View example: Location of weather stations in Brazil, plus rainfall data for a sample area
FAO's Agrometeorology Group converted the IIASA tables into grids:
Gridded rainfall
data for the sample area, Brazil
Then, using Image Display and Analysis (IDA) software, it assigned
to the estimated value of each cell a colour code: for instance, yellow
for monthly rainfall below 95mm, and green above.
Colour-coded map
of January rainfall data for the sample area
Map
of average January rainfall, northeastern Brazil
Applying this procedure to all the IIASA data, the Agrometeorology Group has produced a series of global climate images.
2. Raw data maps
The IIASA
database includes three key climatic elements: average monthly
rainfall total, average monthly temperature, and monthly average
sunshine.
For each of the stations used in the gridding exercise, data have been
assembled over a long time period - usually between 1961 and 1990 - and
then
averaged. Annual totals for rainfall, and the averages for temperature
and
sunshine, were derived from the monthly values. We present here the
complete set of raw data maps:
Rainfall
Annual average rainfall total
Animation
of monthly rainfall total (156K)
Average monthly rainfall total:
January / February
/ March / April /
May / June / July
/ August / September
/ October / November
/ December
Temperature
Annual average temperature
Animation
of monthly temperature (139K)
Average monthly temperature: January
/ February / March
/ April / May / June
/ July / August /
September / October
/ November / December
Sunshine fraction
Note: Sunshine fraction is the percentage of time when bright sunshine is
recorded during the day. It is directly linked to cloudiness, with full
cloud cover being equal to 0% of sunshine fraction.
Annual average sunshine fraction
Animation
of monthly sunshine fraction (127K)
Average monthly sunshine fraction:
January / February
/ March / April /
May / June / July
/ August / September
/ October / November
/ December
3. Derived products
Two products derived by the FAO Agrometeorology Group from the IIASA data sets are a "climate classification"
according to Koeppen, and "Potential biomass" according to Lieth.
Climate classification
The most widely used system of climate
classification is that of the German climatologist Koeppen (1936) -
virtually all more recent classifications
are refinements or variants of the "Koeppen system". The classification
is based on monthly rainfall and temperatures, including the following
five inputs:
Average monthly temperature of the warmest month
Average monthly temperature of the coldest month
Average thermal amplitude between the coldest
and warmest months
Number of months with
temperature exceeding 10°C
Winter
and summer rains
Computer elaboration of these inputs produces:
Koeppen Climate Classification map
The Koeppen system assigns codes to the main climates: Tropical
(A), Dry (B), Temperate (C), Cold (D) and Polar (E):
Brief guide to Koeppen Climate Classification system (text)
Each of the main climate classes are divided into sub-classes based
mainly on the distribution of rainfall and temperature over the year:
A: Tropical
Temperature of the coldest month is greater than 18°C. Reddish
tones (e.g. Central Africa, Southeast Asia) are areas with no dry
season and at least 60 mm of rainfall in the driest month. Areas
indicated with blue tones have monsoon-type climates - a short dry
season but sufficient moisture to keep ground wet throughout the year.
Yellow indicates zones - such as Northeastern Brazil - with a distinct
dry season (one month with precipitation less than 60 mm); green
represents an isothermal subtype, with an annual range of temperature
less than 5°C
B: Dry
Annual evaporation exceeds annual precipitation. Colours
correspond to dominant vegetation types. Zones shaded in greys and blue
have a steppe climate, while those in shades of orange are desert.
Zones in yellow (for example, coastal Namibia) are desert areas with a
cool, dry climate; the temperature of the warmest month does not exceed
18°C.
C: Temperate
Average temperature of the coldest month < 18°C and > -3°C , and the average temperature of warmest month exceeds 10°C.
Note that, since the Temperate class is defined only by temperature
range, the "raw" temperate climate extends into other areas - e.g.
Australia, North Africa - which, owing to severely limited rainfall,
are classified as Dry (B) climates. Temperate areas shaded in blues
(e.g. Northern India) have a winter dry season with at least 10 times
as much precipitation in the wettest summer month as in the driest
winter month. Areas indicated in magenta are characterized by a summer
dry season - at least three times as much rain falls in the wettest
month of winter as in the driest month of summer, the latter having
less than 30mm precipitation. Green tones (Northern Europe, Eastern
USA) indicate areas with at least 30 mm. of precipitation in the driest
month.
D: Cold
Average temperature of the warmest month > 10°C and that of coldest
month < -3°C.
This climate class has two main subclasses: areas with at least 30mm of
rain in the driest month (indicated in green), and those with a winter
dry season - at least ten times as much precipitation in the wettest
month of summer as in the driest month of winter (blue tones).
E: Polar
Average temperature of the warmest month < 10°C. Zones
indicated in violet (e.g. Siberia) are tundra with an average
temperature in the warmest month greater than 0°C, while those in
blue-green (the coast of Greenland) have no month with temperature
above 10°C. A subtype of the latter subclass is the Greenland interior
(light blue), where the average temperature of the coldest
month is less than -38°C.
Biomass potential
Potential biomass is the amount of plant biomass that can be accumulated
in one year under the assumption of ideal conditions prevailing for photosynthesis,
i.e. absorption of solar energy by plants and storage of the energy as plant
material. The map given below uses one of the earliest methods, developed
by H. Lieth and published in 1972.
Although this approach is now largely superseded by more complex approaches
involving solar energy conversion efficiencies, Lieth's method is interesting
in that it clearly shows whether temperature - cold or warm - or water is
the main limiting factor. (The unit of measurement is grams of dry matter per sq. metre per year.)
Map of biomass potential
Temperature-limited biomass potential
Rainfall-limited
biomass potential
4. Downloading the digital
maps and technical data
FAO's
Environment and Natural Resources Service (SDRN) is making the global
climate data base available as geo-referenced digital images and maps.
The data are in Image Display and Analysis (IDA) format, DOS-based
public domain software developed jointly by FAO and USAID's Famine
Early Warning System (FEWS) for displaying, processing and analysing
satellite images.
The data can be analysed using WinDisp (view sample screen),
a Windows-based successor to IDA that was developed with funding from
the European Union as part of the FAO Global
Information and Early Warning System (GIEWS) Workstation Project. The
SADC Food Security Technical Unit, FAO-ARTEMIS, USAID-FEWS, the USGS
EROS Data Center and the US Forest Service have contributed funds to
add
additional analytical features to WinDisp.
WinDisp contains all the analytical functions of IDA, as well as many new
additional features that provide a simpler, yet more powerful interface in
the Windows environment. It allows users to extract statistics from
images by polygons (usually administrative boundaries), to compare
differences, convert formats and export images to ASCII grids and the GIS
software IDRISI.
Download...
1. Georeferenced maps (1.5MB)
Includes
data sets for biomass, rainfall, sunshine fraction, temperature and
Koeppen climate classification, with colour palletes and 'read me'
2. Windisp Version 4.0 (2.02MB)
Contains sample satellite images, maps and color tables. Download also Windisp manual (Word 97 or PDF). For detailed information on Windisp, see GIEWS/Software
References
Leemans, R. and Cramer, W., 1991. "The IIASA database for mean monthly
values of temperature, precipitation and cloudiness on a global terrestrial
grid". Research Report RR-91-18. November 1991. International Institute of Applied Systems Analyses, Laxenburg, pp. 61.
Lieth, H., 1972. "Modelling the primary productivity of the earth. Nature and
resources", UNESCO, VIII, 2:5-10.
Produced by SD-Dimensions and the Agrometeorology
Group of FAO's Sustainable Development Department. Text: R. Gommes. GIFs: F. Petrassi, G. Thomas. Thanks to Thorsten Lemke (GraphicConverter). For further information,
contact Agromet@fao.org
