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Exploring climate model data

Sensitivity of power distribution infrastructure to days of high temperature variability


Developing and analysing an indicator to describe vulnerability of the energy sector to climate change in term of power distribution.


In order to provide the private sector with the best benefits of the progress of climate science, a collaborative project with climatologists and industries (INVULNERABLe) has been launched to develop physical and vulnerability indicators with operational relevance. Especially, we propose to study energy sector vulnerability to climate change. A first case study has been launched to analyse the impact of climate change on the management of power distribution installation (distribution running). This activity is sensitive to fast temperature changes. A climatic indice has been defined. It describes the number of day with diurnal range temperature values much higher than the normal ones. The analysis includes indice actual values and future projections over Europe and China. Both long (end of 21st century) and midrange-term (2030-2050) are examined.


Céline Déandreis (IPSL, France).


Energy sector firms (mainly environment department manager) and climatologists from IPSL, CERFACS and CNRM, Model Developer from IPSL.

Related use cases


Data needs:

  • data type
    daily maximum and minimum temperatures
    period: 1961-2100
    spatial covering: global data and regional data at higher horizontal resolution for Europe and China
  • data set
    Simulated data from the stream 2 data set of the European project ENSEMBLE. It includes 7 models and several simulations per models
    Observations are gridded data for model correction. Three available datasets: the Hadley Centre data set (HADGHCND) and the reanalyses ERA40 or NCEP

Typical course of events

  • Indices definition
    The definition of the indices needs to set up the dialog between industrial partners and climatologists (dealing with cultural, vocabulary differences…; reciprocal education on climate sciences, modelling limits, company interest, economics…), a conception of aid materials (synthetic report, glossary,…) in order to facilitate the dialog. Fact sheets have been written on several topics addressed in the 4th IPCC report, review of scientific studies on diurnal temperature range and other physical processes. Finding the « best interlocutor » within the industrial companies (department leader, engineer, plant workers… )
    Detecting vulnerabilities of industrial partners (events, zones and periods of interest…) In our study: the « difficulty to adapt the running of power distribution installation when fast temperature changes happen (during the day or between consecutive days) » has been mentioned as a main problem
    The indice compute in this use case is the DTR (Diurnal Temperature Range). It is defined as DTR > DTRref + threshold. Indices are ratified by the industrial partners
  • Data sets selection
    Analysing the main strengths and weaknesses of each data-sets (confidence in the data relatively to the study aim, availability)
    Selection of the most appropriate dataset for the study
    Assess the dataset performance for describing physical fields and indices defined in the first step of the process
  • Correction of modelling data
    Selection of a correction method functions of the data biases, the geographical zones of interest and the data availability
    Selection of a data set to perform the correction : observations, re-analyses
    Development or adaptation of correction code source
    Validation of corrected data
    Writing synthetic report on targeted physical fields (Diurnal Temperature Range) as a complement to the review of scientific knowledge. This report contains information about variability, statistical distribution, main geographical structures, physical processes
    Indices calculation from corrected data
    Developing source code
    Running source code
  • Indices statistical analysis
    Trend and variability: Evaluation of the signal trend compared to natural variability
    Significativity and robustness of the results are tested
    Interpretation and graphical display describe results to industrial partners (staff from strategy, risk, insurance or environmental departments) in order to define vulnerability and adaptation measures




  • Quantile-quantile correction method
  • Method to compare several data-sets (trend, seasonal cycle, inter-annual variability)
  • Multi-model analysis
  • Kendall test for trend significativity evaluation


  • IDL to change format data from ascii to netcdf
  • Fortran program for the data correction and the indicator calculation
  • CDO, NCO, ferret, for data treatment and data analyses (DTR, indicator..)
  • NCL, EXCEL for graphical display

File Format

NetCDF Ascii Miscellaneous Notes.

Sources of Uncertainty

Several sources of uncertainty are accounted for in this study
  • observation dataset
  • model biases
  • model structural differences

Multi-reanalyses datasets and multi-model analyses are used to provide a measure of these uncertainties.


The ENES3 project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 824084.