2018 Vol. 35, No. 7
Display Method:
2018, 35(7): 757-770.
doi: 10.1007/s00376-018-7160-4
Abstract:
Using a set of numerical experiments from 39 CMIP5 climate models, we project the emergence time for 4°C global warming with respect to pre-industrial levels and associated climate changes under the RCP8.5 greenhouse gas concentration scenario. Results show that, according to the 39 models, the median year in which 4°C global warming will occur is 2084. Based on the median results of models that project a 4°C global warming by 2100, land areas will generally exhibit stronger warming than the oceans annually and seasonally, and the strongest enhancement occurs in the Arctic, with the exception of the summer season. Change signals for temperature go outside its natural internal variabilities globally, and the signal-to-noise ratio averages 9.6 for the annual mean and ranges from 6.3 to 7.2 for the seasonal mean over the globe, with the greatest values appearing at low latitudes because of low noise. Decreased precipitation generally occurs in the subtropics, whilst increased precipitation mainly appears at high latitudes. The precipitation changes in most of the high latitudes are greater than the background variability, and the global mean signal-to-noise ratio is 0.5 and ranges from 0.2 to 0.4 for the annual and seasonal means, respectively. Attention should be paid to limiting global warming to 1.5°C, in which case temperature and precipitation will experience a far more moderate change than the natural internal variability. Large inter-model disagreement appears at high latitudes for temperature changes and at mid and low latitudes for precipitation changes. Overall, the inter-model consistency is better for temperature than for precipitation.
Using a set of numerical experiments from 39 CMIP5 climate models, we project the emergence time for 4°C global warming with respect to pre-industrial levels and associated climate changes under the RCP8.5 greenhouse gas concentration scenario. Results show that, according to the 39 models, the median year in which 4°C global warming will occur is 2084. Based on the median results of models that project a 4°C global warming by 2100, land areas will generally exhibit stronger warming than the oceans annually and seasonally, and the strongest enhancement occurs in the Arctic, with the exception of the summer season. Change signals for temperature go outside its natural internal variabilities globally, and the signal-to-noise ratio averages 9.6 for the annual mean and ranges from 6.3 to 7.2 for the seasonal mean over the globe, with the greatest values appearing at low latitudes because of low noise. Decreased precipitation generally occurs in the subtropics, whilst increased precipitation mainly appears at high latitudes. The precipitation changes in most of the high latitudes are greater than the background variability, and the global mean signal-to-noise ratio is 0.5 and ranges from 0.2 to 0.4 for the annual and seasonal means, respectively. Attention should be paid to limiting global warming to 1.5°C, in which case temperature and precipitation will experience a far more moderate change than the natural internal variability. Large inter-model disagreement appears at high latitudes for temperature changes and at mid and low latitudes for precipitation changes. Overall, the inter-model consistency is better for temperature than for precipitation.
2018, 35(7): 771-784.
doi: 10.1007/s00376-017-7177-0
Abstract:
The UK Met Office Unified Model (UM) is employed by many weather forecasting agencies around the globe. This model is designed to run across spatial and time scales and known to produce skillful predictions for large-scale weather systems. However, the model has only recently begun running operationally at horizontal grid spacings of ∼1.5 km [e.g., at the UK Met Office and the Korea Meteorological Administration (KMA)]. As its microphysics scheme was originally designed and tuned for large-scale precipitation systems, we investigate the performance of UM microphysics to determine potential inherent biases or weaknesses. Two rainfall cases from the KMA forecasting system are considered in this study: a Changma (quasi-stationary) front, and Typhoon Sanba (2012). The UM output is compared to polarimetric radar observations in terms of simulated polarimetric radar variables. Results show that the UM generally underpredicts median reflectivity in stratiform rain, producing high reflectivity cores and precipitation gaps between them. This is partially due to the diagnostic rain intercept parameter formulation used in the one-moment microphysics scheme. Model drop size is generally both under- and overpredicted compared to observations. UM frozen hydrometeors favor generic ice (crystals and snow) rather than graupel, which is reasonable for Changma and typhoon cases. The model performed best with the typhoon case in terms of simulated precipitation coverage.
The UK Met Office Unified Model (UM) is employed by many weather forecasting agencies around the globe. This model is designed to run across spatial and time scales and known to produce skillful predictions for large-scale weather systems. However, the model has only recently begun running operationally at horizontal grid spacings of ∼1.5 km [e.g., at the UK Met Office and the Korea Meteorological Administration (KMA)]. As its microphysics scheme was originally designed and tuned for large-scale precipitation systems, we investigate the performance of UM microphysics to determine potential inherent biases or weaknesses. Two rainfall cases from the KMA forecasting system are considered in this study: a Changma (quasi-stationary) front, and Typhoon Sanba (2012). The UM output is compared to polarimetric radar observations in terms of simulated polarimetric radar variables. Results show that the UM generally underpredicts median reflectivity in stratiform rain, producing high reflectivity cores and precipitation gaps between them. This is partially due to the diagnostic rain intercept parameter formulation used in the one-moment microphysics scheme. Model drop size is generally both under- and overpredicted compared to observations. UM frozen hydrometeors favor generic ice (crystals and snow) rather than graupel, which is reasonable for Changma and typhoon cases. The model performed best with the typhoon case in terms of simulated precipitation coverage.
2018, 35(7): 785-795.
doi: 10.1007/s00376-017-6327-8
Abstract:
A comparative study was carried out to explore carbon monoxide total columnar amount (CO TC) in background and polluted atmosphere, including the stations of ZSS (Zvenigorod), ZOTTO (Central Siberia), Peterhof, Beijing, and Moscow, during 1998-2014, on the basis of ground- and satellite-based spectroscopic measurements. Interannual variations of CO TC in different regions of Eurasia were obtained from ground-based spectroscopic observations, combined with satellite data from the sensors MOPITT (2001-14), AIRS (2003-14), and IASI MetOp-A (2010-13). A decreasing trend in CO TC (1998-2014) was found at the urban site of Beijing, where CO TC decreased by 1.14% 0.87% yr-1. Meanwhile, at the Moscow site, CO TC decreased remarkably by 3.73% 0.39% yr-1. In the background regions (ZSS, ZOTTO, Peterhof), the reduction was 0.9%-1.7% yr-1 during the same period. Based on the AIRSv6 satellite data for the period 2003-14, a slight decrease (0.4%-0.6% yr-1) of CO TC was detected over the midlatitudes of Eurasia, while a reduction of 0.9%-1.2% yr-1 was found in Southeast Asia. The degree of correlation between the CO TC derived from satellite products (MOPITTv6 Joint, AIRSv6 and IASI MetOp-A) and ground-based measurements was calculated, revealing significant correlation in unpolluted regions. While in polluted areas, IASI MetOp-A and AIRSv6 data underestimated CO TC by a factor of 1.5-2.8. On average, the correlation coefficient between ground- and satellite-based data increased significantly for cases with PBL heights greater than 500 m.
A comparative study was carried out to explore carbon monoxide total columnar amount (CO TC) in background and polluted atmosphere, including the stations of ZSS (Zvenigorod), ZOTTO (Central Siberia), Peterhof, Beijing, and Moscow, during 1998-2014, on the basis of ground- and satellite-based spectroscopic measurements. Interannual variations of CO TC in different regions of Eurasia were obtained from ground-based spectroscopic observations, combined with satellite data from the sensors MOPITT (2001-14), AIRS (2003-14), and IASI MetOp-A (2010-13). A decreasing trend in CO TC (1998-2014) was found at the urban site of Beijing, where CO TC decreased by 1.14% 0.87% yr-1. Meanwhile, at the Moscow site, CO TC decreased remarkably by 3.73% 0.39% yr-1. In the background regions (ZSS, ZOTTO, Peterhof), the reduction was 0.9%-1.7% yr-1 during the same period. Based on the AIRSv6 satellite data for the period 2003-14, a slight decrease (0.4%-0.6% yr-1) of CO TC was detected over the midlatitudes of Eurasia, while a reduction of 0.9%-1.2% yr-1 was found in Southeast Asia. The degree of correlation between the CO TC derived from satellite products (MOPITTv6 Joint, AIRSv6 and IASI MetOp-A) and ground-based measurements was calculated, revealing significant correlation in unpolluted regions. While in polluted areas, IASI MetOp-A and AIRSv6 data underestimated CO TC by a factor of 1.5-2.8. On average, the correlation coefficient between ground- and satellite-based data increased significantly for cases with PBL heights greater than 500 m.
2018, 35(7): 796-812.
doi: 10.1007/s00376-017-7128-9
Abstract:
This paper examines an asymmetric spatiotemporal connection and climatic impact between the winter atmospheric blocking activity in the Euro-Atlantic sector and the life cycle of the North Atlantic Oscillation (NAO) during the period 1950-2012. Results show that, for positive NAO (NAO+) events, the instantaneous blocking (IB) frequency exhibits an enhancement along the southwest-northeast (SW-NE) direction from the eastern Atlantic to northeastern Europe (SW-NE pattern, hereafter), which is particularly evident during the NAO+ decaying stage. By contrast, for negative NAO (NAO-) events, the IB frequency exhibits a spatially asymmetric southeast-northwest (SE-NW) distribution from central Europe to the North Atlantic and Greenland (SE-NW pattern, hereafter). Moreover, for NAO- (NAO+) events, the most marked decrease (increase) in the surface air temperature (SAT) in winter over northern Europe is in the decaying stage. For NAO+ events, the dominant positive temperature and precipitation anomalies exhibit the SW-NE-oriented distribution from western to northeastern Europe, which is parallel to the NAO+-related blocking frequency distribution. For NAO- events, the dominant negative temperature anomaly is in northern and central Europe, whereas the dominant positive precipitation anomaly is distributed over southern Europe along the SW-NE direction. In addition, the downward infrared radiation controlled by the NAO's circulation plays a crucial role in the SAT anomaly distribution. It is further shown that the NAO's phase can act as an asymmetric impact on the European climate through producing this asymmetric spatiotemporal connection with the Euro-Atlantic IB frequency.
This paper examines an asymmetric spatiotemporal connection and climatic impact between the winter atmospheric blocking activity in the Euro-Atlantic sector and the life cycle of the North Atlantic Oscillation (NAO) during the period 1950-2012. Results show that, for positive NAO (NAO+) events, the instantaneous blocking (IB) frequency exhibits an enhancement along the southwest-northeast (SW-NE) direction from the eastern Atlantic to northeastern Europe (SW-NE pattern, hereafter), which is particularly evident during the NAO+ decaying stage. By contrast, for negative NAO (NAO-) events, the IB frequency exhibits a spatially asymmetric southeast-northwest (SE-NW) distribution from central Europe to the North Atlantic and Greenland (SE-NW pattern, hereafter). Moreover, for NAO- (NAO+) events, the most marked decrease (increase) in the surface air temperature (SAT) in winter over northern Europe is in the decaying stage. For NAO+ events, the dominant positive temperature and precipitation anomalies exhibit the SW-NE-oriented distribution from western to northeastern Europe, which is parallel to the NAO+-related blocking frequency distribution. For NAO- events, the dominant negative temperature anomaly is in northern and central Europe, whereas the dominant positive precipitation anomaly is distributed over southern Europe along the SW-NE direction. In addition, the downward infrared radiation controlled by the NAO's circulation plays a crucial role in the SAT anomaly distribution. It is further shown that the NAO's phase can act as an asymmetric impact on the European climate through producing this asymmetric spatiotemporal connection with the Euro-Atlantic IB frequency.
2018, 35(7): 813-825.
doi: 10.1007/s00376-017-7179-y
Abstract:
Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation (DA) and model output statistics (MOS). However, the relative importance and combined effects of the two techniques have not been clarified. Here, a one-month air quality forecast with the Weather Research and Forecasting-Chemistry (WRF-Chem) model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational (3DVar) DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3DVar DA in improving the operational forecasting ability of WRF-Chem.
Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation (DA) and model output statistics (MOS). However, the relative importance and combined effects of the two techniques have not been clarified. Here, a one-month air quality forecast with the Weather Research and Forecasting-Chemistry (WRF-Chem) model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational (3DVar) DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3DVar DA in improving the operational forecasting ability of WRF-Chem.
2018, 35(7): 826-838.
doi: 10.1007/s00376-017-7173-4
Abstract:
The asymmetrical structure of typhoon-induced ocean eddies (TIOEs) in the East China Sea (including the Yellow Sea) and the accompanying air-sea interaction are studied using reanalysis products. Thirteen TIOEs are analyzed and divided into three groups with the k-prototype method: Group A with typhoons passing through the central Yellow Sea; Group B with typhoons re-entering the sea from the western Yellow Sea after landing on continental China; and Group C with typhoons occurring across the eastern Yellow Sea near to the Korean Peninsula. The study region is divided into three zones (Zones I, II and III) according to water depth and the Kuroshio position. The TIOEs in Group A are the strongest and could reverse part of the Kuroshio stream, while TIOEs in the other two groups are easily deformed by topography. The strong currents of the TIOEs impact on the latent heat flux distribution and upward transport, which facilitates the typhoon development. The strong divergence within the TIOEs favors an upwelling-induced cooling. A typical TIOE analysis shows that the intensity of the upwelling of TIOEs is proportional to the water depth, but its magnitude is weaker than the upwelling induced by the topography. In Zones I and II, the vertical dimensions of TIOEs and their strong currents are much less than the water depths. In shallow water Zone III, a reversed circulation appears in the lower layer. The strong currents can lead to a greater, faster, and deeper energy transfer downwards than at the center of TIOEs.
The asymmetrical structure of typhoon-induced ocean eddies (TIOEs) in the East China Sea (including the Yellow Sea) and the accompanying air-sea interaction are studied using reanalysis products. Thirteen TIOEs are analyzed and divided into three groups with the k-prototype method: Group A with typhoons passing through the central Yellow Sea; Group B with typhoons re-entering the sea from the western Yellow Sea after landing on continental China; and Group C with typhoons occurring across the eastern Yellow Sea near to the Korean Peninsula. The study region is divided into three zones (Zones I, II and III) according to water depth and the Kuroshio position. The TIOEs in Group A are the strongest and could reverse part of the Kuroshio stream, while TIOEs in the other two groups are easily deformed by topography. The strong currents of the TIOEs impact on the latent heat flux distribution and upward transport, which facilitates the typhoon development. The strong divergence within the TIOEs favors an upwelling-induced cooling. A typical TIOE analysis shows that the intensity of the upwelling of TIOEs is proportional to the water depth, but its magnitude is weaker than the upwelling induced by the topography. In Zones I and II, the vertical dimensions of TIOEs and their strong currents are much less than the water depths. In shallow water Zone III, a reversed circulation appears in the lower layer. The strong currents can lead to a greater, faster, and deeper energy transfer downwards than at the center of TIOEs.
2018, 35(7): 839-852.
doi: 10.1007/s00376-017-7107-1
Abstract:
To investigate the impact of soil moisture uncertainty on summertime short-range ensemble forecasts (SREFs), a five-member SREF experiment with perturbed initial soil moisture (ISM) was performed over a northern China domain in summertime from July to August 2014. Five soil moisture analyses from three different operational/research centers were used as the ISM for the ensemble. The ISM perturbation produced notable ensemble spread in near-surface variables and atmospheric variables below 800 hPa, and produced skillful ensemble-mean 24-h accumulated precipitation (APCP24) forecasts that outperformed any single ensemble member. Compared with a second SREF experiment with mixed microphysics parameterization options, the ISM-perturbed ensemble produced comparable ensemble spread in APCP24 forecasts, and had better Brier scores and resolution in probabilistic APCP24 forecasts for 10-mm, 25-mm and 50-mm thresholds. The ISM-perturbed ensemble produced obviously larger ensemble spread in near-surface variables. It was, however, still under-dispersed, indicating that perturbing ISM alone may not be adequate in representing all the uncertainty at the near-surface level, indicating further SREF studies are needed to better represent the uncertainties in land surface processes and their coupling with the atmosphere.
To investigate the impact of soil moisture uncertainty on summertime short-range ensemble forecasts (SREFs), a five-member SREF experiment with perturbed initial soil moisture (ISM) was performed over a northern China domain in summertime from July to August 2014. Five soil moisture analyses from three different operational/research centers were used as the ISM for the ensemble. The ISM perturbation produced notable ensemble spread in near-surface variables and atmospheric variables below 800 hPa, and produced skillful ensemble-mean 24-h accumulated precipitation (APCP24) forecasts that outperformed any single ensemble member. Compared with a second SREF experiment with mixed microphysics parameterization options, the ISM-perturbed ensemble produced comparable ensemble spread in APCP24 forecasts, and had better Brier scores and resolution in probabilistic APCP24 forecasts for 10-mm, 25-mm and 50-mm thresholds. The ISM-perturbed ensemble produced obviously larger ensemble spread in near-surface variables. It was, however, still under-dispersed, indicating that perturbing ISM alone may not be adequate in representing all the uncertainty at the near-surface level, indicating further SREF studies are needed to better represent the uncertainties in land surface processes and their coupling with the atmosphere.
2018, 35(7): 853-867.
doi: 10.1007/s00376-017-7138-7
Abstract:
Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas (so-called "sensitive areas"). And yet, few studies have quantified improvements in prediction skill in the context of an optimal observing system. In this study, the impact on prediction skill is explored using an intermediate coupled model in which errors in initial conditions formed to make ENSO predictions are removed in certain areas. Based on ideal observing system simulation experiments, the importance of various observational networks on improvement of El Niño prediction skill is examined. The results indicate that the initial states in the central and eastern equatorial Pacific are important to improve El Niño prediction skill effectively. When removing the initial condition errors in the central equatorial Pacific, ENSO prediction errors can be reduced by 25%. Furthermore, combinations of various subregions are considered to demonstrate the efficiency on ENSO prediction skill. Particularly, seasonally varying observational networks are suggested to improve the prediction skill more effectively. For example, in addition to observing in the central equatorial Pacific and its north throughout the year, increasing observations in the eastern equatorial Pacific during April to October is crucially important, which can improve the prediction accuracy by 62%. These results also demonstrate the effectiveness of the conditional nonlinear optimal perturbation approach on detecting sensitive areas for target observations.
Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas (so-called "sensitive areas"). And yet, few studies have quantified improvements in prediction skill in the context of an optimal observing system. In this study, the impact on prediction skill is explored using an intermediate coupled model in which errors in initial conditions formed to make ENSO predictions are removed in certain areas. Based on ideal observing system simulation experiments, the importance of various observational networks on improvement of El Niño prediction skill is examined. The results indicate that the initial states in the central and eastern equatorial Pacific are important to improve El Niño prediction skill effectively. When removing the initial condition errors in the central equatorial Pacific, ENSO prediction errors can be reduced by 25%. Furthermore, combinations of various subregions are considered to demonstrate the efficiency on ENSO prediction skill. Particularly, seasonally varying observational networks are suggested to improve the prediction skill more effectively. For example, in addition to observing in the central equatorial Pacific and its north throughout the year, increasing observations in the eastern equatorial Pacific during April to October is crucially important, which can improve the prediction accuracy by 62%. These results also demonstrate the effectiveness of the conditional nonlinear optimal perturbation approach on detecting sensitive areas for target observations.
2018, 35(7): 868-880.
doi: 10.1007/s00376-017-7184-1
Abstract:
The present study investigates the persistence of summer sea surface temperature anomalies (SSTAs) in the midlatitude North Pacific and its interdecadal variability. Summer SSTAs can persist for a long time (approximately 8-14 months) around the Kuroshio Extension (KE) region. This long persistence may be strongly related to atmospheric forcing because the mixed layer is too shallow in the summer to be influenced by the anomalies at depths in the ocean. Changes in atmospheric circulation, latent heat flux, and longwave radiation flux all contribute to the long persistence of summer SSTAs. Among these factors, the longwave radiation flux has a dominant influence. The effects of sensible heat flux and shortwave radiation flux anomalies are not significant. The persistence of summer SSTAs displays pronounced interdecadal variability around the KE region, and the variability is very weak during 1950-82 but becomes stronger during 1983-2016. The changes in atmospheric circulation, latent heat flux, and longwave radiation flux are also responsible for this interdecadal variability because their forcings on the summer SSTAs are sustained for much longer after 1982.
The present study investigates the persistence of summer sea surface temperature anomalies (SSTAs) in the midlatitude North Pacific and its interdecadal variability. Summer SSTAs can persist for a long time (approximately 8-14 months) around the Kuroshio Extension (KE) region. This long persistence may be strongly related to atmospheric forcing because the mixed layer is too shallow in the summer to be influenced by the anomalies at depths in the ocean. Changes in atmospheric circulation, latent heat flux, and longwave radiation flux all contribute to the long persistence of summer SSTAs. Among these factors, the longwave radiation flux has a dominant influence. The effects of sensible heat flux and shortwave radiation flux anomalies are not significant. The persistence of summer SSTAs displays pronounced interdecadal variability around the KE region, and the variability is very weak during 1950-82 but becomes stronger during 1983-2016. The changes in atmospheric circulation, latent heat flux, and longwave radiation flux are also responsible for this interdecadal variability because their forcings on the summer SSTAs are sustained for much longer after 1982.
2018, 35(7): 881-895.
doi: 10.1007/s00376-017-7162-7
Abstract:
The variability in the Southern Ocean (SO) sea surface temperature (SST) has drawn increased attention due to its unique physical features; therefore, the temporal characteristics of the SO SST anomalies (SSTA) and their influence on extratropical atmospheric circulation are addressed in this study. Results from empirical orthogonal function analysis show that the principal mode of the SO SSTA exhibits a dipole-like structure, suggesting a negative correlation between the SSTA in the middle and high latitudes, which is referred to as the SO Dipole (SOD) in this study. The SOD features strong zonal symmetry, and could reflect more than 50% of total zonal-mean SSTA variability. We find that stronger (weaker) Subantarctic and Antarctic polar fronts are related to the positive (negative) phases of the SOD index, as well as the primary variability of the large-scale SO SSTA meridional gradient. During December-January-February, the Ferrel cell and the polar jet shift toward the Antarctic due to changes in the SSTA that could be associated with a positive phase of the SOD, and are also accompanied by a poleward shift of the subtropical jet. During June-July-August, in association with a positive SOD, the Ferrel cell and the polar jet are strengthened, accompanied by a strengthened subtropical jet. These seasonal differences are linked to the differences in the configuration of the polar jet and the subtropical jet in the Southern Hemisphere.
The variability in the Southern Ocean (SO) sea surface temperature (SST) has drawn increased attention due to its unique physical features; therefore, the temporal characteristics of the SO SST anomalies (SSTA) and their influence on extratropical atmospheric circulation are addressed in this study. Results from empirical orthogonal function analysis show that the principal mode of the SO SSTA exhibits a dipole-like structure, suggesting a negative correlation between the SSTA in the middle and high latitudes, which is referred to as the SO Dipole (SOD) in this study. The SOD features strong zonal symmetry, and could reflect more than 50% of total zonal-mean SSTA variability. We find that stronger (weaker) Subantarctic and Antarctic polar fronts are related to the positive (negative) phases of the SOD index, as well as the primary variability of the large-scale SO SSTA meridional gradient. During December-January-February, the Ferrel cell and the polar jet shift toward the Antarctic due to changes in the SSTA that could be associated with a positive phase of the SOD, and are also accompanied by a poleward shift of the subtropical jet. During June-July-August, in association with a positive SOD, the Ferrel cell and the polar jet are strengthened, accompanied by a strengthened subtropical jet. These seasonal differences are linked to the differences in the configuration of the polar jet and the subtropical jet in the Southern Hemisphere.
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