Intro

ICCLIM (Indice Calculation CLIMate) is a Python library for computing a number of climate indices.

Currently, the 49 climate indices as defined by European Climate Assessment & Dataset based on air temperature and precipitation variables are included:

  • 11 cold indices (GD4, CFD, FD, HD17, ID, CSDI, TG10p, TN10p, TX10p, TXn, TNn)
  • 1 drought indice (CDD)
  • 9 heat indices (SU, TR, WSDI, TG90p, TN90p, TX90p, TXx, TNx, CSU)
  • 14 rain indices (PRCPTOT, RR1, SDII, CWD, R10mm, R20mm, RX1day, RX5day, R75p, R75pTOT, R95p, R95pTOT, R99p, R99pTOT)
  • 4 snow indices (SD, SD1, SD5cm, SD50cm)
  • 6 temperature indices (TG, TN, TX, DTR, ETR, vDTR)
  • 4 compound indices (CD, CW, WD, WW)

Detailed description of each indice is available at http://eca.knmi.nl/documents/atbd.pdf.

Initialy ICCLIM was designed for online computing of climate indices through the climate4impact portal. In spite of existence of other packages able to compute climate indices (CDO, R package ), it was decided to develop a new software in Python. Python language was first of all choosen to interface with PyWPS: Python implementation of Web Proccessing Service (WPS) Standard. Another reason was to interface eventially with the OpenClimateGIS.

The ICCLIM developer repository can be found here: https://github.com/cerfacs-globc/icclim

Notes about ICCLIM

  1. Input dataserts must be compliant to the CF convention.
  2. Currently, ICCLIM doesn’t support spatial subsetting, i.e. it processes whole spatial area.
  3. ICCLIM works with unsecured OPeNDAP datasets as well.

What is a climate indice

A climate index is a calculated value that can be used to describe the state and the changes in the climate system.

The climate at a defined place is the average state of the atmosphere over a longer period of, for example, months or years. Changes on climate are much slower than on the weather, that can change strongly day by day.

Climate indices allow a statistical study of variations of the dependent climatological aspects, such as analysis and comparison of time series, means, extremes and trends.

Note

A good introduction for climate indices is on the website of the Integrated Climate Data Center (ICDC) of the University of Hamburg.