Updated World Map of the Koppen-Geiger Climate Classification

Abstract

Although now over 100 years old, the classification of climate originally formulated by Wladimir Köppen and modified by his collaborators and successors, is still in widespread use. It is widely used in teaching school and undergraduate courses on climate. It is also still in regular use by researchers across a range of disciplines as a basis for climatic regionalisation of variables and for assessing the output of global climate models. Here we have produced a new global map of climate using the Köppen-Geiger system based on a large global data set of long-term monthly precipitation and temperature station time series. Climatic variables used in the Köppen-Geiger system were calculated at each station and interpolated between stations using a two-dimensional (latitude and longitude) thin-plate spline with tension onto a 0.1°×0.1° grid for each continent. We discuss some problems in dealing with sites that are not uniquely classified into one climate type by the Köppen-Geiger system and assess the outcomes on a continent by continent basis. Globally the most common climate type by land area is BWh (14.2%, Hot desert) followed by Aw (11.5%, Tropical savannah). The updated world Köppen-Geiger climate map is freely available electronically at http://www.hydrol-earth-syst-sci.net/????.

Full text

Hydrol. Earth Syst. Sci., 11, 1633–1644, 2007
www.hydrol-earth-syst-sci.net/11/1633/2007/
© Author(s) 2007. This work is licensed
under a Creative Commons License.
Hydrology and
Earth System
Sciences
Updated world map of the K¨
oppen-Geiger climate classification
M. C. Peel1, B. L. Finlayson2, and T. A. McMahon1
1Department of Civil and Environmental Engineering, The University of Melbourne, Victoria, Australia
2School of Anthropology, Geography and Environmental Studies, The University of Melbourne, Victoria, Australia
Received: 15 February 2007 – Published in Hydrol. Earth Syst. Sci. Discuss.: 1 March 2007
Revised: 28 September 2007 – Accepted: 4 October 2007 – Published: 11 October 2007
Abstract. Although now over 100 years old, the classifica-
tion of climate originally formulated by Wladimir K¨
oppen
and modified by his collaborators and successors, is still in
widespread use. It is widely used in teaching school and
undergraduate courses on climate. It is also still in regular
use by researchers across a range of disciplines as a basis
for climatic regionalisation of variables and for assessing the
output of global climate models. Here we have produced a
new global map of climate using the K¨
oppen-Geiger system
based on a large global data set of long-term monthly pre-
cipitation and temperature station time series. Climatic vari-
ables used in the K¨
oppen-Geiger system were calculated at
each station and interpolated between stations using a two-
dimensional (latitude and longitude) thin-plate spline with
tension onto a 0.1◦×0.1◦grid for each continent. We discuss
some problems in dealing with sites that are not uniquely
classified into one climate type by the K¨
oppen-Geiger sys-
tem and assess the outcomes on a continent by continent
basis. Globally the most common climate type by land
area is BWh (14.2%, Hot desert) followed by Aw (11.5%,
Tropical savannah). The updated world K¨
oppen-Geiger cli-
mate map is freely available electronically in the Supplemen-
tary Material Section (http://www.hydrol-earth-syst-sci.net/
11/1633/2007/hess-11-1633-2007-supplement.zip).
1 Introduction
The climate classification based on the work of Wladimir
K¨
oppen, and dating from 1900, continues to be the most
widely used climate classification over a century later. Es-
senwanger (2001) has provided a comprehensive review of
the classification of climate from prior to K¨
oppen through
to the present. The period of greatest activity was from the
Correspondence to: M. C. Peel
(mpeel@unimelb.edu.au)
mid-nineteenth century through to the 1950s. What is some-
what surprising about this time profile of activity is that as
both the availability of data and computing power to process
them has become increasingly widely available post-1960,
the level of activity in the development of new climate clas-
sifications has markedly declined. The continued popularity
and widespread use of the K¨
oppen classification is remark-
able. There is no doubt an element of historical inertia in
this as each generation of students is taught global climate
using this system and it is the basis of most common global
climate maps. To replace it with a new system would be a
significant task. Arthur Wilcock (1968) was probably cor-
rect in surmising: “If .. . . ... one is convinced that there are
in principle strict limits to what can be achieved by any sim-
ple classification, one may consider it profitless to seek minor
improvements at the cost of confusion.” (p. 13).
K¨
oppen’s inspiration for developing a world map of cli-
mate classification in 1900 owed much to the global vegeta-
tion map of Grisebach published in 1866 and K¨
oppen’s own
background in plant sciences (Wilcock, 1968). Thornthwaite
(1943) claims that K¨
oppen’s use of the first five letters of
the alphabet to label his climate zones is taken from the five
vegetation groups delineated by the late nineteenth century
French/Swiss botanist Alphonse De Candolle who in turn
based these on the climate zones of the ancient Greeks. It is
inconceivable that K¨
oppen could have produced his original
classification and map without using other landscape signals
of climate (particularly vegetation) since there would have
been so little observed climate data available at that time. In
Fig. 3 of this paper we show the relative number of stations
with temperature and precipitation data starting from 1800.
Compared with what is available now, there would have been
data from few stations available to K¨
oppen and the global
distribution would have been much more inconsistent than
is the case now. In the light of this, the persistence of his
scheme of classification is even more remarkable.
Published by Copernicus Publications on behalf of the European Geosciences Union.

1634 M. C. Peel et al.: Updated world K¨
oppen-Geiger climate classification map
While Sanderson (1999) has argued that it is time for mod-
ern atmospheric scientists to develop a new classification of
climates, the K¨
oppen classification continues to be the one
most widely used in teaching. If we take as an example the
textbooks of Arthur Strahler that are in very wide use in the
English speaking world, it is the case that despite Strahler’s
own attempt to produce a new climate classification (see,
for example, Strahler, 1971) the latest edition of this series
of texts still uses the K¨
oppen system (Strahler and Strahler,
2005).
The use of K¨
oppen’s classification is not confined to teach-
ing. Many researchers routinely use it for their own par-
ticular research purposes. The present authors have used it
as the basis for grouping rivers by climate type around the
world in order to facilitate comparisons of runoff character-
istics (McMahon et al., 1992; Peel et al., 2004). Lohmann
et al. (1993) have applied the K¨
oppen classification to the
output from both atmosphere general circulation models and
coupled atmosphere-ocean circulation models and compared
these to maps of the K¨
oppen classification using modern
data sets and to K¨
oppen’s 1923 map. They modelled both
present conditions and enhanced greenhouse scenarios and
concluded: “However, the K¨
oppen classification is easier to
apply and is still a useful tool for estimating the ability of
climate models to reproduce the present climate as well as
indicate the impact of climate changes on the biosphere.”
(p. 191) No doubt K¨
oppen would have been pleased with this
assessment.
In a similar study to that of Lohmann et al. (1993),
Kalvova et al. (2003) compared global climate model out-
puts to maps of K¨
oppen’s classification drawn from gridded
observed data and to K¨
oppen’s 1923 map. They were at-
tracted to the K¨
oppen system because of its known links to
natural vegetation patterns as they have attempted to assess
the impact of global warming on major biomes. They also
compare the map they produced of K¨
oppen’s climate zones
based on modern data with his 1923 map and show that the
differences are only around 0.5% of the area distribution.
Similar uses of the K¨
oppen classification have been made
by Wang and Overland (2004), Gnanadesikan and Stouffer
(2006) and Kleidon et al. (2000) where it is the relation be-
tween the K¨
oppen zones and natural vegetation systems that
has made it useful to their purposes. It is noteworthy that
Kleidon et al. (2000) also used the K¨
oppen 1923 map as a
basis for comparison.
A more critical approach to the K¨
oppen classification has
been taken by Triantafyllou and Tsonis (1994) who claim to
be the first to evaluate the K¨
oppen classification using mod-
ern temperature and precipitation data (for the northern hemi-
sphere). They classified climate stations on a year by year ba-
sis and then analysed the frequency with which they shifted
between the major K¨
oppen climate types (e.g. A to B) in or-
der to assess the adequacy of the K¨
oppen system. In North
American and North Africa they found low variability within
a climate type and narrow regions of high variability between
climate types, indicating the K¨
oppen system performed ade-
quately. For Europe and Asia they found the pattern of vari-
ability less defined, indicating either high within climate type
variability or wide regions between climate types resulting in
an inadequate performance of the K¨
oppen system. It is the
case however that the K¨
oppen classification was intended to
represent long term mean climate conditions and not year-to-
year variability though it can be put to good use as the ba-
sis for assessing climate variability on a year-to-year (Dick,
1964) or multi-decadal basis (Gentilli, 1971). Triantafyl-
lou and Tsonis (1994) conclude, with Sanderson (1999), that
there is a need for a new scheme to represent the world’s cli-
mates.
That may be so, but when Fovell and Fovell (1993) used
cluster analysis to objectively determine climate zones for
the conterminous United States based on modern climate
data they returned to the K¨
oppen classification to assess the
outcomes. Similarly Stern et al. (2000), with all the data
resources of the Australian Bureau of Meteorology at their
disposal, used a modification of the K¨
oppen classification to
draw a new map of the climates of Australia. Their assess-
ment that “.. . the telling evidence that the K¨
oppen clas-
sification’s merits outweigh its deficiencies lies in its wide
acceptance.” (p. 2).
It is against this background that we have chosen to re-
draw the K¨
oppen-Geiger world map using global long-term
monthly precipitation and temperature station data. Recently,
four K¨
oppen world maps based on gridded data have been
produced for various resolutions, periods and levels of com-
plexity. Kalvova et al. (2003) using Climate Research Unit
(CRU, the University of East Anglia) gridded data for the
period 1961–1990 presented a map of the 5 major K¨
oppen
climate types (with E divided into 2 types) at a resolution of
2.5◦latitude by 2.5◦longitude. Gnanadesikan and Stouffer
(2006) presented a K¨
oppen map of 14 climate types based on
the same CRU data and period as Kalvova et al. (2003), but
at a resolution of 0.5◦latitude by 0.5◦longitude. Fraedrich
et al. (2001) using CRU data for the period 1901–1995 pre-
sented a K¨
oppen map of 16 climate types at a resolution of
0.5◦latitude by 0.5◦longitude and investigated the change
in climate types over the period 1981–1995 relative to the
complete period of record. The most comprehensive K¨
oppen
world map drawn from gridded data to date is that of Kottek
et al. (2006) who presented a map with 31 climate types at a
resolution of 0.5◦latitude by 0.5◦longitude based on both the
CRU and Global Precipitation Climatology Centre (GPCC)
VASClimO v1.1 data sets for the period 1951–2000.
All four maps based on gridded data are for restricted pe-
riods (1901–1995, 1961–1990 or 1951–2000) and any sub-
grid resolution climate type variability has been obscured.
So here we present an updated world map of the K¨
oppen-
Geiger climate classification based on station data for the
whole period of record. The data and methodology used to
construct this map are described in the next section. Individ-
ual continental K¨
oppen-Geiger climate maps are presented
Hydrol. Earth Syst. Sci., 11, 1633–1644, 2007 www.hydrol-earth-syst-sci.net/11/1633/2007/

M. C. Peel et al.: Updated world K¨
oppen-Geiger climate classification map 1635
Figure 1. Location of precipitation stations with 30 or more values for each month.
25
Fig. 1. Location of precipitation stations with 30 or more values for
each month.
Figure 2. Location of temperature stations with 30 or more values for each month. Symbols
are “T only” = temperature data only and “P + T” = both temperature and precipitation data.
26
Fig. 2. Location of temperature stations with 30 or more values for
each month. Symbols are “T only” = temperature data only and “P
+ T” = both temperature and precipitation data.
and discussed. The continental maps are then combined to
form the new world K¨
oppen-Geiger map, which is followed
by a discussion, a link to the map for free download and a
conclusion.
2 Data and methodology
The philosophy behind the construction of this updated ver-
sion of the K¨
oppen-Geiger climate map is to rely on observed
data, rather than experience, wherever possible and minimise
the number of subjective decisions. To this end, a large,
globally extensive, climatic dataset was used to describe the
observed climate and the methodology used to interpolate
the observations was chosen to be simple and flexible, but
not beyond what the data could support. There have been
many modifications proposed to the K¨
oppen system but here
we have used criteria that follow K¨
oppen’s last publication
about his classification system in the K¨
oppen-Geiger Hand-
book (K¨
oppen, 1936), with the exception of the boundary be-
tween the temperate (C) and cold (D) climates. We have fol-
lowed Russell (1931) and used the temperature of the coldest
month >0◦C, rather than >–3◦C as used by K¨
oppen in defin-
ing the temperate – cold climate boundary (see Wilcox, 1968
and Essenwanger, 2001 for a history of this modification).
Figure 3. Percentage of precipitation and temperature stations with a monthly value.
27
Fig. 3. Percentage of precipitation and temperature stations with a
monthly value.
The quality of the final map depends on the quality of
the input data and to this end long-term station records of
monthly precipitation and monthly temperature were ob-
tained from the Global Historical Climatology Network
(GHCN) version 2.0 dataset (Peterson and Vose, 1997). Sta-
tions from this dataset with at least 30 observations for each
month were used in the analysis (12 396 precipitation and
4844 temperature stations). Figures 1 and 2 show the global
spatial distribution of precipitation and temperature stations
respectively. In Fig. 2 temperature stations that also have pre-
cipitation data are denoted separately from the temperature
only stations. Regions of high station density are the USA,
southern Canada, northeast Brazil (precipitation only), Eu-
rope, India (precipitation only), Japan and eastern Australia.
Desert, polar and some tropical regions, like Saharan Africa,
Saudi Arabia, central Australia, northern Canada, northern
Russia and the Amazon region of Brazil have sparse station
density.
In the following analysis the complete period of record at
each precipitation and temperature station is used. The sta-
tions exhibit a wide range of record lengths from a minimum
of 30 values for each month up to 299 for precipitation and
297 for temperature. In Fig. 3, the percentage of stations with
a monthly value is plotted over time and shows that the his-
torical period that the data are most representative of are from
1909 to 1991 for precipitation and 1923 to 1993 for temper-
ature. Spatially there is variation in the period of record cov-
ered, with Australia, Europe, Japan and the USA generally
having the longest records.
The whole-of-record approach assumes that data from one
period is comparable with data from any other period. This
assumption can be violated by global or local trends, like
the recent observed warming of global surface temperature,
largely attributable to increasing concentrations of green-
house gases (Barnett et al., 2005). However, at the level
of broad climate types (1st letter, see Table 1) the K¨
oppen-
Geiger climate classification has been found to be relatively
insensitive to temperature trends (Triantafyllou and Tsonis,
www.hydrol-earth-syst-sci.net/11/1633/2007/ Hydrol. Earth Syst. Sci., 11, 1633–1644, 2007

1636 M. C. Peel et al.: Updated world K¨
oppen-Geiger climate classification map
Table 1. Description of K¨
oppen climate symbols and defining criteria.
1st 2nd 3rd Description Criteria*
A Tropical Tcold≥18
f - Rainforest Pdry≥60
m - Monsoon Not (Af) & Pdry≥100–MAP/25
w - Savannah Not (Af) & Pdry<100–MAP/25
B Arid MAP<10×Pthreshold
W - Desert MAP<5×Pthreshold
S - Steppe MAP≥5×Pthreshold
h - Hot MAT≥18
k - Cold MAT<18
C Temperate Thot>10 & 0<Tcold<18
s - Dry Summer Psdry<40 & Psdry<Pwwet/3
w - Dry Winter Pwdry<Pswet/10
f - Without dry season Not (Cs) or (Cw)
a - Hot Summer Thot≥22
b - Warm Summer Not (a) & Tmon10≥4
c - Cold Summer Not (a or b) & 1≤Tmon10<4
D Cold Thot>10 & Tcold≤0
s - Dry Summer Psdry<40 & Psdry<Pwwet/3
w - Dry Winter Pwdry<Pswet/10
f - Without dry season Not (Ds) or (Dw)
a - Hot Summer Thot≥22
b - Warm Summer Not (a) & Tmon10≥4
c - Cold Summer Not (a, b or d)
d - Very Cold Winter Not (a or b) & Tcold<–38
E Polar Thot<10
T - Tundra Thot>0
F - Frost Thot≤0
*MAP = mean annual precipitation, MAT = mean annual temperature, Thot = temperature of the hottest month, Tcold = temperature of the coldest month, Tmon10 = number of
months where the temperature is above 10, Pdry = precipitation of the driest month, Psdry = precipitation of the driest month in summer, Pwdry = precipitation of the driest month
in winter, Pswet = precipitation of the wettest month in summer, Pwwet = precipitation of the wettest month in winter, Pthreshold = varies according to the following rules (if 70% of
MAP occurs in winter then Pthreshold = 2 x MAT, if 70% of MAP occurs in summer then Pthreshold = 2 x MAT + 28, otherwise Pthreshold = 2 x MAT + 14). Summer (winter) is
defined as the warmer (cooler) six month period of ONDJFM and AMJJAS.
1994). The greatest sensitivity of the resultant map to cli-
matic trends is likely to be in the transition zones between
climate types, rather than within a climate type (as seen
in Fraedrich et al., 2001). Any benefits of restricting the
data to a common period would be achieved at the expense
of spatial representativeness, since stations with data that
did not sufficiently overlap the common period would have
been excluded from the analysis. In using the complete pe-
riod of record the resultant K¨
oppen-Geiger climate type map
presents the long-term climate type for the maximum number
of locations around the world.
A total of 4279 locations have data for both precipita-
tion and temperature (indicated with an “x” in Fig. 2). At
these locations the K¨
oppen-Geiger climate type can be de-
termined from the raw station data. Although an updated
K¨
oppen-Geiger map based on 4279 locations would be a sig-
nificant improvement over earlier maps, considerable infor-
mation would be lost if locations with only precipitation or
temperature were ignored. To avoid this loss of informa-
tion the precipitation and temperature based variables used
in the criteria to determine the K¨
oppen-Geiger climate type
(see Table 1) were calculated at each precipitation and tem-
perature station. Then each variable was interpolated using
a two-dimensional (latitude and longitude) thin-plate spline
with tension (Mitas and Mitasova, 1988) onto a 0.1×0.1 de-
gree of latitude and longitude grid for each continent. The
K¨
oppen-Geiger criteria were then applied to the splined vari-
ables. The tension spline interpolations were performed in
ESRI ArcMap version 9.1 using settings of “weight” = 1
and “points” = 10 for all interpolations. The chosen tension
spline settings provided flexible and smooth interpolations of
the climatic variables across the wide range of station densi-
ties and variable values experienced within and between con-
tinents. Although optimisation of the spline settings for each
variable and continent may have improved the interpolation
of individual climatic variables, it was not expected to sig-
nificantly alter the final K¨
oppen-Geiger climate type maps
(which is a combination of the variables) and so was not
Hydrol. Earth Syst. Sci., 11, 1633–1644, 2007 www.hydrol-earth-syst-sci.net/11/1633/2007/

M. C. Peel et al.: Updated world K¨
oppen-Geiger climate classification map 1637
Table 2. Average monthly and annual precipitation (P) and temperature (T) for Herberton Post Office, Australia.
J F M A M J J A S O N D Ann
P (mm) 238.4 229.7 214.4 86.0 46.9 33.3 22.0 18.2 16.5 25.3 77.3 137.9 1146
T (◦C) 23.2 22.8 21.9 20.0 17.9 16.0 15.5 16.5 18.6 21.0 22.4 23.3 19.9
pursued in this paper. However, in continental sub-regions
with high station density more flexible spline settings would
have provided better local results.
A potential improvement to the above methodology would
be to apply a three-dimensional spline, using elevation as the
third dimension (based on a digital elevation model like HY-
DRO1k, USGS, 2000). Elevation plays a key role in the ob-
served spatial pattern of precipitation and temperature and
is an important variable in the interpolation of precipitation
and temperature fields (Daly, 2006). In this analysis long-
term average climatic variables are interpolated, which dis-
play considerably less spatial variability than is typically ob-
served in daily, monthly or even annual fields to which 3-
D interpolation is usually applied. However, due to sparse
station density, particularly in high elevation regions, a more
complex 3-D interpolation procedure was not used in this pa-
per.
Another potential improvement would be to conduct cross-
validation on the individual splined variables to investigate
the sensitivity of each spline to the underlying data as well
as cross-validation of the final K¨
oppen-Geiger climate type
map to assess the sensitivity of the map to each individual
station. Such a sensitivity analysis would probably indicate
that the K¨
oppen-Geiger climate type map is most sensitive in
regions of low station density, transition zones between cli-
mate types and also in regions where there are large elevation
differences. A cross-validation exercise was not conducted
as part of this paper due to the considerable amount of time
required for such an exercise.
A description of the symbols and the criteria used to define
the K¨
oppen-Geiger climate types is provided in Table 1. The
30 possible climate types in Table 1 are divided into 3 trop-
ical (Af, Am and Aw), 4 arid (BWh, BWk, BSh and BSk),
9 temperate (Csa, Csb, Csc, Cfa, Cfb, Cfc, Cwa, Cwb and
Cwc), 12 cold (Dsa, Dsb, Dsc, Dsd, Dfa, Dfb, Dfc, Dfd,
Dwa, Dwb, Dwc and Dwd) and 2 polar (ET and EF). All
precipitation variables are in units of millimetres (mm) and
all temperature variables are in units of degrees Celsius (◦C).
Since all locations that satisfy the B climate criteria will also
satisfy one of the other (A, C, D or E) climate criteria, the B
climates must be identified first. The set of locations defined
as having a B climate is based on a combination of mean an-
nual precipitation and mean annual temperature. The sets of
A, C, D and E climate locations are mutually exclusive and
are based on temperature criteria only.
In applying the criteria to the global dataset it became ap-
parent that in some C climate locations it is possible to sat-
isfy both the Cs and Cw criteria simultaneously. An exam-
ple of this is Herberton Post Office (17.38S, 145.38 E, eleva-
tion 900m) in North Queensland, Australia. Table 2 shows
the monthly averages of precipitation and temperature for the
Herberton Post Office record. Each average is based on about
105 monthly values for precipitation and 78 monthly values
for temperature, so these averages are from a long-term sta-
tion. Following the K¨
oppen-Geiger criteria in Table 1, sum-
mer is the period ONDJFM and over 70% of the mean annual
precipitation falls during summer. Since the mean annual
precipitation (1146 mm) is larger than the B climate type pre-
cipitation threshold of 678 mm (10 * (2 * 19.9 + 28)) (see Ta-
ble 1) the station is not a B climate. With the coldest monthly
temperature 15.5◦C, the station is not an A climate, but does
satisfy the criteria for a C climate. When the second letter
is allocated, the driest summer month is 25.3 mm, which is
below 40mm and is less than one third of the wettest winter
month (86.0mm), so the station satisfies a Cs climate type.
However, the driest winter month (16.5 mm) is less than one
tenth of the wettest summer month (238.4 mm), so this sta-
tion also satisfies the Cw climate type. Considering that over
70% of the precipitation falls during summer a classification
of Cw is appropriate, while a classification of Cs is not.
For cases such as Herberton Post Office an additional test
was developed and applied to determine whether to use Cs
or Cw at a given location. When a location satisfied both
Cs and Cw criteria, the precipitation for the six months con-
taining summer and the six months containing winter were
compared, with Cw assigned if the summer precipitation was
greater than the winter precipitation. This additional rule was
also applied to locations that satisfied both the Ds and Dw
climate criteria as well.
In the presence of steep gradients of a variable, splines
are known to over- or under-estimate, particularly if there is
limited station density. Initially, these spline artefacts were
ignored at the individual variable level. However, once a
continental map of K¨
oppen-Geiger climate type was pro-
duced, the map was checked for locations where over or
under-estimation in a splined variable had caused an aber-
rant climate type to be defined. These aberrant areas were
generally found to be small and were patched by hand based
on surrounding observations. Conversely, in some low sta-
tion density regions, a climate type would extend further than
expected due to a lack of observations to inform the spline.
In these situations, discussed later, the climate type was left
www.hydrol-earth-syst-sci.net/11/1633/2007/ Hydrol. Earth Syst. Sci., 11, 1633–1644, 2007

1638 M. C. Peel et al.: Updated world K¨
oppen-Geiger climate classification map
Figure 4. Köppen-Geiger climate type map of Africa.
28
Fig. 4. K¨
oppen-Geiger climate type map of Africa.
unmodified, due to a lack of information on which to base
any corrections.
Contrary to some earlier forms of the K¨
oppen-Geiger cli-
mate type map the concept of a high altitude climate type is
not used in this paper. Designated as a separate 1st letter cli-
mate type H (Highland) or as a sub category of the E climate
type (ETH or EFH) the highland climate type requires ele-
vation information to be defined. Although global elevation
data are available, defining all locations above a specified el-
evation as an H climate provides little information about the
climate at those locations relative to using the full suite of A,
B, C, D and E climate types. Therefore no highland climate
type is used in this paper.
Once the K¨
oppen-Geiger climate type map for each conti-
nent was constructed, the percentage of land area covered by
the major climate types was calculated. Since the area of a
0.1×0.1 degree pixel changes with latitude, a map of 0.1×0.1
degree pixel area was constructed and then projected onto a
Cylindrical Equal Area projection of the world to determine
the area (in km2) of each 0.1×0.1 degree pixel. These pixel
areas were then summed for each climate type to provide an
estimate of the land area covered by each climate type.
3 Continental maps
The K¨
oppen-Geiger climate type map was determined us-
ing the methodology described in the previous section for
each continent. Continental definitions used in this paper fol-
low those used in the HYDRO1k DEM of the world (USGS,
2000). These maps are presented and discussed in this sec-
tion.
3.1 Africa
The map of K¨
oppen-Geiger climate type for Africa (Fig. 4)
shows that only three (A, B and C) of the main climate types
are present in Africa. Of these three the dominant climate
type by land area is the arid B (57.2%), followed by tropical
A (31.0%) and temperate C (11.8%).
Figure 4 is based on 1436 precipitation and 331 temper-
ature stations. Of these stations a total of 313 had both
precipitation and temperature data for the same location,
from which the climate type could be independently calcu-
lated and checked against the map. The climate type at 309
of these locations matched the map exactly and in the re-
maining 4 locations the correct climate type was present in a
neighbouring cell.
The thin-plate spline with tension settings used to inter-
polate individual variables does not force the spline through
the exact station values. Therefore, slight differences in cli-
mate type between station and spline-based estimates are ex-
pected. If the station and map climate types do not match
exactly, then the station-based climate type usually appears
in a neighbouring cell of the spline-based map. However,
if the station-based climate type is not present in a neigh-
bouring cell, then the spline-based estimate is incorrect, in-
dicating that at least one of the splines has not successfully
interpolated the observed data. For Africa, all of the station
estimates match the map exactly or are in a neighbouring
cell.
The low density of temperature stations in Africa resulted
in some climate types extending further than expected, which
could not be corrected due to lack of data. The two regions
where this is most evident are the temperate regions in the
Eastern Rift Valley south of Nairobi, Kenya and around An-
tananarivo, Madagascar. In both of these cases the tempera-
ture stations are in high elevation locations (Nairobi and An-
tananarivo) and experience a temperate climate type. How-
ever, due to the lack of nearby lower elevation temperature
stations, the temperate influence of both these high elevation
stations extends well beyond their immediate location and
large regions of temperate climate type result in regions that
are more likely to be tropical.
Another interesting feature in Fig. 4 is the division of the
temperate region in the Ethiopian highlands into Cs and Cw
climate types. This region experiences a strong seasonal pre-
cipitation regime (most precipitation occurs during April–
September) but a very even monthly temperature regime (the
hottest month is ∼5◦C hotter than the coldest month). On the
western side of the highlands, the six months from October–
March are warmer than April–September, so summer is de-
fined as October–March, thus the precipitation falls in win-
ter. Whereas on the eastern side of the highlands the six
months from April–September are warmer, so summer is de-
fined as April–September, thus the precipitation falls in sum-
mer. Therefore, in Fig. 4 the western side of the highlands
have a Cs climate and the eastern side have a Cw climate.
Hydrol. Earth Syst. Sci., 11, 1633–1644, 2007 www.hydrol-earth-syst-sci.net/11/1633/2007/

M. C. Peel et al.: Updated world K¨
oppen-Geiger climate classification map 1639
3.2 Asia
In this paper Asia is defined as the region east of a north-
south line through the Urals Mountains down to the Ara-
bian Sea. The map of K¨
oppen-Geiger climate type for Asia
(Fig. 5) shows that all five of the main climate types are
present in Asia. The dominant climate type by land area is
the cold D (43.8%), followed by arid B (23.9%), tropical A
(16.3%), temperate C (12.3%) and polar E (3.8%).
Figure 5 is based on 3650 precipitation and 944 temper-
ature stations. Of these stations a total of 748 had both
precipitation and temperature data for the same location,
from which the climate type could be independently calcu-
lated and checked against the map. The climate type at 732
of these locations matched the map exactly and in 14 of the
remaining 16 locations the correct climate type was present
in a neighbouring cell. However, at 2 locations (Kodaikanal,
India and Pune, India) the observed climate type differs from
the map, indicating that at least one of the splines has not
successfully interpolated the observed data.
In the case of Kodaikanal (10.2N, 77.5 E), located at
2343 m elevation on the eastern side of the Western Ghats
in Tamil Nadu State, the seasonality of precipitation is much
less distinct than that observed in the surrounding lowlands
(∼100 m elevation). So although the observations indicate a
Cfb climate type at Kodaikanal, the map indicates a Cwb cli-
mate type. The disparity is due to the high density of precip-
itation stations in the surrounding lowlands, which influence
the precipitation splines, particularly the winter dry month
spline, to underestimate the precipitation at Kodaikanal. This
under-estimation, particularly the winter dry month precipi-
tation, leads to a Cw rather than a Cf climate type. The uni-
versal spline settings used for the analysis are not flexible
enough to fully capture the local variation in precipitation,
resulting in the misallocation of this site. Correcting the final
climate type map around Kodaikanal would be speculative
and has not been attempted.
At Pune (18.5N, 73.9 E), in Maharashtra State, there is
a very strong orographic gradient in mean annual precipi-
tation. The thin coastal strip to the west (25 km) of Pune re-
ceives mean annual precipitation of between 3000–6000 mm.
At Pune the mean annual precipitation is 700 mm. The ob-
servations at Pune indicate a BSh climate, whereas the map
indicates an Aw climate. This difference is due to the mean
annual precipitation spline overestimating the precipitation
at Pune, due to the very steep precipitation gradient, and thus
placing Pune in an A rather than a B climate type. Again,
the universal spline settings used are not flexible enough to
fully capture the local variation in precipitation, resulting in
the misallocation of this site. Likewise, correcting the final
climate type map around Pune would be speculative and has
not been attempted.
The low density of temperature stations in southern India
resulted in a region of temperate climate type extending fur-
ther south than expected. As mentioned above, Kodaikanal in
Figure 5. Köppen-Geiger climate type map of Asia.
29
Fig. 5. K¨
oppen-Geiger climate type map of Asia.
Figure 6. Köppen-Geiger climate type map of North America.
30
Fig. 6. K¨
oppen-Geiger climate type map of North America.
Tamil Nadu is a high elevation station in a temperate climate
type. However, it is up to 140km from its nearest neighbour
to the west and over 400km away from the nearest station to
the northeast. All the surrounding temperature stations are in
a tropical climate type. The large area of temperate, rather
than tropical climate in southern India is due to this single
station and the lack of temperature stations in the surround-
ing lowland areas, which could not be corrected due to lack
of data.
3.3 North America
North America is defined in this paper as Canada, the USA,
the countries of Central America and the Caribbean Islands.
The map of K¨
oppen-Geiger climate type for North Amer-
ica (Fig. 6) shows that all five of the main climate types are
present in North America. The dominant climate type by
land area is cold D (54.5%), followed by arid B (15.3%),
temperate C (13.4%), polar E (11.0%) and tropical A (5.9%).
Figure 6 is based on 3034 precipitation and 2236 temper-
ature stations. Of these stations a total of 2078 had both
www.hydrol-earth-syst-sci.net/11/1633/2007/ Hydrol. Earth Syst. Sci., 11, 1633–1644, 2007

1640 M. C. Peel et al.: Updated world K¨
oppen-Geiger climate classification map
Figure 7. Köppen-Geiger climate type map of South America.
31
Fig. 7. K¨
oppen-Geiger climate type map of South America.
precipitation and temperature data for the same location,
from which the climate type could be independently calcu-
lated and checked against the map. The climate type at 2020
of these locations matched the map exactly and in all the re-
maining 58 locations the correct climate type was present in
a neighbouring cell.
In Guatemala, there is a low density of temperature sta-
tions, which results in a region of temperate climate type
extending further than expected around Guatemala City
(14.5 N, −90.6 W). At an elevation of 1500 m, Guatemala
City has a temperate Cwb climate. However, it is up to
175 km from its nearest neighbour to the southeast in El
Salvador. All the surrounding temperature stations are in
tropical climate types. Although much of the area around
Guatemala City is likely to be temperate due to the high el-
evation, the temperate region on the map extends into what
are likely to be tropical areas near the coast where there are
no temperature stations to define the boundary more closely.
A region of BWh south of Oaxaca, Mexico (17.1 N,
−96.7 W) may be an artefact of the spline process, but since
there are no precipitation or temperature stations in this arid
region it is impossible to be certain. At an elevation of
1550 m, Oaxaca experiences an arid BSh climate type, but
whether the region to the south is as arid as BWh remains
uncertain.
In the methodology section, an additional criterion to deal
with locations that satisfy both Cs and Cw or Ds and Dw cri-
teria was described. In North America this criterion was suc-
cessfully applied at 11 temperate locations (mainly in Mex-
ico) with observed precipitation and temperature data. How-
ever, other locations in North America show that the criteria
for selecting the second letter of the C and D climate types
are not always providing an appropriate outcome. For exam-
ple, at Batopilas in the state of Chihuahua, Mexico (27.0 N,
107.7 W), where only precipitation data are available, the cli-
mate type in Fig. 6 is given as Csa. Nearby temperature sta-
tions confirm that summer is in the six-month period from
April through to September. At Batopilas the average sum-
mer rainfall is 441mm and the average winter rainfall is
191 mm. Nearby temperate locations are all Cw, which is
consistent with the summer dominant rainfall at Batopilas.
Following the temperate second letter criteria in Table 1, the
driest month in summer (5.3 mm) is below 40 mm and is less
than one third of the wettest month in winter (49.3 mm), so
a Cs climate type is satisfied. When the Cw climate crite-
rion is applied, the driest month in winter (18.4 mm) is not
one tenth of the wettest month in summer (154.6 mm), so a
Cw climate type is not satisfied. Since this site only satis-
fies the Cs criteria, the additional check of the summer and
winter precipitation, described in the methodology section,
is not applied. So at Batopilas, the summer rainfall is greater
than the winter rainfall, by a factor of 2, yet it is defined by
the K¨
oppen-Geiger criteria as a temperate climate with a dry
summer (Csa).
A slightly different concern with the temperate second let-
ter criteria is observed at White River, in the state of Arizona,
USA (33.84N, 109.97 W) where the K¨
oppen-Geiger criteria
suggest a Csa climate type. However, a Cs climate appears
inappropriate since summer precipitation (228 mm) is greater
than the winter precipitation (201mm). A more appropriate
climate type would be Cfa since the precipitation is evenly
distributed throughout the two six month periods. Other loca-
tions where concerns about the second letter criteria for the C
or D climate types were observed are Prescott (Cs −34.57 N,
112.44 W), USA and the Ds regions in northern Canada and
Alaska centred on Fort Yukon (66.6N, 145.3 W), Anchorage
(61.2, 149.9 W), Yellowknife (62.47 N, 114.45 W), Carcross
(60.18 N, 134.7 W), Mayo (63.62 N, 135.87 W) and west of
Fairbanks (64.9N, 147.7 W).
3.4 South America
The map of K¨
oppen-Geiger climate type for South Amer-
ica (Fig. 7) shows that three main climate types A, B and C
dominate the climate of South America. Of these three the
dominant climate type by land area is tropical A (60.1%), fol-
lowed by temperate C (24.1%) and arid B (15.0%). The Polar
E (0.8%) climate type occurs in four places in South Amer-
ica, twice in the Andes, due to the high elevation, along the
Hydrol. Earth Syst. Sci., 11, 1633–1644, 2007 www.hydrol-earth-syst-sci.net/11/1633/2007/

M. C. Peel et al.: Updated world K¨
oppen-Geiger climate classification map 1641
southern edge of Chile (Strait of Magellan)/Argentina (Tierra
del Fuego) and in the Falkland Islands and South Georgia.
Figure 7 is based on 1115 precipitation and 192 tempera-
ture stations. Of these stations a total of 171 had both precip-
itation and temperature data for the same location for which
the climate type is known and can be checked against the
map. The climate type at 165 of these locations matched the
map exactly and in 5 of the remaining 6 locations the correct
climate type was present in a neighbouring cell. In the case
of Aracaju (10.9 S, 37.1 W), in the state of Sergipe, Brazil,
the observed precipitation and temperature satisfy the Am
climate type (borderline Aw), whereas the map shows this
site in a region of Aw climate type. The splined variables
indicate that this station is clearly Aw, which is not consis-
tent with the observations. The reason the splined variables
are inconsistent with the observations is that this region has
a very high density of precipitation stations (Fig. 1). The
universal spline settings used for the analysis are not flexible
enough to fully capture the local variation in precipitation,
resulting in the misallocation of this site.
The misallocation of Aracaju due to the spline settings not
being flexible enough to adequately represent the local varia-
tion in precipitation is indicative of neighbouring parts of the
Nordeste region of Brazil. The climate types represented in
this region BWh, BSh, Aw, Am and Af are consistent with
the observations. However, the spatial distribution of these
climate types would be more in line with the precipitation
observations had a more flexible spline setting been used for
the precipitation data in this region. The sparse distribution
of temperature stations in this region would make any cor-
rections to the final climate type map speculative.
The generally low density of temperature stations in South
America resulted in some climate types extending further
than expected, which could not be corrected due to the lack of
data. Two regions where this is most evident are the temper-
ate regions northeast of Bogota, Colombia and around Quito,
Ecuador. In both of these cases the temperature stations are
in high elevation locations and experience a temperate cli-
mate type. However, due to the lack of nearby lower ele-
vation temperature stations, the temperate influence of both
these high elevation stations extends well beyond their im-
mediate location and large regions of temperate climate type
result in regions that are more likely to be tropical. In the case
of Bogota, a region of ET climate type is observed within the
temperate region. This region is largely coincident with the
higher sections of the Cordillera Oriental, which is likely to
have a climate type of ET.
3.5 Europe
In this paper, Europe is defined as the region west of a north-
south line through the Urals Mountains down to the Arabian
Sea and includes the Arabian Peninsula and the countries of
the Middle East. The K¨
oppen-Geiger climate type map of
Europe (Fig. 8) shows that only four main climate types are
Figure 8. Köppen-Geiger climate type map of Europe.
32
Fig. 8. K¨
oppen-Geiger climate type map of Europe.
found in Europe. The dominant climate type by land area
is cold D (44.4%), followed by arid B (36.3%), temperate C
(17.0%) and polar E (2.3%).
Figure 8 is based on 1209 precipitation and 684 tempera-
ture stations. Of these stations a total of 496 had both precip-
itation and temperature data for the same location for which
the climate type is known and can be checked against the
map. The climate type at 488 of these locations matched the
map exactly and in the remaining 8 locations the correct cli-
mate type was present in a neighbouring cell.
As is the case for other continents, in Europe there are
some climate types extending further than expected due to
a low density of temperature stations. For example, around
Mussala (42.18N, 23.58 E), Bulgaria there is a region of ET
climate type. There are three temperature stations within this
ET region, which are all located at over 2000m in elevation,
and their observations confirm an ET climate type. However,
rather than being part of a single range of mountains, these
three stations are located on top of three separate peaks with
extensive lowlands in between. As there are no temperature
stations located in the lowlands the splines ignore them and
the entire region is set to ET, rather than a mixture of ET and
Df. A similar situation occurs in the Central Massif of France
around Le Puy de Dome (45.8N, 2.9 E) and Mont Aigoual
(44.12 N, 3.58 E) where two temperature stations atop iso-
lated peaks extend a region of Df climate between them. This
region should be a mixture of Df (on the mountains) and Cf
in the lowlands, but there are no temperature stations in the
lowlands.
In Iceland the temperature stations are all located in
coastal areas. Without any inland temperature data to inform
the splines, the coastal climate type of Dfc extends inland
where a climate type of ET is more likely to be appropriate.
The region of ET climate type in southern Norway extends
to the coast at the northwestern edge, an area that would be
expected to be Cfb if temperature data were available.
www.hydrol-earth-syst-sci.net/11/1633/2007/ Hydrol. Earth Syst. Sci., 11, 1633–1644, 2007

1642 M. C. Peel et al.: Updated world K¨
oppen-Geiger climate classification map
Figure 9. Köppen-Geiger climate type map of Australia.
33
Fig. 9. K¨
oppen-Geiger climate type map of Australia.
Along the French-Italian border the low density of temper-
ature stations effectively reduces the area of ET climate type.
At St Bernard (45.7 N, 6.9E) in Switzerland the climate type
is ET, while the nearest station in a direct line to the south,
Nice (43.65 N, 7.2 E) in France, experiences a Csa climate.
Despite the Alps stretching almost to the coast, the region of
ET climate mapped does not, due to the lack of temperature
stations in the region.
3.6 Australia
The K¨
oppen-Geiger climate type map of Australia (Fig. 9)
shows that only three main climate types are found in Aus-
tralia. The dominant climate type by land area is arid B
(77.8%), followed by temperate C (13.9%) and tropical A
(8.3%).
Figure 9 is based on 1807 precipitation and 351 tempera-
ture stations. Of these stations a total of 345 had both precip-
itation and temperature data for the same location for which
the climate type is known and can be checked against the
map. The climate type at 340 of these locations matched the
map exactly and in the remaining 5 locations the correct cli-
mate type was present in a neighbouring cell.
The spatial distribution of precipitation and temperature
stations in the arid western central region of Australia is
sparse, so boundaries between the different arid climate types
are less reliable than in the more data rich eastern and south
western regions (Fig. 2).
3.7 Islands, Greenland and Antarctica
In the spline analyses used to estimate the K¨
oppen-Geiger
climate type for each continent, data from small offshore is-
lands were generally removed prior to analysis, since they
Figure 10. Köppen-Geiger climate type map of the World.
34
Fig. 10. K¨
oppen-Geiger climate type map of the World.
tended to influence the splines over the mainland in low sta-
tion density regions. Therefore, islands like the Canary Is-
lands, Cape Verde, Hawaii and the islands of the South Pa-
cific were allocated a K¨
oppen-Geiger climate type by hand
based on observations from the islands.
In Greenland there were 10 precipitation and 9 tempera-
ture stations, 8 of which have both variables at the same lo-
cation. All of these stations are in coastal locations, so there
was no climatic information about the interior of Greenland
on which to base a spline analysis. In Antarctica, there were
21 temperature stations and no precipitation stations with all
but 2 stations being in coastal locations. Like Greenland
there was little interior climatic information on which to base
a spline analysis. The climate type at all the Greenland sta-
tions was ET, while in Antarctica they were a mixture of ET
and EF.
In order to interpolate the climate type over the whole of
Greenland and Antarctica an alternate method was required.
In the case of Greenland, elevation data was used, with any
location <1000 m given a climate type of ET and a location
≥1000 m given a climate type of EF. In Antarctica the whole
continent was set to a climate type of EF, except around sta-
tions that were known to be ET. In Table 1 the criterion that
divides ET from EF is the hottest month being greater than
0◦C. Of the Antarctic stations the hottest month at any sta-
tion was 1.6◦C, so most of the ET stations are borderline EF.
Based on this observation, any location near an ET station
with an elevation of <100m was set to ET.
4 The world map: discussion and conclusion
The continental and island maps are combined together to
form the world K¨
oppen-Geiger climate type map (Fig. 10).
Globally the dominant climate class by land area is arid B
(30.2%) followed by cold D (24.6%), tropical A (19.0%),
temperate C (13.4%) and polar E (12.8%). The most com-
mon individual climate type by land area is BWh (14.2%),
followed by Aw (11.5%). Of the 30 possible climate types
Hydrol. Earth Syst. Sci., 11, 1633–1644, 2007 www.hydrol-earth-syst-sci.net/11/1633/2007/

M. C. Peel et al.: Updated world K¨
oppen-Geiger climate classification map 1643
the Csc climate type never occurs and the Cwc climate type
occurs in only 25 pixels (representing 0.002% of total land
area).
In the methodology, a new criterion for locations that sat-
isfy both Cs and Cw (or Ds and Dw) criteria simultaneously
was described and it was applied successfully in several lo-
cations. However, the criteria for the second letter of the C
and D climates was still found to produce inappropriate re-
sults, particularly in the high latitudes of North America, and
needs to be further revised. Future revisions based on mutu-
ally exclusive total seasonal precipitation thresholds, similar
to the 70% criteria used for the B climate, may prove more
robust than criteria based on the wettest and driest months of
summer and winter.
The updated world K¨
oppen-Geiger climate type map is
based on the climatology at stations over their entire period
of record, with each variable individually interpolated and
differs from the recent work of Kottek et al. (2006), which
is based on 0.5×0.5 degree gridded temperature and precip-
itation data for the period 1951 to 2000. Although broadly
similar to the map of Kottek et al. (2006), the present map
also deals with locations that satisfy both Cs and Cw (or Ds
and Dw) and has a finer resolution.
The single setting used for the 2-D thin-plate spline with
tension was able to successfully interpolate the observed data
in the vast majority of cases. For a total of 4279 stations
with both precipitation and temperature data, the map dif-
fers significantly at only 3 stations (Kodaikanal, Pune and
Aracaju) and in each case a more flexible spline setting may
have improved the map. However, in mountainous regions
and in regions of low station density the addition of elevation
into the interpolation procedure may improve the extrapola-
tion of K¨
oppen-Geiger climate type. In addition to improved
estimates of precipitation and temperate in mountainous re-
gions, elevation information would limit the influence of iso-
lated high elevation stations extending their climate type into
lowland regions.
The electronic form (in ESRI Arc Grid (raster)
and JPG formats) of the world K¨
oppen-Geiger climate
type map is available in the Supplementary Material
section (http://www.hydrol-earth-syst-sci.net/11/1633/2007/
hess-11-1633-2007-supplement.zip). Also available at this
site are files containing the precipitation and temperature
variables for all stations used in the construction of the map.
Acknowledgements. Australian Research Council Discovery grants
DP0449685 and DP0773016 financially supported this work.
F. Peel assisted with the colour scheme used for the maps.
Edited by: R. T. Clarke
References
Barnett, T., Zwiers, F., Hegerl, G., Allen, M., Crowley, T., Gillett,
N., Hasselmann, K., Jones, P., Santer, B., Schnur, R., Stott, P.,
Taylor, K., and Tett. S.: Detecting and attributing external in-
fluences on the climate system: a review of recent advances, J.
Climate, 18, 1291–1314, 2005.
Daly, C.: Guidelines for assessing the suitability of spatial climate
data sets, Int. J. Climatol., 26, 707–721, 2006.
Dick, R. S.: Frequency patterns of arid, semi-arid and humid cli-
mates in Queensland, Capricornia, 1, 21–30, 1964.
Essenwanger, O. M.: Classification of climates. In World Survey of
Climatology 1C, General Climatology, Elsevier, Amsterdam, pp.
102, 2001.
Fovell, R. G. and Fovell, M.-Y. C.: Climate zones of the contermi-
nous United States defined using cluster analysis, J. Climate, 6,
2103–2135, 1993.
Fraedrich, K., Gerstengarbe, F. -W. and Werner, P. C.: Climate
shifts during the last century, Climatic Change, 50, 405–417,
2001.
Gentilli, J. (Ed.): Climates of Australia and New Zealand, World
Survey of Climatology, Vol. 13. Elsevier, Amsterdam, 405p,
1971.
Gnandesikan, A. and Stouffer, R. J.: Diagnosing atmosphere-ocean
general circulation model errors relevant to the terrestrial bio-
sphere using the K¨
oppen climate classification, Geophys. Res.
Lett., 33, L22701, doi:10.1029/2006GL028098, 2006.
Kalvova, J., Halenka, T., Bezpalcova, K., and Nemesova, I.:
K¨
oppen Climate types in observed and simulated climates, Stud.
Geophys. Geod., 47, 185–202, 2003.
Kleidon, A., Fraedrich, K., and Heimann, M.: A green planet versus
a desert world: estimating the maximum effect of vegetation on
the land surface climate, Climatic Change, 44, 471–493, 2000.
K¨
oppen, W.: Das geographisca System der Klimate, in: Handbuch
der Klimatologie, edited by: K¨
oppen, W. and Geiger, G., 1. C.
Gebr, Borntraeger, 1–44, 1936.
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World
map of the K¨
oppen-Geiger climate classification updated, Mete-
orol. Zeitschr., 15(3), 259–263, 2006.
Lohmann, U., Sausen, R., Bengtsson, L., Cubasch, U., Perlwitz, J.,
and Roeckner, E.: The K¨
oppen climate classification as a diag-
nostic tool for general circulation models, Clim. Res., 3, 177–
193, 1993.
McMahon, T. A., Finlayson, B. L., Haines, A. T., and Srikanthan,
R.: Global Runoff – Continental Comparisons of Annual Flows
and Peak Discharges, Catena Verlag, Cremlingen, 166pp, 1992.
Mitas, L. and Mitasova, H.: General variational approach to the in-
terpolation problem, Comput. Math. Applic., 16, 983–992, 1988.
Peel, M. C., McMahon, T. A., and Finlayson, B. L.: Continental
differences in the variability of annual runoff – update and re-
assessment, J. Hydrol., 295, 185–197, 2004.
Peterson, T. C. and Vose, R. S.: An overview of the Global Histor-
ical Climatology Network temperature database, Bull. Am. Me-
teorol. Soc., 78(12), 2837–2849, 1997.
Russell, R. J.: Dry climates of the United States: I climatic map,
University of California, Publications in Geography, 5, 1–41,
1931.
Sanderson, M.: The classification of climates from Pythagoras to
Koeppen, Bull. Am. Meteorol. Soc., 80, 669–673, 1999.
Stern, H., De Hoedt, G., and Ernst, J.: Objective classification of
Australian climates, Aust. Meteorol. Mag., 49, 87–96, 2000.
Strahler, A. N.: The Earth Sciences. Harper and Row, New York,
824pp, 1971.
www.hydrol-earth-syst-sci.net/11/1633/2007/ Hydrol. Earth Syst. Sci., 11, 1633–1644, 2007

1644 M. C. Peel et al.: Updated world K¨
oppen-Geiger climate classification map
Strahler, A. H. and Strahler, A. N.: Physical Geography : Sci-
ence and Systems of the Human Environment. Wiley, New York,
794pp, 2005.
Thornthwaite, C. W.: Problems in the classification of climates, Ge-
ogr. Rev., 33(2), 233–255, 1943.
Triantafyllou, G. N. and Tsonis, A. A.: Assessing the ability of the
K¨
oppen system to delineate the general world pattern of climates,
Geophys. Res. Lett., 21(25), 2809–2812, 1994.
USGS (2000), HYDRO1k, http://edc.usgs.gov/products/elevation/
gtopo30/hydro/ (accessed 16/10/2006).
Wang, M. and Overland, J. E.: Detecting Arctic climate change
using K¨
oppen climate classification, Climatic Change, 67, 43–
62, 2004.
Wilcock, A. A.: K¨
oppen after fifty years, Ann. Assoc. Am. Geog.,
58(1), 12–28, 1968.
Hydrol. Earth Syst. Sci., 11, 1633–1644, 2007 www.hydrol-earth-syst-sci.net/11/1633/2007/