resolution distribution for the atmospheric dataset.
Resolution Distribution (Degree) Extraction point (Degree)
City
Latitude
distribution-1
Latitude
distribution-2
Longitude
distribution-1
Longitude
distribution-2 Latitude longitude
Caxias 38.595 38.705 9.245 9.355 38.65 9.3
Saint Nazaire 47.255 47.365 2.115 2.225 47.31 2.17
Vigo 42.135 42.245 8.725 8.835 42.19 8.78
Brighton 50.795 50.905 0.055 0.165 50.85 0.11
Dublin 53.235 53.345 6.245 6.355 53.29 6.3
Cork 51.815 51.925 8.485 8.595 51.8 7 8.54
6 B. Habeeb and E. Bastidas-Arteaga / Data in Brief 43 (2022) 108339
Tabl e 6
Dataset metadata sample.
Dataset Metadata
Climate model HadGEM2-AO
Project CMIP5
Product Output1
Institute NIMR-KMA
Experiment Historical & All RCP
Time-frequency Monthly
Reaim Ocean
Ensemble r1i1p1
Version 20,121,018
Data source HadGEM2-AO r6.6.3 (2010): atmosphere: HadGAM (HadGAM2, N96L38); ocean:
HadGOM (HadGOM2, 1 ×1L40, increased resolution at Equator); sea ice: part of
HadGOM2; land: MOSES-2
Forcing Nat, Ant,
GHG, SA, Oz, LU, Sl, Vl, SS, Ds, BC, MD, OC
2. 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 cli-
mate 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 Re-
gional 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 web-
site 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 com-
mon 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 col-
lected from social media platforms.
B. Habeeb and E. Bastidas-Arteaga / Data in Brief 43 (2022) 108339 7
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal rela-
tionships that could have appeared to influence the work reported in this paper.
Data Availability
Climate change indicators database (Reference data) (SIRMA project website).
CRediT Author Statement
Bassel Habeeb: Conceptualization, Methodology, Software, Data curation, Writing original
draft; Emilio Bastidas-Arteaga: Conceptualization, Methodology, Supervision, Validation, Fund-
ing acquisition, Writing –review & editing.
Acknowledgments
This work was carried out in the framework of the Strengthening the Terri to ry’ s Resilience
to Risks of Natural, Climate and Human Origin (SIRMA) project, which is co-financed by the Eu-
ropean Regional Development Fund (ERDF) through INTERREG Atlantic Area Program with ap-
plication code: EAPA_826/2018. The sole responsibility for the content of this publication lies
with the authors. It does not necessarily reflect the opinion of the European Union. Neither the
INTERREG Europe program authorities are responsible for any use that may be made of the in-
formation contained therein.
References
[1] M.G. Stewart, E. Bastidas-Arteaga, Corrosion of concrete and steel structures in a changing climate, in: E. Bastidas-
Arteaga, M.G. Stewart (Eds.), Climate Adaptation Engineering : Risks and Economics for Infrastructure Decision-
Making, Butterworth-Heinemann, 2019, pp. 99–125, doi: 10.1016/B978- 0- 12- 816782- 3.0 0 0 04- 8 .
[2] E. Bastidas-Arteaga, et al., Towa rd s climate change adaptation of existing and new deteriorating infrastructure, in:
J.C. Matos, P.B. Lourenço, D.V. Oliveira, J. Branco, D. Proske, R.A. Silva, et al. (Eds.), 18th International Probabilistic
Workshop: IPW 2020, Springer International Publishing, Cham, 2021, pp. 39–51, doi: 10.1007/978- 3- 030- 73616- 3 _ 3 .
[3] O. Hoegh-Guldberg , D. Jacob , M. Tayl or , M. Bindi , S. Brown , I. Camilloni , A. Diedhiou , R. Djalante , K.L. Ebi , F. En-
gelbrecht , J. Guiot , Y. Hijioka , S. Mehrotra , A. Payne , S.I. Seneviratne , A. Thomas , R. Warren , G. Zhou , Impacts of
1.5 °C global warming on natural and human systems, in: V. Masson-Delmotte, P. Zhai, H.-O. Pörtner, D. Roberts,
J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou,
M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, T. Waterfi eld (Eds.), In: global Wa rming of 1.5 °C. An IPCC special report
on the impacts of global warmi ng of 1.5 °C above pre-industrial levels and related global greenhouse gas emission
pathways in the context of strengthening the global response to the threat of climate change, sustainable
develop-
ment, and effort s to eradicate poverty, 2018, pp. 175–311 .
[4] IPCC, Climate ChangeThe Physical Science Basis. Contribution of Worki ng Group I to the Fifth Assessment Report of
the Intergovernmental Panel On Climate Change, Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA, 2013 2013 .
ResearchGate has not been able to resolve any citations for this publication.
Chapter
Full-text available
Infrastructure assets are essential components to the economical development of modern societies. They are designed to ensure target levels of serviceability and safety on the basis on past experiences and current knowledge on design, construction and maintenance practices. However, changes in climate could modify the lifetime performance of infrastructure by increasing or decreasing failure risks. Therefore, a rational and scientific approach is necessary to deal with the adaptation of existing and new deteriorating infrastructure in a comprehensive way. This keynote paper provides an overview of recent works on this area including: (1) assessment of climate change effects, (2) adaptation to new environmental conditions for future climate change scenarios and (3) decision-making under a changing climate. Several examples for different kind of deteriorating infrastructure assets are also presented and discussed in this paper.
Corrosion of concrete and steel structures in a changing climate
  • Stewart
Climate Adaptation Engineering : Risks and Economics for Infrastructure Decision-Making
  • M G Stewart
  • E Bastidas-Arteaga
M.G. Stewart, E. Bastidas-Arteaga, Corrosion of concrete and steel structures in a changing climate, in: E. Bastidas-Arteaga, M.G. Stewart (Eds.), Climate Adaptation Engineering : Risks and Economics for Infrastructure Decision-Making, Butterworth-Heinemann, 2019, pp. 99-125, doi: 10.1016/B978-0-12-816782-3.0 0 0 04-8.
Climate ChangeThe Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel On Climate Change
IPCC, Climate ChangeThe Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel On Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2013 2013.