Climpact allows you to calculate indices that are annual or monthly statistics of modelled or observed climate data. Here you can find descriptions and formulae for each of the indices.
Climpact allows you to calculate indices that are annual or monthly statistics of modelled or observed climate data. Here you can find descriptions and formulae for each of the indices.
The indices calculated by Climpact are annual statistics of weather. They answer questions like:
As well as being calculated over the full year, many indices are available for a given month—the hottest day each January, for example.
Climpact offers indices derived from daily temperature and precipitation data. These indices are a standardised set recommended by the Expert Team on Sector-Specific Climate Indices (ET-SCI). The standardisation of these indices allows researchers to compare results across time periods, regions and source datasets.
Annual count of days when TN (daily minimum temperature) < 0°C. Let TN_{i}_{j} be daily minimum temperature on day i in year j. Count the number of days where TN_{i}_{j} < 0 °C.
The number of days when TN < 2 °C.
The number of days when TN < -2 °C.
The number of days when TN < -20 °C.
Annual count of days when TX (daily maximum temperature) > 25°C. Let TX_{i}_{j} be daily minimum temperature on day i in year j. Count the number of days where TX_{i}_{j} > 25 °C.
Annual count of days when TX (daily maximum temperature) < 0 °C. Let TX_{i}_{j}be daily maximum temperature on day i in year j. Count the number of days where TX_{i}_{j} < 0 °C.
Annual count of days when TN (daily minimum temperature) > 20 °C. Let TN_{i}_{j} be daily minimum temperature on day i in year j. Count the number of days where TN_{i}_{j} > 20 °C.
Annual* count between the first span of at least 6 days with daily mean temperature TG >5 °C and the first span after July 1^{st} (Jan 1^{st} in SH) of 6 days with TG <5 °C.
Let TG_{i}_{j} be daily mean temperature on day i in year j. Count the number of days between the first occurrence of at least 6 consecutive days with TG_{i}_{j} > 5 °C and the first occurrence after 1^{st} July (Jan 1^{st} in SH) of at least 6 consecutive days with TG_{i}_{j} < 5 °C.
* Annual means Jan 1^{st} to Dec 31^{st} in the Northern Hemisphere (NH); July 1^{st} to June 30^{th} in the Southern Hemisphere (SH).
Let TX_{x} be the daily maximum temperatures in month k, period j. The maximum daily maximum temperature each month is then TX_{xkj} = max(TX_{xkj}).
Let TN_{x} be the daily minimum temperatures in month k, period j. The maximum daily minimum temperature each month is then TN_{xkj} = max(TN_{xkj}).
Let TX_{n} be the daily maximum temperatures in month k, period j. The minimum daily maximum temperature each month is then TX_{nkj} = min(TX_{nkj}).
Let TN_{n} be the daily minimum temperatures in month k, period j. The minimum daily minimum temperature each month is then TN_{nkj}=min(TN_{nkj}).
The mean daily mean temperature.
The mean daily maximum temperature.
The mean daily minimum temperature.
Let TN_{ij} be the daily minimum temperature on day i in period j and let TN_{in}10 be the calendar day 10^{th} percentile centred on a 5-day window for the base period 1961-1990. The percentage of time for the base period is determined where: TN_{ij} < TN_{in}10. To avoid possible inhomogeneity across the in-base and out-base periods, the calculation for the base period (1961-1990) requires the use of a bootstrap procedure. Details are described in Zhang et al. (2005).
Let TX_{ij} be the daily maximum temperature on day i in period j and let TX_{in}10 be the calendar day 10^{th} percentile centred on a 5-day window for the base period 1961-1990. The percentage of time for the base period is determined where TX_{ij} < TX_{in}10. To avoid possible inhomogeneity across the in-base and out-base periods, the calculation for the base period (1961-1990) requires the use of a bootstrap processure. Details are described in Zhang et al. (2005).
Let TN_{ij} be the daily minimum temperature on day i in period j and let TN_{in}90 be the calendar day 90^{th} percentile centred on a 5-day window for the base period 1961-1990. The percentage of time for the base period is determined where TN_{ij} > TN_{in}90. To avoid possible inhomogeneity across the in-base and out-base periods, the calculation for the base period (1961-1990) requires the use of a bootstrap processure. Details are described in Zhang et al. (2005)
Let TX_{ij} be the daily maximum temperature on day i in period j and let TX_{in}90 be the calendar day 90^{th} percentile centred on a 5-day window for the base period 1961-1990. The percentage of time for the base period is determined where TX_{ij} > TX_{in}90. To avoid possible inhomogeneity across the in-base and out-base periods, the calculation for the base period (1961-1990) requires the use of a bootstrap processure. Details are described in Zhang et al. (2005).
Let TX_{ij} be the daily maximum temperature on day i in period j and let TX_{in}90 be the calendar day 90^{th} percentile centred on a 5-day window for the base period 1961-1990. Then the number of days per period is summed where, in intervals of at least 6 consecutive days, TX_{ij} > TX_{in}90.
Annual number of days contributing to events where d or more consecutive days experience TX > 90th percentile.
Let TN_{ij} be the daily maximum temperature on day i in period j and let TN_{in}10 be the calendar day 10^{th} percentile centred on a 5-day window for the base period 1961-1990. Then the number of days per period is summed where, in intervals of at least 6 consecutive days, TN_{ij} < TN_{in}10.
Annual number of days contributing to events where d or more consecutive days experience TN < 10th percentile.
Percentage of days when TX > 50th percentile.
The value of the 95th percentile of TX.
Number of days when TM (daily mean temperature) ≥ 5 °C.
Number of days when TM (daily mean temperature) < 5 °C.
Number of days when TM (daily mean temperature) ≥ 10 °C.
Number of days when TM (daily mean temperature) < 10 °C.
Number of days when TX ≥ 30 ≥C.
Number of days when TX ≥ 35 ≥C.
Annual count of d consecutive days where both TX > 95th percentile and TN < 95th percentile, where 10 ≥ d ≥ 2.
Annual sum of n - TM, where n is a user-defined, location-specific base temperature and TM < n. A measure of the energy demand needed to heat a building.
Annual sum of TM - n, where n is a user-defined, location-specific base temperature and TM > n. A measure of the energy demand needed to cool a building.
Annual sum of TM - n, where n is a user-defined, location-specific base temperature and TM > n. A measure of the energy demand needed to cool a building.
Let TX_{ij} and TN_{ij} be the daily maximum and minimum temperature respectively on day i in period j. If i represents the number of days in j, then:
$${\mathrm{DTR}}_{j}=\frac{\sum _{i=1}^{I}({\mathrm{TX}}_{ij}-{\mathrm{TN}}_{ij})}{I}$$Let RR_{ij} be the daily precipitation amount on day i in period j. The maximum 1-day value for period j are Rx1day_{j} = max (RR_{ij}).
Let RR_{kj} be the precipitation amount for the 5-day interval ending _{k}, period j. Then maximum 5-day values for period j are Rx5day_{j} = max (RR_{kj}).
Maximum d-day PR total.
Measure of "drought" using the Standardised Precipitation Index on time scales of 3, 6 and 12 months. A drought measure specified as a precipitation deficit. See McKee et al. (1993) and the SPI User Guide (World Meteorological Organization 2012) for details.
Measure of "drought" using the Standardised Precipitation Evapotranspiration Index on time scales of 3, 6 and 12 months. A drought measure specified using precipitation and evaporation. See Vicente-Serrano et al. (2010) for details.
Let RR_{wj} be the daily precipitation amount on wet days, w (RR ≥ 1mm) in period j. If W represents number of wet days in j, then:
$${\mathrm{SDII}}_{j}=\frac{\sum _{w=1}^{W}{\mathrm{RR}}_{wj}}{W}$$Let RR_{ij} be the daily precipitation amount on day i in period j. Count the number of days where RR_{ij} ≥ 10 mm
Let RR_{ij} be the daily precipitation amount on day i in period j. Count the number of days where RR_{ij} ≥ 20 mm
Let RR_{ij} be the daily precipitation amount on day i in period j. Count the number of days where RR_{ij} ≥ nn mm.
Let RR_{ij} be the daily precipitation amount on day i in period j. Count the largest number of consecutive days where RR_{ij} < 1mm.
Let RR_{ij} be the daily precipitation amount on day i in period j. Count the largest number of consecutive days where RR_{ij} ≥ 1mm.
Let RR_{wj} be the daily precipitation amount on a wet day w (RR ≥ 1.0mm) in period i and let RR_{wn}95 be the 95^{th} percentile of precipitation on wet days in the 1961-1990 period. If W represents the number of wet days in the period, then:
$${\mathrm{R95}}_{p}=\sum _{w=1}^{W}{\mathrm{RR}}_{wj}\mathrm{where}{\mathrm{RR}}_{wj}>{\mathrm{RR}}_{wn}95$$Let RR_{wj} be the daily precipitation amount on a wet day w (RR ≥ 1.0mm) in period i and let RR_{wn}99 be the 99^{th} percentile of precipitation on wet days in the 1961-1990 period. If W represents the number of wet days in the period, then:
$${\mathrm{R99}}_{p}=\sum _{w=1}^{W}{\mathrm{RR}}_{wj}\mathrm{where}{\mathrm{RR}}_{wj}>{\mathrm{RR}}_{wn}99$$Let RR_{ij} be the daily pre If i represents the number of days in j, then:
$${\mathrm{PRCPTOT}}_{j}=\sum _{i=1}^{I}{\mathrm{RR}}_{ij}$$Annual count of d consecutive days where both TX < 5th percentile and TN < 5th percentile, where 10 ≥ d ≥ 2.
The number of individual heatwaves that occur each summer (Nov–Mar in southern hemisphere and May–Sep in northern hemisphere). A heatwave is defined as 3 or more days where either the EHF is positive, TX > 90th percentile of TX or where TN < 90th percentile of TN, where percentiles are calculated from base period specified by user.
See Perkins & Alexander (2013) for more details.
The number of days that contribute to heatwaves, as defined by HWN.
See Perkins & Alexander (2013) for more details.
The length of the longest heatwave identified by HWN.
See Perkins & Alexander (2013) for more details.
The mean temperature of all heatwaves identified by HWN.
See Perkins & Alexander (2013) for more details.
The peak daily value in the hottest heatwave (defined as the heatwave with the highest HWM.
See Perkins & Alexander (2013) for more details.
The number of individual 'coldwaves' that occur each year.
See Nairn & Fawcett (2013) for more details.
The number of days that contribute to 'coldwaves', as defined by CWN_ECF.
See Nairn & Fawcett (2013) for more details.
The length of the longest 'coldwave' identified by CWN_ECF.
See Nairn & Fawcett (2013) for more details.
The mean temperature of all 'coldwaves' identified by CWN_ECF.
See Nairn & Fawcett (2013) for more details.
The minimum daily value in the coldest heatwave (defined as the coldwave with the lowest EWM_ECF.
See Nairn & Fawcett (2013) for more details.