6.2 Results and Discussion
6.2.3 Seasonal Variation of UTH
6.2 Results and Discussion 63
not systematic nature.
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Figure 6.4: Monthly UTH retrieved from NOAA-15, 2000–2006. From top to bottom: January, April, July, October.
6.2 Results and Discussion 65
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Figure 6.5: Zonal averaged UTH. From left to right: NOAA-15, NOAA-16, and NOAA-17.
In April, the second plot from top in Figure 6.4, we do not find very dry or very moist patterns of UTH. All humidity patterns found in January are still present in April, but with no extreme values of UTH. Compared to January data, in the tropics we can see that the dry patterns shifted southward, and the humid patterns shifted northward with respect to the ITCZ.
In July, the third plot from top in Figure 6.4, we can see the reverse of the UTH distribution compared to that of January. Namely, the low humidity belt moved from the tropics in the Northern hemisphere to the tropics in the Southern hemisphere. In the tropics in the Northern hemisphere there are two patterns of low humidity: one stretching from the Middle Atlantic over the North Africa to Central Asia, and one on the east side of Pacific close to North America.
We see that high humidity zones in monsoon areas over South Africa,
mar-itime continent, and Central America moved northward with respect to the ITCZ, and reached their northward shift limit. The highest values of UTH one can find in the Indian Monsoon system where high humidity moved from the maritime continent to North India. A low pressure system developed over the North of India is the driving force for this move [Lutgens and Tarbuck, 1986].
In both midlatitudes, UTH typically is within 35–45 %RH.
In the Southern subtropics we can find a permanent low humidity belt with UTH less than 15 %RH with interruptions over South America and the Western Pacific. Very low UTH values, less than 10 %RH are found over the North Arabian Peninsula, in the Indian Ocean close to Australia, and in the East Pacific close to South America.
In October, the bottom plot in Figure 6.4, we can see the Monsoon systems over Central America, the maritime continent, and South Africa moving high humidity patterns back to the Southern hemisphere. The highest values of UTH are still found in the Indian Monsoon system. However, the high humidity pat-terns are not as moist as those at their peak in Northern summer or winter. Typ-ically, UTH in the Monsoon systems in this month does not exceed 50 %RH.
In this month, we can see that the low humidity belt in the Southern subtropics breaks over the continents. In the Northern subtropics, a pattern of very low hu-midity found over the North Arabian Peninsula in summer moistens and moves to the East. Also, a low humidity pattern over the North Atlantic moistens. In the East and Middle Pacific, a low humidity pattern develops.
From the UTH distribution in January and July discussed above, one can con-clude that UTH in the tropics has its high values in Southern and Northern sum-mer seasons. One also can conclude that the low humidity distribution in the Southern extratropics is more or less permanent, but that in the Northern extrat-ropics has a seasonal variability.
These results of a seasonal variation of UTH are very consistent with the re-sults ofChen et al.[1999]. Chen et al.[1999] conducted a very thorough inves-tigation of a seasonal variation of the upper tropospheric water vapor (UTWV), high clouds, and deep convection. They retrieved the UTWV from the Mi-crowave Limb Sounder (MLS) on board Upper Atmospheric Research Satellite (UARS). In the tropics, the high humidity patterns we found match very well with the high cloud amount patterns reported by Chen et al. [1999]. As ex-plained byChen et al.[1999], the high humidity and the high cloud amount are directly forced by the deep convection. Therefore, there is such a match.
One common practice to study a latitude dependent seasonal variation of the humidity is to look at the zonal averaged distribution of the humidity. Such a plot for NOAA-15, 16 and 17 is shown in Figure 6.5. This figure shows the latitude averaged UTH plotted against twelve months in a year. The monthly UTH was calculated using Equation 6.1.
6.2 Results and Discussion 67
In the tropics we can see several distinct features. First, the latitudinal vari-ation of humidity follows the ITCZ. Second, the maximum humidity, greater than 45 %RH, occurs in June-July-August period of time and is found at around 10 degrees in the Northern hemisphere. These maxima are well associated with the Indian Monsoon period. Third, the minimum humidity occurs in December-January-February period of time and is found at around 20 degrees in the South-ern hemisphere.
In the midlatitudes there are also several distinct features. First, the latitudinal variation of humidity greater than 30 %RH is asymmetric in the North and South hemispheres. From the figure, in the Northern hemisphere the time dependent maximum of the humidity can be seen very well. However, in the Southern hemisphere this dependency is not so obvious. This can be explained by the fact that there is a stronger convection over the continents which dominate the North-ern hemisphere than over the oceans which dominate the SouthNorth-ern hemisphere.
This can be seen in the extratropical latitudes. The maxima of humidity in the high latitudes occur with about one month delay from the tropical maxima. This delay can be due to a time spent to transport the humidity from the tropical to the higher latitudes and the time delay between summer solstice and averaged temperature maximum in the midlatitudes.
These results from the UTH data to a greater extent agree very well and are consistent with the similar study by Sandor et al.[1998] using the MLS data.
Algthough, both UTH data are retrieved from the microwave observations, the instrumentation and the retrieval methodologies are different.
Despite the time periods of the data used differ with the satellites, we can see a very obvious consistency of the results for all satellites. This once again confirms that there are no inter-satellite differences in this scale of comparison which is enough for climatology studies.
Once we already have a confidence in the seasonal distribution of the UTH data, we can proceed with studying how such a distribution behaves with time.
Figure 6.6 shows a time series of the monthly retrieved UTH for the available data for different latitude bands and different satellites. This exercise enables us to quantitatively estimate the UTH values for different latitude bands.
The top left plot of Figure 6.6 shows a global monthly UTH behavior with the time. The first thing we see in this plot is the annual cycle of the UTH, which we have already seen in the spacial distribution of the UTH. The global mean of the UTH reaches its minimum in a late Northern hemispheric Summer and early Northern hemispheric Autumn of the year. The maximum of the global mean of the UTH can be observed in Northern hemispheric Winter and early North-ern hemispheric Spring of the year. The UTH varies between 30 and 36 %RH.
The average value of UTH is about 33 %RH. Interestingly, the period of high humidity is longer than that of low humidity in the annual cycle of the global
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Figure 6.6: Monthly mean UTH plotted against time for different latitude bands.
Top left: global (-60–60), top right: tropics (-30–30), middle left:
Northern and Southern midlatitudes (-60– -30 and 30–60), middle right: Northern midlatitudes (30–60), bottom left: Southern midlat-itudes (-60– -30). Solid, dotted, and dashed lines correspond to the data from NOAA-15, 16, and 17, respectively.
6.2 Results and Discussion 69
Extratropics North (25 - 30 degrees lat.)
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Figure 6.7: The same as in Figure 6.6, but for the extratropical latitudes. Left and right plots are for the extratropics in the North (25–30) and South (-25– -30) hemispheres, respectively.
UTH.
All three NOAA satellites show the same annual cycle of the UTH. From this figure one does not see any difference between NOAA-16 and 17. However, there is a visible difference between NOAA-15 and the other two satellites. The UTH retrieved from the AMSU-B on this satellite is constantly off by less than 1 %RH.
Three straight lines in all plots of Figure 6.6 show the result of a simple lin-ear fit applied to the data. All of them show a slight decreasing trend in the global UTH. Note that the linear fit lines for NOAA-16 and 17 are very well in agreement, but one for NOAA-15 is by less than 1 %RH off below.
The top right plot of Figure 6.6 shows a UTH behavior with time for the trop-ics, which is defined as 30◦N–30◦S. Here, a variation of the UTH in the annual cycle is much less prominent. It is an expected behavior, since temperature vari-ability is very weak in these latitudes. The amplitude of the UTH varivari-ability is less than 4 %RH. The average value of UTH is about 28 %RH. Also for these latitudes a linear fit shows a slight decreasing trend in the UTH.
The middle left plot of Figure 6.6 shows the UTH behavior with time for the midlatitudes in both hemispheres. The midlatitudes are defined as 30◦–60◦ in both hemispheres. Here, we see more prominent variability in the annual cycle of the UTH compared to one we have seen in the previous plot for the tropics. It is also expected behavior, since there is a stronger temperature gradient over the continents found mostly in these latitudes. Hence, there is a strong amplitude
in the annual UTH variation. The UTH in these latitudes vary between 35 and 42 %RH. The average value of UTH is 38 %RH. A linear fit applied to the data shows the same behavior as discussed earlier.
The middle right and bottom left plots of Figure 6.6 show a UTH behav-ior with time for the midlatitudes in the Northern and Southern hemispheres, respectively. The data for the Northern midlatitudes show more prominent and obvious annual cycle of the UTH. Moreover, they show that the amplitude of the season dependent UTH variability is higher in the Northern hemisphere than that in the Southern hemisphere. In the Northern and Southern hemispheres this vari-ability is between 35 and 45 %RH, and between 36 and 40 %RH, respectively.
The average value of UTH in the Northern and the Southern midlatitudes are 40 and 38 %RH, respectively. This difference can be explained by the fact that the larger land masses are mainly in the Northern hemisphere and it is known that seasonal temperature gradient is much stronger over the land than that over the ocean. Also in the midlatitudes, a linear fit analysis of the data shows a slight decreasing trend in the UTH.
Comparing the UTH of the midlatitudes in both hemispheres one can see that in general these plots show that humidity in the Northern midlatitudes is higher than that in the Southern midlatitudes. The reason is again the larger land masses mainly located in the Northern hemisphere. Hence, since there is a stronger convection over the land than over the ocean, more humidity is transported to the upper troposphere over the land than over the ocean.
The bottom right plot of Figure 6.6 shows the UTH behavior with time for the deep tropics, which is defined as 10◦N–10◦S. The amplitude of the UTH variability here is higher compared to the tropical data. This is mainly due to the seasonal humidity variation around the ITCZ which is located within these latitudes. The average value of UTH in these latitudes is 34 %RH.
The left and right plots in Figure 6.7 show a UTH behavior with time for the extratropics in the Northern and Southern hemispheres, respectively. The Northern and the Southern extratropics are defined as 25◦N–30◦N and 25◦S–
30◦S. The average value of UTH in the Northern and the Southern extratropics are 28 and 25 %RH, respectively. These numbers show that the Southern ex-tratropics are drier than the Northern one. The reason is the same as explained above for the midlatitudes in both hemispheres.
6.2.4 Simple Trend Analysis of UTH
So far a linear fit analysis systematically showed a slight decreasing trend in the UTH for different latitudes. However, such an analysis done for the extratropical latitudes in the Northern hemisphere shows an opposite result. It shows there is a slight increasing trend in the UTH at these latitudes. It is consistent with the
6.2 Results and Discussion 71
data retrieved from the AMSU-B on board different satellites. There is no such a trend for the extratropical latitudes in the Southern hemisphere. Although the data for NOAA-17 show a slight increasing trend, it is very early to make any conclusions from these data since the data time period for this satellite is much shorter than that for the other two satellites.
To further study a possible trend in the UTH data one has to avoid a seasonal variation in the data we have seen. One way to accomplish this is to look at the anomalies in the data. A series of an anomaly in the UTH data is defined as follows
UTHanomal.Jan.2000 = UTHJan.2000−UTHJan.
... (6.2)
UTHanomal.Feb.2007 = UTHFeb.2007−UTHFeb.
where UTH for the corresponding month is calculated using Equation (6.1).
Basically, an anomaly shows how different UTH for the certain month is com-pared to the mean UTH for that month.
Figure 6.8 and Figure 6.9 are similar to Figure 6.6 and Figure 6.7, but an anomaly defined above is plotted against the time series. From these figures and at this stage of the study it is very difficult to make a clear picture of the anomalies in the UTH data. The amplitude of the anomaly is latitude dependent.
For wider latitude bands it is small, since more data smoothens the variability.
Studying a linear fit analysis applied to this data confirms the findings from the previous discussion. Namely, these figures show more clearly that there is a slight decreasing trend in the UTH data for all latitude bands considered except the extratropics in the Northern hemisphere. Note that a linear fit analysis applied to the UTH data retrieved from the AMSU-B on board NOAA-15 and 16 agree to a larger extend very well. However, it is not the case for the NOAA-17 data. This suggests that the time period of the NOAA-NOAA-17 data is too short for such trend studies.
To have a clear picture of the trend in the global UTH data, analysis of lati-tude dependent data only is not enough. One has to look at longilati-tude-latilati-tude dependent trend. There is already a published study of the UTH trend byBates and Jackson[2001]. In that study, the authors derived the UTH from the infrared instrument HIRS for the time period of 1979–1998. To calculate a trend, they used the method ofWeatherhead et al. [1998]. It gives a trend in %yr−1. It is expected to have different results from the trend analysis, since the time periods of the current data and data of Weatherhead et al. [1998] are quite different.
However, it is also expected to find similar patterns of the UTH trends, since
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Figure 6.8: Anomaly in the monthly UTH data. The same as in Figure 6.6, but for the series of the UTH data anomaly defined in Equation (6.2).
6.2 Results and Discussion 73
Extratropics North (25 - 30 degrees lat.)
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Figure 6.9: Anomaly in the monthly UTH data. The same as in Figure 6.7, but for the series of the UTH data anomaly defined in Equation (6.2).
the UTH is expected not to change dramatically in a large scale between the last two decades.
Figure 6.10 shows a linear trend analysis in the longitude-latitude space. The quantity shown is in %RH/month, which was calculated by applying a linear fit to the available monthly data on each grid cell. It shows the absolute change in UTH in one month. The negative and positive values mean the decrease and increase in UTH, respectively.
Different plots in this figure are for different NOAA satellites. As it was mentioned earlier, the time period of the NOAA-17 data is the shortest (Aug.
2002–Feb. 2007). Therefore, the trend patterns the NOAA-17 plot shows are very much different and not consistent with the NOAA-15 (Jan. 2000–Feb.
2007) and 16 (Oct. 2000–Feb. 2007) plots. If one uses the same time period, the UTH trend patterns for different satellites look the same (see Figure B.3 in Appendix B).
The time period difference between the NOAA-15 and 16 data is less than one year. Therefore, they show very similar patterns of the increasing and decreasing trends in the UTH data.
In the trend analysis studies, it is important to take into account the instru-mentation errors developing with time. In the cross-track scanning sensors such error is the asymmetry in the measurements, since the viewing angle dependent radiance to UTH scaling coefficients were derived assuming symmetrical mea-surements around the nadir. Buehler et al.[2005b] showed that there is practi-cally no asymmetry in the AMSU-B 183.31±1 GHz measurements. Moreover,
it does not develop with time for NOAA-16 and 17. There is an increase of the asymmetry with time for NOAA-15, though. It can account for about 3.5 % sys-tematic relative difference in the recent UTH data for the viewing angles more than 35◦. Slightly higher values of the trends for NOAA-15 compared to ones for NOAA-16 can suggest that these enhancements in NOAA-15 trends might be of the instrumental origin. However, taking into account the similarity of the trend patterns for NOAA-15 and 16, and the fact that the NOAA-16 data are free from the asymmetry error one can conclude that the trends due to the asymmetry in the measurements are much smaller than the atmospheric ones.
The results in Figure 6.10 show that patterns of the positive trend of less than 0.06 %RH per month are mainly located outside of the tropics. They are some-what between the subtopics and midlatitudes. In the Northern hemisphere they are mainly found over the continents. They are somewhat stronger in the North-ern than in the SouthNorth-ern hemisphere. Recall that we have seen this behavior previously in Figure 6.7 and 6.9. The locations of these positive trend patterns match very well with the locations of the patterns of low humidity in the long-term UTH data shown in Figure 6.1.
However,Bates and Jackson[2001] report that there is a negative UTH trend in the subtropics and the lower midlatitudes with one exception in the subtropics over India. At the current state of the study, it is not possible to explain these different outcomes.
In the tropics over the maritime continent there is a prominent pattern of a negative trend in the UTH data. This negative trend is less than 0.1 %RH per month. Recall that this area is an area of an active convective system associated with the Indian Monsoon system we have seen in the long term UTH data in Figure 6.1.
Figure 6.11 shows the corresponding 1-σ uncertainty estimates of the trends shown in Figure 6.10. It also suggests that most of the changes in UTH with time happens in the tropics and subtropics, since the uncertainty is larger in those latitudes. Note that plots for NOAA-15 and 16 in Figure 6.11 repeat the similar patterns. However, the uncertainty values for NOAA-17 are much higher again suggesting that the time period of the NOAA-17 data is too short.
The observations of positive and negative trends in UTH might be linked to the studies ofHeld and Soden[2006] and Hong et al. [submitted 2007]. Held and Soden[2006] report that they are confident that lower-tropospheric humidity (LTH) will increase as the climate warms. Consequently, it will lead to the increase in horizontal moisture transport. They conclude that the increase in LTH will lead to the decrease in the convective mass fluxes. A relatively strong pattern of the decreasing trend over the maritime continent in Figure 6.10, which corresponds to the area of high convective system, very possibly confirms this conclusion.