 August 2006 
Jürgen Grieser, René Gommes, Stephen Cofield and Michele Bernardi
The Agromet Group, SDRN
FAO of the UN, Viale delle Terme di Caracalla, 00100 Rome, Italy
Contact: Juergen.grieser@fao.org
Introduction
The Koeppen climatologies and the
climatic net primary production
maps of FAO are based on different periods and precipitation
datasets. Here we provide the datasets in different formats.
Furthermore some derived information like temperature of the coldest
and warmest months, Martonnes aridity index and Gorczynskis continentality
index are provided.
The original data are brought to a common grid based on
USGS gtopo30
and provided as tables in csv format (.5° resolution). For the users
convenience the derived data are also provided as georeferenced data
in IDA/Windisp
format and as GeoTIFF
(5’ resolution, resampled).
The table provides the links to the datasets used to derive the Koeppen
climatologies and npp maps. Each of the files consists of 13 columns.
The first column contains the gridpoint number, the remaining 12 columns
contain the mean annual cycle of the variable at that grid point. In the
case of temperature, it is the mean monthly temperature in °C or the
standard deviation of temperature over the respective period. Precipitation
is provided in mm per month. The
meta data file consists of 4 columns with
gridpoint number, longitude (in .01°), latitude (in .01°) and land fraction
(in %).
All data are provided as comma separated value (csv) in .5°x.5°
resolution. The temporal standard deviation of the variable at the
grid cell within the period is provided too. This allows a wide range
of investigations. For example, it can be used to compare the average
with the variability by estimating the coefficient of variability
(standard deviation / average) in the case of precipitation. Furthermore
it can be used to estimate uncertainty intervals for the average of
each grid cell.
Download the metadata file with grid point coordinates.
For the annual mean temperature und the annual precipitation sum we
also provide resampled georeferenced data in 5’x5’ resolution as
Windisp/IDA images and GeoTiff images.
Aridity and Continentality
From the variety of existing indices to quantify aridity and
continentality we only provide the aridity index of De Martonne
(1926) and the continentality index of Gorczynski (1920).
Aridity indices provide a simple way to express the ratio of
precipitation to evaporation. Since evaporation is rarely observed
it is a common tradition to approximate it. In the approximation by
De Martonne evaporation is set to mean annual temperature T_{A} in °C +10.
The aridity index of De Martonne A_{M} is therefore defined as the ratio
of the annual precipitation sum P_{A} in mm and the annual mean Temperature
in °C +10. It is obvious that one disadvantage of this definition is
that the equation has a pole at –10°C where the index is undefined.
Lower temperatures lead automatically to negative indices. One may
argue that the whole concept of aridity/humidity may not make much
sense in cold regions. However, since we draw global maps we have to
deal with this problem. In order to use the index world wide we define
Note that the higher this coefficient is, the higher is the precipitation
compared to evaporation and thus the less arid is the climate.
This means that by definition a high aridity index means a humid
climate while a low aridity index means an arid climate. The following
map shows the aridity index for the 50 year period from 1951 to 2000
based on temperature data of the CRU and precipitation data from GPCC
VASClimO. It can be downloaded as a bitmap
here.
The continentality index of Gorczynski K_{G} is a simple but efficient
way to estimate the influence of the ocean on the local climate.
The index depends linearly on the annual temperature amplitude A
(difference of monthly mean temperature of warmest and coldest
month). However, A not only depends on the strength of the influence
of the ocean but also on the annual cycle of incoming solar radiation.
Since the amplitude of the annual cycle of incoming solar radiation
depends on latitude, with a maximum in the polar regions, the inverse
of the sine of the latitude gets in as well. The definition in the
version of Gorczynski is
This original equation comes with some drawbacks. Since the sine
approaches zero as the latitude approaches the equator, the values
close to the equator tend to infinity. At the equator the definition
breaks down. We therefore suggest not using the index values within
a latitude range of plus/minus 10 degrees. In order to apply the
definition also to the southern hemisphere we use the absolute of
the latitude instead of the latitude itself. The following map shows
the continentality index for the 50 year period from 1951 to 2000
based on temperature data of the CRU. It can be downloaded as a bitmap
here.
Download colour tables for IDA images of De Martonne aridity index
and of Gorczynski continentality index
here.
Download this file as
pdf.
References
Beck, C., J. Grieser and B. Rudolf, 2005: A New Monthly Precipitation
Climatology for the Global Land Areas for the Period 1951 to 2000.
Klimastatusbericht 2004, 181190, DWD. [pdf]
De Martonne, E. (1941) : Nouvelle carte mondiale de l’indice s’aridité.
Météorol. 1941, 326.
Gorczynski, W. (1920) : Sur le calcul du degré de continentalisme et
son application dans la climatologie. Geogr. Annaler 2, 324331.
Mitchell, T., and P. Jones, 2005: An improved method of constructing
a database of monthly climate observations and associated highresolution
grids. Int. J. Climatol., 25, 693712.
http://www.cru.uea.ac.uk/
Rudolf, B., C. Beck, J. Grieser, U. Schneider, 2005: Global
Precipitation Analysis Products of the GPCC. Internet publication
at
http://gpcc.dwd.de/
[pdf]
