Long-term surface air temperatures at 1.5 m screen level over land are used
in calculating a global average surface temperature trend. This global trend
is used by the IPCC and others to monitor, assess, and describe global
warming or warming hiatus. Current knowledge of near-surface temperature
trends with respect to height, however, is limited and inadequately
understood because surface temperature observations at different heights in
the surface layer of the world are rare especially from a high-quality and
long-term climate monitoring network. Here we use high-quality two-height
Oklahoma Mesonet observations, synchronized in time, fixed in height, and
situated in relatively flat terrain, to assess temperature trends and
differentiating temperature trends with respect to heights (i.e.,
near-surface lapse rate trend) over the period 1997 to 2013. We show that
the near-surface lapse rate has significantly decreased with a trend of
Physical properties of the atmosphere and dynamic processes mix heat vertically and horizontally, yielding the highest temperatures, on average, at the surface with marked seasonal and spatial variations (IPCC, 2013; Karl et al., 2006). The thermal structure near the surface is affected by various surface forcings (e.g., radiation absorbed and emitted, turbulent mixing, and vegetation interaction) which result in the near-surface lapse rate varying considerably with location and season as well as with atmospheric humidity (Stone and Carlson, 1979; Karl et al., 2006; Mahrt, 2006; Pielke Sr. et al., 2007). In the entire troposphere, climate models indicate a distinct height-dependent temperature response to surface temperature increases (refers to air temperature at a screen height near ground surface) (Gaffen et al., 2000; Santer et al., 2005; Karl et al., 2006; Thorne et al., 2011; Seidel et al., 2012; Mitchell et al., 2013). Most of these height-dependent temperature studies focused on tropospheric temperature trends by using radiosonde and satellite observations and climate models (Thorne et al., 2011), however, the near-surface temperature lapse rate has rarely been studied in the surface layer of the atmosphere.
Natural internal climate variability and noise in the data make the detectability of long-term temperature trends in the surface boundary layer difficult. One reason is that the boundary layer typically changes from a convective turbulent regime, with a gain of sensible heat (daytime), to a thermodynamically stable, long-wave radiationally cooled regime (nighttime) with a loss of sensible heat (Pielke Sr. et al., 2007; Baldocchi and Ma, 2013). The high-quality two-height surface observations in the Oklahoma Mesonet (Shafer et al., 2000; Lin et al., 2007), however, provide for the first time an accurate observational network to extract the temperature trend signal at two heights in the surface layer. The temperature observations are synchronized in time, fixed in height, and situated in relatively flat terrain, thus providing a unique opportunity to evaluate near-surface temperature trends and thus the lapse rate trends.
This study is the first observational investigation of two-height, near-surface temperatures to examine lapse rate trends and variability over more than a decade period, a 17-year timescale from 1997 to 2013, which substantially increases the signal-to-noise ratio for trend analysis (Santer et al., 2011) compared to a decade observation (Lin et al., 2007). In this study, our objective is to provide observational evidence for near-surface lapse rate and temperature trends over 1997 to 2013 in Oklahoma.
We selected stations from the Oklahoma Mesonet, which is a world-class
network of environmental monitoring stations. As reported in 2009, the
National Research Council (NRC) recommended the Oklahoma Mesonet as the
“gold standard” for statewide weather and climate networks (
44 Oklahoma Mesonet stations (filled circles) and 44 USHCN stations (open circles), in which the Oklahoma Mesonet stations include 34 grassland stations (green) and 10 cropland stations (black circles). The MODIS Land Cover product (MOD12Q1) (Friedl et al., 2010) in 2005 was used to classify the Oklahoma Mesonet stations into grassland (34 stations) and cropland stations (10 stations). The thin lines indicate the borders of nine climate divisions in Oklahoma.
Monthly time series of
Changes of monthly lapse rate (LR) (
Individual station trends of monthly lapse rate anomalies
(
Trends and variations of monthly lapse rate (LR) (
Changes of monthly lapse rates (LR) (
The same as Fig. 7 but only for 10 cropland station averages.
Monthly smoothed anomaly time series over 1997–2013 of
The US Historical Climatology Network (USHCN, version 2.5) consists of 44 high-quality stations in Oklahoma and the data quality of monthly average temperatures has been rigorously examined (Menne et al., 2009) (Fig. 1). These 44 USHCN stations have long been commonly selected for use in evaluating climate changes on the global, regional, and state scales and thus the USHCN temperature is considered as a reference temperature change when evaluating climate change. It was assumed that both the 44 USHCN stations and the 44 Oklahoma Mesonet stations are representative of the Oklahoma state region in this study.
In the USHCN data set, the instrument change adjustments in a climate series
“is a regional average” (Quayle et al., 1991; Hubbard and Lin, 2002, 2006). The exact effect at individual stations may vary
depending on local environmental or climate factors such as the direction of
sunlight and wind speeds around the radiation shields. Temperature data used
in the study from the Oklahoma Mesonet are quality controlled and
thermometers used in the network have been calibrated every 24 to 60 months.
The air temperature at 9 m height was measured by a thermistor in a
naturally ventilated radiation shield from 1997 to 2013. Air temperature
instruments at 1.5 m height were changed from a naturally ventilated
radiation shield into an aspirated radiation shield in late 2008. Therefore,
homogeneity tests of monthly temperatures for individual Mesonet stations in
both
The 44
The lapse rate is defined as
Monthly anomalies for lapse rates, temperatures, and other climatic variables were departures from monthly climatology for the period from January 1997 to December 2013. The regional time series were aggregated by using an equally weighted station average from each station when the observations were available.
The computation of complementary variables shown in this study is briefly
described here. The total energy content of a unit parcel of air (per kg) is
provided by the sum of the kinetic energy, latent heat, enthalpy, and
gravitational potential energy (Peterson et al., 2011). Without considering
the gravitational potential energy and kinetic energy, the air heat content
(
The water vapor pressure deficit (VPD) was calculated using
For the trend analysis, the adjusted standard error and adjusted degrees of freedom method was used for evaluating the statistical significance of regional temporal trends and individual station trends at the 95 % or otherwise specified confidence levels (Santer et al., 2000; Karl et al., 2006). This approach is a modification of the ordinary least squares linear regression to substitute the effective sample size by correcting for the effect of temporal autocorrelation in the anomaly time series or its residual series (Santer et al., 2000; Karl et al., 2006).
Here we present the first observational investigation of two-height,
near-surface temperatures to examine lapse rate trends and variability over
more than a decade period. For the period of 1997 to 2013, when trends of
surface temperature anomalies are evaluated by individual surface
temperatures at 1.5 m (
In terms of month-to-month variability of these three time series (Fig. 2a
to c), the standard deviations over the period studied were 1.63, 1.64, and
1.65
The air heat content variability was very similar to the air temperature's
month-to-month variability although it was coupled with air humidity (Fig. 2d and e). The temperature difference between measurements at 1.5 m of the
Oklahoma Mesonet and USHCN (
Figure 3 shows lapse rate changes and changes in monthly anomalies
for daily, daytime, and nighttime conditions. The lapse rate is defined
as
The statistically significant trend of the daily lapse rate was
In Fig. 3b, the metadata inventory of thermometer changes suggests that
there could be systematic biases which might compromise trend analysis. In
addition to the routine quality-control and instrument calibrations of
That the lapse rate trend is statistically significant is initially
surprising, since the individual two-height temperatures have no significant
trends (Fig. 3a and b). We explained how this can occur in Appendix A
(see Figs. A1 and A2). Results in Fig. 3 indicated that the temperature
difference between
The seasonality shown in the daytime lapse rate was clearer than in the nighttime lapse rate (Figs. 3 and 4), suggesting that strong turbulent mixing controlled the daytime mixing layer but as expected, there was stabilized surface air (weak turbulence) in the nocturnal boundary layer (Stone and Carlson, 1979; Stull, 1988; Karl et al., 2006; McNider et al., 2012). Thus, the nighttime lapse rate clearly consistently varied much more than the daytime lapse rate over 1997 to 2013 (Figs. 3a and 4a). Figure 4 indicates that part of the daytime was unstably stratified in the surface boundary layer, however, for most of the time over a 24 h period, the lapse rates show a stable surface boundary layer for all months in the Oklahoma region. During the spring season, the daytime lapse rates were relatively suppressed while nighttime lapse rates were suppressed during the fall season in Oklahoma (Fig. 4a). All daytime, daily, and nighttime lapse rates showed a change between the averages of the first 10 years and the last 10 years (Fig. 4b).
To examine spatial aspects of lapse rate changes, the lapse rate trends in
44 individual stations are shown in Fig. 5 for daily, daytime, and
nighttime lapse rates. All but one station lapse rate trend showed a
decrease irrespective of whether they were the daily, daytime, or nighttime
analyses. About 16, 36, and 23 % of all stations showed
statistically non-significant trends for the daily, daytime, and nighttime
time series, respectively. The majority of stations showed significant
decreasing trends, especially for daily lapse rates (Fig. 5). The histogram
of individual trends for nighttime indicated trends were more negative
relative to daily and daytime lapse trends (meaning the higher level
temperature increased more (or decreased less) than the lower level
temperature). Across Oklahoma, the lower latitude region showed more
negative lapse rate trends. When dividing all of Oklahoma into northern and
southern areas by a 35.4
Daytime and nighttime lapse rate trends demonstrate different properties
largely due to the diurnal solar cycle, wind speed and its interaction with
the land surface (Pepin, 2001; Karl et al., 2006; Mahrt, 2006; McNider et
al., 2012). Wind strongly influences turbulent mixing and surface boundary
layer depth (Stull, 1988; Pepin, 2001). Figure 6 shows the lapse rate trend
and variations under windy and calm conditions. There was no significant
lapse rate trend observed under windy daytime conditions (Fig. 6c). The most
negative lapse rate trend,
The MODIS Land Cover product (MOD12Q1) was used for the year 2005 (Friedl et al., 2010) to classify all 44 Oklahoma Mesonet stations into 34 grassland stations and 10 cropland stations to examine possible effects of land use and land cover on lapse rates (Fig. 1). Figures 7 and 8 showed that there were no statistical differences among respective lapse rate trends between grassland and cropland stations.
Due to the complexity of the surface vertical temperature profile variations
(Stone and Carlson, 1979; Pepin, 2001; Mahrt, 2014), here we simply
presented a monthly smoothed anomaly time series of climatic variables
including solar radiation (SR, W m
In summary, for related climate variables, it is understandable that solar radiation is the most correlated due to its strong role on turbulent sensible heat flux from the ground surface associated with vertical temperature gradients and stability. The wind speed did play a role for lapse rate changes in the surface boundary layer (Pepin, 2001; Pielke Sr. et al., 2007; McNider et al., 2012; Baldocchi and Ma, 2013). Precipitation changes can provide information about soil moisture changes and its effect on variations of the daytime surface energy budget and heating of atmospheric temperatures (McNider et al., 2012; Baldocchi and Ma, 2013). Nevertheless, the mechanism of decreased lapse rates and latitudinal gradients of surface lapse rate trends observed in Oklahoma from 1997 to 2013 warrants further study and longer observation data in the future.
Our study has the following major findings. First, using the lapse rate (defined as the difference in temperature at two levels) trends can be diagnosed with more statistical confidence than considering temperature trends from each level separately. Second, trends of surface temperature depend on the height at which the measurements are made. A greater warming at the 9 m level, or larger cooling at the 1.5 m screen level would explain such an observation. This is important as the surface temperature is used to diagnose and model global warming (IPCC, 2013). Using just the 1.5 m level trends would provide a different magnitude of trend than if obtained from the temperatures at 9 m (at least in Oklahoma and this may be true elsewhere). Third, the near-surface lapse rate trends were altered by wind speed. Fourth, lapse rate trends in southern Oklahoma were significantly more negative than further north in the state. Our study suggests a positive temperature trend at 9 m could be due in part to a change in wind speed during the time period such that the 9 m level more often remains above the nocturnal cool layer later during the observing period.
Finally, since land surface temperatures are often not taken at the same height above the ground, if the magnitude of long-term trends depends on the height of the measurement, it further complicates the ability to accurately quantify global warming using a global average surface temperature trend from a single height of observation at each location used in the construction of the global assessment (IPCC, 2013). This research should provide impetus for building additional or vertical expansion of current in situ observational infrastructure for a more robust understanding of climate change.
One might question how measurements from two individual heights can show no
significant trends but the difference does. To evaluate this, we first
generated two monthly temperature anomaly series, representing measurements
at 9 m height (
An example of two non-significant trends in
These figures illustrate the frequency of outcomes
(shown as the
The third step was to run simulations 1000 times to generate 1000 pairs of
Finally, trend analyses were conducted for the
In summary, a differential process (
This work was partially supported by the United States Geological Survey (USGS), National Science Foundation (NSF), the U.S. Climate Program Office (CPO), and the USDA Ogallala Initiative Program. The authors thank Monica Deming at the Oklahoma Climatological Survey for permission to use Oklahoma Mesonet data and for valuable discussions. The authors benefitted from insightful comments and discussion from Oklahoma Mesonet supporting staff. We also thank Urs Neu at Swiss Academy of Sciences for our constructive discussions in the past on the subject of this paper.Edited by: J. P. Huang