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Based on a revised and extended multi‐station Hawaiʻi Temperature Index (HTI), the mean air temperature in the Hawaiian Islands has warmed significantly at 0.052°C/decade (p<0.01) over the past 100 years (1917–2016). The year 2016 was the warmest year on record at 0.924°C above the 100‐year mean (0.202°C). During each of the last four decades, mean statewide positive air temperature anomalies were greater than those of any of the previous decades. Significant warming trends for the last 100 years are evident at low (0.056°C/decade, p<0.001) and high elevations (0.047°C/decade, p<0.01). Warming in Hawai‘i is largely attributed to significant increases in minimum temperature (0.072°C/decade, p<0.001) resulting in a corresponding downward trend in diurnal temperature range (‐0.055°C/decade, p<0.001) over the 100‐year period. Significant positive correlations were found between HTI, the Pacific Decadal Oscillation, and the Multivariate ENSO Index, indicating that natural climate variability has a significant impact on temperature in Hawaiʻi. Analysis of surface air temperatures from NCEP/NCAR reanalysis data for the region of Hawaiʻi over the last 69 years (1948–2016) and a mean atmospheric layer temperature time series calculated from radiosonde‐measured thickness (distance between constant pressure surfaces) data over the last 40 years (1977–2016) give results consistent with the HTI. Lastly, we compare temperature trends for Hawaii’s highest elevation station, Mauna Loa Observatory (3397 m), to those on another mountainous subtropical island station in the Atlantic, Mt. Izaña Observatory (2373 m), Tenerife, Canary Islands. Both stations sit above the local temperature inversion layer and have virtually identical significant warming trends of 0.19°C/decade (p<0.001) between 1955 and 2016.
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Temperature trends in Hawaiʻi: A century of change, 19172016
Marie M. McKenzie | Thomas W. Giambelluca | Henry F. Diaz
Department of Geography and Environment,
University of HawaiʻiatM
anoa, Honolulu, Hawaii
Marie M. McKenzie, Department of Geography
and Environment, University of HawaiʻiatM
2424 Maile Way, Saunders Hall 445, Honolulu, HI
Funding information
University of Hawaii at Hilo
Based on a revised and extended multi-station Hawaiʻi Temperature Index (HTI),
the mean air temperature in the Hawaiian Islands has warmed significantly at
0.052C/decade (p< 0.01) over the past 100 years (19172016). The year 2016
was the warmest year on record at 0.924C above the 100-year mean (0.202C).
During each of the last four decades, mean state-wide positive air temperature
anomalies were greater than those of any of the previous decades. Significant
warming trends for the last 100 years are evident at low- (0.056C/decade,
p< 0.001) and high-elevations (0.047C/decade, p< 0.01). Warming in Hawaii
is largely attributed to significant increases in minimum temperature (0.072C/
decade, p< 0.001) resulting in a corresponding downward trend in diurnal temper-
ature range (0.055C/decade, p< 0.001) over the 100-year period. Significant
positive correlations were found between HTI, the Pacific Decadal Oscillation, and
the Multivariate ENSO Index, indicating that natural climate variability has a sig-
nificant impact on temperature in Hawaiʻi. Analysis of surface air temperatures
from NCEP/NCAR reanalysis data for the region of Hawaiʻi over the last 69 years
(19482016) and a mean atmospheric layer temperature time series calculated from
radiosonde-measured thickness (distance between constant pressure surfaces) data
over the last 40 years (19772016) give results consistent with the HTI. Finally,
we compare temperature trends for Hawaii's highest elevation station, Mauna Loa
Observatory (3,397 m), to those on another mountainous subtropical island station
in the Atlantic, Mt. Izaña Observatory (2,373 m), Tenerife, Canary Islands. Both
stations sit above the local temperature inversion layer and have virtually identical
significant warming trends of 0.19C/decade (p< 0.001) between 1955 and 2016.
climate change, El Niño-southern oscillation, Hawaiʻi, Pacific decadal
oscillation, radiosonde observations, temperature trends
The Pacific Ocean region plays an important role in ongoing
global air and sea surface temperature changes. The Hawai-
ian Islands occupy a unique geographic position in the
North-Central Pacific and possess a dense, long-term histori-
cal climate data record at a range of elevations spanning a
mid-tropospheric temperature inversion produced by the des-
cending arm of the Hadley cell. As such, Hawaii is geo-
graphically well positioned to provide observations that
enhance understanding of regional and global temperature
trends, both at the surface and higher elevations in the atmo-
sphere. In addition to yielding insights for regional and
global climate, temperature change in the Hawaiian archipel-
ago can have critically important impacts on terrestrial eco-
systems, water supply, agriculture, and economic health
(Keener et al., 2012).
The Hawaiian temperature record also provides an
opportunity to examine the effects of large sources of inter-
nal climate variabilitythe El NiñoSouthern Oscillation
(ENSO) and the Pacific Decadal Oscillation (PDO; Mantua
et al., 1997). Hawaii is close to the centres of action of
Received: 16 March 2018 Revised: 9 February 2019 Accepted: 19 February 2019 Published on: 28 March 2019
DOI: 10.1002/joc.6053
Int J Climatol. 2019;39:39874001. © 2019 Royal Meteorological Society 3987
ENSO, the strongest driver of natural global climate variabil-
ity on time scales of two to seven years (Salinger, 2005).
Whether PDO is a dynamic mode of climate variability inde-
pendent of ENSO has been questioned (Schneider and Cor-
nuelle, 2005). Nevertheless, the PDO Index (Zhang et al.,
1997; Mantua and Hare, 2002) provides a convenient metric
for basin-wide climate variation at the decadal scale, while
an ENSO index such as the Multivariate ENSO Index (MEI)
provides a statistic representing interannual variations. PDO
phase changes have been associated with oceanic circulation
changes linked to variations in the rate of global temperature
increase, including the recently ended period of slow global
warming (Dai et al., 2015).
In support of a state-wide surface air temperature analy-
sis, Giambelluca et al. (2008) ; hereafter referred to as GDL,
developed the Hawaiʻi Temperature Index (HTI) to evaluate
mean (T
), minimum (T
), and maximum (T
) tem-
perature trends in the islands. Observations from 21 stations
across five of the main Hawaiian Islands were included in
the index, calculated as the mean of monthly station anoma-
lies, averaged to annual values. GDL showed increases in
, and T
at rates of 0.043, 0.094, and
0.005C/decade state-wide, respectively, over the 88-year
period, 19192006. The more rapidly rising T
and the
consequent reduction in the diurnal temperature range
(DTR) were key findings of GDL. In the more recent
decades of their study (19752006), mean warming rates
were much steeper state-wide (0.164C/decade), approach-
ing the global mean trend of 0.177C/decade (19762005;
Pachauri et al., 2014), with enhanced warming at high-
elevation stations (0.268C/decade). Mountain temperatures
in Hawaiʻi vary over short distances due to factors, such as
the vertical structure of the atmosphere and the interaction of
winds with the topography of the islands. GDL found posi-
tive temperature trends, at both low- and high-elevations,
comparable to changes observed in other tropical and sub-
tropical oceanic islands (Huber et al., 2006; Martín et al.,
2012; Diaz et al., 2014), and examined fluctuations in the
rate of warming over the 19192006 period. While previous
work has linked ENSO- and PDO-related natural variability
with fluctuations in temperature (Giambelluca et al., 2008 ;
Diaz et al., 2011) and precipitation (Diaz and Giambelluca,
2012; Frazier et al., 2018) in Hawaii, apparent shifts in
these teleconnections are not well understood. GDL noted,
for example, that in the most recent decades of their study,
temperature appeared to be less sensitive to phase changes in
the PDO.
With GDL as the starting point for this analysis, we
sought to extend the 88-year HTI record to 100 years. The
original HTI network of 21 stations actually had available
between 8 and 18 stations in any given year, and the number
of stations in the index had declined steadily from its peak in
the 1950s due to station closings. With further declines in
recent years, the homogeneity of the HTI, based on its
changing network, became questionable. Hence, it is neces-
sary to re-evaluate and refine the index to address problems
associated with the network. To do so, we develop a new
HTI by merging temperature anomaly time series from two
networks, spanning the early and late portions of the
100-year study period, respectively. Extending the time
series even by a few years is significant because the addition
of recent years captures both the period of a widely observed
global warming slowdown, which has been linked to PDO-
related changes in Pacific Ocean circulation patterns (Dai
et al., 2015), and the 20152016 El Niño, ranked among the
three strongest El Niño events in the historical record
(L'Heureux et al., 2017). Including these events in the time
series adds significantly to the available information on
regional temperature effects of ENSO and PDO.
To further expand on the work of GDL, in this study, we
have conducted additional analyses to validate the HTI
trends, by comparing the station-based index with time
series derived from NCEP/NCAR reanalysis data and a
mean atmospheric layer temperature time series derived
from the difference in geopotential height observations at
two pressure levels. We also re-examined the GDL conclu-
sions on the difference in warming trends between low- and
high-elevations in Hawaii, considering possible spurious
effects of small sample size and data gaps. Finally, in light
of the phenomenon of enhanced warming with elevation
more generally (Pepin et al., 2015), we examine temperature
trends for the highest elevation station in the islands, Mauna
Loa Observatory (19.54N, 155.58W, 3397 m), and com-
pared results with that of a high-elevation subtropical island
station in the Atlantic, Mt. Izaña Observatory (28.30N,
16.50W, 2373 m), Tenerife, Canary Islands. Both of these
stations are located above the local trade wind inversion
level and, hence, subject to effects of changes in Hadley cell
circulation. The similarity of long-term temperature trends in
the Canary Islands, as shown by Martín et al. (2012), to
those previously found for Hawaii led us to compare tem-
perature changes, particularly at higher elevations, at these
two widely separated, but similarly situated sites.
GDL used a temperature index (19192006) derived from
21 stations (Table 1) distributed across the main Hawaiian
Islands. In this analysis, we added 12 additional years of data
from the National Oceanographic and Atmospheric Adminis-
tration's (NOAA's) National Centers for Environmental Infor-
mation (NCEI, and the
Western Regional Climate Center (WRCC, https://wrcc.dri.
edu/summary/Climsmhi.html) from 19171918 and 2007
2016 to the GDL HTI time series. Monthly T
surface air temperature (SAT) are analysed, and station
annual temperature anomalies were calculated as the mean of
the monthly anomalies in each year. Stations missing more
than one monthly value in a given year were excluded.
Monthly anomalies were calculated as departures from the
baseline period (19441980) mean monthly values at each
station. The baseline period corresponds to the period with
the greatest number of index stations available. For each sta-
tion, the annual temperature anomaly time series was calcu-
lated as the departure relative to the station annual mean over
the baseline period. To address outliers, any values outside of
the 99% confidence interval (set as ±2.326 standard devia-
tions derived from a running 31-year sample), were excluded
from the analysis. Following GDL, temperature indices were
computed separately for state-wide, low- (below 800 m), and
high-elevation stations (above 800 m). Because of the scarcity
of high-elevation stations, the limit was set in GDL at 800 m
to include four stations, and retained here for the same reason.
The HTI anomalies were calculated as the weighted mean of
the low- and high-elevation indices with weights of 0.575 and
0.425 corresponding to the relative proportion of land area
below and above 800 m elevation, respectively.
Due to the various establishment and discontinuation
dates of stations (Table 1) the use of a single GDL network
for this extended analysis would have led to increasingly
greater effects of changes in network membership over time
(Figure 1a). Homogeneity is questionable if the number of
stations in the index changes significantly over the period
under study. Therefore, we re-evaluated the reference
network for this study. Many long-term stations were avail-
able early in the period but discontinued in the middle of
the study period, while other stations started in the middle
and continued toward the end of the period. To address this
problem, while trying to maximize the spatial coverage
throughout the period, we established two networks to rep-
resent the early and late parts of the record (Figure 1b, c).
The time series of the early and late networks were merged
with a total of 14 stations (Figure 2), four of which were
common to the two networks (Table 1), after adjustment of
the earlier network index that is based on a linear regression
between the two networks during the period of overlap
(19521983) to achieve a homogeneous 100-year index
time series. This process was done separately for T
time series, state-wide, and for low- and
high-elevation stations. Subsequently, the F-ratio test was
used to assess the significance of the difference in the vari-
ances between the regression-adjusted temperature time
series of the earlier period and that of the original time
series of the later period.
As a measure of temperature variation in the broader
region, mean SAT, and mean sea surface temperatures (SST)
based on NCEP/NCAR reanalysis data (Kistler et al., 2001;
NCEP reanalysis data provided by the NOAA/OAR/ESRL
PSD, Boulder, Colorado, USA, from their website at http:// were analysed. We also examined
TABLE 1 Locations and elevations for the 21 climate stations used in the study by Giambelluca et al. (2008) and the 14 of which (marked) were used in
this study
SKN Station name Status Elevation (m) Latitude (N) Longitude (W)
39 Mauna Loa Slope Observatory
1955 to present 3,341 19.54 155.58
338 Haleakal
a Ranger Station
1939 to present 2,143 20.76 156.25
54 Hawaiʻi Volcanoes Nat'l Park HQ
19492014 1,211 19.43 155.26
267 Kula Sanatorium/Hospital
1916 to present 916 20.70 156.36
436 Kaʻiliʻili 19252012 744 20.51 156.16
434 Haleakal
a BES/Farm Exp 4 19391992 640 20.51 156.18
672 L
anaʻi City 19302014 494 20.50 156.55
91 Mountain View 19061985 466 19.33 155.07
714 Tantalus 19361957 427 21.21 157.49
446 Kailua
1905 to present 213 20.89 156.22
73.2 Kainaliu 19392013 457 19.54 155.93
870 ʻŌpaeʻula
1949 to present 288 21.57 158.04
21 P
19051985 264 19.12 155.29
175.1 Kohala Mission
19051978 164 20.14 155.48
223 ʻŌʻ
19491993 131 20.10 155.17
1,020 Līhuʻe
19051963 63 21.59 159.22
781 K
aneʻohe Mauka 19281998 60 21.25 157.49
1,020.1 Līhuʻe Airport
1950 to present 30 21.98 159.34
86 Hilo
1905 to 1966 12 19.44 155.05
87 Hilo Airport
1941 to present 12 19.72 155.06
703 Honolulu WB Airport
1946 to present 2 21.32 157.93
SKN: state key number used to identify climate stations.
Included in the 8-station network.
Included in the 10-station network.
the NOAA Extended Reconstructed Sea Surface Tempera-
ture v5 dataset provided by the NOAA/OAR/ESRL PSD,
Boulder, Colorado, USA, from their website at https://www. (ERSST v5, Huang et al., 2017) around
Hawaiʻi for comparison with the HTI results. The datasets
were obtained for the HTI period (19172016).
The multivariate ENSO Index (MEI; Wolter and Timlin,
1998) was used to represent ENSO status in this study. The
MEI includes six observed fields (SLP, zonal and meridional
surface wind, SST, and total cloudiness) in the classification of
ENSO modes. MEI data were obtained from NOAA (http:// PDO index
values were obtained for the HTI period of record from the
Joint Institution for the Study of the Atmosphere and Ocean
Toumi et al. (1999) showed that observed pressure varia-
tions at high-elevation could be used to estimate temperature
change in the underlying air layer. Here, we followed a simi-
lar approach and analysed the difference in geopotential
height observations at two pressure levels (700 and
1,000 hPa) from Hawaiian radiosonde data to derive the
time series of mean 1,000700 hPa (approximately the
lowest 3,000 m) air layer temperature (MLT). The MLT
time series (19772016) was calculated as a function of the
geopotential height difference or layer thickness based on
the assumption of the hydrostatic equilibrium:
where dp/dzis the vertical pressure gradient, ρis air density,
and gis acceleration due to gravity 9.81 m s
. Applying this
relationship to a specific layer and substituting for ρusing the
equation of state (ρ=p
Tv, where R
is the gas constant for
dry air, 287 J kg
, and
Tvis the mean virtual tempera-
ture of the air layer) results in (Wallace and Hobbs, 1977):
gln p1
 ð2Þ
where z
and z
are the geopotential heights
(m) corresponding to pressure levels p
, and p
(hPa). Re-
Tvis obtained as:
gln p1
 ð3Þ
This equation was applied using height observations at
the 1,000 hPa (~150 m a.s.l.) and 700 hPa (~3,200 m a.s.l.)
levels measured twice daily 0:00 UTC (2:00 p.m. HST) and
12:00 UTC (2:00 a.m. HST) from an atmospheric sounding
station at Hilo (19.72N, 155.05W) on Hawaiʻi Island
Statistical analyses were performed in the R statistical
programming environment (R core Team, 2014). Secular
trends were calculated through linear regression using the
gls function in the nlme package (Pinheiro et al., 2017) to
explicitly account for temporal autocorrelation and fitted
the regression equation using restricted maximum likeli-
hood (REML). We assessed statistical significance at levels
of 0.001, 0.01, and 0.05, for the null hypothesis that the
trend is zero. In addition to the secular trend analyses, the
FIGURE 2 Elevation map of the temperature stations (red triangles) across
the Hawaiian islands used in this study [Colour figure can be viewed at]
FIGURE 1 (a) Number of stations available in the 21-station network (GDL) during 19172016, (b) total number of stations in each year for the early
10-station subset network, and (c) total number of stations in each year for the later 8-station subset network. Vertical dashed line indicates the period (1977)
where the two networks were merged
Pearson productmoment correlation coefficients were
computed for assessing the relationships between atmo-
spheric variables and temperature indices.
3.1 |Network testing
Two temperature subset station networks were evaluated to
represent the early and late parts of the study period. A
10-station network was selected with nearly continuously
available data at all stations during 19171983. Similarly, an
eight-station network was identified with nearly complete data
during 19522016. Data from these network stations were
screened, and anomalies outside of the 99th percentile were
removed from the analysis. For each of T
and for state-wide, low- and high-elevation stations, linear
regressions between the annual time series of the two net-
works during the overlapping period, 19521983, were per-
formed (Figure 3). Results from the linear regressions showed
good relationships and were used to adjust the early time
series to correct for any systematic heterogeneity arising from
merging the two records. The state-wide mean bias errors
(MBE) between the two time series for T
during the 19521983 period before the adjustments were
0.012, 0.001, and 0.005C, respectively. After the adjust-
ments, state-wide MBE values were reduced to 0.007,
0.000001, and 0.00003C, respectively, for T
(see Table 2 for MBE values for all time series before
and after adjustments). The F-ratio test was used to compare
the variances of the time series derived from the two networks
during the overlapping period after the adjustments and found
no statistically significant differences. Therefore, for state-
FIGURE 3 Linear regression plots for annual mean temperature anomaly time series for the 10-station (xvariable) and 8-station (yvariable) networks during
the overlap period (19521983). The 10-station time series were subsequently adjusted using the linear regression equations shown here [Colour figure can be
viewed at]
TABLE 2 Mean bias error (MBE) values (C) for all time series during
overlapping periods of the earlier (10-station) and original later (8-station)
networks before and after linear regression adjustments
Annual series T
State-wide Before adjustment 0.01180 0.00114 0.00497
After adjustment 0.00692 0.0000014 0.00003
Low-elevation Before adjustment 0.01238 0.03379 0.02158
After adjustment 0.00002 0.00002 0.00002
High-elevation Before adjustment 0.00748 0.00002 0.01750
After adjustment 0.00003 0.04841 0.0000098
wide, low-, and high-elevation stations, the two time series
were merged at the end of 1977, approximately in the middle
of the period of overlap, to create 100-year time series of
. The new HTI (T
state-wide, low- and high-elevation stations) have strong rela-
tionships with the previously defined HTI of GDL (Figure 4).
The new anomaly time series for state-wide T
temperatures were tested against the corresponding GDL
time series, yielding coefficients of determination (r
0.94 and 0.87, respectively.
3.2 |Station surface air temperature trends
The trends in near-surface temperature for state-wide
(Figure 5) and both low- and high- elevations stations
(Figure 6) are summarized in Table 3. Based on the long-
term (19172016) data, including the additional 12 years
included in this analysis, Hawaii's 100-year T
has a sta-
tistically significant positive trend of 0.052C/decade
(p< 0.01; Figure 5a). As demonstrated in GDL, warming is
largely attributed to an increase in T
, which has seen a
significant long-term warming trend of 0.072C/decade
(p< 0.001) state-wide (Figure 5b). T
(Figure 5c), on the
other hand, was found to have no significant long-term
trend. The increase in T
and the consistency in T
a significant reduction in DTR over the long-term period
(Figure 5d) with a trend of 0.054C/decade (p< 0.001).
at low-elevation stations over the 100-year period
(19172016) shows a positive trend of 0.056C/decade
(p< 0.001; Figure 6a). At high-elevation stations, T
over the long-term period was found to have a significant
positive trend of 0.047C/decade (p< 0.01; Figure 6b). A
significant rate of warming for T
(0.090C/decade; p
< 0.001) is also found at low-elevation stations (Figure 6c).
at high-elevation stations did not show a significant
trend over the long-term (Figure 6d). T
, for both low- and
high-elevation stations, shows no significant trend between
1917 and 2016 (Figure 7ef).
Trends during subsets of the 100-year study period,
defined according to apparent periods of warming (+) and
cooling (), 19171939 (+), 19401957 (), 19581999
(+), and 20002013 (), are also summarized in Table 3.
While temperature has fluctuated over the past 100 years,
the long-term trend is clearly upward. Although the period
FIGURE 4 Comparison of annual mean temperature anomalies used in the GDL HTI (yvariable) and the revised and extended index presented in this study
(xvariable) from 1919 to 2006, which have good to strong relationships [Colour figure can be viewed at]
20002013 had no significant changes in T
state-wide, the last 3 years of the analysis, with 2015
and 2016 being the warmest years on record, strongly sug-
gest that rapid warming has resumed. The years 20142016
were exceptionally warm. However, even with 20152016
removed from the time series, the long-term (19172014)
trends in T
(0.042C/decade; p< 0.01) and T
(0.066C/decade; p< 0.001) remain significant.
During the last four decades, state-wide decadal mean
and T
anomalies were all higher than those of any
of the previous decades (Table 4). A particularly interesting
feature is the accelerated rate of increase in the last few
decades for T
state-wide and at low-elevation stations. At
both low- and high-elevation stations, T
and T
in the
three most recent decades (excluding the recent decade for
high-elevation stations) were higher than in all previous
decades. Decadal means for T
, on the other hand, have
stabilized since 1987.
To assess the effect of adjusting the time series based on
the 10-station network (representing the earlier part of the
study period), trends were also calculated from the unad-
justed time series. Long-term trends for the merged time
series without adjustments (for T
, and T
, tem-
peratures state-wide and at both elevation ranges) are
slightly lower than the long-term trends derived from the
regression-adjusted time series, excluding the long-term
trend at low-elevation, which remains the same (Table 5).
3.3 |Temperature trends derived from other data sets
An alternative regional temperature time series was derived
using NCEP/NCAR reanalysis data (Kistler et al., 2001) for
comparison with our HTI results. Annual SATs over the
Hawaiʻi region, 1525N, 170140W, was analysed during
19482016. While not entirely independent of the station
data, the mean of gridded estimates for the region provide a
metric of the broader regional temperature variation with
assimilation of all available observations. The resulting tem-
perature time series (Figure 7) has a positive trend of
0.103C/decade (p< 0.01) over this 69-year period, similar
to the positive HTI trend for the same period
(0.122C/decade; p< 0.001). The two time series are also
well correlated (r = 0.84; p<0.001).
Geopotential height observations for the period
19772016 from radiosonde data provided an additional
temperature estimate for comparison with the HTI results.
The 7001,000 hPa atmospheric MLT corresponds well
with the state-wide HTI over this period (Figure 8). While
the correlation between the two temperature estimates is rea-
sonably high (r= 0.64, p< 0.001), neither time series had a
significant trend over the 40-year overlap period. MLT has a
non-significant negative trend of 0.015C/decade. Please
note that estimation based on the geopotential height differ-
ence gives virtual temperature of the air layer between 1,000
and 700 hPa (
Tv), which can be affected by changes in spe-
cific humidity within the layer over time. Diaz et al. (2011)
FIGURE 5 Temperature anomalies for HTI in Hawaiʻi are shown as the annual mean relative to 19441980, with individual years shown as grey bars. The
black curve represents the 7-year running mean, and the lines correspond to the linear regressions for the 100-year period (19172016). The red lines indicate
significant trends. Non-significant trend is marked n.s. (p> 0.05) [Colour figure can be viewed at]
showed that specific humidity in the 1,000700 hPa layer
increased by about 0.15 g kg
during 19582009. Assum-
ing the specific humidity increased during 19772016 at the
same rate as during 19582009, the humidity change would
have accounted for an increase in
Tvof about 0.02C (Elliott
et al., 1994, Equation 3).
The HTI time series is compared with the two large-scale
modes of natural internal climate variability, PDO and
ENSO, for the 100-year study period (19172016) in
Figure 9. HTI has a reasonably strong relationship with PDO
(Figure 9a) (r= 0.53, p< 0.001), whereas the HTI and
MEI (Figure 9b) have a weaker, but significant positive cor-
relation (r= 0.34, p< 0.001). GDL and Diaz et al. (2011)
showed that Hawaii temperature variations are strongly
coupled to the PDO and SST, but that in recent decades air
temperature in Hawaii had increased relative to the
variations in the PDO index. After revising and extending
the HTI time series, temperature still appears to be rising rel-
ative to the variations driven by the PDO (Figure 9a). Com-
paring recent HTI time series with NCEP/NCAR SST
anomalies averaged over 1624N and 160140W for the
period 19482016 (Figure 10a) shows that while air temper-
ature in the islands is positively correlated with SST of the
surrounding area (r= 0.53; p< 0.001), the SST trend of
0.032C/decade is much lower that the HTI trend,
0.122C/decade, for the same period. We also compared
HTI with another SST data set available for a longer period,
NOAA ERSST v5, averaged over 1530N and
170140W. Over the whole study period (19172016), it
also showed a positive correlation (r= 0.65; p< 0.001;
Figure 10b), and, in this case, a trend very similar to the HTI
trend (ERSST: 0.056C/decade vs. HTI: 0.052C/decade).
FIGURE 6 Temperature anomalies for HTI in Hawaiʻi for low-elevations (left column; (a), (c), and (e) and high-elevations (right column; (b), (d), and (f )
shown as the annual anomaly relative to 19441980, with individual years shown as grey bars. The black curve represents the 7-year running mean, and the
lines correspond to linear regressions for the 100-year period (19172016). The red lines indicate significant trends. Non-significant trends are marked
n.s. (p> 0.05) [Colour figure can be viewed at]
In comparison with either of the SST data sets, HTI
increases faster than SST starting around 1950, as was also
noted by GDL. It remains unclear why air temperature and
SST have similar centennial scale trends, but apparently dif-
fer on shorter time scales. Possible explanations include cli-
mate variations that affect land areas differently than the
surrounding ocean areas (affecting for example night time
cloud cover over the islands), and land cover changes. We
note that irrigated sugarcane acreage expanded rapidly
before around 1940 (Juvik and Juvik, 1998, p. 246), which
might have suppressed the air temperature trend relative to
the SST trend during that time. Hawaii's urbanized area has
experienced rapid expansion since the time of statehood
(1959) coincident with a decline in irrigated sugarcane, per-
haps contributing to enhanced air temperature warming rela-
tive to SST since that time.
Of particular interest is the apparent tuning of the
Hawaii air temperature time series to the global warming
signal. The long-term (19172016) global land-ocean tem-
perature index (GLOTI) in Figure 11a and the global ocean-
only temperature index (GOTI) in Figure 11b time series
(NOAA/NCEI, 2017; have
somewhat stronger warming trends than Hawaii's at
0.098C/decade and 0.081C/decade, respectively. The
20-year running trends are determined and compared
between the HTI, GLOTI, GOTI, and lowess-smoothed
PDO time series in Figure 11. Prior to around 1970, Hawaii's
temperature responses to natural climate variability appear to
have been more pronounced than those of the global indices.
Following 1970, however, HTI tracks GLOTI and GOTI
closely, lending confidence to the apparent link between
regional and global warming.
Figure 12 shows mean surface temperature from
Mt. Izaña Observatory (2,373 m), Tenerife, Canary Islands
(source of data: Emilio Cuevas, AEMET, Canary Islands,
Spain) for comparison with Mauna Loa Observatory (MLO)
in Hawaii, elevation 3,397 m. Mt. Izaña, shown in
Figure 12a, has a significant positive T
of 0.15C/decade
(p< 0.001) over 19172016, and 0.19C/decade (p
< 0.001) for the 62-year period (19552016). MLO, in
Figure 12b, also has a mean warming trend of
0.19C/decade (p< 0.001) for the 62-year period
TABLE 3 Linear air temperatures trends for elevation ranges in Hawaiʻi and the whole state, for the entire period of record and for the periods with apparent
warming and cooling
Annual series T
(C/decade) T
(C/decade) T
(C/decade) DTR (C/decade)
State-wide 19172016 0.052** 0.072*** 0.017 0.054***
19171939 0.115 0.137* 0.025 0.161
19401957 0.428*** 0.323*** 0.589*** 0.264*
19581999 0.119** 0.197*** 0.059 0.144*
20002013 0.271 0.420 0.053 0.560***
Low-elevation 19172016 0.056*** 0.090*** 0.019
19171939 0.102 0.117 0.020
19401957 0.410*** 0.256*** 0.541**
19581999 0.120* 0.177*** 0.053
20002013 0.217 0.252 0.196
High-elevation 19172016 0.047** 0.051 0.016
19171939 0.073 0.085 0.081
19401957 0.266** 0.264 * 0.610 ***
19581999 0.120*** 0.220** 0.060
20002013 0.387 0.768 0.059
Symbols following fitted coefficient indicate statistical significance level at alpha: ***0.001; **0.01; *0.05; other values not significant.
FIGURE 7 Mean annual SAT anomalies from NCEP/NCAR reanalysis
data (black solid line) for Hawaiʻi region (1525N, 170140W) from
1948 to 2016 compared to the HTI (black dotted line). Linear trend for
NCEP/NCAR reanalysis data temperature is 0.103C/decade (red solid line)
and the HTI trend is 0.122C/decade (red dotted line) over the 69-year
period, 19482016 (both significant, p< 0.001.). There is a strong
correlation between two the data sets (r= 0.84; p< 0.001) [Colour figure
can be viewed at]
(19552016). MLO and Mt. Izaña's temperatures anomalies
are plotted together in Figure 12c to show the similarities
and are significantly correlated with each other (r= 0.51, p
< 0.001; Figure 12d).
The result presented here of a mean long-term period
(19172016) warming rate for Hawaii of 0.052C/decade
(p< 0.01) updates the previously analysed temperature
change in Hawaiʻi for 19192006 (GDL; 0.043C/decade,
p= 0.05). The warming trend in Hawaii amounts to more
than half the global rate over the past century of
0.098C/decade (NOAA/NCEI, 2017). HTI time series and
the GLOTI time series show substantial coherency in the
timing of periods of accelerated and reduced warming
(Figure 11a).
GDL found that the rate of warming was much greater at
high-elevations than low-elevations during 19752006.
Their finding is consistent with expectations and observa-
tions of amplified warming with elevation (Pepin et al.,
2015). Diaz et al. (2011) analysed free atmosphere tempera-
ture trends along the vertical profile over Hawaii using
NCEP reanalysis data. They found warming throughout the
profile with a distinct peak at the 850 hPa level, which dif-
fers from the station-based findings of GDL. The extended
HTI analysis shows (see Table 3) that T
at low-elevation
stations has a significant warming trend of 0.056C/decade
(p< 0.001) compared to 0.043C/decade in the GDL series.
At high-elevation stations (n= 4), T
over 19172016
increased at 0.047C/decade (p< 0.01), slightly higher than
for the 85-year study period analysed by GDL
(0.044C/decade, p= 0.05), but lower than the low-
TABLE 4 Decadal mean surface mean, minimum, and maximum
temperature anomalies (C) 19172016 state-wide and for high and low
elevation stations
Annual series T
State-wide 19171926 0.058 0.040 0.178
19271936 0.197 0.213 0.276
19371946 0.250 0.203 0.281
19471956 0.189 0.143 0.247
19571966 0.041 0.082 0.059
19671976 0.106 0.172 0.041
19771986 0.300 0.249 0.482
19871996 0.458 0.707 0.314
19972006 0.415 0.720 0.189
20072016 0.470 0.476 0.295
Low-elevation 19171926 0.055 0.037 0.201
19271936 0.174 0.032 0.349
19371946 0.231 0.153 0.278
19471956 0.225 0.095 0.309
19571966 0.052 0.084 0.046
19671976 0.169 0.078 0.214
19771986 0.345 0.246 0.449
19871996 0.510 0.579 0.461
19972006 0.400 0.641 0.176
20072016 0.491 0.704 0.281
High-elevation 19171926 0.116 0.125 0.138
19271936 0.206 0.313 0.162
19371946 0.195 0.170 0.268
19471956 0.122 0.088 0.142
19571966 0.008 0.008 0.076
19671976 0.063 0.204 0.192
19771986 0.233 0.219 0.522
19871996 0.388 0.880 0.116
19972006 0.475 0.826 0.207
20072016 0.442 0.167 0.314
TABLE 5 Surface temperature trend anomalies for merged time series without linear regression-adjustments for 19172016 state-wide, and for high- and
low-elevation stations
Annual series T
(C/decade) T
(C/decade) T
(C/decade) DTR (C/decade)
State-wide 19172016 0.046** 0.067** 0.016 0.050**
19171939 0.145 0.202* 0.027 0.225
19401957 0.580*** 0.476*** 0.644*** 0.161
19581999 0.114** 0.205*** 0.060 0.160*
20002013 0.272 0.419 0.053 0.560***
Low-elevation 19172016 0.032 0.090*** 0.021
19171939 0.120 0.211 0.023
19401957 0.481*** 0.461*** 0.610**
19581999 0.118* 0.184*** 0.060
20002013 0.217 0.252 0.196
High-elevation 19172016 0.038 0.033 0.011
19171939 0.123 0.200 0.091
19401957 0.446** 0.611* 0.688***
19581999 0.118*** 0.242** 0.054
20002013 0.388 0.768 0.150
Symbols following fitted coefficient indicate statistical significance level at alpha: ***0.001; **0.01; *0.05; other values are not significant.
elevation trend. This result is similar to that of GDL for
19192006, that is, the long-term trends do not indicate
warming enhancement with elevation. But in contrast to
GDL, high-elevation trends are also not higher than low-ele-
vation trends in recent decades (Table 3). We note, however,
that the highest station in Hawaii (MLO) has been rapidly
warming much faster (0.19C/decade) than the high-eleva-
tion average (0.12C/decade, p< 0.001) for the 19552016
time period. This suggests that the selected elevation range
is too wide to properly see enhanced high-elevation warm-
ing. Indeed, an analysis of trends in air temperature lapse
rates in Hawaii (Kagawa-Viviani and Giambelluca, in
review) suggests that air temperature trends in the elevation
range of approximately 8001,600 m have been very low or
even negative during the past 50 years. Thus, if enhanced
warming is occurring at the high-elevations, it is being can-
celled by flat or negative trends in the lower part of high-
elevationnetwork used in our analysis. However, it also
might be an artefact of the noisy signal produced by the
small sample of high-elevation stations. While a higher limit
would suffer greater uncertainty due to an even smaller sam-
ple of stations, it might better capture the elevation effect.
Because it infers a more stable atmosphere, faster warming
at high-elevations is consistent with the observed drier con-
ditions during the past several decades in Hawaii (Frazier
and Giambelluca, 2017). Greater scrutiny of elevation-
related differences in Hawaii's air temperature trends is
ongoing. In a separate study, Kagawa-Viviani and Giambel-
luca (in review) focus specifically on the spatial patterns of
temperature change in Hawaiʻi.
To gain more insight into the possible systematic temper-
ature change for high-elevation tropical island sites, we com-
pared temperature change at MLO (3,397 m), Hawaii and
the Mt. Izaña Observatory (2,373 m), Tenerife, Canary
Islands, Spain. Mt. Izaña Observatory is the only high-eleva-
tion subtropical island site other than MLO with a long sur-
face air temperature record, as far as we know. At these two
stations, which differ in elevation by nearly 1,000 m, but are
both located above the typical local trade wind inversion
(TWI) height, the warming rates are virtually identical and
quite high (0.19C/decade, p< 0.001) for the period of
overlapping records (19552016; Figure 12). While a sam-
ple of only two stations is not adequate to make sweeping
generalizations, the rapid warming observed at these two
sites suggests that they are affected in a similar way by
ongoing large-scale climate warming. Furthermore, they are
similarly located latitudinally, in the descending branch of
the northern Hadley cell, albeit in different oceans, implying
that the ongoing warming of the layer above the TWI might
be systematic and widespread. It also indicates that the real
FIGURE 8 Mean atmospheric layer temperature (MLT) anomalies from
Hilo radiosonde height (m) between the 1,000 and 700 hPa surfaces relative
to 19852005 base period and mean surface temperature anomalies in
Hawaiʻi (HTI). Linear trend for MLT ( )is0.015C/decade over the
40-year period, 19772016 (not significant; p= 0.90) and the linear trend
for temperature ( ) is 0.079C/decade over the 40-year period,
19772016 (not significant; p= 0.17). There is a fairly good correlation
between the MLT from radiosonde data and HTI (r= 0.641, p< 0.001)
FIGURE 9 Annual and smoothed (with a 7-year running mean) HTI and
both PDO trends and MEI trends for the study period. (a) Significant
correlation are found between PDO and HTI (r= 0.53, p< 0.001), ( )
PDO index, ( ) PDO smoothed, ( ) HTI, and ( ) HTI smoothed
and (b) there is a positive correlation between MEI and HTI (r= 0.34,
p< 0.001), ( ) MEI index, ( ) MEI smoothed, ( ) HTI, and
() HTI smoothed [Colour figure can be viewed at]
break in lowland/highland warming rates is not at 800 m,
but rather near or above the inversion level, which is at a
much higher altitude around 2,0002,300 m in Hawaii
(Longman et al., 2015) and around 1,500 m in the Canary
Islands (Sanroma et al., 2010; Martín et al., 2012).
Although rapid warming in the 800 m and above layer
found by GDL has apparently not persisted based on the
extended HTI, enhanced warming at the highest elevations
(MLO) in Hawaii comports with the abundant evidence of
elevation dependent warming in mountains throughout the
world (Pepin et al., 2015). While several possible mecha-
nisms have been identified (Rangwala and Miller, 2012;
Pepin et al., 2015), none point specifically to the zone above
the TWI. We note that, areas above the TWI are warmer than
they would be without the TWI. Hence, a more persistent
TWI would enhance warming above the mean TWI level.
Subsiding air in the descending branch of the regional Had-
ley circulation, which maintains the TWI, influences both
Hawaiʻi and the Canary Islands. Longman et al. (2015)
found consistent increases in the intensity and persistence of
the TWI over the 19732013 period and found a good corre-
lation between the TWI frequency of occurrence and the
strength of Hadley cell subsidence in Hawaiʻi. Should the
apparent uptick in Hadley cell overturning and consequent
increase in TWI frequency of occurrence prove to be linked
FIGURE 11 (a) HTI (base period 19172000), PDO and GLOTI time
series (base period 19012000) comparison for the 19172016 time period.
The GLOTI trend is 0.098C/decade for the 100-year period. ( ) HTI, linear
trend: 19172016: 0.052C/decade, ( ) GLOTI, linear trend: 19172016:
0.098C/decade, ( ) HTI 20-year running mean, ( ) GLOTI 20-year
running mean, ( ) PDO LOWEES-smoothed. (b) HTI (base period
19172000), PDO and GOTI time series (base period 19012000)
comparison for the 19172016 time period. The global ocean trend is
0.081C/decade for the 100-year period. ( ) HTI, linear trend: 19172016:
0.052C/decade, ( ) GOTI, linear trend: 19172016: 0.081C/decade,
() HTI 20-year running mean, ( ) GLOTI 20-year running mean,
() PDO LOWEES-smoothed [Colour figure can be viewed at]
FIGURE 10 (a) SST anomaly for 1624N, 160140W using
NCEP/NCAR reanalysis data in comparison with HTI for the 67-year
period (19482016). SST trend is 0.032C/decade (not significant, p= 0.4),
and the warming trend of 0.122C/decade for HTI between 1948 and 2016
(p< 0.001). There is positive correlation between HTI and SST (r= 0.53,
p< 0.001). ( ) SST, ( ) SST smoothed, ( ) SST, linear trend:
19482016: 0.032C/decade, ( ) HTI, ( ) HTI smoothed, ( )
HTI, linear trend: 19482016: 0.122C/decade. (b) ERSST v5 for 1530N,
170140W in comparison with HTI over the 100-year (19172016) record
resulted in a warming trend of 0.052C/decade (p< 0.001) and strong
correlation between the two variables (r= 65, p< 0.001). ( ) ERSST,
() ERSST smoothed, ( ) ERSST, linear trend: 19172016:
0.056C/decade, ( ) HTI, ( ) HTI smoothed, ( ) HTI, linear
trend: 19172016: 0.052C/decade [Colour figure can be viewed at]
to global climate change, as suggested by downscaled cli-
mate projections for Hawaii (Lauer et al., 2013), rapid
warming at high-elevations in Hawaii is likely to continue.
As the global climate warms, Hawaiʻi is likely to see
increasing atmospheric stability, due in part to a more persis-
tent TWI (Cao et al., 2007; Lauer et al., 2013). In the Canary
Islands, an intensification of the trade wind circulation in the
northern windward slopes of the Canary Islands is expected
with increases in wet and humid air below the inversion, but
above the inversion, with the strengthening of the Hadley
cell, the air is dry and clear (Sanroma et al., 2010). Although
mean temperature and precipitation are known to be strongly
modulated by ENSO in Hawaiʻi, interannual to decadal
drivers of climatic variability in Canary Islands are generally
different. Nonetheless, the similarity in the observed rate of
warming in response to global climate change at these two
widely separated sites is striking.
in Hawaii has increased over the long-term period,
while T
has not increased significantly. These results
translate to a considerable drop in the DTR. Vose et al.
(2005) found a global trend in DTR of 0.066C/decade
over land areas between 1950 and 2004 as a result of
increased rapid T
rise. Malamud et al. (2011) showed that
greater increases in T
compared with T
at Mauna Loa
Observatory had caused DTR to decrease by
0.50C/decade over the period 19772006. Martín et al.
(2012) also found greater night time warming for high-eleva-
tion areas of Tenerife.
Throughout the 100-year record, temperature anomalies
have experienced periods of both positive and negative
FIGURE 12 (a) and (b) annual mean surface temperature anomalies (grey bars), 7-year running means (black curve) and trends (red lines) at Mt. Izaña
(2,373 m), Canary Islands, Spain, and Mauna Loa Observatory (MLO; 3,397 m), Hawaiʻi island, relative to a 19611990 baseline period. (a) Mt. Izaña has a
significant warming rate of 0.15C/decade (solid red line) for the century (p< 0.001; 19172016), and a significant trend of 0.19C/decade (p< 0.001;
dashed red line) over the recent 62-year period (19552016). (b) MLO also has a warming rate of 0.19C/decade (p< 0.001; solid red line) over the
19552016 period. (c) Comparison of annual mean surface temperature anomalies on Mt. Izaña and MLO. (d) Scatterplot of MLO vs. Mt. Izaña annual
temperature time series during 19552016 (r= 0.51, p< 0.001) [Colour figure can be viewed at]
trends. However, the long-term trend is positive, with new
record highs in recent years. Based on the HTI, 2015 and
2016, which were influenced by one of the strongest El Niño
events on record, were the warmest years throughout the
100-year study period at 0.788 and 0.924C above the
100-year mean, respectively. The extreme positive tempera-
ture anomaly associated with the 20152016 event reached
1.5C during JulyOctober 2015 (Zhu and Li, 2017).
We refined a previous HTI index and extended the record by
12 years from the earlier reported 88-year time series
(19192006, GDL 2008) to a 100-year trend series
(19172016). To minimize the effects of discontinuous sta-
tion data records, an improved HTI was developed. We com-
pared the station-based results with other datasets
(e.g., radiosonde and Reanalysis data). The various resulting
temperature series were consistent with the HTI results, both
well correlated and exhibiting similar trends. The long-term
trends for T
anomalies for state-wide, low-
, and high-elevation stations were assessed. T
and T
for the 100-year study period state-wide showed significant
rates of warming (0.052C/decade and 0.072C/decade,
respectively). T
trends by elevation showed higher rates
of warming at low-elevations (0.056C/decade) compared to
the high-elevation stations (0.047C/decade) over the last
century. However, much higher rates of warming are evident
above the TWI at Hawaii's highest elevation station, MLO,
and at Mt. Izaña Observatory in the Canary Islands. The
regional effects of the global warming slowdown beginning
in the early 2000s coinciding with a phase change in the
PDO continue into the additional recent years added to the
HTI record. However, rapid warming appears to have
resumed in Hawaii early in 2014.
This research was made possible through funding by the
Office of Maunakea Management (OMKM). We thank Emi-
lio Cuevas of AEMET, Canary Islands, Spain for providing
the Mt. Izaña temperature data and John Barnes, Research
Scientist, NOAA/ESRL, for access to MLO data. We thank
Aurora Kagawa-Viviani for providing insightful comments
and edits. We also thank Alexandra Hedgpeth for help at the
start of this project, and Camilo Mora for the radiosonde
data excel macro code. Special thanks to Jim Juvik of the
University of Hawaiʻi at Hilo and Jessica Kirkpatrick
of OMKM.
Marie M. McKenzie
Cao, G., Giambelluca, T.W., Stevens, D.E. and Schroeder, T.A. (2007) Inversion
variability in the Hawaiian trade wind regime. Journal of Climate, 20,
Dai, A., Fyfe, J.C., Xie, S.-P. and Dai, X. (2015) Decadal modulation of global
surface temperature by internal climate variability. Nature Climate Change,
5, 555559.
Diaz, H.F. and Giambelluca, T.W. (2012) Changes in atmospheric circulation
patterns associated with high and low rainfall regimes in the Hawaiian
islands region on multiple time scales. Global and Planetary Change, 98-99,
Diaz, H.F., Giambelluca, T.W. and Eischeid, J.K. (2011) Changes in the vertical
profiles of mean temperature and humidity in the Hawaiian islands. Global
and Planetary Change, 77(12), 2125.
Diaz, H.F., Bradley, R.S. and Ning, L. (2014) Climatic changes in mountain
regions of the American cordillera and the tropics. Arctic, Antarctic, and
Alpine Research, 46, 735743.
Elliott, W.P., Gaffen, D.J., Angell, J.K. and Kahl, J.D.W. (1994) The effect of
moisture on layer thicknesses used to monitor global temperatures. Journal
of Climate, 7(2), 304308.<03
Frazier, A.G. and Giambelluca, T.W. (2017) Spatial trend analysis of Hawaiian
rainfall from 1920 to 2012. International Journal of Climatology, 37,
Frazier, A.G., Elison Timm, O., Giambelluca, T.W. and Diaz, H.F. (2018) The
influence of ENSO, PDO and PNA on secular rainfall variations in Hawaii.
Climate Dynamics, 51, 21272140.
Giambelluca, T.W., Diaz, H.F. and Luke, M.S.A. (2008) Secular temperature
changes in Hawaii. Geophysical Research Letters, 35(12), L12702. https://
Huang, B., Thorne, P.W., Banzon, V.F., Boyer, T., Chepurin, G., Lawrimore, J.
H., Menne, M.J., Smith, T.M., Vose, R.S. and Zhang, H.-M. (2017) NOAA
extended reconstructed sea surface temperature (ERSST), Version 5. [SST/-
Skin T, 19162016]. NOAA, National Centers for Environmental Informa-
tion. accessed August 14, 2018.
Huber, U.M., Bugmann, H.K. and Reasoner, M.A. (2006) Global change and
mountain regions: an overview of current knowledge. Dordrecht, The
Netherlands: Springer.
Juvik, S.P. and Juvik, J.O. (1998) Atlas of Hawaii, 3rd edition. Honolulu,
Hawaii: University of Hawaii Press.
Keener, V.W., Marra, J.J., Finucane, M.L., Spooner, D. and Smith, M.H. (Eds.).
(2012) Climate change and pacific islands: indicators and impacts: Report
for the 2012 Pacific Islands Regional climate assessment. Washington, DC:
Island Press.
Kistler, R., Collins, W., Saha, S., White, G., Woollen, J., Kalnay, E., et al.
(2001) The NCEP-NCAR 50-year reanalysis: monthly means CD-ROM and
documentation. Bulletin of the American Meteorological Society, 82(2),
Lauer, A., Zhang, C., Elison-Timm, O., Wang, Y. and Hamilton, K. (2013)
Downscaling of climate change in the Hawaii region using CMIP5 results:
on the choice of the forcing fields. Journal of Climate, 26(24),
L'Heureux, M.L., et al. (2017) Observing and predicting the 2015/16 El Niño.
Bulletin of the American Meteorological Society, 98(7), 13631382. https://
Longman, R.J., Diaz, H.F. and Giambelluca, T.W. (2015) Sustained increases in
lower-tropospheric subsidence over the central tropical North Pacific drive a
decline in high-elevation rainfall in Hawaii. Journal of Climate, 28(22),
Malamud, B.D., Turcotte, D.L. and Grimmond, C.S.B. (2011) Temperature
trends at the Mauna Loa observatory. Hawaii. Climate Past, 7(3), 975983.
Mantua, N.J. and Hare, S. (2002) Pacific-Decadal Oscillation (PDO). Encyclope-
dia of global environmental change, 1, 592594.
Mantua, N.J., Hare, S.R., Zhang, Y., Wallace, J.M. and Francis, R.C. (1997) A
Pacific Interdecadal climate oscillation with impacts on Salmon production.
Bulletin of the American Meteorological Society, 78(6), 10691079. https://<1069:APICOW>2.0.CO;2.
Martín, J.L., Bethencourt, J. and Cuevas-Agulló, E. (2012) Assessment of global
warming on the Island of Tenerife, Canary Islands (Spain). Trends in mini-
mum, maximum and mean temperatures since 1944. Climatic Change, 114
(2), 343355.
National Centers for Environmental Information (NOAA), Climate at a Glance:
U.S. Time Series, 2017, retrieved on February 16, 2017, from http://www.
Pachauri, R.K., Allen, M.R., Barros, V.R., Broome, J., Cramer, W., Christ, R.,
et al. (2014) Climate Change 2014: Synthesis Report. In: Pachauri, R. and
Meyer, L. (Eds.) Contribution of Working Groups I, II and III to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change.
Geneva, Switzerland: IPCC, p. 151. isbn:978929169-143-2.
Pepin, N., Bradley, R.S., Diaz, H.F., Baraer, M., Caceres, E.B., Forsythe, N.,
Greenwood, G., et al. (2015) Elevation-dependent warming in mountain
regions of the world. Nature Climate Change, 5, 424430.
Pinheiro J, Bates D, DebRoy S, Sarkar D and R Core Team (2017). Nlme: linear
and nonlinear mixed effects models. R package version 3.1-131, URL:
R Core Team. (2014) R: a language and environment for statistical computing.
Vienna, Austria: R Foundation for Statistical Computing URL http://www.
Rangwala, I. and Miller, J.R. (2012) Climate change in mountains: a review of
elevation-dependent warming and its possible causes. Climatic Change, 114,
Salinger, M.J. (2005) Climate variability and change: past, present and future
an overview. In: Salinger, J. , Sivakumar, M.V.K. and Motha, R.P. (Eds.)
Increasing climate variability and change: Reducing the vulnerability of
agriculture and forestry. Dordrecht: Springer Netherlands, pp. 929. https://
Sanroma, E., Palle, E. and Sanchez-Lorenzo, A. (2010) Long-term changes in
insolation and temperatures at different altitudes. Environmental Research
Letters, 5(2), 024006.
Schneider, N. and Cornuelle, B.D. (2005) The forcing of the Pacific decadal
oscillation. Journal of Climate, 18(21), 43554373.
Toumi, R., Hartell, N. and Bignell, K. (1999) Mountain Station pressure as an
indicator of climate change. Geophysical Research Letters, 26(12),
Vose, R.S., Easterling, D.R. and Gleason, B. (2005) Maximum and minimum
temperature trends for the globe: an update through 2004. Geophysical
Research Letters, 32(23), L23822.
Wallace, J.M. and Hobbs, P.V. (1977) Atmospheric science, an introductory sur-
vey. New York: Academic Press.
Wolter, K. and Timlin, M.S. (1998) Measuring the strength of ENSO events:
how does 1997/98 rank? Weather, 53(9), 315324.
Zhang, Y., Wallace, J.M. and Battisti, D.S. (1997) ENSO-like Interdecadal Vari-
ability: 1900-93. Journal of Climate, 10(5), 10041020.
Zhu, Z. and Li, T. (2017) The record-breaking hot summer in 2015 over Hawaii
and its physical causes. Journal of Climate, 30(11), 42534266. https://doi.
How to cite this article: McKenzie MM,
Giambelluca TW, Diaz HF. Temperature trends in
Hawaiʻi: A century of change, 19172016. Int
J Climatol. 2019;39:39874001.
... As a result, the number of extreme heat days (days in a year when the ratio of daily maximum and minimum temperatures exceeds the historical records) was projected to increase in tropical regions (IPCC, 2018). In Hawai'i, the historical records showed that the mean surface air temperature had increased significantly at +0.052 °C per decade over the past 100 yr (1917McKenzie et al., 2019). The recent four decades (1977 to 2016) have been warmer than other decades in the instrumental records (McKenzie et al., 2019), and warming has primarily attributed to an increase in the minimum temperature (McKenzie et al., 2019). ...
... In Hawai'i, the historical records showed that the mean surface air temperature had increased significantly at +0.052 °C per decade over the past 100 yr (1917McKenzie et al., 2019). The recent four decades (1977 to 2016) have been warmer than other decades in the instrumental records (McKenzie et al., 2019), and warming has primarily attributed to an increase in the minimum temperature (McKenzie et al., 2019). Warming in Hawai'i is not uniform across the landscape, showing the highest temperature increase at the lowest elevations (McKenzie et al., 2019;Kagawa-Viviani and Giambelluca, 2020). ...
... In Hawai'i, the historical records showed that the mean surface air temperature had increased significantly at +0.052 °C per decade over the past 100 yr (1917McKenzie et al., 2019). The recent four decades (1977 to 2016) have been warmer than other decades in the instrumental records (McKenzie et al., 2019), and warming has primarily attributed to an increase in the minimum temperature (McKenzie et al., 2019). Warming in Hawai'i is not uniform across the landscape, showing the highest temperature increase at the lowest elevations (McKenzie et al., 2019;Kagawa-Viviani and Giambelluca, 2020). ...
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Proper knowledge and understanding of climatic variability across different seasons are important in farm management. To learn more about the potential effects of climate change on dairying in Hawaii, we conducted a study on site-specific climate characterization using several variables including rainfall, wind speed, solar radiation, and temperature, at two dairy farms located on Hawai`i Island, Hawai`i, in Ookala named “OK DAIRY,” and in Upolu Point named “UP DAIRY.” Temperature-Humidity Index (THI) and wind speed variations in the hottest four months (JUN-SEP) were analyzed to determine when critical thresholds that affect animal health are exceeded. Rainfall data were used to estimate the capacity of forage production in 6-month wet (NOV-APR) and dry (MAY-OCT) seasons. Future projections of temperature and rainfall were assessed using mid- and end-century gridded data products for low (RCP 4.5) and high emissions (RCP 8.5) scenarios. Our results showed that the “OK DAIRY” site received higher rainfall than the “UP DAIRY” site, favoring grass growth and forage availability. In addition, the “UP DAIRY” site was more stressful for animals during the summer (THI 69 to 73) than the “OK DAIRY” site (THI 67 to 70) as the THI exceeded the critical threshold of 68 which is conducive for high lactating cattle. On the “UP DAIRY” site, the THI did not drop below 68 during the summer nights, which created fewer opportunities for cattle to recover from heat stress. Future projections indicated that air temperature would increase 1.3 to 1.8 °C by mid-century and 1.6 to 3.2 °C by the end of the century at both farms, and rainfall will increase at the “OK DAIRY” site and decrease at the “UP DAIRY” site by the end-of-century. The agriculture and livestock industries, particularly the dairy and beef subsectors in Hawai`i, are vulnerable to climate changes as higher temperatures and less rainfall will have adverse effects on cattle. The findings in this study demonstrated how both observed and projected changes in climates support the development of long-term strategies for breeding and holistic livestock management practices to adapt to changing climate conditions.
... differing rates of change in mean versus extreme temperatures) in climate restrict forest regeneration and lead to negative effects on forest regeneration or restoration (Hagger et al., 2018). For example, annual mean temperatures have risen throughout Hawaiʻi with warming more pronounced at higher elevations (Diaz et al., 2011;Giambelluca et al., 2008;McKenzie et al., 2019) but cloud cover and precipitation appear to be decreasing at mid to high elevations (Cao et al., 2007;Giambelluca et al., 2013). Reductions in cloud cover, especially during winter months, could result in more extreme cold temperatures even as the overall climate warms, because clouds act as an insulating cover for landsurface temperatures. ...
... The decoupling of regional and sitespecific climates from global trends means that cold temperatures may become more extreme in the near future (Kodra et al., 2011). We are cautious in making broad conclusions about our limited, 17-year temperature record as climate in the Hawaiian islands is strongly linked to longer temporal scale events (McKenzie et al., 2019). However, this 17-year temperature record taken from a climate station within a few kilometers of our site does offer ecologically relevant data on a timeframe that overlaps significantly with restoration efforts (A. ...
... Even with limited climate data, our site may be a good example of the disparity between regional and site-specific locations in a heterogenous mountain environment. Climates throughout Hawaiʻi are warming (Diaz et al., 2011;Kagawa-Viviani and Giambelluca, 2020;McKenzie et al., 2019), but our 17-year climatic record shows no trend in mean temperatures while monthly minimum temperatures appear to be becoming more extreme. ...
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Tropical montane forests are being lost at an alarming rate but harbor some of the globe’s most unique biodiversity. The Hawaiian archipelago is a prime example of the importance of high elevation forests to species conservation and persistence as they serve as the last refugia for Hawaiian birds. Yet these forests have been converted to invasive dominated pastures, and efforts to restore them have been met with limited success. Unsuccessful forest restoration may be due to freezing temperatures acting as a demographic bottleneck by killing seedlings recruiting into pastures. We determined freezing tolerances of eight common native woody plants at a high-elevation forest on Hawaiʻi Island and compared these freezing tolerances to two years of site-specific winter temperatures and 17 years of regional temperature records. Low temperature extremes were more severe and common in pastures than under nearby 30-year-old canopy trees. Freezing temperatures over two years were severe enough to damage leaf tissues of six of eight species tested. Those species that displayed the greatest freezing tolerance were also those found naturally recruiting into open pastures. Temperature trends over the past 17 years show monthly minimum temperatures are not increasing as predicted by climate change. Persistent severe freezing events may limit seedling recruitment in the pasture, slowing native woody plant expansion into these abandoned pastures. The species-level differences in freezing tolerance show that current management actions are using species that are at high risk to freezing damage outside of the forest canopy and that alternative species should be considered.
... Globally, surface air temperatures are increasing, and Hawaii and other oceanic islands are no exception. Multiple station-based observations indicate long-term warming in Hawaii (Diaz et al., 2011;Giambelluca et al., 2008;McKenzie et al., 2019;Safeeq et al., 2013) and other oceanic tropical and subtropical islands (Cropper & Hanna, 2014;Folland et al., 2003;Kumar et al., 2014;Vincent et al., 2011;Weng, 2010), although the magnitudes of warming vary with trends ranging from 0.08 to 0.4°C/decade. Across these islands and island groups, trends are overlain by natural sources of variability at interannual to multidecadal time scales, including for Hawaii, the El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) (Chu & Chen, 2005), and for Macaronesia, the North Atlantic Oscillation (NAO; Cropper & Hanna, 2014). ...
... This study takes advantage of existing gridded products developed for Hawaii RF and evapotranspiration mapping Giambelluca et al., 2013;Giambelluca et al., 2014), extends existing mean annual and monthly temperature maps Giambelluca et al., 2014), builds on prior Hawaii temperature trend analyses (Giambelluca et al., 2008;McKenzie et al., 2019), and using new sea level and surface lapse rate time series, identifies oceanic and atmospheric processes contributing to local warming and cooling across the Hawaiian Islands. ...
... Table 2). These calculated trends for sea level air temperatures are twofold to fourfold higher than recently reported for low elevation (0-800 m) T min and T max trends across the Hawaiian Islands (compare to +0.072 and +0.017 [ns] °C/decade, respectively, for 1917McKenzie et al., 2019). Our stronger sea level T min,z0 trends may reflect, in part, urbanization concentrated in low elevations across Hawaii; urbanization is known to enhance T min trends relative to T max (Karl et al., 1988). ...
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While the Hawaiian Islands are experiencing long‐term warming, spatial and temporal patterns are poorly characterized. Drawing on daily temperature records from 309 stations (1905–2017), we explored relationships of surface air temperatures (Tmax, Tmin, Tavg, and diurnal temperature range) to atmospheric, oceanic, and land surface variables. Statistical modeling of spatial patterns (2006–2017) highlighted the strong negative influence of elevation and moisture on air temperature and the effects of distance inland, cloud frequency, wind speed, and the local trade wind inversion on the elevation dependence of surface air temperature. We developed time series of sea level air temperature and surface lapse rate by modeling surface air temperature as a simple function of elevation and found a strong long‐term (1905–2017) warming trend in sea level Tmin, twice that of Tmax (+0.17 vs +0.07°C/decade), suggesting regional warming, possibly enhanced by urbanization and cloud cover effects. Removing this trend, sea level Tmax and Tmin tracked SST and rainfall at decadal time scales, while Tmax increased with periods of weakened trade winds. Sea level air temperatures correlated with North Pacific climate indices, reflecting the influence of regional circulation via SST, rain, clouds, and trade winds that modulate environmental warming across the Hawaiian Islands. Increasing (steeper) Tmax surface lapse rates for the 0‐ to 1,600‐m elevation range (into the cloud zone) over 1978–2017 coincide with observations of marine boundary layer drying and rising cloud base heights, suggesting a need to better understand elevation‐dependent warming in this tropical/subtropical maritime environment and associated changes to cloud formation and persistence.
... Temperatures have risen rapidly in the Hawaiian Islands since the mid 1970s (Giambelluca et al 2008, McKenzie et al 2019 and a long-term drying trend has persisted since the early 1920s (Frazier and Giambelluca 2017), resulting in reduced forest biomass and productivity (Barbosa and Asner 2017). These same drying and warming trends have increased the frequency and intensity of wildland fire (Trauernicht et al 2015, Trauernicht 2019 with predictable negative effects on ecosystem carbon balance . ...
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The State of Hawai'i passed legislation to be carbon neutral by 2045, a goal that will partly depend on carbon sequestration by terrestrial ecosystems. However, there is considerable uncertainty surrounding the future direction and magnitude of the land carbon sink in the Hawaiian Islands. We used the Land Use and Carbon Scenario Simulator (LUCAS), a spatially explicit stochastic simulation model that integrates landscape change and carbon gain-loss, to assess how projected future changes in climate and land use will influence ecosystem carbon balance in the Hawaiian Islands under all combinations of two radiative forcing scenarios (RCPs 4.5 and 8.5) and two land use scenarios (low and high) over a 90 year timespan from 2010 to 2100. Collectively, terrestrial ecosystems of the Hawaiian Islands acted as a net carbon sink under low radiative forcing (RCP 4.5) for the entire 90 year simulation period, with low land use change further enhancing carbon sink strength. In contrast, Hawaiian terrestrial ecosystems transitioned from a net sink to a net source of CO2 to the atmosphere under high radiative forcing (RCP 8.5), with high land use accelerating this transition and exacerbating net carbon loss. A sensitivity test of the CO2 fertilization effect on plant productivity revealed it to be a major source of uncertainty in projections of ecosystem carbon balance, highlighting the need for greater mechanistic understanding of plant productivity responses to rising atmospheric CO2. Long-term model projections such as ours that incorporate the interactive effects of land use and climate change on regional ecosystem carbon balance will be critical to evaluating the potential of ecosystem-based climate mitigation strategies.
... These problems are acute in the Canary Islands, where warming in high mountains is occurring rapidly (0.14 ± 0.07°C/ decade on Tenerife summits), twice as fast as the rest of the island (Martín et al. 2012). This pattern has been reported for other temperate (Halloy and Mark 2003) and tropical-subtropical islands (Diaz et al. 2014;McKenzie et al. 2019). Invasive plant species are scarce in the Tenerife high mountain zone, due to the severe environmental constraints (Arévalo et al 2005). ...
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Invasive alien species and climate change are two of the main current threats to conservation of biodiversity worldwide. Their effects have been extensively studied individually, but we know less about their combined effect. This study analyzes the population changes in the plant community of the high mountain legume shrub habitat of Tenerife over 10 years (between 2009 and 2018), using alien herbivore exclusion plots distributed over two sectors with different patterns of climate. Our outcomes show contrasting effects of herbivory and climate in plant communities, with significant shifts in community composition. The dominant species, Teide broom (Spartocytisus supranubius), is negatively affected by both climate and alien herbivores, leading to a regression of its abundance. In contrast, a formerly rare species, Pterocephalus lasiospermus, is benefiting from warmer temperatures and from herbivore presence owing to its low palatability. Simultaneously, some thermal native species from the neighboring pine forest are invading the alpine ecosystem. We conclude that the alpine habitat is changing very quickly and differently according to whether it is in warmer or colder sectors of the summit of Tenerife. This work reveals the need to simultaneously consider multiple drivers to understand the response of mountain ecosystems to global change.
A large majority of climate change studies carried out to date are on changes in mean climate, which have comparatively downplayed variability. In terms of trend analysis or forecast, the scientific output and common knowledge for global warming are much more robust than for changes in temperature variability. Quantification of temperature variability adds another dimension of temporal scale, requiring immense labor and presenting great uncertainty. Regardless, this endeavor is necessary since changes in ambient temperature variabilities could also contribute to current and future human health burden besides changes in mean quantities. Here, we review the current literature on trends of surface air temperature variability defined at a range of timescales, aiming to tease out the welter of evidence and thus improving the scientific recognition of changes in air temperature variability in the context of climate change. The findings of reviewed studies from numerous regions differ substantially over various temporal scales. In general, the ambient temperature variability on short time scales (e.g., diurnal or inter-day) shows a downward trend, while it is increasing on longer time scales (e.g., inter-annual). We then move beyond the review and deliver an extended discussion of potential implications for future research related to ambient temperature variability. We highlight the need to consider the methodological choices, especially timescales of interest, in the trend analysis as well as health impact studies. Continued research focusing on temperature variability at multiple timescales, with concerted efforts from scientists of all relevant stripes, is meaningful in synthesizing knowledge and reducing uncertainties surrounding air temperature variability.
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The Nevado de Toluca weather station (4283 masl, 19 ºN) has recorded meteorological data for over half a century, and this combination of elevation and duration provides a rare opportunity to study climate trends in a tropical high-mountain environment. The climatic variability during the period 1965-2015 at the Nevado de Toluca volcano was analyzed. Nine standard climate indices for temperature and seven for precipitation were calculated from daily data from its weather station. The results, with a high level of statistical significance, show an increase in the number of days with night frost and cold periods; likewise, results indicate an increase in the diurnal thermal oscillation. Total accumulated precipitation shows an increasing tendency over time, although the periods with precipitation are increasingly isolated. This suggests that seasonal snow on the summit of the volcano will be increasingly isolated but, at the same time, the snowpack will persist longer. This work is expected to serve as a reference for other high-mountain tropical environmental studies, where air temperature and precipitation are crucial issues.
Alpine ecosystems in the Pacific Islands are isolated and unique, characterized by high levels of endemism. Only Hawai‘i and New Zealand have elevations high enough to contain substantial alpine climates, and about 11% of the land area of both island groups is located above treeline. Both of these volcanically active archipelagos are characterized by complex topography, with peaks over 3700 m. These alpine ecosystems have significant cultural, social, and economic value; however, they are threatened by invasion of exotic species, climate change, and human impacts. Nonnative ungulates reduce native shrubland and grassland cover, and threaten populations of endangered birds. Exotic plants alter water yields and increase fire risk, and increased recreational visitation to these remote areas facilitates the introduction of exotic plant seeds, pests, and pathogens. Both New Zealand and Hawai‘i have experienced strong warming at higher elevations, and future projections indicate that these robust warming trends will continue. Glacial retreat has been noted in the Southern Alps, with 34% ice volume lost since 1977, and New Zealand may lose 88% of its ice volume by 2100. Snowfall on Hawai‘i's mountain peaks is projected to almost entirely disappear by 2100. Changes are occurring rapidly, and additional monitoring and research are needed to conserve these uniquely sensitive, remote regions.
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Over the last century, significant declines in rainfall across the state of Hawai‘i have been observed, and it is unknown whether these declines are due to natural variations in climate, or manifestations of human-induced climate change. Here, a statistical analysis of the observed rainfall variability was applied as first step towards better understanding causes for these long-term trends. Gridded seasonal rainfall from 1920 to 2012 is used to perform an empirical orthogonal function (EOF) analysis. The leading EOF components are correlated with three indices of natural climate variations (El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Pacific North American (PNA)), and multiple linear regression (MLR) is used to model the leading components with climate indices. PNA is the dominant mode of wet season (November–April) variability, while ENSO is most significant in the dry season (May–October). To assess whether there is an anthropogenic influence on rainfall, two methods are used: a linear trend term is included in the MLR, and pattern correlation coefficients (PCC) are calculated between recent rainfall trends and future changes in rainfall projected by downscaling methods. PCC results indicate that recent observed rainfall trends in the wet season are positively correlated with future expected changes in rainfall, while dry season PCC results do not show a clear pattern. The MLR results, however, show that the trend term adds significantly to model skill only in the dry season. Overall, MLR and PCC results give weak and inconclusive evidence for detection of anthropogenic signals in the observed rainfall trends.
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Hawaiian surface air temperature (HST) during summer 2015 (from July to October) was about 1.5°C higher than the climatological mean, which was the hottest since records began in 1948. In the context of record-breaking seasonal-mean high temperature, 98 exceptional local heatwave days occurred during summer 2015. Based on diagnoses and simulations, we demonstrate in this paper that the record-high summer HST of 2015 arose mainly from the combined effects of the interannual and interdecadal variability of sea surface temperature anomalies (SSTA). The interannual variability of SSTA, with an El Niño-like pattern in the tropics and cold (warm) anomalies over the western (eastern) North Pacific, was the primary contributor to the abnormally high HST in summer 2015. This interannual tropical–extratropical SSTA pattern was accompanied by low-level southwesterly anomalies over the central North Pacific, which weakened the climatological northeasterly trade winds and reduced the ventilation effect, warming Hawaii. Numerical experiments further revealed that the SST warming in the subtropical eastern North Pacific was mostly responsible for the weakened trade winds and warming over Hawaii. Interdecadal SST warming in the tropics was a secondary factor. By superimposing the positive SSTA over the Indo-Pacific warm pool and tropical North Atlantic Ocean upon the climatological mean maximum SST regions, it was found that these anomalies led to enhanced convection over the Maritime Continent and the oceans around Mexico, causing anomalous subsidence and reduced cloud cover over the tropical central North Pacific. The reduced cloudiness increased the amount of downward solar radiation, thus warming Hawaii.
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The El Niño of 2015-16 was among the strongest El Niño events observed since 1950, and took place almost two decades after the previous major event in 1997-98. Here, perspectives of the event are shared by scientists from three national meteorological or climate services that issue regular operational updates on the status and prediction of the El Niño-Southern Oscillation (ENSO). Public advisories on the unfolding El Niño were issued in the first half of 2015. This was followed by significant growth in sea surface temperature (SST) anomalies, a peak during November 2015 - January 2016, subsequent decay, and its demise during May 2016. The lifecycle and magnitude of the 2015-16 El Niño was well predicted by most models used by national meteorological services, in contrast to the generally over-exuberant model predictions made the previous year. The evolution of multiple atmospheric and oceanic measures demonstrates the rich complexity of ENSO, as a coupled ocean-atmosphere phenomenon with pronounced global impacts. While some aspects of the 2015-16 El Niño rivaled the events of 1982-83 and 1997-98, we show that it also differed in unique and important ways, with implications for the study and evaluation of past and future ENSO events. Unlike previous major El Niños, remarkably above-average SST anomalies occurred in the western and central equatorial Pacific, but were milder near the coast of South America. While operational ENSO systems have progressed markedly over the past several decades, the 2015-16 El Niño highlights several challenges that will continue to test both the research and operational forecast communities.
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Spatial patterns of rainfall in Hawai‘i are among the most diverse in the world. As the global climate warms, it is important to understand observed rainfall variations to provide context for future changes. This is especially important for isolated oceanic islands where freshwater resources are limited, and understanding the potential impacts of climate change on the supply of freshwater is critical. Utilizing a high-resolution gridded data set of monthly and annual rainfall for Hawai‘i from January 1920 to December 2012, seasonal and annual trends were calculated for every 250-m pixel across the state and mapped to produce spatially continuous trend maps. To assess the stability of these trends, a running trend analysis was performed on 34 selected stations. From 1920 to 2012, over 90% of the state experienced drying trends, with Hawai‘i Island, and in particular the western part of the island, experiencing the largest significant long-term declines in annual and dry season rainfall. The running trend analysis highlighted the multi-decadal variability present in these trends, and revealed that the only region in the state with persistent annual and dry season trends through the study period is the western part of Hawai‘i Island; for most other regions, the drying trends were not significant until the most recent part of the record was included. These results support previous studies that indicate drying across the state over recent decades, and reveal the timing of upward and downward trends as well as important spatial details for natural resource management in Hawai‘i.
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We review some recent work regarding climatic changes in selected mountain regions, with particular attention to the tropics and the American Cordillera. Key aspects of climatic variability and trends in these regions are the amplification of surface warming trends with height, and the strong modulation of temperature trends by tropical sea surface temperature, largely controlled by changes in El Niño–Southern Oscillation on multiple time scales. Corollary aspects of these climate trends include the increase in a critical plant growth temperature threshold, a rise in the freezing level surface, and the possibility of enhanced subtropical drying. Anthropogenic global warming projections indicate a strong likelihood for enhancement of these observed changes.
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There is growing evidence that the rate of warming is amplified with elevation, such that high-mountain environments experience more rapid changes in temperature than environments at lower elevations. Elevation-dependent warming (EDW) can accelerate the rate of change in mountain ecosystems, cryospheric systems, hydrological regimes and biodiversity. Here we review important mechanisms that contribute towards EDW: snow albedo and surface-based feedbacks; water vapour changes and latent heat release; surface water vapour and radiative flux changes; surface heat loss and temperature change; and aerosols. All lead to enhanced warming with elevation (or at a critical elevation), and it is believed that combinations of these mechanisms may account for contrasting regional patterns of EDW. We discuss future needs to increase knowledge of mountain temperature trends and their controlling mechanisms through improved observations, satellite-based remote sensing and model simulations.
Prior to the 20th century Northern Hemisphere average surface air temperatures have varied in the order of 0.5°C back to AD 1000. Various climate reconstructions indicate that slow cooling took place until the beginning of the 20th century. Subsequently, global-average surface air temperature increased by about 0.6°C with the 1990s being the warmest decade on record. The pattern of warming has been greatest over mid-latitude northern continents in the latter part of the century. At the same time the frequency of air frosts has decreased over many land areas, and there has been a drying in the tropics and sub-tropics. The late 20th century changes have been attributed to global warming because of increases in atmospheric greenhouse gas concentrations due to human activities. Underneath these trends is that of decadal scale variability in the Pacific basin at least induced by the Interdecadal Pacific Oscillation (IPO), which causes decadal changes in climate averages. On interannnual timescales El Niño/Southern Oscillation (ENSO) causes much variability throughout many tropical and subtropical regions and some mid-latitude areas. The North Atlantic Oscillation (NAO) provides climate perturbations over Europe and northern Africa. During the course of the 21st century global-average surface temperatures are very likely to increase by 2 to 4.5°C as greenhouse gas concentrations in the atmosphere increase. At the same time there will be changes in precipitation, and climate extremes such as hot days, heavy rainfall and drought are expected to increase in many areas. The combination of global warming, superimposed on decadal climate variability (IPO) and interannual fluctuations (ENSO, NAO) are expected lead to a century of increasing climate variability and change that will be unprecedented in the history of human settlement. Although the changes of the past and present have stressed food and fibre production at times, the 21st century changes will be extremely challenging to agriculture and forestry.
Consistent increases in the strength and frequency of occurrence of the trade wind inversion (TWI) are identified across a ~40-yr period (1973-2013) in Hawaii. Changepoint analysis indicates that a marked shift occurred in the early 1990s resulting in a 20% increase in the mean TWI frequency between the periods 1973-90 and 1991-2013, based on the average of changes at two sounding stations and two 6-month (dry and wet) seasons. Regional increases in the atmospheric subsidence are identified in four reanalysis datasets over the same ~40-yr time period. The post-1990 period mean for the NCEP-NCAR reanalysis shows increases in subsidence of 33% and 41% for the dry and wet seasons, respectively. Good agreement was found between the time series of TWI frequency of occurrence and omega, suggesting that previously reported increases in the intensity of Hadley cell subsidence are driving the observed increases in TWI frequency. Correlations between omega and large-scale modes of internal climate variability such as El Niño-Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO) do not explain the abrupt shift in TWI frequency in the early 1990s in both seasons. Reported increases in TWI frequency of occurrence may provide some explanation for climate change-related precipitation change at high elevations in Hawaii. On average, post-1990 rainfall was 6% lower in the dry season and 31% lower in the wet season at nine high-elevation sites. Rainfall was significantly correlated with TWI frequency at all of the stations analyzed.