Large-scale warming is not urban

Article (PDF Available)inNature 432(7015):290 · December 2004with14 Reads
DOI: 10.1038/432290a · Source: PubMed
Controversy has persisted over the influence of urban warming on reported large-scale surface-air temperature trends. Urban heat islands occur mainly at night and are reduced in windy conditions. Here we show that, globally, temperatures over land have risen as much on windy nights as on calm nights, indicating that the observed overall warming is not a consequence of urban development.


*CEFE–CNRS, 1919 route de Mende, 34293
Montpellier, France
LSCE–CEA–CNRS CE Saclay l’Orme des
Merisiers, 91191 Gif-sur-Yvette, France
INRA Site Agroparc, domaine Saint-Paul, 84914
Avignon Cedex 9, France
§Collège de France, 75231 Paris Cedex 05, France
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Competing financial interests: declared none.
more than 27% of global land area) in
1958–99.For 1950–2000, the trends of global
annual average T
for windy, calm and all
conditions were identical (0.190.06 °C per
decade; Fig. 1a). So, urbanization has not
systematically exaggerated the observed
global warming trends in T
.The same can
be said for poor instrumental exposure and
microclimatic effects,which are also reduced
when instruments are well ventilated
When the criterion for calm was
changed to the lightest decile of wind
strength, the global trend in T
unchanged. The analysis is therefore robust
to the criterion for calm. To assess the effect
of time differences between the reanalysis
daily-average winds and T
,I repeated
the analysis using 26 stations in North
America and Siberia that have hourly or
six-hourly reports of simultaneous temper-
ature and wind. Again, windy and calm
nights warmed at the same rate, in this case
by 0.20 °C per decade.
Because a small sample was used, I com-
pared global trends for the reduced period
1950–93 with published all-conditions
trends for that period based on a sample of
over 5,000 stations
.All differences were
within 0.02 °C per decade.This robustness
arises because of the spatial coherence of sur-
face temperature variations and trends
The global annual result conceals a
relative warming of windy nights in winter
in the extratropical Northern Hemisphere
(Fig. 1b), mainly in western Eurasia. The
observed tendency to an increased positive
phase of the North Atlantic Oscillation
implies that the windier days in western
Eurasia had increased warm advection from
the ocean
,yielding greater warming. In
summer in the extratropical Northern
Hemisphere (Fig. 1c), there was no relative
change in T
on windy nights. At that time
of year, atmospheric circulation changes are
less influential, but an urban warming signal
is still absent. In the tropics, calm nights
warmed relative to windy nights on an annu-
al average, but only by 0.020.01 °C per
decade, which is much less than the overall
tropical warming in T
(0.160.03 °C).
This analysis demonstrates that urban
warming has not introduced significant biases
into estimates of recent global warming.
The reality and magnitude of global-scale
warming is supported by the near-equality of
temperature trends on windy nights with
trends based on all data.
David E. Parker
Hadley Centre, Meteorological Office,
Exeter EX1 3PB, UK
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Data (Clarendon, Oxford, 1999).
Competing financial interests: declared none.
brief communications
VOL 432
18 NOVEMBER 2004
Large-scale warming
is not urban
ontroversy has persisted
over the
influence of urban warming on
reported large-scale surface-air tem-
perature trends. Urban heat islands occur
mainly at night and are reduced in windy
.Here we show that, globally,
temperatures over land have risen as much
on windy nights as on calm nights, indicat-
ing that the observed overall warming is not
a consequence of urban development.
Observations of the minimum tempera-
ture (T
) over 24 hours at 264 stations
worldwide since 1950 were expressed as
anomalies, relative to the period 1961–90
where possible. Coverage of T
data was
good north of 20° N, in Australasia and in
the western tropical Pacific, but poor in
Africa, South America, Antarctica and parts
of southern Asia. Reanalysed
near-surface wind components were used
to classify the T
anomalies into ‘windy’
(upper tercile) and calm’ (lower tercile)
conditions. Daily average wind speeds were
used because the timings of temperature
extremes are not known. For stations
between 140° E and the dateline, T
which occurs most frequently in the early
morning — was matched with the previous
day’s speed. This is because the early morn-
ing in terms of universal time (equivalent
to Greenwich Mean Time) is still in the
previous day in the Far East.
Annual and seasonal anomalies of T
were gridded on a 5°5° resolution for
windy, calm and ‘all’ conditions. Coverage
was at least 200 grid boxes (equivalent to
1960 1970 1980 1990 2000
Atmospheric science
Early peak in Antarctic
oscillation index
he principal extratropical atmospheric
circulation mode in the Southern
Hemisphere, the Antarctic oscillation
(or Southern Hemisphere annular mode),
represents fluctuations in the strength of
the circumpolar vortex and has shown a
trend towards a positive index in austral
summer in recent decades, which has been
linked to stratospheric ozone depletion
and to increased atmospheric greenhouse-
gas concentrations
.Here we reconstruct
the austral summer (December–January)
Antarctic oscillation index from sea-level
pressure measurements over the twentieth
and find that large positive values,
and positive trends of a similar magnitude
Figure 1 Anomalies in T
for windy (red) and calm (blue)
conditions. a, Annual global data; b, winter data (December to
February) for Northern Hemisphere land north of 20° N; c, sum-
mer data (June to August) for Northern Hemisphere land north of
20° N. The linear trend fits, and the 2
error ranges given in
the text, were estimated by restricted maximum likelihood
taking into account autocorrelation in the residuals. As expected
from the reduced stratification of the boundary layer, T
is, on
average, warmer on windy nights than on calm nights.
18.11 brief comms 289 newnew 15/11/04 10:16 am Page 290
© 2004
to those of past decades, also occurred
around 1960, and that strong negative
trends occurred afterwards. This positive
Antarctic oscillation index and large posi-
tive trend during a period before ozone-
depleting chemicals were released into the
atmosphere and before marked anthro-
pogenic warming, together with the later
negative trend, indicate that natural forcing
factors or internal mechanisms in the
climate system must also strongly influence
the state of the Antarctic oscillation.
Until recently
, it has not been possible to
put the Antarctic oscillation index (AAOI)
trends in past decades into a longer-term
context, as comprehensive Southern Hemi-
sphere data are limited to the reanalysis period
(1948/58–present; NCAR–NCEP/ERA40 re-
analysis). Our reconstructions are intended
to cover the reanalysis period with a consis-
tent estimate of the AAOI, as this has been
, and to extend this estimate fur-
ther back.The new reconstructions are more
reliable as they use more predictor stations
and a statistical model fitted using ERA40
reanalysis, whose AAOI estimates are better
than those from NCEP reanalysis
We define the Antarctic oscillation as the
first empirical orthogonal function,and the
AAOI as the first principal component of
the December–January mean extratropical
sea-level pressure. A positive or negative
AAOI indicates a strengthening or weaken-
ing, respectively, of circumpolar westerly
flow. For our reconstructions, we used
multiple regression to estimate the AAOI
from the leading principal components of
normalized station pressure. The model is
fitted using detrended data, but the recon-
struction is derived using undetrended data.
One reconstruction (1905–2000) uses 22
stations (Fig. 1a); the second (1951–2000)
uses 41 and provides improved coverage of
the Antarctic oscillation centres of action
(Fig. 1b). Cross-validation gives a correla-
tion of 0.88 and 0.90 for the 1905 and 1951
reconstructions, respectively. (For methods,
see supplementary information).
Both reconstructions show that the cur-
rent positive values for the AAOI are not
unprecedented (Fig. 1c). After the relatively
stable first half of the twentieth century,
there is a period of positive values (relative
to the 1958–2000 mean) from 1958 to 1963,
followed by a sharp drop to predominantly
negative values until the mid-1980s, and
then by a mostly positive phase up to the
present. The maximum positive 25-year
trends over recent years are of similar magni-
tude to those between the low values of the
1940s and the peak in the 1960s.Note that the
trend over the past decades is caused by a
combination of negative values in the 1970s
and current positive values.
A positive AAOI around 1960, followed
by a negative index, is also present in the
NCEP and the ERA40 data, in a zonal index-
based AAOI
and in earlier reconstructions
Consistent with this Antarctic oscillation
behaviour, station pressures around 1960
have positive anomalies in the mid-latitude
centres of action and negative anomalies in
the Antarctic centre of action. By contrast
with our reconstructions, the 1960s peak is
slightly lower than the 1990s peak in both
reanalyses and the zonal index AAOI.
Despite this small uncertainty about the
exact values, the 1960s peak is a robust fea-
ture in all these data sets.
The fact that the austral summer behav-
iour of the Antarctic oscillation in recent
decades seems not to be unprecedented
indicates that natural forcing factors, such
as solar or volcanic variability, or internal
processes in the climate system, can strongly
influence the state of the Antarctic oscilla-
tion.The question arises as to what the role of
these factors has been over the past decades.
Julie M. Jones, Martin Widmann
Institute for Coastal Research, GKSS Research
Centre, 21502 Geesthacht, Germany
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Supplementary information accompanies this communication on
Nature’s website.
Competing financial interests: declared none.
Antarctic oscillation index
1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Figure 1 Reconstruction of the December–January Antarctic oscillation index (AAOI). a, The Antarctic oscillation pattern and regression
weights for normalized station sea-level pressure used for the 1905 AAOI reconstruction. Isolines show the sea-level pressure anomaly (in
hundreds of pascals) for the AAOI1. The red circles denote positive values and the pink circles denote negative ones; the area is pro-
portional to the weight; b, as in a, but for the 1951 AAOI reconstruction, with dark green denoting positive values and light green denoting
negative ones. c, Reconstructed December–January AAOI. Red bars show the 1905 reconstruction; green bars, the 1951 reconstruction.
The thick red line is the nine-year low-pass-filtered 1905 reconstruction; the green, the 1951 reconstruction. The thin red and green lines
show the 95% confidence intervals for the filtered data. Years are dated from December.
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The line in Fig. 1a shows unity (xy) and is not a
regression line, as the legend describes it.
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    • "This UHI–population regression is applicable only if there are a large number of stations to calculate the regional averages. On the premise that urban warming is intense on calm days but suppressed on windy days, Parker (2004 Parker ( , 2006) compared global and regional trends of daily surface air temperatures on calm and windy days, separately, for the period 1950–2000. It was found that the temperature trends were almost unaffected by such subsampling, indicating that local urban warming contributed little to the observed warming trends. "
    [Show abstract] [Hide abstract] ABSTRACT: There have long been arguments about the impact of urbanization on local meteorological observations. This letter reviews up-to-date studies of the urbanization-related warming in the observed land surface air temperature series in China. Many previous studies have suggested that, over the past few decades, the local warming due to urbanization could have been about 0.1 °C/10 yr, or even larger. However, based on recently developed homogenized temperature records, the estimated urban bias is smaller. Major uncertainties arise from either the data quality or the techniques used to estimate the urbanization effect. A key example is the ‘observation-minus-reanalysis’ method, which tends to overestimate the urban signal in this region, partly due to systematic bias in the multi-decadal variability of surface air temperature in the reanalysis data. It is expected that improved numerical modeling with high-resolution information regarding the changing land surface in the region will help to further understand and quantify the effect of urbanization in local temperature records.摘要城市化影响局地气温观测记录,是气候变化领域关注的问题之一。曾有研究认为,近几十年中国气温观测中城市化增温达0.1°C/10年甚至更高。然而,近年基于均一化观测而得到的结果要小得多。其中的不确定性主要源于数据质量和评估方法。典型例子如‘观测减再分析(OMR)’方法。由于再分析资料中的多年代际变率相对于实际气温观测存在系统性偏差,该方法通常会高估城市化效应。近年发展的高分辨率气候模拟及动态陆面演变信息将有助于理解和量化城市化效应。
    Article · Mar 2016
    • "When compared with results using many stations, the differences are small [see the review by Parker (2010)]. As noted above, many assessments of urbanization effects at the large scale have considered rural-only sites and compared these to averages based on all sites, or on urban-only sites [see, for example, Jones et al. (1990), Parker (2004, 2006, 2010), and Peterson and Owen (2005)]. Differences are always small, and always an order of magnitude smaller than any long-term warming—implying that any urbanization effect is small. "
    [Show abstract] [Hide abstract] ABSTRACT: The purpose of this review article is to discuss the development and associated estimation of uncertainties in the global and hemispheric surface temperature records. The review begins by detailing the groups that produce surface temperature datasets. After discussing the reasons for similarities and differences between the various products, the main issues that must be addressed when deriving accurate estimates, particularly for hemispheric and global averages, are then considered. These issues are discussed in the order of their importance for temperature records at these spatial scales: biases in SST data, particularly before the 1940s; the exposure of land-based thermometers before the development of louvred screens in the late 19th century; and urbanization effects in some regions in recent decades. The homogeneity of land-based records is also discussed; however, at these large scales it is relatively unimportant. The article concludes by illustrating hemispheric and global temperature records from the four groups that produce series in near-real time.
    Full-text · Article · Mar 2016
    • "The effects of urbanization on temperatures have been found to be negligible when compared with the global or regional trends in average temperature [Hansen et al., 2010; Jones et al., 1990; Li et al., 2004; Parker, 2004; Peterson et al., 1999]. In contrast, some studies have indicated that urbanization may induce a large warming bias in regional or local temperature time series [Balling and Idso, 1989; Ren et al., 2007] . "
    [Show abstract] [Hide abstract] ABSTRACT: This study simulated the effects of changes in the underlying surface induced by long-term urbanization on trends in surface air temperature (SAT) over three extensive urban agglomerations (Beijing-Tianjin-Hebei, BTH; the Yangtze River Delta, YRD; and the Pearl River Delta, PRD) in China during 1980–2009. To isolate the effects of continuous urban expansion on SAT with the least computation cost, we employed the Community Land Model (CLM4.5) in an offline mode for a relatively long period. Based on a high-quality land use dataset dating back to the 1980s, two scenarios were designed to represent the distributions of both nonurban and historically urban land-use. By comparing the results of two numerical experiments, urban-induced warming in daily mean SAT (Tmean) over the three urban agglomerations, BTH, YRD and PRD, were found to be 0.13°C/30a, 0.12°C/30a, and 0.09°C/30a, contributing about 9.70%, 10.3% and 9.68% to the mean long-term SAT trends, respectively. In addition, a higher contribution of urban-related warming was found in winter for BTH and in summer for the other two regions. However, urban-related warming had no significant effect on the trends of daily maximum SAT (Tmax) when compared with daily minimum SAT (Tmin). Specifically, at a local scale, the contributions of urban warming to the background warming in three representative cities, Beijing, Shanghai and Guangzhou, were 12.7%, 29.0%, and 23.6%, respectively.
    Full-text · Article · Feb 2016
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