Statistical analysis in climate research: future aridity conditions in the Iberian Peninsula

Abstract

Researchers in atmospheric sciences often use the popular format named Network Common Data Form (NetCDF) developed by University Corporation for Atmospheric research (UCAR) to create, manage, store and distribute scientific data. It is a platform independent format, available for several operational systems, and it was designed to represent multidimensional, array-oriented scientific data. Usually an array has two dimensions (2D), in atmospheric sciences that can means a temperature, precipitation or pressure field given certain coordinates: latitude and longitude. Arrays having more than two dimensions, e.g., when to the previous fields it is added altitude (3D) or even time (4D) these arrays are called multidimensional arrays. Programming and work with multidimensional data can be challenging, although NetCDF data is self-describing and support direct access to small subset or larger datasets (since storage is made as arrays). Consequently, some common statistical analysis can still be performed in climate research but from another view point. Aridity plays a key role to characterize the climate of a region, since it has a major impact on water resources and human activities. In this case study, several statistical methods are going to be applied to several aridity indices, in two different periods between 1951 and 2070 in the Iberian Peninsula. Gridded precipitation totals and air temperature datasets are used on a daily and monthly basis to compute this index. Results revealed that climate was subjected to both spatial and temporal variabilities and statistically significant trends were detected, mainly between 2041 and 2070. A regional division of the Iberian Peninsula according to aridity conditions was attained by a hierarchical cluster analysis and is going to be presented. The selection of the clusters following Ward method showed high spatial coherence, and allowed the study of the general spatial behavior of aridity conditions in Iberia during this period. These results are in clear accordance with some outcomes achieved by regarding other climatic indices.

Full text

Geophysical Research Abstracts
Vol. 20, EGU2018-17235, 2018
EGU General Assembly 2018
© Author(s) 2018. CC Attribution 4.0 license.
Statistical analysis in climate research: future aridity conditions in the
Iberian Peninsula
Francisco Carvalho (1,2), Cristina Andrade (3,4,5), Joana Contente (3), João Corte-Real (6,7)
(1) Centro de Matemática e Aplicações, Universidade Nova de Lisboa, Portugal (fpcarvalho@ipt.pt), (2) Instituto Politécnico
de Tomar, (3) NHRC.ipt - Instituto Politécnico de Tomar, (4) Centre for the Research and Technology of Agro-Environmental
and Biological Sciences (CITAB), University of Trás-os-Montes e Alto Douro, Vila Real, Portugal, (5) University of Aveiro,
CESAM, Aveiro, Portugal, (4) Centro de Matemática e Aplicações (CMA), Universidade Nova de Lisboa, Lisboa, Portugal,
(6) ICAAM, University of Évora, Évora, Portugal, (7) University Lusófona of Humanities and Technologies, DAT/DREAMS,
Lisboa, Portugal
Researchers in atmospheric sciences often use the popular format named Network Common Data Form (NetCDF)
developed by University Corporation for Atmospheric research (UCAR) to create, manage, store and distribute
scientific data. It is a platform independent format, available for several operational systems, and it was designed
to represent multidimensional, array-oriented scientific data. Usually an array has two dimensions (2D), in
atmospheric sciences that can means a temperature, precipitation or pressure field given certain coordinates:
latitude and longitude. Arrays having more than two dimensions, e.g., when to the previous fields it is added
altitude (3D) or even time (4D) these arrays are called multidimensional arrays. Programming and work with
multidimensional data can be challenging, although NetCDF data is self-describing and support direct access to
small subset or larger datasets (since storage is made as arrays). Consequently, some common statistical analysis
can still be performed in climate research but from another view point.
Aridity plays a key role to characterize the climate of a region, since it has a major impact on water re-
sources and human activities. In this case study, several statistical methods are going to be applied to several
aridity indices, in two different periods between 1951 and 2070 in the Iberian Peninsula. Gridded precipitation
totals and air temperature datasets are used on a daily and monthly basis to compute this index.
Results revealed that climate was subjected to both spatial and temporal variabilities and statistically sig-
nificant trends were detected, mainly between 2041 and 2070. A regional division of the Iberian Peninsula
according to aridity conditions was attained by a hierarchical cluster analysis and is going to be presented. The
selection of the clusters following Ward method showed high spatial coherence, and allowed the study of the
general spatial behavior of aridity conditions in Iberia during this period. These results are in clear accordance
with some outcomes achieved by regarding other climatic indices.
This work is supported by: European Investment Funds by FEDER/COMPETE/POCI-Operational Compet-
itiveness and Internationalization Programme, under Project POCI-01-0145-FEDER-006958 and National Funds
by FCT - Portuguese Foundation for Science and Technology, under the project UID/AGR/04033/2013. This
work is supported by: European Investment Funds by FEDER/COMPETE/POCI Operational Competitiveness
and Internationalization Program, under POCentro-PT2020-FEDER project Centro-01-0145-FEDER-
024253.