1
Human-induced changes in Wind,
Temperature and Relative Humidity during
Santa Ana events
Mimi Hughes1, Alex Hall
2, and Jinwon Kim
2
1 National Research Council/National Oceanic and Atmospheric Administration,
Earth System Research Laboratory, Physical Sciences Division, 325 Broadway,
Boulder CO 80305
(303)497-4865
2 University of California, Los Angeles, Department of Atmospheric and Oceanic
Sciences, Box 951565, Los Angeles, CA 90095
Abstract: The frequency and character of Southern California’s Santa Ana wind events are
investigated within a 12-km-resolution downscaling of late-20th
and mid-21st century time periods
of the National Center for Atmospheric Research Community Climate System Model global
climate change scenario run. The number of Santa Ana days per winter season is approximately
20% fewer in the mid-21st century compared to the late-20
th century. Since the only systematic
and sustained difference between these two periods is the level of anthropogenic forcing, this
effect is anthropogenic in origin. In both time periods, Santa Ana winds are partly katabatically-
driven by a temperature difference between the cold wintertime air pooling in the desert against
coastal mountains and the adjacent warm air over the ocean. However, this katabatic mechanism
is significantly weaker during the mid-21st century time period. This occurs because of the well-
documented differential warming associated with transient climate change, with more warming in
the desert interior than over the ocean. Thus the mechanism responsible for the decrease in Santa
Ana frequency originates from a well-known aspect of the climate response to increasing
greenhouse gases, but cannot be understood or simulated without mesoscale atmospheric
dynamics. In addition to the change in Santa Ana frequency, we investigate changes during Santa
Anas in two other meteorological variables known to be relevant to fire weather conditions --
relative humidity and temperature. We find a decrease in the relative humidity and an increase in
temperature. Both these changes would favor fire. A fire behavior model accounting for changes
in wind, temperature, and relative humidity simultaneously is necessary to draw firm conclusions
about future fire risk and growth associated with Santa Ana events.
Keywords: regional climate, climate change, downslope winds, fire weather
Abbreviations:
2
1.0 Introduction
The cool, relatively moist fall and winter climate of Southern California is
often disrupted by dry, hot days with strong winds, known as Santa Anas, blowing
out of the desert. The Santa Ana winds are a dominant feature of the fall and
wintertime climate of Southern California (Conil and Hall 2006), and they have
important ecological impacts. The most familiar is their influence on wildfires:
Following the hot, dry Southern California summer, the extremely low relative
humidities and strong, gusty winds associated with Santa Anas introduce extreme
fire risk, often culminating in wildfires with large economic loss (Westerling et al.
2004). Less widely-known but just as important is their impact on coastal-ocean
ecosystems: The strong winds induce cold filaments in sea-surface temperature
(SST) with an associated increase in biological activity (Castro et al. 2006;
Trasvina et al. 2003). This decrease in SST and increase in biological activity is
likely due in part to increased mixing in the oceanic boundary layer, although an
increase in dust deposition on the ocean surface during these events could also
increase biological activity (Hu and Liu 2003; Jickells et al. 2005).
A recent investigation of the dynamics of Santa Ana winds (Hughes and
Hall 2009) found that both local and synoptic conditions control their formation.
When strong synoptically-forced offshore flow impinges on Southern California’s
topography, offshore momentum can be transported to the surface, causing Santa
Ana conditions. However, Hughes and Hall (2009) found that there are many days
with Santa Ana conditions that are not associated with this type of strong synoptic
forcing. Rather, for a large fraction of the Santa Ana days, offshore winds are
forced by a local temperature gradient between the cold desert and warmer air
over the ocean at the same altitude. The temperature gradient induces a
hydrostatic pressure gradient pointing from the desert to the ocean, which is
reinforced by the negative buoyancy of the cold air as it flows down the sloped
surface of the major topographical gaps.
This study investigates the response of the frequency and intensity of
Santa Ana events and associated meteorological conditions to anthropogenic
forcing. Santa Ana wind events cannot be simulated without resolving the coastal
mountain ranges separating the Mojave desert from the Southern California Bight.
This requires resolution higher than roughly 10-15km. Hughes and Hall (2009)
3
showed that even the relatively high-resolution North American Regional
Reanalysis (NARR, 32 km horizontal resolution) has unrealistically weak Santa
Ana winds. Because its coarse resolution does not adequately resolve the coastal
topography, it cannot develop the tight desert-ocean temperature gradient often
driving Santa Anas. However, when the NARR data are dynamically downscaled
with a much higher resolution regional atmospheric model, Santa Ana events are
well-reproduced. This indicates that conditions leading to Santa Anas are implicit
in the coarser resolution NARR data set, even if the reanalysis model itself is
incapable of generating them realistically. This dynamical downscaling technique
has also been proven numerous times to give a more realistic view of local climate
conditions in other contexts (e.g., Diffenbaugh et al. 2005; Leung and Ghan 1999;
Kim and Lee 2003).
It follows that global models even coarser in resolution than the NARR
product, such as those used for global climate change simulations, likely have
almost no signature of these strong offshore winds, even if they do contain
information highly relevant for Santa Ana formation. In our case, we are
examining changes in Santa Anas implicit in the National Center for Atmospheric
Research (NCAR) Community Climate System Model (CCSM) global climate
change scenario run. This model has a grid-point equivalent resolution of roughly
1.4 degrees, obviously much too coarse to resolve Southern California’s coastal
ranges. To resolve the topography and draw out the simulation’s implications for
Santa Anas, we therefore must downscale it with a regional atmospheric model.
As we demonstrate, the regional simulation and the dynamical framework
describing Santa Ana wind development of Hughes and Hall (2009) together
allow us to identify how large-scale changes in the simulated future climate of the
CCSM simulation affect the frequency and intensity of Santa Ana events. Our
results indicate that as the climate adjusts to anthropogenic forcing, the frequency
of Santa Ana days is reduced. Although the reduction in Santa Ana events could
suggest a reduction in fire occurrence as the climate responds to anthropogenic
forcing, we further investigate two other meteorological variables known to affect
fire in the region, temperature and relative humidity (Moritz et. al. 2010). In both
cases, these meteorological variables change in ways favorable for fire
occurrence, with relative humidity decreasing on days with Santa Ana conditions,
4
and temperature increasing. Thus the implications of anthropogenic climate
change for fire conditions in the region are ambiguous.
2.0 Weather Research and Forecast (WRF)
Simulation
The climate change experiment is carried out by downscaling late 20th
century and mid 21st century time slices from a climate change scenario
simulation done with the NCAR Community Climate System Model 3 (CCSM3).
This dynamical downscaling was performed with the Weather Research and
Forecast (WRF) model, version 2.2.1 (Skamarock et al. 2005). The model solves a
non-hydrostatic momentum equation in conjunction with the thermodynamic
energy equation. The model features multiple options for advection and
parameterized atmospheric physical processes. The physics options selected in
this experiment include the NOAH land-surface scheme (Chang et al. 1999), the
simplified Arakawa Schubert (SAS) convection scheme (Hong and Pan 1998), the
Rapid Radiative Transfer Model (RRTM) longwave radiation scheme (Mlawer et
al. 1997), Dudhia (1989) shortwave radiation, and the WRF Single-Moment
(WSM) 3-class with simple ice cloud microphysics scheme. For more details on
the physics options, readers are referred to the website http://wrf-model.org. The
model domain covers the western United States at a 36-km horizontal resolution,
with the inner 12-km nest spanning the entire state of California and adjacent
coastal zone. Both domains have 28 atmospheric and 4 soil layers in the vertical.
WRF was driven by the global climate data generated when the Special
Report on Emissions Scenarios (SRES) A1B emission scenario is imposed on
CCSM3 (Nakicenovic and Swart 2000). The emission scenario assumes balanced
energy generation between fossil and non-fossil fuel; the resulting carbon dioxide
(CO2) emissions are located near the averages of all SRES emission scenarios.
The CO2 concentrations in the WRF simulations were fixed at 330 parts per
million, volume (ppmv) and 430 ppmv during the late 20th
century and mid-
twenty-first century periods, respectively.
Regional climate for the late 20th
century and mid-21st century periods is
calculated from a total of 20 cold season (October–March) WRF simulations
spanning two time periods: 1971–1981 and 2045–2055. Individual WRF runs
were initialized at 00UTC October 1 of the corresponding years using the CCSM3
5
output data. All simulations continued for the remaining six-month period without
re-initialization by updating the large-scale forcing along the lateral boundaries at
three-hour intervals. The focus on the cold season simulations is appropriate in
this case because Santa Ana winds have a very strong seasonality, with peak
occurrence in December, and no strong offshore winds from April to September.
3.0 Santa Ana response to a changing climate
The first step in quantifying anthropogenic changes in Santa Ana (SA)
wind occurrence and characteristics is to create a SA index (SAt). As in Hughes
and Hall (2009), our SA index is simply the offshore wind strength at the exit of
the largest gap in southern California (blue box, Figure 1). The advantage of this
index, in contrast to previously-defined SA indices (e.g. Miller and Schlegel 2006;
Raphael 2003; Sommers 1978; D. Danielson, personal communication), is that it
provides information about SA occurrence and intensity, but does not introduce
assumptions about mechanisms possibly causing Santa Anas, whether local or
synoptic. Figure 1 shows the composite surface winds for days with SAt greater
than 8 m s-1
for the 10 cold season WRF simulations corresponding to the late
20th century time slice (1971-1981). The composite SA wind field exhibits
characteristics we expect for SA events: strong offshore (that is, roughly
northeasterly) winds throughout most of Southern California, with the strongest
winds on the leeward slopes of the mountains and through the gaps in the
topography, most notably across the Santa Monica mountains.
Is there any change in the number of SA days due to anthropogenic
forcing? To answer this question, we quantify the number of SA days per season
in the WRF downscaling of CCSM. To the extent that there is a difference in
climate between the two WRF simulations, we know it is due to the effect of
anthropogenic forcing, since that is the only sustained and systematic difference
between the two simulations. Figure 2 shows the average number of days with
SAt greater than 10 m s-1
for the late-20th and mid-21st century WRF simulations.
There is nearly a 20% reduction in the total number of SA days per year in the
mid-21st century run. In the following sections, we explore the mechanism by
which the frequency of SA wind events is reduced to lend more credibility to this
result.
6
4.0 Understanding Reduced Santa Ana Frequency
The atmospheric dynamics associated with SA winds were recently
investigated by Hughes and Hall (2009). They validated and analyzed SA events
in a 6-km resolution Southern California climate reconstruction. The
reconstruction was accomplished by downscaling reanalysis data corresponding to
the years 1995-2006 using WRF’s predecessor, the NCAR/Pennsylvania State
University Mesoscale Model, Version 5 (MM5, Grell et. al. 1994). They found
that SAs arise from a combination of two mechanisms – one with a synoptic
extent covering much of the western U.S., and another more local process
confined to Southern California. Here we briefly review important results from
this study, as they are relevant to our explanation of reduced SA frequency
resulting from anthropogenic forcing.
Previous studies identified large-scale mid-tropospheric conditions as the
driver of SAs (e.g., Sommers, 1978). If there is a large synoptic-scale pressure
gradient causing strong offshore winds over Southern California at mountain-top
level, this causes surface flow as the offshore momentum is transferred to the
surface in a stably stratified atmosphere. This often occurs when a high surface
pressure anomaly is located over the Great Basin, with a corresponding high
geopotential height anomaly at 700 hPa centered over Oregon. Though this
synoptic mechanism contributes to offshore flow in Southern California, Hughes
and Hall (2009) also found that if a large temperature gradient exists between the
cold desert surface and the warm ocean air at the same altitude (approximately 1.2
km), it causes a localized offshore pressure gradient near the surface. This
generates katabatic offshore flow in a thin layer near the surface, as the negatively
buoyant, cold desert air flows down the sloped surface of the gaps. The negative
buoyancy can be written as a pressure gradient force (Parish and Cassano, 2003):
g
0sin Eq. 1
where g=9.8 m s-1
is gravitational acceleration, ’ is the temperature deficit of the
cold layer, 0 is the average temperature in the cold layer, and is the slope of the
topography. To calculate , Hughes and Hall (2009) used the average desert
surface temperature for 0, the average slope of the topography through the largest
gap for (approximately 1 degree, or 1 km drop over 50 km; see Figure 1) and
7
the temperature difference between the air over the cold desert surface (i.e., the
cold layer) and air over the ocean at the same altitude (representative of the
ambient atmosphere) for ’.
These two mechanisms can act independently (and often do), causing mild
SA winds, or combine to force the largest magnitude offshore winds. Their joint
contribution to SAt can be modeled statistically by a bivariate regression model to
predict SAt based on two parameters representative of each mechanism
S ˆ A t(u,) A * u B * C Eq. 2
where u, the offshore (and mainly geostrophic) wind speed at 2 km, represents the
synoptic forcing and represents the local thermodynamic forcing. In the MM5
reconstruction, this regression model captured almost all variability in SAt
(correlation between SAt and SÂt was 0.93), with about 1/8 of the variability
accounted for by the synoptic mechanism, more than half by the local mechanism,
and the remainder by in-phase variability of the two mechanisms impossible to
unambiguously ascribe to one or the other.
Figure 3 shows the regression model’s representation of SAt, SÂt, plotted
against SAt for the WRF late 20th century and mid 21st century cold season
simulations. The high degree of correspondence (correlation coefficient = 0.88,
and 0.86 for the late 20th and mid 21st simulations, respectively) confirms that, as
with the MM5 reconstruction, the two mechanisms are primarily responsible for
determining SAt in the WRF simulations forced by CCSM3 data. The regression
model coefficients are shown in Table 1 for both decades. Parameters A and B are
within 10% of one another, and C is close to zero for both decades. Table 1 also
shows the variance explained by each of the terms of the regression model.
Similar to Hughes and Hall (2009), more variance is explained by the local
mechanism than by the synoptic mechanism, although in the case of the current
WRF downscaling of CCSM data, the variance is more equally partitioned.
Because the terms within it correspond to distinct physical processes, the
regression model allows us to identify the changes in forcing responsible for the
reduction in SAt. To understand which term of the regression model is causing
reduced SÂt, we calculate the total contribution of and u to SÂt separately and
then sum over days with SÂt greater than 14 m s-1
for the two time periods
(Figure 4).
8
Turning our attention first to the synoptic forcing represented by u in Eq. 1
and the second column grouping in Figure 4, we see that the WRF simulation
shows almost no change between the present and future simulations. Thus the
reduction in SAt between present and future simulations is not primarily due to a
systematic change in the low-to-mid-troposphere geostrophic wind and associated
synoptic-scale pressure gradients when offshore winds blow. Focusing instead on
the katabatic forcing of in Eq. 1 and the first column grouping in Figure 4, we
see that the 21st century time period shows over 1/3 less contribution to SÂt from
than the present day scenario. This is likely because land masses warm more
quickly in response to increased radiative forcing than the oceans (e.g. Trenberth
et al. 2007), reducing the temperature gradient between the cold desert and warm
ocean.
A signature of the differential warming of land and ocean is illustrated in
Figure 5, which shows changes in seasonal mean desert surface air temperature
(SAT) as well as the air temperature over the ocean at the same altitude—the two
components of ’ used to calculate — between the late 20th and mid 21st century
simulations. Both locations exhibit warming, but the desert SAT warms about a
degree more than the air over the ocean at the same altitude, consistent with
previous regional climate change projection studies (e.g., Kim et al. 2002). The
magnitude of the katabatic pressure gradient force, , is directly proportional to
the difference between these two temperatures. As the desert warms faster than
the air over the ocean, a large temperature gradient between the two areas
becomes less likely in wintertime, and large becomes less frequent. Because the
only sustained difference between the two periods is the signature of the
anthropogenic increase in radiative forcing, this decrease in the desert-ocean
temperature gradient must be anthropogenically forced. It follows that the
reduction in and the resulting decrease in SA frequency also arises from
anthropogenic forcing.
5.0 Temperature, relative humidity, and implications
for fire
In the previous section, we showed that, because the air close to the desert
surface warms more quickly in response to anthropogenic greenhouse gas forcing
9
than the air 1.2 km above the ocean, the number of strong offshore wind events
per year will likely decline over the following decades. Because wildfire activity
in this region is strongly tied to the occurrence of SA winds (e.g., Moritz et. al.
2010; Westerling 2004; Keeley 2004), it is tempting to conclude that this
reduction could lead to a reduction in wildfire activity. However, this may be a
misleading conclusion because the reduction in SAs is accompanied by changes in
other meteorological variables also determining fire risk. For example, fire
severity tends to increase as temperature increases (Moritz et. al. 2010).
Therefore, the local increases in simulated temperature (Fig. 5) should also favor
more severe wildfire outbreaks.
Another meteorological variable critical to determining fire conditions is
the near-surface relative humidity (Moritz et. al. 2010). To examine whether this
variable exhibits any changes in our regional climate change experiment, we
calculate average relative humidity 2m above the surface (2mRH) at high
elevations on days with SA conditions (Fig. 6a). The WRF simulations show a
reduction in average 2mRH during SA days. The future scenario is nearly 4
percentage points drier at high elevations during SA events than the present-day
scenario, and a two-tailed t-test proves that the means are different with 99%
confidence.
Why does the 2mRH decrease under climate change when simulated Santa
Ana events occur in Southern California? Although global RH is not expected to
change significantly under climate change conditions (Held and Soden 2006),
over dry land surfaces this may not be the case due to an absence of a moisture
source to maintain constant RH levels in warmer air. In fact, if we look at the
distribution of 2mRH change in the WRF scenario (Fig. 6b) we see that the largest
reductions in 2mRH on SA days are located in the Mojave desert interior. The
desert surface responds to positive radiative forcing by warming strongly (Fig. 5),
and because the desert soil has almost no moisture to release to the atmosphere,
2mRH must decrease. Because much of the air in the coastal areas during SA
conditions has its origin in the desert interior (Fig. 1), 2mRH in most of the
domain is also reduced, albeit less so than in the desert interior. Notably, the
largest non-desert reductions in 2mRH tend to be located in the areas with
strongest winds on SA days (cf. Fig. 1 and Fig. 6b).
10
The resulting implications for fire incidence of the combination of these
three meteorological variables (wind, RH, and temperature) are ambiguous, since
strong offshore wind frequency decreases while RH decreases and temperature
increases. To get quantitative estimates on the meteorological implications for
fire incidence, a fire behavior model would be necessary to untangle the
respective roles these three variables will play in the future. Westerling et. al.
(2006) recently showed that wildfire activity has increased in Southern California
over the past three decades. If the RH and temperature effects dominate the wind
speed effects, wildfire activity may well continue to increase as climate change
accelerates.
6.0 Conclusions
This study investigates the frequency of Southern California’s SA wind
events within a high-resolution dynamical downscaling of two time slices of the
NCAR CCSM3 climate change scenario simulation. One time slice corresponds
to the late-20th
century, the other to the mid-21st century. This particular CCSM
simulation imposes the SRES-A1B emission scenario. In the high resolution
simulation the SA events per year are reduced approximately 20% in the mid-21st
compared with the late-20th
century. Because the only difference between the two
time slices is the level of anthropogenic forcing, the change in SA events is
caused by anthropogenic forcing. The reduction in the frequency of SAs is also
associated with a reduction in their mean intensity.
We use a bivariate regression model to reproduce the Santa Ana time
series, where two known forcing mechanisms as used as independent (predictor)
variables: (1) synoptically-forced strong offshore winds at the mountain tops
whose momentum is transported to the surface, and (2) local thermodynamically
forced winds caused by a katabatic pressure gradient that arises from the thermal
contrast between cold desert air and warmer air over the adjacent ocean. The
regression model reproduces approximately 80% of the variability in the Santa
Ana time index and also reproduces the significant reduction in SA events
between the two time slices. We use the regression model to partition the
anthropogenic change in SA events into contributions from synoptic and katabatic
components. We find a large reduction in katabatic forcing. This is caused by the
larger transient warming of the desert surface than the air over the ocean at the
11
same altitude. This reduces the likelihood of a large temperature deficit
developing in the desert in wintertime and therefore reduces the likelihood of
large katabatic forcing. We see no change in the likelihood of synoptically forced
SAs.
These results are not inconsistent with previous results: While Miller and
Schlegel (2006) did identify an anthropogenically-forced change in large-scale
pressure patterns associated with SAs, the change they detected was a seasonal
shift, which would not appear in our annual-total analysis. Moreover, these
investigators did not examine the katabatic forcing mechanism, which we found to
be of primary importance in creating anthropogenic change in SAs.
The role SA winds play in spreading wildfire in the region (e.g.,
Westerling 2004; Keeley 2004) suggests their reduction in frequency could lead to
reduced wildfire in Southern California. However, we find that two other
meteorological variables important to fire risk and growth, RH and temperature,
also change significantly and systematically in the region during SA events. RH
on SA days is reduced, and temperature increases; both of these changes would
favor fire development. Also, this study does not address changes in other
parameters critical to fire behavior, such as available fuel. Moreover, ignition
events (i.e., humans lighting matches in the coastal chaparral shrubland), another
major factor affecting fire frequency, will probably increase with population
(Syphard et al. 2007). Therefore, a fire behavior model is needed to predict how
fire incidence will change under anthropogenic climate change conditions, and no
conclusions can be drawn from this study alone about future wildfire occurrence
associated with SA events.
There are other societal implications of this anthropogenic reduction of SA
wind events that could be significant and should be explored further. A reduction
in the frequency and intensity of SA winds has implications for coastal marine
ecosystems, which respond favorably to SA conditions, and to the air quality in
the Los Angeles basin, which is better during SA events. The ecological effects of
nutrient loss for the Southern California Bight and the decline in air quality during
winter could be quantified with regional simulations of oceanic biogeochemistry
and atmospheric chemistry.
To the extent that the smaller temperature increase in the atmosphere over
the ocean is due to the larger oceanic heat capacity, the reduction in
12
thermodynamic forcing of SAs might be a feature of the transient climate change
that will return to pre-industrial levels once the climate equilibrates. Nevertheless,
the reduction in SA wind events due to anthropogenic climate change is
significant because it illustrates an observed and explainable regional change in
climate due to plausible mesoscale processes. Further, despite the fact that surface
temperature changes are due to a well-known response of the climate system to an
increase in greenhouse gases, the resultant change in SA wind events cannot be
simulated or understood without mesoscale atmospheric processes, thus requiring
a high spatial resolution atmospheric model for its detection.
Acknowledgements
Mimi Hughes is supported by a National Research Council Postdoctoral
Associateship and National Science Foundation ATM-0735056, which also
supports Alex Hall. Part of this work was performed using the National Center for
Atmospheric Research supercomputer allocation 35681070. The research
described in this paper was performed as an activity of the Joint Institute for
Regional Earth System Science and Engineering, through an agreement between
the University of California, Los Angeles, and the Jet Propulsion Laboratory,
California Institute of Technology, and was sponsored by the National
Aeronautics and Space Administration. Preprocessing of the Community Climate
System Model data was also partially funded by the "National Comprehensive
Measures against Climate Change" Program by Ministry of Environment, Korea
(Grant No. 1600-1637-301-210-13) National Institute of Environmental Research,
Korea. Computational resources for this study have been provided by Jet
Propulsion Laboratory’s Supercomputing and Visualization Facility and the
National Aeronautics and Space Administration’s Advanced Supercomputing
Division.
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15
Table 1. Parameters of the bivariate regression model (Eq. 2), and variance explained by its terms.
Units are shown in the header row.
Late 20th
Mid 21st
Regression model
parameters
A (dimensionless) 0.33 0.36
B (s-1
) 2704 2443
C (m s-1
) 0.08 0.15
Percent variance
explained by terms
of regression model
u 23 27
β 40 33
covariance 14 14
error 23 26
16
Figure 1. Average winds for days with Santa Ana time series greater than 8 m s-1
for late 20th
century simulation. Arrows show total wind; color contours show wind speed. Only every third
grid point is plotted for clarity. Black contours show model terrain, plotted every 800 meters (m)
starting at 100 m. The thick black contour shows coastline at 12-km resolution.
17
Figure 2. Number of days per season with SAt greater than 10 m s-1
in the (green bar) late 20th
and (yellow bar) mid 21st century simulations. Difference in number of days per season is
significant at the 90% confidence level using a two-tailed t-test.
18
Figure 3. Actual SAt plotted against that predicted by the bivariate regression model (SÂt ) for (a)
late 20th
century simulation, and (b) mid 21st century simulation. Red dashed line shows SAt=SÂt.
19
Figure 4. Total contribution of (left grouping) and (right grouping) u to SAt. Contributions were
calculated by summing the product of the regression model parameters and (left column) or
(right column) u for days with SAt greater than 14 m s-1
.
20
Figure 5. Change in mean temperature between the late 20th
and mid 21st century simulations at the
desert surface (blue bar) and 1.2 km over the ocean surface (red bar). Mean changes are
significant at beyond the 99% level using a two-tailed t test.
21
Figure 6: a) Average relative humidity (RH, %) 2m above the surface (hereafter, 2mRH) on days
with SAt > 10 m s-1
, at locations higher than 1500m, for the late 20th
century (green bar) and mid
21st century scenarios (yellow bar). Difference is significant at beyond the 99% level using a two-
tailed t test. b) Difference between the average 2mRH on days with SAt > 10 m s-1
in the mid 21st
century simulation and the late 20th
century simulation. Terrain and coastline are shown as in Fig.
1. Negative values indicate the mid 21st century simulation values are less than the late 20
th century
values.