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Simulation Of Synoptic Scale Circulation Features Biology Essay

Simulation Of Synoptic Scale Circulation Features Biology Essay

Published: 23rd March, 2015 Last Edited: 23rd March, 2015

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The climate of Southern Africa strongly depends on seasonal variation of synoptic scale features over the region. Examples of these features are cut-off lows, deep tropical lows, deep temperate lows and Tropical Temperate Troughs (TTTs). The features have a time scale of about 3-5 days, a mean horizontal scale of about 1000-3000 km, and usually propagate westward with a speed of about 5-8 m s-1 (Orlanski, 1975) over Southern Africa. In most cases, they are responsible for the extreme weather conditions over the region. Although they are short lived, their complex interactions with the large scale circulation features (with a longer time scale) determine the climate of Southern African climate (Hudson and Jones, 2002; Reason and Jagadheesha, 2005; Anyah and Semazzi, 2006). It is therefore essential that a global model for seasonal forecast over the region gives a realistic simulation of the futures, at least their seasonal variability. This study investigates how well the features are simulated in the Hadley Centre Atmospheric Model version 3 (HadAM3) and the NCAR the Community Atmospheric model version 3 (CAM3).

These predominant synoptic scale features are well recognized at 500hPa height (Tyson, 1981; Tyson and Preston-Whyte, 2000). Therefore, it is important to study the anomalies in the geopotential height field at 500hPa and 850hPa for 30 years. Anomalies in this field help in explaining how seasons vary from the annual mean.

The frequency, duration and intensity of the synoptic features either induce or suppress rainfall, thus leading to rainfall variability over Southern Africa (Harrison 1984; Tyson and Preston-Whyte, 2000). For example, Taljaard (1985) showed that the westerly disturbances and the formation of cut-off lows induce rainfall. Cut-off lows are low pressure systems that develop from the westerlies (Hobbs et al. 1998; Harrison 1984a; 1984b; Todd and Washington, 1999; Tyson and Preston-Whyte 2000; Jury and Nkosi 2000). They are seen in the upper troposphere as troughs, deepens until they form closed circulations (Hobbs et al. 1998; Tyson and Preston-Whyte, 2000; Browning and Mason, 1980; Fuenzalida et al. 2005; Smith and Reeder, 1988; Garreaud, 2000). These deep closed circulations that develop from upper westerly troughs are very intense synoptic features over Southern Africa. They induce unstable troposphere at low levels, produce severe convective events that lead to heavy rainfall and floods over large areas, and may trigger severe cyclonegenesis that induces strong wind (Nieto et al. 2005; Tyson and Preston-Whyte, 2000).

Deep tropical lows and deep temperate lows are low level circulation centres in the Rossby waves. Tropical lows are caused by disturbances in the easterly wave, driven mostly by thermal heating of the continent. The deep tropical and temperate low can intensify into a cyclone and if more intense, can lead to a storm. They help in tropical moisture and energy transfer. This process significantly facilitates rainfall over the region (Mason and Jury, 1997; Van den Heever et al. 1997). Also, Miron and Tyson (1984) found that synoptic situations responsible for rain-bearing winds are consistent with easterly low disturbances. The tropical and temperate lows are responsible for rainfall over the interior during summer. Harrison (1984) reported in his study that tropical and temperate perturbations are major determinants of Southern African rainfall.

When the upper westerly wave coincides with an easterly wave or depression in lower levels, it results in the formation of a tropical temperate trough. This feature is associated with tropical moisture and energy transfer and it has been known to significantly contribute to summer rainfall over Southern Africa (Harangozo and Harrison, 1983; Harrison, 1984b).

The study reports the capability of HadAM3 and CAM3 in simulating the mean number of cut-off lows, number of days with tropical lows, temperate lows and TTTs from 1971 through 2000. It also attempts to discuss how the synoptic scale features are related to the rainfall variability over Southern Africa. HadAM3 is the atmosphere component of the Hadley Centre Coupled Model version 3 (Gordon et al. 2000; Pope et al. 2000), which was developed at the Hadley Centre for Climate Prediction and Research, UK. The model employs spherical polar coordinates on a regular latitude-longitude grid. The horizontal resolution is 3.75o longitude, 2.5o latitude, and 8 layers in the vertical, which are based on a hybrid vertical coordinate system (Simmons and Burridge, 1981). The development and description of the HadAM3 model can be found in Gordon et al. 2000; Pope et al. 2000; Jones et al. 2005; Murphy et al. 2002. The HadAM3 has been used successfully in some studies over the Southern Africa. Examples can be seen in Tennant, 2003; Reason et al. 2003; Reason and Jagadheesha, 2005. The model is generally able to capture the circulation dynamics of the Southern African climate. CAM3 is the atmosphere component of the Community Climate System Model, version 3.0 (Collins et al. 2004), which was developed at the US National Centre for Atmospheric Research (NCAR). The study used the finite volume dynamic core option of the CAM3, with horizontal resolution of 2.0o x 2.5o and 26 vertical levels. This model has not been used extensively to study the climate of Southern Africa. The study therefore reports the model's capability to concerned institutions in Southern Africa, which will decide on the possibility of the model to be added in seasonal forecasting over the region.

Few studies over Southern Africa show that the rainfall seasonality varies greatly from one region to another which would explain why the seasonal rainfall over all areas of the region is difficult to forecast. Based on this difficulty, in some parts of the study, the region is divided into sub-regions. This is to capture the variations in the rainfall and temperature seasonality over different parts of the region. The selection of the sub-regions has been explained in section 2. Section 2 also explains the techniques used in estimating the synoptic features from the two global models. Following the techniques is the results and discussion for the mean climatologies and the seasonal variations of the features and rainfall. The final section is the conclusions, which will highlight the important outcomes of this study.

2. Techniques

Both HadAM3 and CAM3 were applied to produce 30 years (1971-2000) climate simulations. A five member ensemble is integrated forward with observed daily Reynold's Sea Surface Temperature (SST). The ensemble members were produced from perturbing the initial conditions of the SSTs as input to the models for each ensemble run.

It has been shown that ensemble forecasting is one of the best methods for reducing errors associated with climate uncertainties over individual model ensemble prediction. Therefore, increase in the number of ensemble members, directly affect the result positively. Only five members are used for the study because of insufficient computing space to simulate more ensemble members.

Statistical averages of these ensemble members' estimation of the features were used in the analysis. For better comparison of the models with the reanalysis data, the models results were interpolated to the resolution (2.5o x 2.5o) of the NCAR reanalysis I data (NCEP). In the investigation of the models to reproduce the synoptic scale features, the simulated 500hPa and 850hPa geopotential heights were analyzed and the results compared with those from NCEP reanalysis.

Anomalies of the geopotential height from the climatological mean for 30 years are calculated and a relationship is created from the pattern with that of rainfall.

The standardized anomalies calculated from the mean of daily geopotential height from a time series of 1971 through 2000 period were done to take care of the seasonal variations within the dataset. This removes dispersion in the dataset and helps to recognize the magnitude of the anomalies.

Additionally, we use the Laplace equation to track cut-off lows, temperate and tropical lows from geopotential height. The identification of low pressures was based on an 8 neighbour grid value of a two dimensional geopotential field at 500hPa for cut-off lows and tropical temperate troughs; 500hPa and 850hPa for deep tropical and temperate lows. The second derivatives from the Laplace equation allow the minimum value for the depression. The limitation used in considering the cut-off low is the minimum geopotential height at a grid point and the closed circulation westerlies in the upper troposphere. On each day, a given grid point was identified as a geopotential minimum (gpm) if it is within a minimum of six out of the eight surrounding grid points. Once this set of cut-off low points was chosen, only the grid points that showed a minimum geopotential height are retained. The tropical temperate trough forms when the upper westerlies coincide with the easterlies wave in lower levels. The algorithm records days with tropical temperate troughs on days where a link is established between a tropical low and a westerly wave trough (van den Heever, 1997). The tropical region is defined in this study as 0o - 25oS and 0o - 50oE and the temperate region as 25oS - 50oS and 0o - 50oE.

Correlation coefficient is calculated to measure the strength of the linear association between the synoptic features and rainfall. The significance of correlation coefficients is obtained from the table of critical values of correlation after calculating the two-tailed test. The confidence level used is 95% and with 12 months as the sample space, the degree of freedom is 10.

REG1 is between latitude 0, 20oS and longitude 0, 55oE. REG2 is between latitude 20oS, 40oS and longitude 0, 20oE and REG2 positioned at latitude 20oS, 40oS and longitude 20oE, 55oE.

The tropical temperate troughs form when a tropical low is coupled to a temperate westerly wave via a subtropical trough (Figure 5a), forming a northwest to southeast cloud band along the leading edge of the westerly trough (Harangozo and Harrison, 1983; Harrison, 1984c; van den Heever, 1994).

3. Results a. Seasonal Rainfall pattern

Figure 1 compares the simulated seasonal rainfall patterns in the models with the observed (NCEP reanalysis). Generally in NCEP for all seasons, total rainfall is greater in the tropics. This gradually decreases westward such that most of the central and western regions are semi-desert with low rainfall. Dryness to low rainfall is observed over most central and south-west of the region.

In general, the simulated rainfall patterns are closer to the observed in HadAM3 than in CAM3. CAM3, the rainfall pattern is different from that of NCEP. The model over-predicts rainfall over the central in December-January-February (DJF). In the same season, a zone of maximum rainfall lies along the western half and the north eastern part of the Southern Africa. The most prominent feature in the March-April-May (MAM) is the zone of maximum rainfall located between 5o and 15oS. Along the zone, rainfall decreases from 8.0 mm/day at east coast to about 4.0 mm/day. In HadAM3, the simulated rainfall distribution is closer to that from NCEP than that from CAM3. In June-July-August (JJA), the entire region is dry receiving less than 1mm/day of rainfall in that season. Some amount of rainfall is only found north of 10oS and south of 30oS. Both models estimate almost no rainfall in JJA over most part of the region. However, CAM3 has about 3-5mm/day of rainfall at the north-eastern region. The models capture the small amount of rainfall at the south-most part of the region but CAM3 fails to capture the rains over Western Cape. In September-October-November (SON), rainfall is observed to be maximum in the tropics and it extends to the central part of the region and a small region at the south-eastern part can be seen with some amount of rainfall (about 2mm/day). CAM3 simulates a rainfall region that extends from the tropics to the entire sub-continent, making the entire region receive 1mm/day or more rainfall. HadAM3 simulations capture all the essential features shown in NCEP, except that it generally under estimates the rainfall patterns in all seasons.

b. Seasonal Temperature pattern

In figure 2, the mean of the temperature for DJF, MAM, JJA and SON from 1971 through 2000 is shown from NCEP reanalysis, HadAM3 and CAM3. NCEP shows that during the astral summer in DJF, temperature decreases southwards from the equator. The lowest temperature (18oC) is observed over the south most part of the region in that season.

The models reproduce similar pattern but HadAM3 has 16oC and CAM3 has a minimum of about 20oC. In MAM, NCEP shows a similar pattern but with a minimum of 14oC over South Africa and maximum of 28oC over north-eastern part of Southern Africa. CAM3 reproduces a minimum of 18oC and a maximum of 26oC while HadAM3 reproduces a minimum of 16oC and a maximum of 26oC. In summer months (JJA), a minimum of 14oC is observed over South Africa and a maximum of 24oC at the equator. The models capture these minimum and the maximum temperatures values at almost same positions. During the SON season the centre of the southern Africa experiences a temperature of 24oC. It decreases towards the south of the region. The models reproduce similar pattern in all seasons.

c. Anomalies in HGTs

The contour lines of Figure 3 depict the climatological anomalies obtained from 30 year averages of the 500hPa height fields of DJF, MAM, JJA and SON from NCEP reanalysis, HadAM3 and CAM3 models. In DJF, weak anomalous low is observed in NCEP over the oceans, which increases inland and a weak anomalous high over the tropics. In MAM, high geopotential is seen over southern part and the oceans but a weak high over the tropics. Anomalous values of the geopotential height ranging from -5 to -40gpm are concentrated over most central and southern part of the region in JJA. At 500hPa, subtropical high prevails during these seasons and a corresponding dryness to low rainfall is observed in DJF, MAM and in JJA over most part of the region. During SON, high anomalous values occur over the central part of the region. A high geopotential height appears only over the oceans and over the southern part of the region. The pattern here agrees with that of rainfall (figure 1); maximum in the tropics and extends to the central part of the region except at the western part of the region with some dryness.

HadAM3 seem to have anomalous high in SON but correctly show the patterns. Almost the same pattern and values are simulated for JJA. Although CAM3 have close values as that of NCEP, it simulates slightly different pattern for MAM and SON.

Figure 4 shows the circulation anomalies at the lower troposphere. In DJF, weak high is observed over the whole region. A high emerging from the southern Oceans extends into the central part of the region. Negative anomalies are observed over the tropics. A strong high geopotential height is observed over every part of the region in JJA. The associated circulation at the lower troposphere seems opposite to that at the upper troposphere. In JJA when the anomalous geopotential is low, in the upper troposphere, it is high in the lower troposphere and there is dryness over most part of Southern Africa. When the subtropical high prevails at the lower troposphere, a corresponding dryness to low rainfall is observed in DJF, MAM and in JJA over most part of the region. In SON synoptic anomalous flow of low geopotential height emerge from the ocean to the southern Africa and extends to the tropics. Positive anomalies are observed over the Indian Ocean near Madagascar and over the southern Atlantic Ocean. The low geopotential height experience in SON in most part of the subcontinent and the tropics agrees with the rainfall pattern over the region. Generally, the low over the ocean at 850hPa contributes to the rainfall over the most part of the region and the corresponding high over the ocean contributes to the dryness in that season. A swift change from high in JJA to low in SON of the geopotential at the 850hPa connotes dryness in JJA and rains in SON. The link between the 850hPa geopotential height and rainfall has been reported in a related study by Landman and Goddard (2002) using a pattern analysis from Canonical Correlation Analysis. The models generally capture most of these patterns well for all the seasons. Particularly, HadAM3 shows very similar pattern as NCEP. However, it over predicts and shows a stronger gradient for both high and low geopotential over the region. CAM3 simulates slightly different pattern, especially for SON, a high is simulated over the central part of the region and a low shown over the south and in the tropics.

d. Standard deviations in U and V wind components

Standard deviations of zonal wind (fig.4) range from 5ms-1 at about 25oS to a maximum of 12ms-1 in the south-most part of the region. Standard deviations of meridional component (fig.5) are similar to those of the zonal component.

e. Regional Seasonal Variations

The seasonal variations in the simulated climatological rainfall from the models and NCEP reanalysis for REG1, REG2 and REG3 are shown in figure 4. In figure 4a, rainfall over REG1 is maximum in summer and minimum in winter. The highest is seen in January from NCEP and the models. Rainfall decreases to a minimum in August with NCEP, May-July with CAM3 and June-August with HadAM3. HadAM3 has almost same amounts of rainfall with NCEP from January till May and then underestimates it until November. CAM3 overestimates the summer rainfall and underestimates it in the winter months. REG2 (figure 4b) experiences a general low amount of rainfall. According to NCEP, the region records the lowest rainfall in summer and highest in winter. HadAM3 captures the variations correctly but CAM3 fails to get it. However, the differences are only less than 0.5mm/day. Over REG3 (figure 4c), rainfall variation is similar to that over REG1 except that the amount is less than that over REG1. The models reproduced almost the same rainfall variations as that from NCEP reanalysis.

Fig. 5 shows temperature variations over the 3 sub-regions. Temperature is high in summer and low in winter. The variations are very similar in all the sub-regions. Over all the regions, the models over-simulate the temperature from February till June and then under-stimulate in July, August and September. REG1 (figure 5a) has a short range of temperature variations of above 21oC to below 25oC. Temperature over REG2 ranges from 15oC to 20oC and that over REG3 from 16oC to a value below 23oC.

The seasonal variations of the standardized rainfall and temperature anomalies over southern Africa for the 1971-2000 year period are shown in figure 6. The standardized anomalies are calculated from their daily anomalies and monthly estimates of the climatological standard deviations are presented. In figure 6a, positive standardized anomaly of rainfall almost equal to 1 is observed, from NCEP, in January and it decreases to zero at the end of April. The negative anomaly starts in May and peaks in July, it then rises through to October. Positive anomaly starts again at the beginning of November and it increases in December. The models simulate these anomalies very close to the reanalysis. Both have a correlation coefficient of 0.98 with NCEP reanalysis. In figure 5b, positive standardized anomaly of temperature decreases from 1 in January to zero at the end of April. The negative anomaly starts in May at zero and decreases to near -1.5 in mid July, it then increases gently from -1.5 in July to zero in October. The Positive anomaly starts again in October at zero to a little above 0.5 in December.

Figure 7 compares the models standardized anomalies of their daily geopotential height at 500hPa (figure 7a) and near the surface at 850hPa (figure 7b) with NCEP reanalysis. In figure 7a, NCEP shows a positive anomaly that increases from January till April and in December. The positive anomaly peaks in March at 1.5 and decreases to zero in May. The negative anomaly is between 0 and -1.5, within this range, the standardized anomaly decreases from zero in May to -1.5 in August but rises again between August and November. Similarly, both models simulate a similar pattern in the range of their deviations. In particular, CAM3 reproduces the positive anomalies from January through May and negative anomalies between June and November. December has positive anomalies. The peak of the positive anomalies in this case is in April. Like in NCEP,

CAM3 simulates the negative anomalies from June through November. Moreover, CAM3 shows a correlation coefficient of 0.89 with the NCEP. Interestingly, HadAM3 reproduces a similar pattern as in NCEP with a correlation coefficient of 0.94. In figure 7b, negative anomalies at 850hPa are observed from January through May and from October through December. The winter months starting from May have positive anomalies up to about 1.5. The range is between -1.5 and 1.5. The models simulate the range of the standardized deviation as in NCEP. However, CAM3 simulates the transition from negative to positive anomaly more than a month earlier, sometime in March while HadAM3 has a closer transition time as NCEP.

Figure 8 shows the seasonal mean number of cut-off lows estimated from NCEP and the two-models. A high mean number of cut-off lows is observed from NCEP during the onset of the austral winter season in March and April from NCEP. Singleton and Reason (2007) also found that cut-off lows over southern Africa are most common in the March-May season. A smaller peak is present in October. Both models capture the peak of the mean number of cut-off lows in March but they represent the second peak (in October for NCEP) a bit earlier in September. Although, both models generally under estimate the mean number of cut-off lows over the region, HadAM3 simulates the mean number closer to that of NCEP reanalysis with a correlation coefficient of 0.63 with NCEP.

In figure 9a, the observed number of days with deep tropical lows from NCEP varies between 17 and 30 per month. The highest number of days with tropical lows from NCEP is close to 30 in February. The number of days with deep tropical lows decreases as winter approaches. May, June and July recorded the lowest number of days with tropical lows and 17 days is the observed minimum. The number of days increases from July through December and from January through February. Both models underestimate the number of days with deep tropical lows although they show similar pattern to that of NCEP. For CAM3, the months January, February, March and December have 25 days of deep tropical lows. The number of days with tropical lows decreases to 15 in April and May then increases from 15 to 20 through June. CAM3 has a correlation coefficient of 0.87 with NCEP. In the case of HadAM3, the number of days with deep tropical lows is in the order of 5 to 20 days from January through December. It also reproduces less number of days with tropical lows from the onset of winter season in May till the peak of winter season in July but the correlation coefficient is still high (0.83).

In addition, the number of days with deep temperate lows from NCEP reanalysis and the models is shown in figure 9b. Generally, lower number of days with deep lows is seen from NCEP over the temperate region than over the tropical region (see figure 9a). The number of days with deep temperate lows increases from September through December and also from January through March. The highest number of days with deep temperate lows from NCEP is 8 in March. The NCEP reanalysis shows the lowest number of days with deep temperate lows to be 4 per month during the austral winter season. The models extremely underestimate the number of days with deep temperate lows, although the pattern is clearly represented. CAM3 particularly estimates almost zero number of days per month of temperate lows in winter and close to 1 day per month in summer. HadAM3 estimates about 2 days of temperate lows in summer and winter months have between zero and 1 day of temperate lows per month. The highest estimated number of days of these lows is less than 2 in April. The models have a correlation coefficient of 0.59 and 0.47 for CAM3 and HadAM3, respectively.

Figure 10 shows the seasonal variation of days with TTTs from 1971 to 2000 over the same domain. It is evident that the number of days with TTTs estimated from NCEP increases from September through December. It peaks in March. The number of days with TTTs from NCEP is between 4 and 8 throughout the seasons. The pattern is reproduced fairly well in both models except with CAM3, which overestimates the number of days with TTTs throughout the year and more significantly in January, February, March and December. HadAM3 reproduced the number of days with TTTs between 4 and 8, as seen from NCEP reanalysis.

4. Discussion

Most of the synoptic scale features presented here are rainfall inducing systems, which are noticeable at the 500hPa. The amount of geopotential height variability, as measured by standard deviations, is compared between the seasons of rainfall and temperature.

As seen in the spatial patterns, the mean geopotential height at 500hPa is similar to that of the mean rainfall and temperature over the study region. Negative anomalies in the daily geopotential height at 500hPa correspond to low climatological rainfall between May and November.

In contrast, at the 850hPa (figure 7b), positive anomalies in the daily geopotential height correspond to low climatological rainfall between May and October. The link between the 850hPa geopotential height and rainfall has been reported in a related study by Landman and Goddard (2002) using a pattern analysis from Canonical Correlation Analysis.

Moreover, from figures 8 and 4a, as the mean number of cut-off lows increases, there is an increase in the mean rainfall and when the mean number of cut-off lows decreases, the mean rainfall decreases. However, a correlation coefficient of 0.44 not significant at 95% confidence level is shown for cut-off lows from NCEP and NCEP rainfall (table 1). CAM3 also shows a low correlation coefficient for cut-off lows and rainfall, which is not significant. In table 1, HadAM3 shows a high correlation coefficient for cut-off lows and rainfall which is significant at 95% level of confidence.

In addition, the months with a high number of days of TTTs correspond to months with high rainfall. The link between TTTs and rainfall over southern Africa has been explained through moisture convergence by Todd and Washington (1999). Also, low number of days of temperate lows agrees with low winter rainfall over southern Africa. It can be seen in table 1, that there is a significant correlation coefficient from NCEP for tropical lows, temperate lows, TTTs and NCEP rainfall at 95% confidence level. Similarly, CAM3 also shows high correlation coefficients which are significant at the same confidence level for the same features and rainfall. However, HadAM3 shows a strong correlation coefficient for tropical lows and for TTTs but no relationship at all for temperate lows. The pattern of the features discussed above can be confirmed from related studies (Harrison 1984a; Miron and Tyson 1984; Tyson 1986; Mason and Joubert 1997; Todd and Washington 1999).

5. Conclusion

The models have shown their capabilities in reproducing the synoptic scale features over southern Africa in comparison with the NCEP reanalysis. The variations in the geopotential height has been studied with the models and compared with NCEP reanalysis. The variability of the synoptic scale features is associated with the position and frequency of rainfall over the region. The models correlate well with NCEP in the standardized geopotential height anomalies at both 500hPa and 850hPa. The variations in the geopotential height have been linked with rainfall and temperature through their means and standardized anomalies. The standardized anomalies in the geopotential height at 500hPa have been shown to be in phase with the standardized anomalies of rainfall and that at 850hPa to be out of phase with that of rainfall and temperature. In the summer months (December-January-February), when the region experiences maximum rainfall, synoptic scale features like cut-off lows, tropical lows and TTTs show increased intensity. Also during winter, low rainfall corresponds to low intensity of the synoptic scale features. The seasonal variation in tropical lows, temperate lows and TTTs has also been shown to relate with the mean rainfall with strong correlation coefficient, which are significant at 95% confidence level.

Generally, the models are able to reproduce the synoptic scale circulation features and have estimated the relationship between them and rainfall. These features are crucial for reliable seasonal forecast over southern Africa. Moreover, the accuracy of forecast produced from these global models will depend on the ability of these GCMs to simulate the synoptic scale circulation features that play dominant roles in determining the climate over the region.

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