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Northern Hemisphere atmospheric stilling partly attributed to an increase in surface roughness



Surface winds have declined in China, the Netherlands, the Czech Republic, the United States and Australia over the past few decades. The precise cause of the stilling is uncertain. Here, we analyse the extent and potential cause of changes in surface wind speeds over the northern mid-latitudes between 1979 and 2008, using data from 822 surface weather stations. We show that surface wind speeds have declined by 5g-15% over almost all continental areas in the northern mid-latitudes, and that strong winds have slowed faster than weak winds. In contrast, upper-air winds calculated from sea-level pressure gradients, and winds from weather reanalyses, exhibited no such trend. Changes in atmospheric circulation that are captured by reanalysis data explain 10g-50% of the surface wind slowdown. In addition, mesoscale model simulations suggest that an increase in surface roughnessg-the magnitude of which is estimated from increases in biomass and land-use change in Eurasiag-could explain between 25 and 60% of the stilling. Moreover, regions of pronounced stilling generally coincided with regions where biomass has increased over the past 30 years, supporting the role of vegetation increases in wind slowdown.
Northern Hemisphere atmospheric stilling partly
attributed to an increase in surface roughness
Robert Vautard1*, Julien Cattiaux1, Pascal Yiou1, Jean-Noël Thépaut2and Philippe Ciais1
Surface winds have declined in China, the Netherlands, the
Czech Republic, the United States and Australia over the
past few decades1–4. The precise cause of the stilling is
uncertain. Here, we analyse the extent and potential cause of
changes in surface wind speeds over the northern mid-latitudes
between 1979 and 2008, using data from 822 surface weather
stations. We show that surface wind speeds have declined
by 5–15% over almost all continental areas in the northern
mid-latitudes, and that strong winds have slowed faster than
weak winds. In contrast, upper-air winds calculated from sea-
level pressure gradients, and winds from weather reanalyses,
exhibited no such trend. Changes in atmospheric circulation
that are captured by reanalysis data explain 10–50% of
the surface wind slowdown. In addition, mesoscale model
simulations suggest that an increase in surface roughness—the
magnitude of which is estimated from increases in biomass
and land-use change in Eurasia—could explain between 25 and
60% of the stilling. Moreover, regions of pronounced stilling
generally coincided with regions where biomass has increased
over the past 30 years, supporting the role of vegetation
increases in wind slowdown.
The decline of surface wind observed in many regions of
the world is a potential concern for wind power electricity
production5, and has been shown to be the main cause of decreasing
pan evaporation6–8. In China, a persistent decrease of monsoon
winds was observed in all seasons3,9. Stilling winds were also
evidenced over the Netherlands2, in the Czech Republic10, over
the conterminous US (refs 1,11) and most of Australia4. In
Mediterranean regions, wind trends were non-monotonic over
the past decades12. At high latitudes, the surface winds were
found to increase13,14.
Such surface wind trends can be due to (1) changes in mean
circulation15,16 and/or to the decrease of synoptic weather system
intensity, both as a consequence of climate change, (2) changes
in near-surface wind due to increasing surface roughness in the
near field of each station2,4,11 and/or in boundary layer structure,
and/or (3) instrumental or observational drifts17,18. Over China,
wind decline was attributed to a north–south warming gradient in
winter, and to sunlight dimming caused by air pollution lingering
over central areas3,9 in summer. No clear explanation was given for
the wind decline in other studied regions of interest.
The attribution of the stilling drivers requires a global investi-
gation of available surface and upper-air wind data, which has not
been conducted so far. Here we use global data sets of in situ wind
measurements (see the Methods section and Supplementary Infor-
mation). A set of 822 worldwide surface stations with continuous
wind records was selected after careful elimination of stations with
obvious breaks and large gaps. This data set covers most of the
northern mid-latitudes over the 1979–2008 period.
1LSCE/IPSL, Laboratoire CEA/CNRS/UVSQ, 91191 Gif/Yvette Cedex, France, 2ECMWF, Shinfield Park, Reading RG2 9AX, UK.
We found that annual mean wind speeds have declined at 73% of
surface stations over the past 30 years (Fig. 1a). In Europe, Central
Asia, Eastern Asia and in North America (Fig. 1a) the annual mean
surface wind speed has decreased on average at a rate of 0.09,
0.16, 0.12 and 0.07 m s1decade1, respectively (2.9, 5.9,
4.2 and 1.8% per decade), that is, a decrease of about 10%
in 30 years and up to almost 20% in Central Asia, where wind
speed trends have not been studied so far. These numbers are all
statistically significant (p<0.1% for the regression coefficient).
Tropical areas are not well covered by the data set. However the
wind decline over South Asia is also about 5% per decade, and
0.08 m s1per decade.
Zonal means of wind trends exhibit a rather homogeneous
behaviour across latitudes, in the range of 0.06 to 0.11 to
m s1decade1. A higher frequency of positive trends is found
only over the 15 northern polar latitude stations. A few wind
series starting earlier (after 1959) further indicate that stilling
actually started at least as early as in the 1960s (Supplementary Fig.
S1). Negative trends were also found in mid to high percentiles
of the wind speed distribution (see Supplementary Table S1),
as well as in the frequency of observations above fixed mid to
high thresholds (5–15 m s1, see Fig. 2). Trend results are robust
to changes in the station selection method and parameters (see
Supplementary Table S1).
If we assume that the primary cause of the surface wind stilling is
a slowdown in atmospheric general circulation and/or a weakening
in synoptic weather activity, then a stilling trend should also
show up (1) in the reanalyses of three-dimensional wind fields
of the National Centers for Environmental Prediction/National
Center of Atmospheric Research (NCEP/NCAR) and European
Center for Medium-range Weather Forecast (ECMWF)—because
observations of upper-air winds are used in the reanalysis procedure
and there is a high connectivity between winds at 10 m and
at 850 hPa, see Supplementary Fig. S2, (2) in upper-air wind
observations from rawinsonde and (3) in geostrophic winds
deduced from pressure gradients.
NCEP/NCAR reanalysis19 does not exhibit any trend in surface
(10 m) winds over land (Fig. 1b), as found in previous studies2,4,11.
The recent ECMWF ERA-interim reanalysis20 exhibits negative
trends with magnitudes between 10% (for North America) and
50% (for Europe) of the observed ones over the past two decades,
with quite different spatial patterns (Fig. 1c, Supplementary
Fig. S1 and Table S1). Over Australia similar weak trends
(0.02 m s1decade1) were obtained by ERA-interim as by ERA40
in a previous study4. In contrast the inter-annual variability
of surface winds is fairly well reproduced in both reanalyses
(Supplementary Figs S2 and S3). This indicates that part of the
wind trend changes, captured in the ERA-interim reanalyses,
is due to large-scale circulation changes, but the reanalysis
© 2010 Macmillan Publishers Limited. All rights reserved.
Central Asia
East Asia
North America
Wind speed trend (m s
per decade)
Wind speed trend (m s¬1 per decade) Wind speed trend (m s¬1 per decade)
Figure 1 |Observed and reanalysis surface wind speed trends. a, 30-year surface (1979–2008) wind speed linear trend calculated over all of the available
observations and each of the selected stations, in m s1perdecade. The specific regions studied in this Letter are shown by rectangles delimiting the areas
over which statistics are calculated. b, NCEP/NCAR reanalyses trends calculated using mean daily 10 m wind speed values available over a grid of 193×47
grid points over the Northern Hemisphere (data available at c, The same as in bfor
ERA-interim reanalyses trends calculated over the past 20 years alone. The area boundaries for the four regions of focus are: Europe 20W–40E,
30–75 N, 276 stations; Central Asia 40–100E, 30–75N, 96 stations; Eastern Asia 100–160E, 30–75N, 190 stations; North America, 170–50W,
30–75N, 170 stations. The South Asia area quoted in the main text covers 40–160E, 0–30N, 40 stations.
models/procedures have deficiencies and/or missing key processes
that prevent the surface wind trends from being fully resolved2,4,11.
Land-use changes, not taken into account in reanalyses, are
potential candidates of such processes.
Upper-air (850 hPa and above) wind speed observations from
rawinsonde data do not either exhibit such a systematic wind
decline pattern (Fig. 3), and even show large regions with increasing
trends (Western Europe, North America). At nearly collocated sites,
winds normalized by their grand mean have trends with much
higher amplitudes at the surface than above (Fig. 3c). However,
regionally coherent negative trends are found over Eastern Asia
at 850 hPa, where surface trends could be linked with upper-air
trends, as a result of regional monsoon decline due to climate
change and air pollution3. However, reanalyses over China do not
exhibit trends, which remains unexplained and points towards
uncertainties in models.
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1980 1990 2000
1980 1990 2000
1980 1990 2000
Frequency (%) Frequency (%)
1980 1990 2000
Frequency (%) Frequency (%)
u 1 m s¬1
u 3 m s¬1
u 5 m s¬1
u 7 m s¬1
u 9 m s¬1
u 11 m s¬1
u 13 m s¬1
u 15 m s¬1
Figure 2 |Wind speed distribution evolution. ad, Evolution, as a function of year, of the annual frequency of wind exceeding a given threshold (various
curves, see legend), for each of the four regions identified in Fig. 1: Europe (a), Central Asia (b), Eastern Asia (c), North America (d). The numbers take into
account all stations in the domains together, as well as all hours and seasons. The linear regression trend coefficients, respectively for the frequency (in
% perdecade) of winds stronger than 1, 3, 5, 7, 9, 11, 13 and 15m s1are: Europe: 0,1,5,7,11,12,11,12; Central Asia:
0,6,13,18,23,24,22,23; Eastern Asia: 2,4,10,15,19,23,30,37; Northern America: 1,2,4,5,3,3,3,11.
Geostrophic winds estimated from observed sea-level pressure
gradients also show a negligible stilling trend2,8 (Supplementary
Fig. S3). However, their interannual variations are consistent with
observed surface winds. These findings suggest that large-scale wind
changes should not be the dominant drivers of observed surface
wind decline.
Despite our stringent station selection procedure of rejecting
stations with large data gaps and obvious breaks (see the Methods
section and Supplementary Information), some time series may
still exhibit heterogeneities, which could explain a widespread
wind stilling trend only if they would be coherent over all
regions, which is unlikely. Systematical instrumental drifts, such
as anemometer performance degradation, especially for near-calm
winds, have been suggested17, but anemometers were replaced
over the past decades (O. Mestre and S. Jourdain, Météo-France,
personal communication), and trends are more pronounced for
stronger winds. A systematic change of observation systems
or round-off procedures18 also could have introduced artificial
trends. However, at most stations the decreasing wind trend was
gradual throughout the past 30 years, as shown for instance in
Fig. 4a for the Central Asian stations with wind trends in the
lowest tercile of the data. It is therefore unlikely that generalized
instrumental heterogeneities and observational procedures explain
the systematic trends.
These findings suggest that changes in surface processes
could play a major role in the surface wind stilling. Surface
winds are sensitive to changes in (1) surface roughness and
(2) sensible heat fluxes modifying vertical momentum fluxes
through boundary layer convection. Wind trends do not exhibit
strong diurnal variations (see Supplementary Table S1), suggesting
that roughness changes should dominate over sensible heat flux
changes as a cause of stilling.
Increase of surface roughness can be associated with factors
such as urbanization, growth of forests, changes in trees and forest
distribution or changes in agricultural practices. To estimate the
sensitivity of surface wind to roughness changes, we carried out
sensitivity simulations with the MM5 model21. Starting from a
control simulation (with standard roughness height z0c), changes
in roughness height z0(from a factor 1 to 2) have been applied in
the model surface boundary conditions during a single year (2007)
over an area covering Eastern Europe and Central Asia, where
the negative wind trends are most pronounced. When averaged
over the area considered (Supplementary Fig. S4), the 10 m wind
speed decrease δUbetween an enhanced roughness simulation and
a control simulation fits the relation δU= −aln(z0/z0c), where
a=0.48 m s1when surface roughness is uniformly changed and
a=0.37 m s1when only grassland roughness is changed and
the effect is averaged over grassland areas (as defined by MM5
land-cover data). Thus, about a doubling of roughness height would
lead to a decrease of 10 m wind by 0.26–0.33 m s1, which is in the
range of observed wind decreases over the past three decades (see
Supplementary Table S1). Over Central Asia, where trends are most
pronounced, a tripling of the roughness height would be required
to fully reach observed stilling, which seems fairly unrealistic.
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North America
Central Asia
East Asia
Pressure (hpa)
Pressure (hpa)
¬0.2 ¬0.1 0 0.1 0.2 0.3 0.4 0.5 ¬0.06
Central Asia
Eastern Asia
North America
¬0.04 -0.02 0 0.02
Normalized wind speed trend (per decade)
Wind speed trend (m s¬1 per decade)
Wind speed trend (m s¬1 per decade)
Figure 3 |Upper-air wind speed trends. a, Trends of the 850hPa wind measured by rawinsonde data, over stations having at least half of the yearly data
over more than 15 years during the two-decade period between 1979 and 2008, in m s1perdecade. b, Mean vertical profile of wind speed trend obtained
from monthly averaged rawinsonde measurements (850 hPa and above) and closest surface site, averaged over all sites in each region of Fig. 1a. c, The
same as in bfor wind speed trends normalized by mean wind speed at each site. Surface values are arbitrarily set to 1,015 hPa pressure in the profile.
An average increase of about 1.1% peryear of forest carbon sink
at mid to high latitudes of the Northern Hemisphere band has
been deduced from two-decade-long remote-sensed normalized
difference vegetation index (NDVI) data22. Over Russia, estimates
of the yearly carbon sink to carbon pool ratio vary between 1.2
and 1.3%. Using a theory developed to link tree properties and
roughness23,24, applied to areas with only sparse roughness elements
(which is generally the case for anemometer neighbourhoods), the
roughness height should scale as z0hλ1.33, where his the height
and λis the frontal area index of roughness elements. Assuming
an isotropic development of the volume Vof roughness elements
and their constant density per area, hscales as V1/3and λscales as
V2/3, implying that z0scales as V1.22. Conversely, assuming only an
increase in the density of roughness elements without any change
in their volume, the increase of z0should scale as the increase of
V. From this scaling one can deduce that the increase of biomass
over Russia would have induced, over 30 years, an increase of
roughness height of 36–50%, explaining 25–40% of the observed
wind stilling. This is also supported by the cropland abandonment
in southwestern Russia25. The contribution of biomass growth to
wind stilling should be higher in other areas such as Europe or
China, where forest growth reaches 1.5% per year (refs 26,27). In
such cases, the induced wind decline could reach between 40% and
60% of the observed one over the past three decades.
Several studies based on the NDVI showed a widespread
increase of vegetation in many northern areas28. Using the Global
Inventory Modelling and Mapping Studies (GIMMS) database29,
obtained from the Advanced Very High Resolution Radiometer
(AVHRR) remote-sensed observations over 1982–2006, we found
that 62% of wind sites witnessed a positive spring–summer NDVI
trend (Fig. 4b). Moreover, the negative wind trend has a median
amplitude about three times larger over sites with a NDVI trend in
the upper 10% than over sites in the lower 10% of its distribution
(Fig. 4c). This also supports the influence of vegetation increase
on surface winds, but a quantitative relationship between NDVI
and roughness is difficult to establish. Note also that the declining
wind trends are found across the entire day (Supplementary Table
S1), suggesting that dynamic roughness changes dominate over
sensible heat flux changes.
Therefore, atmospheric circulation changes captured by the
ERA-interim reanalysis explain between 10 and 50% of wind
stilling in the northern mid-latitudes, and our analysis suggests
that 25–60% could be due to a roughness increase due to
vegetation. Urbanization may be an extra roughness increase
factor, although recent observations do not directly support
this mechanism8. However, even a gross estimate of roughness
increase due to urbanization could not be found. To formally
attribute the causes of declining winds, a sensitivity analysis
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1980 1985 1990 1995 2000 2005
¬0.01 0 0.01 0.02
Spring¬summer NDVI trend (per decade)Year
Wind speed trend (m s¬1 per decade)
Annual mean wind speed (m s¬1)
North America
Central Asia
East Asia
Spring¬summer NDVI trend (per decade)
Figure 4 |Surface wind trends and their relationship with NDVI trends. a, Time evolution of annual average surface wind speed for each of the stations
with the trend in the lower tercile of the wind trend distribution over the Central Asia area. Each curve corresponds to a station. The mean trend is
0.31 m s1decade1.b, Spring–Summer (April–September) trends (in decade1), of the average over a 24×24 km area around the surface wind stations
of the NDVI (as obtained from the GIMMS AVHRR product). c, Median surface wind trends over the stations of each decile of the NDVI trend distribution,
together with the corresponding wind trend distribution (20–40% and 60–80% in yellow, 40–60% in red), versus the median of each decile of NDVI
trends. The regression line for the median (y=−0.104–2.65x) is shown.
using a model of surface wind speeds driven by all potential
variables is required.
An important question is whether wind power energy produc-
tion is likely to be affected by wind stilling in the future. Over the
past 30 years surface winds underwent an average 10% decrease,
with larger trends at stronger winds (Fig. 2). As wind electricity
production is much more efficient at stronger winds, a continuation
of such trends would lead to a major loss of wind power production.
However, wind power is not taken at the surface but between
50 and 100 m, where trends should be weaker according to our
three-dimensional analysis. In any case, the strong influence of
land-cover change on wind speed trends is good news because
land use can much more easily be controlled locally than the
large-scale circulation.
Surface winds. Observation data sets come from the Research Data Archive, which
is maintained by the Computational and Information Systems Laboratory at NCAR
(sponsored by the National Science Foundation). The original data were taken
from the Research Data Archive (
Surface winds are obtained from two global data sets of this archive, gathering
hourly or three-hourly meteorological information on several parameters including
wind speed and direction from anemometers. The first data set (DS463.2) covers
the period 1901–2003. The second data set (DS461.0) covers the period 2000–2008.
Both contain hourly or three-hourly reports from about 10,000 observation sites
worldwide. The database includes data originating from various sources such as
synoptic, airways, Meteorological Routine Weather Report and Supplementary
Marine Reporting Station. A full description of the data sets can be found on
the websites and In particular, care was taken to correct
some heterogeneities induced by changes in wind speed units in this data set (see
related documentation on this latter website).
A stringent objective procedure is applied here to remove stations with gaps
or heterogeneities in the data, as described in the Supplementary Information,
leaving a subset of 822 worldwide stations, most of which are located in the
Northern Hemisphere.
Upper-air winds from rawinsondes. We used the rawinsonde monthly averages
of wind gathered in the Integrated Global Radiosonde Archive30 (IGRA). These
data were available for comparison with surface data over the three-decade period
from 1979 to 2008, and we used values taken at standard levels of 850 hPa, 700hPa,
500 hPa and 200hPa. A site selection procedure is also applied as for surface data,
see Supplementary Information. IGRA also includes surface wind speed taken near
the site. However, we did not use these data because the pairing would have led to
less complete profiles than if using the NCAR surface wind data and a criterion of
300 km for distance. We however checked that the IGRA surface wind speed had
similar trends to the NCAR data.
NCEP/NCAR and ERA-interim reanalyses. Reanalyses issued at
NCEP/NCAR are used, and made available at a 2.5×2.5resolution on the
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the 4 ×daily data set of wind at 10 m is used, for the Northern Hemisphere over
the period 1979–2008. The new ECMWF ERA-interim20 is a global atmospheric
reanalysis covering the period from 1989 until real time. The production
of ERA-interim began in summer 2006. Increased computer power enabled
several enhancements of ERA-interim over ERA-40. In particular, ERA-interim
uses a model version that was operational for weather forecast in the autumn
of 2006, with improved model physics. It also uses a 12-h 4D-VAR data
assimilation system, a horizontal resolution of 80 km, a more extensive use
and better treatment of satellite radiances, a better formulation of background
error constraints and a new humidity analysis. The long-term homogeneity
has improved substantially over that of ERA-40. The reader is referred to for a description of reanalysis
activities and products at ECMWF.
NDVI from AVHRR. NDVI data from the GIMMS database29, obtained from
the AVHRR remote-sensed observations, were used. These data cover the 25-year
period 1982–2006 and were carefully calibrated. They were originally provided
on a 8 km ×8 km grid, but averages over available NDVI data over the nine cells
surrounding the station were used for plotting Fig. 4b,c. We then calculated linear
trends from six-month NDVI averages (April–September).
Received 22 April 2010; accepted 9 September 2010;
published online 17 October 2010
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Author contributions
R.V. designed the experiments and carried out the observation analysis, J.C. did
all model experiments and analysis of outputs, as well as NCEP/NCAR reanalysis
calculations. J-N.T. extracted and analysed all data from ERA-interim reanalyses. P.Y.
initially suggested systematic wind trends in observations, and P.C. suggested that
land-cover changes due to vegetation could be a major driver of wind stilling and
helped design the experiments with NDVI. All co-authors substantially contributed to
the paper writing.
Additional information
The authors declare no competing financial interests. Supplementary information
accompanies this paper on Reprints and permissions
information is available online at
Correspondence and requests for materials should be addressed to R.V.
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... As revealed by many previous studies (Roderick et al., 2007;Vautard et al., 2010;McVicar et al., 2012;Minola et al., 2016;Laapas and Venäläinen, 2017;Azorin-Molina et al., 2018;Zeng et al., 2019;Zhang and Wang, 2020), WS decreased from the 1970s to 2010s and subsequently recovered over many terrestrial regions of the Northern Hemispherethis is known as the WS stilling and recovery. Possible causes of the WS stilling and recovery have been widely discussed and include changes in surface roughness induced by greenness and land use/cover change (Vautard et al., 2010;Wu et al., 2018b;Zhang and Wang, 2021) and large-scale atmospheric circulation changes (Azorin-Molina et al., 2018;Wu et al., 2018a;Zeng et al., 2019), such as the North Atlantic Oscillation (NAO) as revealed in Sweden by Minola et al. (2016. ...
... As revealed by many previous studies (Roderick et al., 2007;Vautard et al., 2010;McVicar et al., 2012;Minola et al., 2016;Laapas and Venäläinen, 2017;Azorin-Molina et al., 2018;Zeng et al., 2019;Zhang and Wang, 2020), WS decreased from the 1970s to 2010s and subsequently recovered over many terrestrial regions of the Northern Hemispherethis is known as the WS stilling and recovery. Possible causes of the WS stilling and recovery have been widely discussed and include changes in surface roughness induced by greenness and land use/cover change (Vautard et al., 2010;Wu et al., 2018b;Zhang and Wang, 2021) and large-scale atmospheric circulation changes (Azorin-Molina et al., 2018;Wu et al., 2018a;Zeng et al., 2019), such as the North Atlantic Oscillation (NAO) as revealed in Sweden by Minola et al. (2016. However, all of the studies relied on available WS series starting in the 1950s or 1960s when the World Meteorological Organization (WMO) began to guide automatic weather monitoring in 1950 (WMO, 2018). ...
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Creating a century-long homogenized near-surface wind speed observation dataset is essential to improve our current knowledge about the uncertainty and causes of wind speed stilling and recovery. Here, we rescued paper-based records of wind speed measurements dating back to the 1920s at 13 stations in Sweden and established a four-step homogenization procedure to generate the first 10-member centennial homogenized wind speed dataset (HomogWS-se) for community use. Results show that about 38 % of the detected change points were confirmed by the known metadata events, and the average segment length split by the change points is ∼11.3 years. Compared with the raw wind speed series, the homogenized series is more continuous and lacks significant non-climatic jumps. The homogenized series presents an initial wind speed stilling and subsequent recovery until the 1990s, whereas the raw series fluctuates with no clear trend before the 1970s. The homogenized series shows a 25 % reduction in the wind speed stilling during 1990–2005 than the raw series, and this reduction is significant when considering the homogenization uncertainty. The homogenized wind speed series exhibits a significantly stronger correlation with the North Atlantic oscillation index than that of the raw series (0.54 vs. 0.29). These results highlight the importance of the century-long homogenized series in increasing our ability to detect and attribute multidecadal variability and changes in wind speed. The proposed homogenization procedure enables other countries or regions to rescue their early climate data and jointly build global long-term high-quality datasets. HomogWS-se is publicly available from the Zenodo repository at (Zhou et al., 2022).
... Near the Equator and in the Southern Hemisphere, wind speed increases because the land surface warming exceeds ocean surface warming 6 . Regionally and locally, other changes in circulation and surface roughness resulting from land-use changes may modify the wind resource 23 . Consequently, there are differences between global and national cf developments. ...
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The capacity factor (cf) is a critical variable for quantifying wind turbine efficiency. Climate change-induced wind resource variations and technical wind turbine fleet development will alter future cfs. Here we define 12 techno-climatic change scenarios to assess regional and global onshore cfs in 2021–2060. Despite a decreasing global wind resource, we find an increase in future global cf caused by fleet development. The increase is significant under all evaluated techno-climatic scenarios. Under the likely emissions scenario Shared Socioeconomic Pathway 2–4.5, global cf increases from 0.251 in 2021 up to 0.310 in 2035 under ambitious fleet development. This cf enhancement is equivalent to a 361 TWh yield improvement under the globally installed capacity of 2020 (698 GW). To increase the contribution of the future wind turbine fleet to the Intergovernmental Panel on Climate Change climate protection goals, we recommend a rapid wind turbine fleet conversion.
... Most of the previous studies focused on the sediment resuspension process caused by wind-driven waves (Qin et al. 2004;Tang et al. 2020). However, the near-surface wind speed was observed to decrease in recent decades (McVicar et al. 2012;Vautard et al. 2010;Woolway et al. 2019). For example, in the Lake Taihu Basin, the wind speed decreased by 1.06 m/s in the past 40 years (Zhang et al. 2020c). ...
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Cyanobacterial bloom accumulation and dissipation frequently occur in Lake Taihu, a typically shallow, eutrophic lake due to wind wave disturbance. However, knowledge of the driving mechanisms of cyanobacterial blooms on underwater light attenuation is still limited. In this study, we collected a high-frequency in situ monitoring of the wind field, underwater light environment, and surface water quality to elucidate how cyanobacterial bloom accumulation and dissipation affect the variations in underwater light attenuation in the littoral zone of Lake Taihu. Results showed that cyanobacterial blooms significantly increased the diffuse attenuation coefficient of ultraviolet-B (Kd(313)), ultraviolet-A (Kd(340)), and photosynthetically active radiation (Kd(PAR)); the scattering of total suspended matter (bbp(λ)); and the absorption of phytoplankton (aph(λ)) and chromophoric dissolved organic matter (CDOM, ag(λ)) (p < 0.01). The Kd(PAR) decreased quickly during the processes of bloom dissipation, but the decrease of Kd(313) and Kd(340) lagged 0.5 day. Our results suggested that cyanobacterial blooms could increase particle matters and elevated the production of autochthonous CDOM, resulting in underwater light attenuation increase. Ultraviolet radiation (UVR) and PAR attenuation both have significant responses to cyanobacterial blooms, but the response processes were distinct due to the different changes of particle and dissolved organic matters. Our study unravels the driving mechanisms of cyanobacterial blooms on underwater light attenuation, improving lake ecosystem management and protection.
... Several studies have found a positive association between anoxia and large cyanobacteria populations in low NO 3 systems (Harris and Trimbee, 1986;Trimbee and Prepas, 1988;Verschoor et al., 2017;Molot et al., 2014Molot et al., , 2021a. Thermal stability has increased since 1988 (David Depew, ECCC, unpublished data), perhaps because of a warming climate and lower wind speeds (Pryor et al., 2009;Vautard et al., 2010). A stronger thermal gradient makes the water column more resistant to wind-induced mixing, thereby lowering transport of oxygenated surface waters to oxygen-depleted bottom waters. ...
Several studies have shown that large, experimental additions of nitrate (NO3) to eutrophic systems can mitigate large populations of nuisance cyanobacteria and that high NO3 concentrations can oxidize anoxic sediments. These studies are consistent with observations from numerous aquatic systems across a broad trophic range showing development of reduced surficial sediments precedes the formation of large cyanobacteria populations. We use 50+ years of data to explore whether high NO3 concentrations may have been instrumental both in the absence of large populations of cyanobacteria in eutrophic Hamilton Harbour, Lake Ontario in the 1970s when total phosphorus (TP) and total nitrogen (TN) concentrations were high, and in delaying large populations until August and September in recent decades despite much lower TP and TN. Our results indicate that large cyanobacteria population events do not occur at the central station in July-September when epilimnetic NO3 > 2.2 mg N L⁻¹. The results further suggest that remedial improvements to wastewater treatment plant oxidation capacity may have been inadvertently responsible for high NO3 concentrations > 2.2 mg N L⁻¹ and thus for mitigating large cyanobacteria populations. This also implies that large cyanobacteria populations may form earlier in the summer if NO3 concentrations are lowered.
... In the time domain, wind resources may vary from sub-hourly to multi-decadal scales [4]. Multi-decadal wind resource changes are mainly due to large-scale atmospheric and oceanic circulations and changing surface roughness induced by land use changes [5,6]. It is thus plausible to assume that the projected global climate development will cause local, regional, and global changes in the future wind energy availability [7,8]. ...
This review analyzed 75 studies published 2017–2021 investigating future wind resource evolution. It provides comprehensive information on the studies' specifications and globally summarizes the reported wind resource changes. The studies show substantial differences in design, limiting their comparability. Most studies evaluated a small number of regional climate models driven by general circulation models from CMIP5 under the worst-case scenario RCP8.5. The use of worst-case scenarios can be justified, intending to study climate change impacts for the most significant climate change signal. To quantify the differences between present and future resources, very short evaluation periods with average durations of 23.8 and 24.9 years were used. Our analysis reveals that the availability and distribution of wind resources would likely change in the future. The changes’ magnitudes are usually greatest under the most pessimistic climate change scenarios. The direction of the changes varies spatially. The strongest wind resource decline was observed in the Western United States. Declining wind resources are likely in most parts of the northern hemisphere. For southern Brazil, a distinct wind resource increase is projected. For the design of future studies, it is recommended to use (1) the more realistic climate change scenarios SSP245, SSP460, and SSP370, (2) multi-model ensembles, which allow testing the statistical significance of the detected changes, (3) longer investigation periods (≥30 years) to reinforce the climate change relevance of the statements made.
... Great attention has been paid to the notable decline in SWS regionally and globally in the changing climate over the past decades, namely the so-called "wind stilling" (Roderick et al., 2007). Apart from relocation and data quality issues (Azorin-Molina et al., 2018), literature have considered that the SWS stilling were mainly attributed to anthropogenic disturbances such as urbanization and "greening" of vegetation (Vautard et al., 2010;Zhang et al., 2019). However, many studies found that the SWS decline has started to reverse in the recent decade, without notable changes in land surface roughness (Azorin-Molina et al., 2018;Zeng et al., 2019). ...
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Great attention has been paid to the long-term decline in terrestrial near-surface wind speed (SWS) in China. However, how the SWS varies with regions and seasons and what modulates these changes remain unclear. Based on quality-controlled and homogenized terrestrial SWS data from 596 stations, the covarying SWS patterns during the Asian Summer Monsoon (ASM) and the Asian Winter Monsoon (AWM) seasons are defined for China using empirical orthogonal function (EOF) analysis for 1961–2016. The dominant SWS features represented by EOF1 patterns in both seasons show a clear decline over most regions of China. The interannual variability of the EOF1 patterns is closely related to the Northeast Asia Low Pressure (NEALP) and the Arctic Oscillation (AO), respectively. The EOF2 and EOF3 patterns during ASM (AWM) season describe a dipole mode of SWS between East Tibetan Plateau and East China Plain (between East Tibetan Plateau and Northeast China), and between Southeast and Northeast China (between Northeast China and the coastal areas of Southeast China), respectively. These dipole structures of SWS changes are closely linked with the oceanic-atmospheric oscillations on interannual scale.
... Apart from the hot topic of 'global stilling' in previous studies (McVicar et al., 2008;Lin et al., 2013;Earl et al., 2013), different SWS trends, such as being reversing in certain regions, have been found recently as the observation prolongs Zeng et al., 2019). The causes of different SWS trends have been investigated from different perspectives, such as contributions of large-scale atmospheric circulation (Zhang and Wang, 2020), surface roughness changes (Vautard et al., 2010), and friction caused by urbanization (Jacobson and Ten Hoeve, 2012). An interesting finding suggested that aerosols could reduce surface wind speed, resulting in an increase in the concentration and duration time of aerosols in the air, forming a positive feedback mechanism (Xu et al., 2006;Yang et al., 2016). ...
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Wind is crucial to human society because it is closely related to energy supply, agricultural production and pollution dispersion. The surface wind speed (SWS) is dependent on the natural variability and the impact of urbanization. Over the coastal areas where nearly half of the world population live, sea land breezes (SLBs) are a common thermodynamic circulation that can affect SWS variation. However, few have distinguished coastal areas from inland areas to particularly investigate the SLB's contribution to SWS variation. This study investigates quantified contributions of both sea-breezing wind (SBW) and land-breezing wind (LBW) to SWS variation by extracting the SLB and the wind from synoptic-scale systems (synoptic wind) from the original long-term wind observation. The results show that there were no unified increasing or decreasing trends of SWS at coastal sites. Both synoptic wind and SLB contributed significantly to SWS variation at sites where both served as major components of local wind fields. However, among typical monsoon areas, synoptic wind remained mainly responsible for SWS variation. SLB contributed more than 82% to SWS variation among areas where SLB solely served as the major component of local wind fields. In contrast, the contribution of synoptic wind ranged from 2.7% to 17.2% for regions dominated by SLB, which was quadratically proportional to the local cloud fraction.
Analyzing the primary factors of potential evapotranspiration (PET) dynamic is fundamental to accurately estimating crop yield, evaluating environmental impacts, and understanding water and carbon cycles. Previous studies have focused on regionally average regional PET and its dominant factors. Spatial distributions of PET trends and their main causes have not been fully investigated. The Mann–Kendall test was used to determine the significance of long-term trends in PET and five meteorological factors (net radiation, wind speed, air temperature, vapor pressure deficit, relative humidity) at 56 meteorological stations in the Sichuan-Chongqing region from 1970 to 2020. Furthermore, this present study combining and quantitatively illustrated sensitivities and contributions of the meteorological factors to change in annual and seasonal PET. There was a positive trend in PET for approximately 58%, 68%, 38%, 73% and 73% of all surveyed stations at annual, spring, summer, autumn and winter, respectively. Contribution analysis exhibited that the driving factors for the PET variation varied spatially and seasonally. For stations with an upward PET trend, vapor pressure deficit was a dominant factor at all time scales. For stations with a downward PET trend, annual changes in PET mainly resulted from decreased wind speed, as did changes in spring, autumn and winter; decreasing net radiation was the dominant factor in summer. The positive effect of the vapor pressure deficit offset the negative effects of wind speed and net radiation, leading to the increasing PET in this area as a whole. Sensitivity analysis showed that net radiation and relative humidity were the two most sensitive variables for PET, followed by vapor pressure deficit in this study area. Results from the two mathematical approaches were not perfect match, because the change magnitude of the meteorological factors is also responsible for the effects of meteorological factors on PET variation to some extent. However, conducting sensitivity and contribution analysis in this study can avoid the uncertainties from using a single method and provides detailed and well-understood information for interpreting the influence of global climate change on the water cycle and improving local water management.
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We describe an undocumented change in how calm periods in near-surface wind speed (and direction) observations have been encoded in a subset of global datasets of sub-daily data after 2013. This has resulted in the under-estimation of the number of calm periods for meteorological stations across much of Asia and Europe. Hence average wind speeds after 2013 have been over-estimated, affecting the assessment of changes in global stilling and reversal phenomena after this date. By addressing this encoding change we show that globally, since 2010, wind speeds have recovered by around 30% less than previously thought.
This study focuses on the trends and the causes of variation in actual evapotranspiration (AET) around the warming hiatus over China by a comprehensive analysis applying various temporal‐spatial methods. It is observed that the annual AET showed a different trend around 2000 for China as a whole. By employing segmented regression analysis for detecting warming hiatus points, high temporal inconsistency can be found in eight climatic regions of China. The impacts of meteorological variables on AET were further identified by affecting the intensity and relative change of meteorological factors. AET was highly correlated (P<0.01) with solar radiation in the southeast (R=0.80) and air specific humidity in the northwest areas (R=0.83). AET changes presented the highest sensitivity to specific humidity in Northwest before 2006 and in North Central China after 2003, with sensitivity coefficients of 1.48 and 1.74, respectively. Three variables, including air specific humidity (with an average contribution rate of ~17% in the Northwest), short‐wave radiation, air temperature can be the main factors that lead to the changes in AET. The specific meteorological factors varied from region to region: the changes in AET can be ascribed to the increased wind and short‐wave radiation in North Central China and East China, the decreased air temperature in Tibetan Plateau, and the increased specific humidity in Southeast China during warming hiatus, etc. After the warming hiatus occurred, the dominant factor of AET trends changed from air specific humidity to short‐wave radiation and other factors. Generally, air specific humidity and air temperature have played leading roles in AET trends during the past 30 years. This article is protected by copyright. All rights reserved.
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The terrestrial carbon sink, as of yet unidentified, represents 15–30% of annual global emissions of carbon from fossil fuels and industrial activities. Some of the missing carbon is sequestered in vegetation biomass and, under the Kyoto Protocol of the United Nations Framework Convention on Climate Change, industrialized nations can use certain forest biomass sinks to meet their greenhouse gas emissions reduction commitments. Therefore, we analyzed 19 years of data from remote-sensing spacecraft and forest inventories to identify the size and location of such sinks. The results, which cover the years 1981–1999, reveal a picture of biomass carbon gains in Eurasian boreal and North American temperate forests and losses in some Canadian boreal forests. For the 1.42 billion hectares of Northern forests, roughly above the 30th parallel, we estimate the biomass sink to be 0.68 ± 0.34 billion tons carbon per year, of which nearly 70% is in Eurasia, in proportion to its forest area and in disproportion to its biomass carbon pool. The relatively high spatial resolution of these estimates permits direct validation with ground data and contributes to a monitoring program of forest biomass sinks under the Kyoto protocol.
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An earlier paper (Pryor et al., 2009) reports linear trends for annual percentiles of 10 m wind speeds from across the United States based on ordinary linear regression applied without consideration of temporal autocorrelation. Herein we show significant temporal autocorrelation in annual metrics from approximately half of all surface and upper air wind speed time series and present analyses that indicate at least some fraction of the temporal autocorrelation at the annual time scale may be due to the influence of persistent low-frequency climate modes as manifest in teleconnection indices. Treatment of the temporal autocorrelation slightly reduces the number of stations for which linear trends in10 m wind speeds are deemed significant but does not alter the trend magnitudes relative to those presented by Pryor et al. (2009). Analyses conducted accounting for the autocorrelation indicate 55% of annual 50th percentile 10 m wind speed time series, and 45% of 90th percentile annual 10 m wind speed time series derived from the National Climate Data Center DS3505 data set exhibit significant downward trends over the period 1973-2005. These trends are consistent with previously reported declines in pan evaporation but are not present in 10 m wind speeds from reanalysis products or upper air wind speeds from the radiosonde network.
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Warming of the arctic climate is having a substantial impact on the Alaskan North Slope coastal region. The warming is associated with increasing amounts of open water in the arctic seas, rising sea level, and thawing permafrost. Coastal geography and increasing development along the coastline are contributing to increased vulnerability of infrastructure, utilities, and supplies of food and gasoline to storms, flooding, and coastal erosion. Secondary impacts of coastal flooding may include harm to animals and their land or sea habitats, if pollutants are released. Further, Inupiat subsistence harvesting of marine sources of food, offshore resource extraction, and marine transportation may be affected. This paper describes a project to understand, support, and enhance the local decision-making process on the North Slope of Alaska on socioeconomic issues that are influenced by warming, climate variability, and extreme weather events.
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Evaporative demand, measured by pan evaporation, has declined in many regions over the last several decades. It is important to understand why. Here we use a generic physical model based on mass and energy balances to attribute pan evaporation changes to changes in radiation, temperature, humidity and wind speed. We tested the approach at 41 Australian sites for the period 1975–2004. Changes in temperature and humidity regimes were generally too small to impact pan evaporation rates. The observed decreases in pan evaporation were mostly due to decreasing wind speed with some regional contributions from decreasing solar irradiance. Decreasing wind speeds of similar magnitude has been reported in the United States, China, the Tibetan Plateau and elsewhere. The pan evaporation record is invaluable in unraveling the aerodynamic and radiative drivers of the hydrologic cycle, and the attribution approach described here can be used for that purpose.
A comprehensive intercomparison of historical wind speed trends over the contiguous United States is presented based on two observational data sets, four reanalysis data sets, and output from two regional climate models (RCMs). This research thus contributes to detection, quantification, and attribution of temporal trends in wind speeds within the historical/contemporary climate and provides an evaluation of the RCMs being used to develop future wind speed scenarios. Under the assumption that changes in wind climates are partly driven by variability and evolution of the global climate system, such changes should be manifest in direct observations, reanalysis products, and RCMs. However, there are substantial differences in temporal trends derived from observational wind speed data, reanalysis products, and RCMs. The two observational data sets both exhibit an overwhelming dominance of trends toward declining values of the 50th and 90th percentile and annual mean wind speeds, which is also the case for simulations conducted using MM5 with NCEP-2 boundary conditions. However, converse trends are seen in output from the North American Regional Reanalysis, other global reanalyses (NCEP-1 and ERA-40), and the Regional Spectral Model. Equally, the relationship between changing annual mean wind speed and interannual variability is not consistent among the different data sets. NCEP-1 and NARR exhibit some tendency toward declining (increasing) annual mean wind speeds being associated with decreased (increased) interannual variability, but this is not the case for the other data sets considered. Possible causes of the differences in temporal trends from the eight data sources analyzed are provided.
Evaporative demand is routinely measured using the evaporation of water from standardised pans. The most common are Class A pans: they are metal dishes 4 feet in diameter and 10 inches deep sitting on a wooden platform. Because of the practical importance in agriculture and engineering, there is a wealth of pan evaporation data. Analysis has shown that in most places, pan evaporation has declined over the last 30 to 50 years despite the well documented increases in near-surface air temperature. It is important to understand why this has occurred. Using generic principles of mass and energy balance, we developed the PenPan model of evaporation from a class A pan for attribution purposes. In this talk we briefly describe the PenPan model and then demonstrate its application at 41 sites, broadly scattered across Australia, for the period 1975-2004. We found that the decline in Australian pan evaporation was mostly due to declining wind speed with some regional contributions from declining radiation. In general, the observed changes in vapour pressure deficit and air temperature were too small to have an appreciable impact on pan evaporation rates. By a comparative analysis, we show that the general global trends of declining evaporative demand are most due to various combination of declining radiation and/or wind.
The Class A pan evaporation rates at many Australian observing stations have reportedly decreased between 1970 and 2002. That pan evaporation rates have decreased at the same time that temperatures have increased has become known as the “pan evaporation paradox.” Pan evaporation is primarily dependant on relative humidity, solar radiation, and wind. In this paper, trends in observed pan evaporation in Australia during the period 1975–2004 were attributed to changes in other climate variables using a Penman-style pan evaporation model. Trends in daily average wind speed (termed wind run) were found to be an important cause of trends in pan evaporation. This result is a significant step toward resolving the pan evaporation paradox for Australia. Data inspection and interstation comparison revealed that some of the significant wind run trends were discontinuous or spatially uncorrelated. These analyses raised the possibility that some of the changes in observed wind run, and by implication some of the significant changes in pan evaporation, may represent changes in the local environment surrounding the observing stations. Daily pressure gradients and NCEP–NCAR reanalysis wind surfaces were analyzed in an attempt to identify any climatological wind run trends associated with large-scale changes in atmospheric circulations. Unfortunately, the trends from the two data sources were not consistent, and the challenge remains to conclusively identify the cause or causes of the changes in observed station wind run in Australia.