Development of Priority Climate Indices for Africa: A CCl/CLIV AR Workshop
119
can be thought of as discontinuities or shifts in the data record or time series of
maximum or minimum temperature. These abrupt or sometimes gradual changes
can be traced to both natural and artificial (human induced) changes. User's are
generally more interested in eliminating or mitigating the effects of the latter
(artificial) and trying to detect and/or explain the former (natural). To examine a
temperature time series for inhomogeneities, ClimDex utilizes both visual inspection
of a temperature time series and statistical test (t-test) to test the difference between
two adjacent period mean values In the first step, ClimDex simply provides the user
with a time series of annual mean (temperature) and accumulated values
(precipitation). These time series can then be examined in conjunction with any
existing metadata to identify potential inhomogeneities. The second step involves
the user defining a "window" size in years. This window size is then split into two
adjacent periods and then the difference between the two mean values are tested for
any differences from 0 (e.g. two-sided t-test, for Beta not equal to 0). The resultant
probabilities from this statistical test are plotted for the user.
5 Results
Each participant has undertaken a Climate Indices Study for his country and
elaborate a report which will be available to the scientific community. Hereafter,
only global African maps of selected indices are reported. Six selected indices are
shown:
~ The Percent of Time the Minimum Temperature is less or equal to the 10th
Percentile. This can show us the trend of the lowest minimum temperatures.
~ The percent of Time TMIN is greater or equal to 90th Percentile to see also the
trend of the warm night temperature.
~ The Percent of Time the Maximum Temperature is less or equal to the 10th
Percentile.
~ The percent of Time T AMX is greater or equal to 90th Percentile to see also the
trend of the Hot night temperature.
~ The number of days that precipitation is greater or equal to a chosen threshold
like 10 mm.
~ The percent of annual precipitation due to events exceeding 95th percentile.
The first two figures concerning minimum temperature show a positive trend of
the warm night temperature and a negative trend of the lowest minimum
temperatures over most countries, including Madagascar and Seychelles, except in
the extreme eastern coast of Tanzania. Similar results are found for maximum
temperature but this time the extreme eastern coast of Tanzania show similar results
to those obtained over all countries. There is evidence of warming in almost all the
countries, this warming is found to be statistically significant in many cases.
The last two figures show the last two precipitation indices listed above. Unlike
the temperature, the trend in these cases are very different from one location to
another even in the same country making it very difficult to make a regional analysis
119
can be thought of as discontinuities or shifts in the data record or time series of
maximum or minimum temperature. These abrupt or sometimes gradual changes
can be traced to both natural and artificial (human induced) changes. User's are
generally more interested in eliminating or mitigating the effects of the latter
(artificial) and trying to detect and/or explain the former (natural). To examine a
temperature time series for inhomogeneities, ClimDex utilizes both visual inspection
of a temperature time series and statistical test (t-test) to test the difference between
two adjacent period mean values In the first step, ClimDex simply provides the user
with a time series of annual mean (temperature) and accumulated values
(precipitation). These time series can then be examined in conjunction with any
existing metadata to identify potential inhomogeneities. The second step involves
the user defining a "window" size in years. This window size is then split into two
adjacent periods and then the difference between the two mean values are tested for
any differences from 0 (e.g. two-sided t-test, for Beta not equal to 0). The resultant
probabilities from this statistical test are plotted for the user.
5 Results
Each participant has undertaken a Climate Indices Study for his country and
elaborate a report which will be available to the scientific community. Hereafter,
only global African maps of selected indices are reported. Six selected indices are
shown:
~ The Percent of Time the Minimum Temperature is less or equal to the 10th
Percentile. This can show us the trend of the lowest minimum temperatures.
~ The percent of Time TMIN is greater or equal to 90th Percentile to see also the
trend of the warm night temperature.
~ The Percent of Time the Maximum Temperature is less or equal to the 10th
Percentile.
~ The percent of Time T AMX is greater or equal to 90th Percentile to see also the
trend of the Hot night temperature.
~ The number of days that precipitation is greater or equal to a chosen threshold
like 10 mm.
~ The percent of annual precipitation due to events exceeding 95th percentile.
The first two figures concerning minimum temperature show a positive trend of
the warm night temperature and a negative trend of the lowest minimum
temperatures over most countries, including Madagascar and Seychelles, except in
the extreme eastern coast of Tanzania. Similar results are found for maximum
temperature but this time the extreme eastern coast of Tanzania show similar results
to those obtained over all countries. There is evidence of warming in almost all the
countries, this warming is found to be statistically significant in many cases.
The last two figures show the last two precipitation indices listed above. Unlike
the temperature, the trend in these cases are very different from one location to
another even in the same country making it very difficult to make a regional analysis