The performance of twenty GCMs that participated in the Coupled Model Intercomparison Phase 5 (CMIP5) is evaluated at Sterling, Virginia, by comparing model outputs with radiosonde observational dataset and reanalysis dataset. We evaluated CMIP5 models in their ability to simulate wind climatology, seasonal cycle, interannual variability, and trends at the pressure levels from 850 hPa to 30 hPa. We also addressed the question of the number of years required to detect statistically significant wind trends using radiosonde wind measurements. Our results show that CMIP5 models and reanalysis successfully reproduced the observed climatological annual mean zonal wind and wind speed vertical distribution. They also capture the observed seasonal zonal, meridional, and wind speed vertical distribution with stronger (weaker) wind during the winter (summer) season. However, there is some disagreement in the magnitude of vertical profiles among CMIP5 models, reanalysis, and radiosonde observation. Overall, the number of years to obtain statistically significant trend decreases with increasing pressure level except for upper troposphere. Although the vertical profile of interannual variability of CMIP5 models and reanalysis agree with the radiosonde observation, the wind trend is not statistically significant. This indicates that detection of trends on local scale is challenging because of small signal-to-noise ratio problems.
A number of studies have evaluated the ability of climate model simulations that participated in Phase Five of Climate Model Intercomparison Project (CMIP5) to reproduce the observed features of North American continental and other regional climate features (e.g., [
Although several studies attempted to quantify the characteristics, variability, and mean state of the tropospheric jet streams (e.g., [
This characterization is important in understanding the influence of stratospheric dynamics on tropospheric patterns [
Vautard et al. [
Recently, there has been growing interest in accurately depicting upper air winds due to their role in estimating the state and changes in general atmospheric circulation [
The objective of this study is therefore to characterize and understand the vertical distribution of zonal, meridional, and wind speeds (wind fields) over Sterling, Virginia, Eastern United State of America. We will focus on the trend and variability at the tropospheric jet core. We will also calculate the number of years needed to detect future wind field trends from radiosonde observation.
This paper is organized as follows; the dataset and study approach are described in Section
Three reanalysis datasets used in this study are NCEP-NCAR [
Monthly mean zonal, meridional, and wind speed data (wind fields) from Sterling, Virginia, (located at latitude 38°58′36′′N, longitude 77°29′09′′ east at an elevation of 88.4 meters above mean sea level) which is part of Integrated Global Archive (IGRA) radiosonde observations [
Wind fields data from CMIP5 model simulations used in this study are summarized in Table
List of CMIP5 models used in this study. Models in bold are those CMIP5 models that have at least five ensemble members for both historical and future (RCP4.5) simulations.
Institute (modeling group), country | Models | Ensemble members | Atmospheric resolutions | |
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Horizontal (lon. × lat.) | Vertical levels | |||
College of Global Change and Earth System Science, Beijing Normal University, China | BNU-ESM | 1 | 2.8 × 2.8 | 26 |
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Canadian Centre for Climate Modelling and Analysis, Canada |
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35 |
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35 | |
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National Center for Atmospheric Research, USA |
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27 |
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Centro Euro-Mediterraneo per I Cambiamenti Climatici, Italy | CMCC-CMS | 1 | 0.75 × 0.75 | 95 |
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Commonwealth Scientific and Industrial Research Organisation in collaboration with the Queensland Climate Change Centre of Excellence, Australia |
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18 |
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Geophysical Fluid Dynamics Laboratory, USA | GFDL-ESM2G |
1 |
2.5 × 2.0 |
24 |
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NASA Goddard Institute for Space Studies, USA |
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40 |
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40 | |
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Met Office Hadley Centre, UK |
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Institut Pierre-Simon Laplace, France | IPSL-CM5A-LR |
1 |
3.75 × 1.875 |
39 |
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Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute for Environmental Studies, Japan | MIROC-ESM |
1 |
2.8 × 2.8 |
80 |
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Max Planck Institute for Meteorology, Germany | MPI-ESM-LR |
1 |
1.9 × 1.9 |
47 |
Taylor diagram is displayed in polar coordinate for pattern comparison between reference and other runs in terms of correlation, standard deviation (SD), and root mean square error (RMSE) for (a) zonal wind and (b) meridional wind. SD ratio is the standard deviation of each model run to that of the radiosonde observation taken as a reference. SD ratio is the radial distance from the origin; the RMSE is the distance to the reference point; the azimuthal position gives the correlation coefficient. The colored symbols represent monthly data used for each run from 1979 to 2005 (annual, black plus; December, January, and February (DJF), blue diamond; March, April, and May (MAM), green upward triangle; June, July, and August (JJA), red circle; September, October, and November (SON), magenta downward triangle). Open (filled) symbols represent positive (negative) correlations between each ensemble member and radiosonde observation (black filled circle).
This model has 10 ensemble members which differ only on the initial conditions of the atmospheric component. The radiosonde observation is taken as the reference and compared with the ten ensemble runs from CSIRO-Mk3.6.0 model in terms of correlation, standard deviation, and root mean square error, three comparison metrics in a single graph (Figures
Second, our result shows that change in the initial condition has impact on zonal and meridional wind speed as indicated by the spread in the seasonal and annual correlation coefficient, RMS error, and standard deviation ratio between observation and each run. The spread among individual simulations also indicates the uncertainty in the initial condition of CMIP5 models (initial condition problems). These uncertainties and errors are further analyzed in Section
The method consists of analysis of annual and seasonal zonal, meridional, and wind speed time series data from historical CMIP5 model simulations, reanalysis (ERA-Interim, NCEP/NCAR, and NCEP/DOE) product, and radiosonde observations. Trends are computed for each model, reanalysis product, and observations. Trend values are calculated using a linear least square analysis method. The trend uncertainty computing was based on standard error, taking into account the autocorrelation of the time series [
Since the CMIP5 simulations used in this study vary in resolution from 0.75° latitude × 0.75° longitude (Model CMCC-CMS) to 3.75° latitude × 2.5° longitude (Model HadCM3) as shown in Table
Figure
(a) Reanalysis and CMIP5 interpolated to 2.5° by 2.5° grid cells to compare with the radiosonde stations data (blue star represents the Sterling, VA, National Weather Station location). (b) Radiosonde burst location at 50 hPa for the Sterling station. The circles in (b) represent JJA, SON, DJF, and MAM for green, red, blue, and purple, respectively, while yellow square and triangle represent Sterling radiosonde stations and Beltsville research site, respectively.
Finally, statistical technique given in (
Even with improvements in the performance of climate models in reproducing the present and past climate due to a combination of sophisticated physical parameterization and higher model resolution [
Before we discuss the results from our study, we summarize in this section different sources of uncertainty in climate models, namely, forcing, initial condition, and model imperfection (model inadequacy and model uncertainty). According to [
For reanalysis and observation, [
According to [
To demonstrate an element of possible initial conditions (intramodel) and intermodel uncertainties in the models used in this study, we evaluated 8 models with 5 ensemble members that differ only in the initial condition of the atmospheric models (i.e., sensitivity of climate model simulations on initial condition). We first quantified the uncertainty related to the internal variability by taking the standard deviation of these five ensemble member for each model. Second, we quantified the intermodel variability by taking the standard deviation of 8 models after averaging first the ensemble members in each model. The results are shown in Figure
Intramodel and intermodal variability for eight models with at least 5 ensemble members.
Zonal wind
Meridional wind
Wind speed
Result indicates that there is larger intermodel variability than intramodel variability for zonal, meridional, and wind speed climatology. While intermodel variability signifies model response uncertainty (structural uncertainty), the intramodel variability is related to uncertainty in the initial conditions. The intermodel variability increases from the surface upwards, reaching maximum at the lower stratosphere for zonal wind and wind speed climatology (Figures
Having demonstrated some inherent problems associated with both the use of model, reanalysis, and observation data in climate applications and analysis, we caution that users should thus be aware of these uncertainties. The results based on first ensemble member for each model will be presented in next section.
Figure
Annual mean climatology (a, b, and c) and climatology difference (bias) relative to radiosonde observation (d, e, and f). The period of study is 1979–2005 from 850 hPa to 30 hPa pressure levels. The radiosonde plus and minus 2 standard deviations around the climatology mean is displayed as the gray shading in the bias plot (d, e, and f).
Overall, there are reasonable agreements in the shape of the vertical profiles of zonal wind and wind speed among CMIP5, reanalysis, and radiosonde observation, with slight difference in their magnitude. In the troposphere (850–250 hPa), CMIP5 models agree reasonably well with each other and are generally within 1–8 m s−1 of the radiosonde observation, while in stratosphere (150–30 hPa), the spread among the models and the model bias compared to radiosonde increased. For example, at 700 hPa, the range of CMIP5 models differs by ~2.8 m s−1, and model-to-model variability is ~0.9 m s−1 while at 100 hPa, the models differ by ~11 m s−1 and model-to-model variability is ~3.2 m s−1. The increased model-to-model variability is in agreement with the result of quantified uncertainty in the upper troposphere and stratosphere discussed in Section
For the meridional wind (Figure
The performances of CMIP5 models in their ability to simulate the seasonal climatology are also analyzed. First, the seasonal wind fields of vertical distribution from CMIP5 models, reanalysis, and radiosonde observation are computed by averaging three months (i.e., for December through February (DJF), March through May (MAM), June through August (JJA), and September through November (SON)). Overall, there is agreement in shape of the seasonal horizontal wind and wind speed vertical distribution among CMIP5 models, reanalysis, and radiosonde observation (not shown). Similar to the annual mean climatology, they show an increase in wind fields with height from 850 to ~200 hPa and then wind fields start to decrease with height till 30 hPa. The CMIP5 models and reanalysis capture the observed seasonal mean wind vertical distribution with stronger (weaker) wind during the DJF (JJA) season (not shown). Although CMIP5 models and reanalysis capture the phase of the seasonal cycle, the amplitude of the seasonal cycle differs among models. For reanalysis, the maximum difference in the amplitude of the seasonal cycle is located at ~200 hPa. In general, CMIP5 and reanalysis bias with respect to radiosonde range from −10 m s−1 to 15 m s−1 throughout the vertical profile.
To describe the differences in the climatology of the horizontal wind and wind speed, we will focus on this pressure level (200 hPa). As stated above, CMIP5 and reanalysis capture the phase of the seasonal cycle with stronger (weaker) wind during winter (summer) months (Figures
Sterling station seasonal cycle at 200 hPa from individual CMIP5, reanalysis, and radiosonde observation for 1979–2005. The gray shading in (b), (d), and (f) is the radiosonde plus and minus 1 standard deviation around the climatology mean.
In addition, while there is variation in the amplitude of the seasonal cycle among individual CMIP5, there is reasonable agreement in amplitude of the seasonal cycle wind fields among the three reanalysis datasets. For zonal wind and wind speed, CMIP5 model bias ranges from −5 to 10 m s−1 while reanalysis bias ranges from 0 to 5 m s−1 (Figures
Figure
Similar to Figure
While the vertical interannual variability profile (distribution) of meridional wind and wind speed shows similar profile with zonal wind, the magnitude of maximum interannual variability differs (Figures
Also, radiosonde observations, reanalysis, and CMIP5 model show larger interannual variability in the upper troposphere for the three wind fields compared to the lower troposphere. The zonal wind bias (Figure
In Figure
The vertical distributions of annual mean horizontal wind and wind speed trends from radiosonde, reanalysis, and CMIP5 models calculated for 1979–2005. Horizontal black line represents the 2-sigma variation in wind.
In general, there is similarity in the shape and magnitude of the vertical zonal wind and wind speed trend profile. The shape of meridional wind trend profile differs slightly from both zonal wind and wind speed. Besides, the magnitude meridional wind trend profile is small. Overall, radiosonde observation shows positive trends from 850 hPa to 50 hPa for zonal wind and wind speed. Further information about the observed trends can also be found by examining the 2-sigma (95%) confidence interval in the observations. The trend confidence interval in zonal, meridional, and wind speed is about twice as large in upper troposphere and lower stratosphere (i.e., at pressure levels between 300 hPa and 100 hPa) compared to the lower troposphere (i.e., at pressure level between 850 hPa and 300 hPa). For example, zonal wind annual mean trend confidence interval at 200 hPa is 0.9 m s−1 decade−1 whereas at 500 hPa, it is about 0.46 m s−1 decade−1. This larger confidence interval in upper troposphere and lower stratosphere (UTLS) indicates that the upper troposphere and lower stratosphere are subjected to larger variability than the troposphere (Figures
Out of the 20 CMIP5 models, seven (7) show positive zonal wind and wind speed trends (Figures
So far the analysis was based on output from historical simulations for the period from 1979 to 2005. The result generally indicates that any climate change trend in the 27 years of observed and simulated wind is not yet distinguishable from the natural interannual to decadal variability using a single point observation. Next section deals with the number of years required to detect statistically significant trends from radiosonde observation.
In this section, we addressed the question of the number of years required to detect statistically significant wind trends with the probability of 0.9 from Sterling radiosonde observation (see (
In this study, ensemble mean zonal, meridional, and wind speed trends from CSIRO-MK3.6.0 model for the period 2016–2050 are used as example for estimation of future trends. The ensemble mean trends are evaluated by averaging the individual 10 run trends found from CSIRO-MK3.6.0 model. The ensemble mean trends and uncertainty in the mean trends for the three wind fields are plotted in Figure
The standard deviation and autocorrelation of the horizontal wind as well as wind speed noise are estimated from Sterling radiosonde observation wind field time series for the period 1979–2012 after rearranging (
Month-to-month variability and autocorrelation for zonal wind (a, b), meridional wind (c, d), and wind speed (e, f), respectively. The observational data was from 1979 to 2012 for Sterling station Virginia Station.
Zonal wind variability
Zonal wind autocorrelation
Meridional wind variability
Meridional wind autocorrelation
Wind speed variability
Wind speed autocorrelation
Future estimated trends and numbers of years for zonal wind (a, b), meridional wind (c, d), and wind speed (e, f), respectively. Black diamond symbol represents the ensemble mean trends calculated by averaging trends found from individual 10 ensemble member. Horizontal black thin line in (a), (c), and (e) represents 1-sigma trend uncertainty. Circle symbols represent number of years (black filled circle represents the mean, blue the minimum, and red the maximum). Note that the blue circles and red circles in (b), (d), and (f) represent the 95% confidence interval for the number of years which is represented by black circle.
CSIRO-MK360 (2016–2050)
Number of years
CSIRO-MK360 (2016–2050)
Number of years
CSIRO-MK360 (2016–2050)
Number of years
Radiosonde wind observation data from 1979 to 2012 are used to estimate month-to-month variability and autocorrelation in wind field dataset. Assuming these two parameters would remain the same in the future and the ensemble mean trends estimated from CSIRO-MK3.6.0 are accurate (Figures
Overall, the zonal wind trend mirrors the wind speed trend while the number of years to obtain statistically significant trend decreases with increasing pressure level except for upper troposphere. In the upper troposphere (~200 hPa) for zonal (Figure
In this study, monthly mean wind data from CMIP5 simulations was used to examine models and their ability to represent annual and seasonal historical wind variability and trends from the 850 hPa to 30 hPa pressure level. We then characterized and compared the observed and simulated wind fields (zonal, meridional, and wind speed) with respect to climatology mean, variability, and trends for both annual mean and seasonal mean. We also calculated the number of years needed to detect trend.
We summarize our results as follows: Overall, zonal wind and wind speed vertical profiles are very similar in terms of shape and magnitude. The vertical profiles of individual CMIP5 models, as well as among CMIP5 models, reanalysis, and radiosonde observation, however, show some disagreement in the sign of the meridional wind magnitude. Although the CMIP5 models reproduce the observed vertical profile of the annual mean zonal wind climatology reasonably well, there are bias and variability among models, which is larger at the stratosphere (above ~200 hPa). When reanalyses are compared to radiosonde, their spread among each other and bias with respect to radiosonde are smaller. This is expected because the radiosonde observation has already been assimilated into these reanalysis products. The CMIP5 models and reanalysis capture the observed seasonal cycle throughout the vertical profile, with stronger (weaker) wind during the winter (summer) season. There is, however, variation in the amplitude of the seasonal cycle among models, and reanalysis with maximum difference in the amplitude of the seasonal cycle located ~200 hPa. As expected, it is challenging to find statistically significant trends at local scale due to small signal-to-noise ratio. Longer wind time series would be required for the trends to be statistically significant for a single point measurement. Our analysis shows that intramodel spread is smaller than intermodel variability suggesting that the role of internal variability is negligible. It thus follows that the model response uncertainty is responsible for the disagreement in the vertical profiles of the wind fields.
The authors declare that there is no conflict of interests regarding the publication of this paper.