Variables and derived variables

The Global Climate Observing System international collaboration has identified 50 Essential Climate Variables (ECVs) comprising

Atmospheric (over land sea and ice)

  1. Surface: Air temperature, wind speed and direction, water vapour, pressure, precipitation, surface radiation budget.
  2. Upper-air: Temperature, wind speed and direction, water vapour, cloud properties, Earth radiation budget (including solar irradiance).
  3. Composition: Carbon dioxide, methane, and other long-lived greenhouse gases, ozone and aerosol, supported by their precursors

Oceanic

  1. Surface: Sea-surface temperature, sea-surface salinity, sea level, sea state, sea ice, surface current, ocean colour, carbon dioxide partial pressure, ocean acidity, phytoplankton.
  2. Sub-surface: Temperature, salinity, current, nutrients, carbon dioxide partial pressure, ocean acidity, oxygen, tracers.

Terrestrial

  1. River discharge, water use, groundwater, lakes, snow cover, glaciers and ice caps, ice sheets, permafrost, albedo, land cover (including vegetation type), fraction of absorbed photosynthetically active radiation (FAPAR), leaf area index (LAI), above-ground biomass, soil carbon, fire disturbance, soil moisture.

All ECVs are technically and economically feasible for systematic observation. It is these variables for which international exchange is required for both current and historical observations.

Climate variables are also described on the Bureau of Meteorology website here .

Extreme climate indices are defined by, and are available for some global climate models from ClimDEX , a project that produces a suite of in situ and gridded land-based global datasets of indices representing the more extreme aspects of climate. These are defined as follows:

Code

Description

Definition

FD

Number of frost days

Annual count of days when daily minimum temperature < 0oC.

SU

Number of summer days

Annual count of days when TX (daily maximum temperature) > 25oC.

ID

Number of icing days

Annual count of days when TX (daily maximum temperature) < 0oC.

TR

Number of tropical nights

Annual count of days when TN (daily minimum temperature) > 20oC.

GSL

Growing season length

Annual (1st Jan to 31st Dec in Northern Hemisphere (NH), 1st July to 30th June in Southern Hemisphere (SH)) count between first span of at least 6 days with daily mean temperature TG>5oC and first span after July 1st (Jan 1st in SH) of 6 days with TG<5oC.

TXx

Monthly maximum value of daily maximum temperature

Let TXx be the daily maximum temperatures in month k, period j. The maximum daily maximum temperature each month is then:

TXxkj=max(TXxkj)

TNx

Monthly maximum value of daily minimum temperature

Let TNx be the daily minimum temperatures in month k, period j. The maximum daily minimum temperature each month is then:

TNxkj=max(TNxkj)

TXn

Monthly minimum value of daily maximum temperature

Let TXn be the daily maximum temperatures in month k, period j. The minimum daily maximum temperature each month is then:

TXnkj=min(TXnkj)

TNn

Monthly minimum value of daily minimum temperature

Let TNn be the daily minimum temperatures in month k, period j. The minimum daily minimum temperature each month is then:

TNnkj=min(TNnkj)

TN10p

Percentage of days when TN < 10th percentile

Let TNij be the daily minimum temperature on day i in period j and let TNin10 be the calendar day 10th percentile centred on a 5-day window for the base period 1961-1990. The percentage of time for the base period is determined where: TNij < TNin10

TX10p

Percentage of days when TX < 10th percentile

Let TXij be the daily maximum temperature on day i in period j and let TXin10 be the calendar day 10th percentile centred on a 5-day window for the base period 1961-1990. The percentage of time for the base period is determined where: TXij < TXin10

TN90p

Percentage of days when TN > 90th percentile

Let TNij be the daily minimum temperature on day i in period j and let TNin90 be the calendar day 90th percentile centred on a 5-day window for the base period 1961-1990. The percentage of time for the base period is determined where: TNij > TNin90

TX90p

Percentage of days when TX > 90th percentile

Let TXij be the daily maximum temperature on day i in period j and let TXin90 be the calendar day 90th percentile centred on a 5-day window for the base period 1961-1990. The percentage of time for the base period is determined where: TXij > TXin90

WSDI

Warm spell duration index

Annual count of days with at least 6 consecutive days when TX > 90th percentile

CSDI

Cold spell duration index

Annual count of days with at least 6 consecutive days when TN < 10th percentile

DTR

Daily temperature range

Monthly mean difference between TX and TN

Rx1day

Monthly maximum 1-day precipitation

Let RRij be the daily precipitation amount on day i in period j. The maximum 1-day value for period j are: Rx1dayj = max (RRij)

Rx5day

Monthly maximum consecutive 5-day precipitation

Let RRkj be the precipitation amount for the 5-day interval ending k, period j. Then maximum 5-day values for period j are: Rx5dayj = max (RRkj)

SDII

Simple precipitation intensity index

Let RRwj be the daily precipitation amount on wet days, w (RR ≥ 1mm) in period j. If W represents number of wet days in j, then:

$$SDII_{j}={∑↙{w=1}↖W RR_{wj}}/W$$

R10mm

Annual count of days when PRCP≥ 10mm

Let RRij be the daily precipitation amount on day i in period j. Count the number of days where: RRij ≥ 10mm

R20mm

Annual count of days when PRCP≥ 20mm

Let RRij be the daily precipitation amount on day i in period j. Count the number of days where: RRij ≥ 20mm

Rnnmm

Annual count of days when PRCP≥ nnmm, nn is a user defined threshold

Let RRij be the daily precipitation amount on day i in period j. Count the number of days where: RRij ≥ nnmm

CDD

Maximum length of dry spell, maximum number of consecutive days with RR < 1mm

Let RRij be the daily precipitation amount on day i in period j. Count the largest number of consecutive days where: RRij < 1mm

CWD

Maximum length of wet spell, maximum number of consecutive days with RR ≥ 1mm

Let RRij be the daily precipitation amount on day i in period j. Count the largest number of consecutive days where: RRij ≥ 1mm

R95pTOT

Annual total PRCP when RR > 95p

Let RRwj be the daily precipitation amount on a wet day w (RR ≥ 1.0mm) in period i and let RRwn95 be the 95th percentile of precipitation on wet days in the 1961-1990 period. If W represents the number of wet days in the period, then:

$R95p_{j}=∑↙{w=1}↖W RR_{wj}$ where $RR_{wj} > RR_{wn}95$

R99pTOT

Annual total PRCP when RR > 99p

Let RRwj be the daily precipitation amount on a wet day w (RR ≥ 1.0mm) in period i and let RRwn99 be the 99th percentile of precipitation on wet days in the 1961-1990 period. If W represents the number of wet days in the period, then:

$R99p_{j}=∑↙{w=1}↖W RR_{wj}$ where $RR_{wj} > RR_{wn}99$

PRCPTOT

Annual total precipitation in wet days

Let RRij be the daily precipitation amount on day i in period j. If I represents the number of days in j, then

$$PRCPTOT_{j}=∑↙{i=1}↖{I} RR_{ij}$$