Development of Priority Climate Indices for Africa: A CCI/CLIV AR Workshop
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airmasses, a majority of the continent is strongly influenced by circulations which
also extend across large parts of the Atlantic and Indian Oceans. These direct
circulations have a pronounced annual cycle and associated variations in rainfall,
often described in terms of the movement of the Inter-Tropical-Convergence-Zone
(ITCZ).
3 Data and Indices
To monitor changes in climate and changes in climate extremes, a set of key indices
has been computed. A good index is expected to have a clear meaning, be highly
relevant to people, provide insights into climate change, be homogeneous, be easy
to interpret, be relevant to the practical concerns of policy makers and do not smooth
out potentially important changes. Other desirable characteristics of an index for
monitoring climate extremes include: a good signal-to-noise ratio for the detection
of a trend from one period to another, relevance to economic activity and other
aspects of human society, sensitivity to likely anthropogenically-induced or natural
variations in climate and it should be calculable from available observational and
model data.
Many types of indices can be drawn up from daily data. Table I is a list of the
climate indices that the software compute. Some indices are based on thresholds,
some on varying extremes, some are just normalize indices, others are combined
indices. The indices are expressed in various ways to facilitate spatial and temporal
trend detection and impact analysis.
Daily rainfall and temperature data is needed to compute the indices above. A
selection of stations from the Region I GSN station list has been done. Most
participants brought daily data for the selected stations existing in their countries.
The period covered by the daily data depend from one country to another and
from one station, in the same country, to another.
4 Software and Analysis
One of the most important step in treating daily data is to make a quality control and
look to inhomogeneities in these data. An inhomogeneity in a time series is defined
as any change in this time series that is not due to change in weather or climate.
Among the causes that may lead to such inhomogeneities we find: changes in
instruments, changes in processing, changes in the environment around the shelter,
changes in observing practices and changes in location of stations.
The software used to control the data, to control the homogeneity of the data and
to compute the indices is called ClimDex software. It runs under Excell and was
developed by Byron Gleason from NCDC/NOAA, USA. ClimDex provides users
with a way to detect temperature (TMAX and TMIN) inhomogeneities. These values
117
airmasses, a majority of the continent is strongly influenced by circulations which
also extend across large parts of the Atlantic and Indian Oceans. These direct
circulations have a pronounced annual cycle and associated variations in rainfall,
often described in terms of the movement of the Inter-Tropical-Convergence-Zone
(ITCZ).
3 Data and Indices
To monitor changes in climate and changes in climate extremes, a set of key indices
has been computed. A good index is expected to have a clear meaning, be highly
relevant to people, provide insights into climate change, be homogeneous, be easy
to interpret, be relevant to the practical concerns of policy makers and do not smooth
out potentially important changes. Other desirable characteristics of an index for
monitoring climate extremes include: a good signal-to-noise ratio for the detection
of a trend from one period to another, relevance to economic activity and other
aspects of human society, sensitivity to likely anthropogenically-induced or natural
variations in climate and it should be calculable from available observational and
model data.
Many types of indices can be drawn up from daily data. Table I is a list of the
climate indices that the software compute. Some indices are based on thresholds,
some on varying extremes, some are just normalize indices, others are combined
indices. The indices are expressed in various ways to facilitate spatial and temporal
trend detection and impact analysis.
Daily rainfall and temperature data is needed to compute the indices above. A
selection of stations from the Region I GSN station list has been done. Most
participants brought daily data for the selected stations existing in their countries.
The period covered by the daily data depend from one country to another and
from one station, in the same country, to another.
4 Software and Analysis
One of the most important step in treating daily data is to make a quality control and
look to inhomogeneities in these data. An inhomogeneity in a time series is defined
as any change in this time series that is not due to change in weather or climate.
Among the causes that may lead to such inhomogeneities we find: changes in
instruments, changes in processing, changes in the environment around the shelter,
changes in observing practices and changes in location of stations.
The software used to control the data, to control the homogeneity of the data and
to compute the indices is called ClimDex software. It runs under Excell and was
developed by Byron Gleason from NCDC/NOAA, USA. ClimDex provides users
with a way to detect temperature (TMAX and TMIN) inhomogeneities. These values