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Parallel Comparison of the Northern Winter Stratospheric Circulation in Reanalysis and in CMIP5 Models


doi: 10.1007/s00376-014-4192-2

  • A parallel comparison is made of the circulation climatology and the leading oscillation mode of the northern winter stratosphere among six reanalysis products and 24 CMIP5 (Coupled Model Intercomparison Project Phase 5) models. The results reveal that the NCEP/NCAR, NECP/DOE, ERA40, ERA-Interim and JRA25 reanalyses are quite consistent in describing the climatology and annual cycle of the stratospheric circulation. The 20CR reanalysis, however, exhibits a remarkable "cold pole" bias accompanied by a much stronger stratospheric polar jet, similar as in some CMIP5 models. Compared to the 1-2 month seasonal drift in most coupled general circulation models (GCMs), the seasonal cycle of the stratospheric zonal wind in most earth system models (ESMs) agrees very well with reanalysis. Similar to the climatology, the amplitude of Polar Vortex Oscillation (PVO) events also varies among CMIP5 models. The PVO amplitude in most GCMs is relatively weaker than in reanalysis, while that in most of the ESMs is more realistic. In relation to the "cold pole" bias and the weaker oscillation in some CMIP5 GCMs, the frequency of PVO events is significantly underestimated by CMIP5 GCMs; while in most ESMs, it is comparable to that in reanalysis. The PVO events in reanalysis (except in 20CR) mainly occur from mid-winter to early spring (January-March); but in some of the CMIP5 models, a 1-2 month delay exists, especially in most of the CMIP5 GCMs. The long-term trend of the PVO time series does not correspond to long-term changes in the frequency of PVO events in most of the CMIP5 models.
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Manuscript received: 26 August 2014
Manuscript revised: 07 November 2014
通讯作者: 陈斌, bchen63@163.com
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Parallel Comparison of the Northern Winter Stratospheric Circulation in Reanalysis and in CMIP5 Models

  • 1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
  • 2. University of Chinese Academy of Sciences, Beijing 100049

Abstract: A parallel comparison is made of the circulation climatology and the leading oscillation mode of the northern winter stratosphere among six reanalysis products and 24 CMIP5 (Coupled Model Intercomparison Project Phase 5) models. The results reveal that the NCEP/NCAR, NECP/DOE, ERA40, ERA-Interim and JRA25 reanalyses are quite consistent in describing the climatology and annual cycle of the stratospheric circulation. The 20CR reanalysis, however, exhibits a remarkable "cold pole" bias accompanied by a much stronger stratospheric polar jet, similar as in some CMIP5 models. Compared to the 1-2 month seasonal drift in most coupled general circulation models (GCMs), the seasonal cycle of the stratospheric zonal wind in most earth system models (ESMs) agrees very well with reanalysis. Similar to the climatology, the amplitude of Polar Vortex Oscillation (PVO) events also varies among CMIP5 models. The PVO amplitude in most GCMs is relatively weaker than in reanalysis, while that in most of the ESMs is more realistic. In relation to the "cold pole" bias and the weaker oscillation in some CMIP5 GCMs, the frequency of PVO events is significantly underestimated by CMIP5 GCMs; while in most ESMs, it is comparable to that in reanalysis. The PVO events in reanalysis (except in 20CR) mainly occur from mid-winter to early spring (January-March); but in some of the CMIP5 models, a 1-2 month delay exists, especially in most of the CMIP5 GCMs. The long-term trend of the PVO time series does not correspond to long-term changes in the frequency of PVO events in most of the CMIP5 models.

1. Introduction
  • The main variability of the stratospheric circulation lies in the northern winter season, which can be represented by the leading oscillation of the stratospheric polar vortex between a strong (cold) and weak (warm) state of the vortex. This leading and recurrent oscillation mode is known as the Polar Vortex Oscillation (PVO) (Ren and Cai, 2006, 2007; Cai and Ren, 2006, 2007), or the Northern Annular Mode in the stratosphere (NAM) (Thompson and Wallace, 1998). Associated with the occurrence of PVO or NAM events, downward propagation of circulation anomalies exists (Kodera et al., 1990; Baldwin and Dunkerton, 1999), as well as simultaneous poleward propagation in the stratosphere synchronized with equatorward propagation of circulation anomalies in the troposphere between the tropics and the polar region (Cai and Ren, 2006, 2007; Ren and Cai, 2007). Due to the intimate coupling of changes between the stratosphere and troposphere, as well as the much longer timescale exhibited by the stratospheric circulation, circulation changes in the stratosphere have been indicated to have significant implications for weather and climate prediction in the troposphere (Thompson and Wallace, 1998; Baldwin and Dunkerton, 2001; Thompson et al., 2002; Cai, 2003; Ren and Cai, 2007).

    However, application of the stratospheric effects in weather and climate prediction is still quite limited due to insufficient knowledge on the dynamics of the stratosphere-troposphere coupling. The limited length of the observational data record currently available is an important factor affecting this limitation of understanding. Nevertheless, with improvements in the performance of numerical models in simulating the stratosphere in recent years, numerical models have begun to play an important role in further investigations of stratospheric dynamics and stratosphere-troposphere coupling processes. An early comprehensive inter-model comparison of the performance of various stratosphere-resolving general circulation models (GCMs) revealed that most of the models generally showed a much stronger and colder stratospheric polar vortex and a less frequent occurrence of "stratospheric sudden warming" (SSW) events (or the negative phase of the PVO/NAM) (Charlton et al., 2007). Based on these results, the frequency of SSW events in observations is about six events per decade, while it is on average only about 1.0-2.6 events per decade in models. This "cold pole" problem has been shown to prevail in many other stratosphere-resolving GCMs (Pawson et al., 2000; Ren et al., 2009). (Eyring et al., 2010) found that, for the ensemble means of several GCMs, the polar temperature biases become smaller (<5 K) and the SSW frequency (five events per decade) becomes much closer to that in observations. Recently, the Coupled Model Intercomparison Project Phase 5 (CMIP5) for the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report released long-term integration results from more than 50 models of various countries, and in a series of standard scenarios. This provides us with valuable long-term datasets for further studies on the stratosphere. Before adoption of these model datasets in stratospheric studies, systematic and objective assessments on the general performance of all the models in reproducing the climatology and changes of stratospheric circulation are obviously needed. Based on the multi-model results for the historical scenario of CMIP5, the current study systematically evaluates the models' performances in simulating the northern winter stratospheric circulation, including the wintertime climatology, the seasonal evolution, and the polar vortex oscillation process.

    The remainder of the paper is organized as follows. Section 2 gives a brief introduction to the CMIP5 models and the historical scenario experiments used in this study. Section 3 presents the reproducibility of the present climatology in the CMIP5 models. In section 4, the CMIP5 models are evaluated based on the annual cycle of the polar stratospheric circulation. Section 5 compares the CMIP5 models in simulating the PVO with reanalysis data. The final section provides further discussion and conclusions.

2. Description of the CMIP5 models and reanalysis datasets
  • CMIP5 were carried out by 25 modeling groups representing more than 50 climate models with the aim of furthering understanding of past and future climate change in key areas of uncertainty (Taylor et al., 2012). The changing conditions prescribed in the experiments include atmospheric composition (including CO2) due to anthropogenic and volcanic forcing, solar forcing, concentrations of short-lived species, and natural and anthropogenic aerosols (Taylor et al., 2012). CMIP5 builds on the previous phase (CMIP3) of experiments in two main ways. First, more modeling centers and models are involved. Second, the models generally run at higher spatial resolution or with more comprehensive physical processes. The historical-run scenario denotes that the coupled atmosphere-ocean model simulations are forced by estimates of the changes in atmospheric composition from natural and anthropogenic sources, volcanoes, greenhouse gases (GHGs), and aerosols, as well as the changes in solar output and land cover during the industrial period (1850-2005). Only anthropogenic GHGs and aerosols are prescribed as common forcings in all models, and other forcings, such as changes of land use, may differ from model to model. For earth system models (ESMs), the carbon cycle and natural aerosols are also simulated by models, and therefore feature feedback processes.

    The historical-run outputs we used are from 24 fully coupled CMIP5 models, including 12 atmosphere-ocean coupled GCMs and 12 ESMs. Some of the models are further coupled with chemistry modules. Detailed descriptions of all of the adopted 24 models are listed in Table 1, including the countries they are from, the types, the horizontal resolutions, and the numbers of vertical levels of the models, as well as the related references. Most of the models have provided multiple ensemble members, but we only used the first member to capture the temporal variability of the stratospheric circulation effectively.

    The reanalysis datasets used include the National Centers for Environmental Prediction-National Center for Atmospheric Research Reanalysis I (NCEP1) (Kalnay et al., 1996), the NCEP-U.S. Department of Energy Reanalysis II (NCEP2) (Kanamitsu et al., 2002), the European Centre for Medium-Range Weather Forecasts 40-Year Reanalysis (ERA40) (Uppala et al., 2005), the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-I) (Dee et al., 2011), the Japanese 25-year Reanalysis (JRA25) (Onogi et al., 2007), and the Twentieth-Century Reanalysis Project, version 2 (20CR) (Compo et al., 2011). Table 2 provides detailed information on these reanalysis datasets. The analysis methods used in this study include least-squares fitting, linear regression, and empirical orthogonal function (EOF) analysis.

3. Winter climatology
  • Figure 1 shows the winter mean (December-February, DJF) zonal-mean air temperature (shading) and zonal-mean zonal wind (contours) in each reanalysis. It can be seen that, in the upper troposphere, all the six reanalysis datasets consistently show a subtropical westerly jet near 30°N. While in the stratosphere, the first five reanalysis datasets all show that the strength of the polar jet is 30 m s-1 at 10 hPa, located at about 65°N, corresponding to the polar cold center of 200 K in the layer of 30-50 hPa. In contrast, 20CR shows a much stronger polar jet (55 m s-1) and a much colder (185 K at 20-10 hPa) polar vortex. The poor performance of 20CR in describing the stratospheric circulation may be related to the fact that the data assimilation is only applied on surface pressure, and the boundary forcing is from monthly-mean sea surface temperature and sea ice distributions (Compo et al., 2011). In this way, the 20CR dataset can provide relatively good estimations of the tropospheric variability, but with larges biases in the stratosphere, also noted in (Compo et al., 2011).

    Figure 1.  Winter (December-January, DJF) climatology of the zonal-mean zonal wind (contours, units: m s-1) in (a) six reanalyses and (b-e) 24 CMIP5 models. Also shown in (a) is the zonal-mean temperature in the six reanalyses (shading, units: K). Shading in (b-e) represents the separate model biases in zonal-mean temperature relative to the ensemble mean of the first five reanalysis datasets without 20CR. The climatologies are derived from the data records of recent 30-year periods (ERA40: 1973-2002; other reanalyses: 1979-2008; models: 1976-2005).

    Comparing the zonal-mean zonal wind patterns in each CMIP5 model with that in the first five reanalyses, it is seen that, generally, most of the CMIP5 models can reproduce reasonably well the strength and the vertical and meridional position of the upper tropospheric subtropical jet in the northern winter (Figs. 1b-e). However, the reproducibility of the models for the winter stratospheric polar jet and the polar temperature varies substantially from model to model, in terms of their magnitudes and location of action centers relative to the climatology in reanalysis.

    To perform an objective evaluation of the performance of the CMIP5 models in reproducing the northern winter stratospheric circulation, several benchmarks are defined: the mean temperature in the tropical stratosphere (30°S-30°N, 70-10 hPa), in the midlatitude stratosphere (30°-60°N, 100-10 hPa), in the upper polar stratosphere (60°-90°N, 30-10 hPa), and in the lower polar stratosphere (60°-90°N, 200-50 hPa); and the strength (averaged over 55°-75°N, 70-10 hPa) and meridional location of the stratospheric polar jet (maximum westerly). Below, we use box plots to present the distributions of these benchmarks in parallel for reanalysis and for the models.

  • Figure 2 shows the benchmarks for the mass-weighted area mean temperature in the tropical stratosphere (T trp, 30°S-30°N, 70-10 hPa, Figs. 2a and b) and in the midlatitude stratosphere (T mid, 30°-60°N, 100-10 hPa, Figs. 2c and d), and for the reanalysis (Figs. 2a and c) and for the CMIP5 models (Figs. 2b and d). The mean values of T trp in NCEP1, NCEP2, ERA40, REA-I and JRA25 are quite consistent at around 211.8 K, but in 20CR it is higher (213.6 K, Fig. 2a). As shown in Fig. 2b, the CMIP5 models capture the tropical stratospheric temperature with varying degrees of success. The mean values of T trp in 20 of the 24 models (BCC-CSM1-1, BCC-CSM1-1-M, CCSM4, CNRM-CM5, FGOALS-s2, GFDL-CM3, HadCM3, INMCM4, IPSL-CM5A-LR, IPSL-CM5A-MR, IPSL-CM5B-LR, MIROC-ESM, MIROC-ESM-CHEM, MIROC5, MRI-CGCM3, MPI-ESM-LR, MPI-ESM-MR, MPI-ESM-P, NorESM1-M and WACCM) exhibit a warm bias relative to the ensemble mean of the first five reanalysis datasets. The largest warm bias in T trp is from GFDL-CM3 (216 K). The other four models exhibit a cold bias of T trp, with the largest cold bias from CSIRO-Mk3.6.0 (208 K).

    Figure 2.  Box plots showing the distribution of mean temperature (a, b) in the tropical (30°S-30°N, 70-10 hPa) and (c, d) midlatitude (30°-60°N, 100-10 hPa) stratosphere in winter (DJF) from (a, c) the six reanalysis datasets and (b, d) the 24 CMIP5 models. The box for each dataset indicates the interquartile range; the central line in each box stands for the median; and the dot stands for the mean value. The whisker of each box indicates the minimum and the maximum value of the distribution. Dark gray shading, light gray shading, long dashes and short dashes show the interquartile range, value range, median, and mean for reanalysis ensemble (excluding 20CR), respectively.

    Similarly, the mean values of T mid in NCEP1, NCEP2, ERA40, ERA-I, and JRA25 are also very consistent (215 K), but 20CR again shows a large positive departure (217.5 K). The mean values of T mid in 17 of the 24 models (CSIRO-Mk3.6.0, BCC-CSM1-1, BCC-CSM1-1-M, CCSM4, FGOALS-s2, GFDL-CM3, HadCM3, INMCM4, IPSL-CM5A-LR, IPSL-CM5A-MR, IPSL-CM5B-LR, MIROC-ESM, MIROC-ESM-CHEM, MPI-ESM-MR, MRI-CGCM3, NorESM1-M and WACCM) are also overestimated relative to the first five reanalyses. The largest warm bias in T mid is again from GFDL-CM3 (218 K), and the other seven models exhibit a cold bias of T mid, with the largest cold bias being from FGOALS-g2 (211.5 K).

    Figure 3.  The same as in Fig. 2, but for (a, b) the upper polar (60°-90°N, 30-10 hPa) and (c, d) lower polar (60°-90°N, 200-50 hPa) stratosphere in winter.

  • The benchmarks of the mass-weighted area mean temperature in the upper (T pl,up, 60°-90°N, 30-10 hPa, Figs. 3a and b) and lower (T pl,lw, 60°-90°N, 200-50 hPa, Figs. 3c and d) polar stratosphere are shown in Fig. 3, including the distributions of T pl,up and T pl,lw for both the reanalysis (Figs. 3a and c) and the CMIP5 models (Figs. 3b and d). Note that the values of T pl,up and T pl,lw can define the intensity of the stratospheric polar vortex. The mean values of T pl,up and T pl,lw are 211.5 K and 214 K, respectively——fairly consistent among the first five reanalyses (NCEP1, NCEP2, ERA40, ERA-I, and JRA25). The mean values of T pl,up (198 K) and T pl,lw (207.5 K) in 20CR are both much smaller. The "cold pole" problem, especially for the lower polar stratosphere, also prevails in most of the CMIP5 models. For example, the mean values of T pl,up and T pl,lw in some models (e.g., BCC-CSM1-1, BCC-CSM1-M, CCSM4, CNRM-CM5, FGOALS-g2) are as low as 205 K and 210 K, respectively, both even exceeding the lower interquartile range of the ensemble mean of the first five reanalysis datasets. The much larger cold deviation of T pl,up and T pl,lw in FGOALS-g2 might be related to the systematic cold biases of the model, because all the mean values of T trp, T mid, T pl,up, and T pl,lw exhibit considerable cold biases. In contrast, although the "cold pole" problem also exists in most of the ESMs, the cold biases of T pl,up and T pl,lw in some of the ESMs and ChmESMs (MIROC-ESM, MIROC-ESM-CHEM, MPI-ESM-LR, MPI-ESM-MR, MPI-ESM-P and WACCM) are relatively much smaller than those in most of the GCMs.

  • The strength of the polar night jet is defined as the mass-weighted area mean zonal wind over (55°-75°N, 70-10 hPa). The mean value of U pn is 20 m s-1 in NCEP1, NCEP2, ERA40, ERA-I, and JRA25 (Fig. 4a). Consistent with the positive (negative) deviation of temperature in the midlatitude (polar) stratosphere in 20CR, the mean value of U pn in 20CR (37 m s-1) is nearly double that in the other reanalysis datasets. Similarly, the overestimated (much stronger) polar jet also prevails in most of the CMIP5 models (e.g., BCC-CSM1-1, BCC-CSM1-1-M, CCSM4, FGOALS-s2, GFDL-CM3, MRI-CGCM3); while across the ESMs (IPSL-CM5A-LR, IPSL-CM5A-MR, IPSL-CM5B-LR, MIROC-ESM, MIROC-ESM-CHEM, MPI-ESM-LR, MPI-ESM-LR, MPI-ESM-P, NorESM1-M and WACCM), the mean U pn is relatively close to that in reanalysis (Fig. 4b).

    The mean central latitude of the polar jet in NCEP1 is around 65°N, consistent with all the other reanalysis datasets, including 20CR, despite 20CR showing a much stronger polar jet (Fig. 4c). The polar jet in most of the models (BCC-CSM1-1, BCC-CSM1-1-m, CCSM4, FGOALS-s2, GFDL-CM3, INMCM4, MIROC-ESM, MIROC-ESM-CHEM, MIROC5, MPI-ESM-LR, MPI-ESM-MR, MPI-ESM-P, MRI-CGCM3, NorESM1-M and WACCM) is located at a similar central latitudinal position, with a mean value falling in the interquartile range of the reanalysis ensemble (20CR excluded); while in some other models (CNRM-CM5, CSIRO-Mk-3.6.0, FGOALS-g2, GISS-E2-H and GISS-E2-R, HadCM3 and IPSL-CM5A-MR), the polar jet tends to lie further equatorward (Fig.4d).

    Figure 4.  The same as in Fig. 2, but for (a, b) the mean zonal wind in the polar night jet region (55°-75°N, 70-10 hPa) and (c, d) the latitude of the polar night jet center in winter from (a, c) the six reanalysis datasets and (b, d) the 24 CMIP5 models.

    Figure 5.  Annual cycle of the zonal-mean zonal wind (units: m s-1, interval: 10) at 10 hPa from (a) the six reanalysis datasets and (b-e) the 24 CMIP5 models. Shading represents values greater than 30 m s-1.

4. Annual cycle
  • Figure 5 shows the annual cycle of the stratospheric zonal-mean zonal wind at 10 hPa. The extratropical zonal wind at 10 hPa clearly exhibits an annual cycle from a summer easterly to a winter westerly, which is quite consistent among NCEP1, NCEP2, ERA40, ERA-I, and JRA25, particularly the maximum tropical easterly in late January, the maximum polar westerly in early December, and the transitions between westerlies and easterlies in the extratropics. Unlike in these reanalyses, the polar jet in 20CR peaks until late January, exhibiting a seasonal drift of 1-2 months. In other words, not only the strength of the polar jet is overestimated, but there also exists a temporal delay of the winter westerly center in 20CR. Meanwhile, an elusive zonal-mean westerly exists over the equator throughout the year in 20CR.

    The CMIP5 models capture the seasonal variation of stratospheric zonal-mean zonal wind with varying degrees of success. The simulated annual cycle in most of the CMIP5 models is quite similar to that in the first five reanalyses, including the transition between the wintertime westerly and the summertime easterly in the extratropics in both the southern and northern hemispheres. However, the strength of the northern polar jet, the easterly in the subtropics, and the seasonal timing of the maximum circumpolar westerly vary from model to model. For example, a common problem seems to exist in some of the GCMs (CCSM4, CNRM-CM5, CSIRO-Mk3.6.0, FGOALS-g2, FGOALS-s2, GFDL-CM3, MIROC5, and MRI-CGCM3) and some of the ESMs (MPI-ESM-LR, MPI-ESM-MR, MPI-ESM-P and NorESM1-M) in that the simulated polar night jet also peaks 1-2 months later relative to that in the reanalysis data.

    Figure 6 further shows a quantitative measure of the performance of the CMIP5 models in reproducing the annual cycle of the extratropical zonal wind and the polar temperature relative to the first five reanalysis datasets. Figure 6a is a Taylor diagram for the simulated mass-weighted zonal wind in the circumpolar region (55°-75°N, 70-10 hPa), and Fig. 6b is the same but for the polar temperature (75°-90°N, 100-20 hPa). The correlation coefficient of the mass-weighted zonal-wind/temperature between each CMIP5 model and the reanalysis ensemble (except 20CR) is denoted by the cosine of the azimuth angle, and the ratio of the corresponding standard deviation in every CMIP5 model to that in the reanalysis ensemble is represented by the radial distance. The radial distance of each model indicates that the seasonal variation of the circumpolar zonal wind and the polar temperature in some models (e.g., BCC-CSM1-1, BCC-CSM1-1-M, CCSM4, FGOALS-s2, GFDL-CM3, IPSL-CM5B-LR) are obviously stronger than that in the reanalysis ensemble (REF in Fig. 6); while in some other GCMs (e.g., CSIROC-Mk3.6.0, GISS-E2-H, GISS-E2-R, HadCM3 and INMCM4), they are relatively much weaker. In contrast, the annual cycles of polar stratospheric circulation in most of the ESMs (e.g., IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC-ESM, MIROC-ESM-CHEM, MPI-ESM-LR, MPI-ESM-MR, MPI-ESM-P, NorESM1-M and WACCM) are reproduced much more realistically. The normalized standard deviations in these ESMs are closer to that of the 20CR-excluded reanalysis ensemble (REF in Fig. 6). In general, the correlation coefficients of the zonal wind between all models and the 20CR-excluded reanalysis ensemble can reach 0.9 (Fig. 6a), which is also true for the polar temperature (Fig. 6b), indicating a well-reproduced transition of the stratospheric circulation in the polar region between summertime and wintertime.

    Figure 6.  Taylor diagram showing the climatological annual cycle of the polar stratospheric circulation: (a) polar night jet defined as the averages of the zonal-mean zonal wind in (55°-75°N, 70-10 hPa) and (b) the polar temperature defined as averages of the air temperature in (75°-90°N, 100-20 hPa). The correlation coefficients and the ratio of the standard deviation between the modeled and reanalysis ensemble (excluding 20CR) result are shown by the cosine of the azimuth angle and the radial distance, respectively. REF indicates the reanalysis ensemble (excluding 20CR) as the reference point.

    Figure 7.  Spatial patterns of the winter (DJF) zonal-mean zonal wind anomalies (units: m s-1, contour interval: 1) obtained from linear regressions against the leading EOF time series of the zonal-mean zonal wind anomalies in the extratropical Northern Hemisphere (20°-90°N) for (a) the six reanalysis datasets and (b-e) the 24 CMIP5 models. Shading denotes the corresponding DJF climatology of the zonal-mean zonal wind. The latitude of the PVO maximum center is shown in the bottom right corner of each plot, and the percentage variance of the leading mode is shown in the top right.

5. Polar Vortex Oscillation events
  • The leading oscillation process, the NAM or PVO (Cai and Ren, 2007; Ren and Cai, 2006), is always related to an oscillation between a stronger and a weaker stratospheric polar vortex accompanied with radical changes of the circumpolar jet between a stronger and a weaker westerly (or even easterly) state. Here, we perform EOF analysis on the monthly zonal-mean zonal wind anomalies north of 20°N for each of the reanalyses and each of the model historical runs. Following (Ren and Yang, 2012) and (Liu et al., 2012), we name the leading mode as the PVO mode. Rather than defining PVO events based on the standard deviations (STDs) of the PVO time series as in their studies, we identify PVO events based on a criterion that represents the average oscillation changes of the stratospheric zonal wind for unit STD of the PVO intensities in the reanalysis datasets, which is obtained by regressing the spatial pattern of the PVO mode against the standardized PVO time series. As a result, the threshold for PVO events in reanalysis is unit STD, which on average corresponds to a central intensity of 8-9 m s-1 for the leading zonal-mean zonal wind oscillation (i.e., PVO time series multiplied by the leading PVO mode). And the PVO events in models are identified only when the central intensity of the leading zonal wind oscillation in the extratropics achieves 10 m s-1, 15 m s-1, or 20 m s-1. In this way, the definitions of PVO events are uniform in terms of their intensity among the reanalyses and the CMIP5 models.

  • The regressed spatial pattern of the DJF zonal-mean zonal wind anomalies against the PVO time series is shown in Fig. 7 for the reanalyses and the CMIP5 models. It can be seen that the dipole pattern, or the out-of-phase relationship of the zonal wind anomalies between the subtropics and the circumpolar region, are largely consistent among the reanalysis datasets. This is also true for the oscillation amplitudes, as indicated by the comparable action centers in the panels of Fig. 7(a). The oscillation center lies north of 67.5°N, close to the climatological location of the DJF polar jet, and the oscillation amplitudes are all 8 m s-1 in the reanalysis datasets, including 20CR. Specifically, the maximum zonal-mean zonal wind anomalies for unit PVO STD are 7.8, 8.0, 8.7, 8.2, 8.5, and 9.0 m s-1 in NCEP1, NCEP2, ERA40, ERA-I, JRA25, and 20CR, respectively. It can be seen from Figs. 7b-e that the oscillation amplitudes are reproduced with varying degrees of success in the CMIP5 models. The PVO intensity in some of the GCMs is much weaker, with the central value of the zonal-wind oscillation for unit STD of the leading time series being only 1.6 m s-1 in CSIRO-Mk3.6.0, 2.5 m s-1 in MIROC5, 6.9 m s-1 in CCSM4, 6.4 m s-1 in CNRM-CM5, 6.8 m s-1 in FGOALS-g2, 7.5 m s-1 in GFDL-CM3, 5.5 m s-1 in GISS-E2-H, 5.6 m s-1 in GISS-E2-R, 4.7 m s-1 in HadCM3, and 5.7 m s-1 in INMCM4. Relatively, the oscillation amplitudes are more realistic in most of the ESMs (e.g., BCC-CSM1-1, BCC-CSM1-1-M, ISPL-CM5A-LR, ISPL-CM5A-MR, IPSL-CM5B-LR, MIROC-ESM, MIROC-ESM-CHEM, MPI-ESM-LR, MPI-ESM-MR, MPI-ESM-P, NorESM1-M and WACCM). The central value of the zonal-wind oscillation for unit STD of the leading time series is around 8-9 m s-1 in these ESMs.

  • Based on the results of Cai and Ren (2006, 2007), PVO events occurs 1-2 times in each winter season in the NCEP2 reanalysis. Table 3 shows the average frequency of positive/negative PVOs in each reanalysis dataset and in each CMIP5 model based on the different thresholds (10, 15 and 20 m s-1) for PVO events. When 10 m s-1 is chosen as the threshold for PVO events, there are on average about 7-8 PVO events in one decade in NCEP1, NCEP2, ERA-I, and JRA25. In 20CR, however, there are only 4-5 PVO events in one decade.

    The reproducibility of the PVO frequency varies among CMIP5 models (Table 3). Specifically, whether a higher (20 m s-1) or a lower (10 m s-1) threshold is used, not even one PVO event can be identified in CSIRO-Mk3.6.0 and MIROC5, again indicating that the zonal-wind oscillation intensity in these models is relatively very weak (Fig. 7). Similar problems also exist in some other GCMs. For the thresholds of 10, 15 or 20 m s-1, the frequency of positive/negative PVO events in the reanalysis ensemble (20CR excluded) is about 6.8/7.6, 3.4/4.2, and 1.5/2.7 per decade, respectively, and it is only about 4.9/5.6, 2.2/3.2, and 0.9/1.8 per decade, respectively, in the model ensemble. This seems to indicate that the polar vortex oscillation in most of the CMIP5 models, particularly the CMIP5 GCMs, is generally much weaker and can barely reach the observed PVO intensity. In other words, aside from the common problem of the cooler or stronger stratospheric polar vortex, the underestimated frequency of positive/negative PVO events is another challenge for the CMIP5 models, especially the CMIP5 GCMs. In contrast, the frequency of positive/negative PVO events that reach the thresholds in most of the ESMs (e.g., BCC-CSM1-1-m, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC-ESM, MIROC-ESM-CHEM, MPI-ESM-LR, MPI-ESM-MR, MPI-ESM-P and WACCM) is comparable with the observation (Table 3).

    Figure 8.  Polar vortex oscillation climatology in frequency of events [units: no. (10 yr)-1] in a given month for (a) each reanalysis dataset and (b-e) each CMIP5 historical-run simulation, based on the 10 m s-1 intensity threshold of the leading oscillation of zonal-mean zonal wind. The reanalysis ensemble (excluding 20CR) climatology is shown as blank bars.

    Next, we compare the seasonal distributions of PVO events among each reanalysis dataset and each CMIP5 model. To do this, the month-by-month distributions of the average numbers of PVO events in each reanalysis dataset (Fig. 8a) and each CMIP5 model (Figs. 8b-e) are shown in Fig. 8. Positive/negative PVO events are denoted with dark/light gray bars. From the distribution of PVO events in NCEP1, NCEP2, ERA40, ERA-I, and JRA25, it can be seen that, typically, most PVO events occur during mid-winter to early spring (January-March), with only a few PVOs occurring in November and April. The seasonal distributions of PVO events in NCEP1, NCEP2, ERA40, ERA-I, and JRA25 are rather consistent, especially among NCEP2, ERA-I, and JRA25, and between NCEP1 and ERA40. Whereas, in 20CR, the PVO events occur mainly in March-May (Fig. 8a). This seasonal drift problem for the occurrence of PVO events is also common in the CMIP5 GCMs (e.g., CCSM4, CNRM-CM5, FGOALS-g2, FGOALS-s2, GFDL-CM3 and MRI-CGCM3), and even in several of the ESMs (e.g., BCC-CSM1-1, BCC-CSM1-1-m and IPSL-CM5A-LR, Figs.8b-8e). This seasonal drift may be related to the 1-2 month delay of the winter extratropical westerly center in these models relative to the reanalysis datasets (Fig. 5). In contrast, the PVO frequency and its seasonal distribution in most of the ESMs (e.g., IPSL-CM5A-MR, IPSL-CM5B-LR, MIROC-ESM, MIROC-ESM-CHEM, MPI-ESM-LR, MPI-ESM-MR, MPI-ESM-P, NorESM1-M and WACCM) are well reproduced.

  • It is known that the global troposphere and the surface have become substantially warmer due to the increase in greenhouse gases (GHGs) in recent decades. Meanwhile, the stratosphere has become steadily cooler, and the northern winter stratospheric polar vortex has become stronger (Randel et al., 1999; Ramaswamy et al., 2001; Langematz et al., 2003; Manzini et al., 2003; Ramaswamy et al., 2006). The strengthening of the polar vortex and the cooling of the polar stratosphere are intimately related to the long-term changes in the low-frequency variability in the troposphere, including the AO or the NAM. Next, we turn our attention to the performance of the CMIP5 models in reproducing this long-term trend, particularly the long-term trend and the changes in frequency of the stratospheric oscillation events in the CMIP5 models.

    Figure 9 shows the existence of a long-term trend of the PVO time series (asterisks) during 1900-2005 in each CMIP5 model, and the corresponding changes in frequency of the PVO events based on the 10 m s-1 intensity threshold explained above. It can be seen that some of the models can reproduce a significant positive trend of the PVO time series (e.g., BCC-CSM1-1-m, CNRM-CM5, CSIRO-Mk3.6.0, FGOALS-s2, HadCM3, MIROC-ESM, MIROC-ESM-CHEM, MIROC5, MPI-ESM-P, MRI-CGCM3 and NorESM1-M), but exhibit an insignificant increase in the frequency of positive PVO events from 1900-50 to 1951-2005. MRI-CGCM3 even shows a decrease in frequency of positive PVO events when there is a positive trend of the PVO time series (Fig. 9a). Meanwhile, there are some models which show a decrease in frequency of negative PVO events (e.g., CCSM4, FGOALS-s2, MRI-CGCM3, CNRM-CM5, BCC-CSM1-1, BCC-CSM1-1-m, NorESM1-M, MIROC-ESM-CHEM, MPI-ESM-P, and WACCM), though also insignificant. Other models even show an insignificant increase in the frequency of negative PVO events (Fig. 9b). As a result, a significant positive trend of the PVO time series can be identified in the model ensemble, but accompanied by insignificant changes in the average frequency of either the positive (Fig. 9a) or the negative (Fig. 9b) PVO events. Further diagnosis of the poleward eddy heat flux (60°N, 50 hPa) by planetary-wave activity in winter shows that nearly all the models, except GISS-E2-R [-3.6 K m s-1 (10 yr)-1, 95% confidence level] reproduce insignificant changes in the northward eddy heat flux during 1900-2005 (not shown). These results basically confirm the results from one single model (FGOALS-s2) in (Ren and Yang, 2012), i.e. that the long-term trend of the polar cooling in recent decades (or the positive trend of the PVO time series) is not related to the decrease in dynamic forcing by planetary-wave activity (or the occurrence of PVO events), but rather to the long-term thermodynamic forcing due to the increase in GHGs.

    Figure 9.  Changes in frequencies for (a) positive and (b) negative PVO events [units: no. (10 yr)-1] from the period 1900-50 to 1951-2005 based on the 10 m s-1 intensity threshold of the leading oscillation of zonal-mean zonal wind. The dashed lines mark the 95% confidence level for changes in PVO frequencies. The asterisks indicate that a positive trend of the PVO time series in 1900-2005 in the corresponding CMIP5 model is statistically significant at the 95% confidence level.

6. Summary
  • Based on six reanalysis datasets and the historical scenario simulations from 24 CMIP5 models, the northern wintertime stratospheric circulation is diagnosed and systematically assessed. The results indicate that NCEP1, NECP2, ERA40, ERA-I, and JRA25 are quite consistent in describing the general features of the circulation climatology from the stratosphere to the troposphere in the Northern Hemisphere winter, particularly the zonal-mean patterns of the zonal wind and temperature. The annual cycle of the stratospheric zonal-mean zonal wind is also highly consistent among those five reanalysis datasets. As one of the most common problems in the 20CR reanalysis and in some of the CMIP5 models (especially the GCMs, e.g., CCSM4, FGOALS-g2, FGOALS-s2, MRI-CGCM3, and CNRM-CM5), a much stronger polar jet is reproduced, accompanied by a much cooler polar stratosphere. Most of the GCMs show a serious seasonal drift of the zonal-mean zonal wind with a 1-2 month delay of the maximum westerly in the circumpolar region. The simulated seasonal cycle of the stratospheric zonal-mean zonal wind in most of the ESMs agrees very well with the first five reanalyses. The observed PVO, defined as the leading mode of the extratropical (20°-90°N) zonal-mean zonal wind in reanalysis, is characterized by a dipole pattern of the zonal-mean zonal wind between the subtropics and the circumpolar region. The amplitude of the circumpolar westerly wind oscillation is reproduced with varying degrees of success in the CMIP5 models. It is generally weaker in some of the GCMs than that in the reanalysis ensemble (excluding 20CR), while it is reproduced much more realistically in most of the ESMs. The frequency of the PVO events in most of the CMIP5 models is also underestimated, especially in most of the GCMs. Specifically, there are on average 6.8/7.6, 3.4/4.2, and 1.5/2.7 positive/negative PVO events in one decade in the reanalysis ensemble (excluding 20CR) when an oscillation intensity threshold of 10, 15, and 20 m s-1 is applied, respectively. The corresponding average number of positive/negative PVO events is only 4.9/5.6, 2.2/3.2, and 0.9/1.8 in one decade, respectively, in the model ensemble.

    In addition, PVO events in NCEP1, NCEP2, ERA40, ERA-I, and JRA25 consistently take place mainly during mid-winter to early spring (January-March), while the peak frequency of PVO events in 20CR and in most GCMs appears 1-2 months later in February-April. The seasonal drift in PVO frequency is consistent with the similar 1-2 months delay, relative to the reanalysis, of the appearance of the strongest winter extratropical westerly center. By contrast, the seasonal drift of the peak frequency of PVO in most of the ESMs is relatively insignificant.

    The model ensemble shows a positive trend of the PVO time series, accompanied by less significant changes in PVO frequency from 1900-50 to 1951-2005. This verifies the results from one single model (Ren and Yang, 2012), which showed that the long-term trend of the polar cooling in recent decades (or the positive trend of the PVO time series) is not related to the decrease in dynamic forcing by planetary-wave activity (or the occurrence of PVO events), but rather to the long-term thermodynamic forcing due to the increase in GHGs.

    In general, the parallel comparison of the climatology and variability of the stratospheric circulation between CIMP5 models and the currently available reanalysis datasets can help model users to understand the model uncertainties in stratospheric processes in CMIP5, and also provide useful information for future model improvements on model ability in describing stratospheric dynamics. The inclusion of the carbon cycle and natural aerosols as well as stratosphere-resolved processes may help to significantly improve model performance in simulating the polar stratosphere. Nevertheless, more detailed or specific analyses are still needed to further understand the uncertainties in any specific stratospheric process in each of the CMIP5 models.

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