4.5. Mining Intervals of Abrupt Change in Climate Data
Contributors: Liess (R-UMN), Shekhar (F-UMN), Zhou (G-UMN), Jiang (G-UMN)
Activities: Abrupt climate changes are unusually large shifts of precipitation, temperature, etc. that occur
during a short time-intervals. Detecting the periods of such abrupt changes in time series is a crucial step
for understanding and attributing climate changes. In a broader sense, abrupt changes can also occur in
geographical space, indicating sharp transitions regions of environment between different ecological
zones (a.k.a. ecotones). As these ecotones may be vulnerable in response to climate change, identifying
their footprints can help us understand the interactions between climate change and ecological systems.
Existing state-of-the-art approaches for abrupt change detection (e.g., in signal processing, image
processing, etc.) aim to find time-points (spatial locations) where abrupt changes or discontinuities occur.
They do not find intervals of abrupt changes. Such intervals are of interest to understand finer-details of
processes underlying abrupt changes. The interval detection problem is challenging since (1) the abrupt
change intervals may have varying lengths without a known maximum length, (2) abrupt changes
intervals may not exhibit monotonicity of change detection, and (3) data volume may be very large.
In year I, an interest measure named sameness score was defined to quantify the consistency of abrupt
change, e.g., change direction or change magnitude. We also designed and evaluated a preliminary
computational approach, namely, Abrupt Change Interval Miner (ACIM), which enumerated and pruned
intervals in a top-down manner exploiting subset relationship among intervals. Case-studies were carried
out to evaluate the usefulness of ACIM.
In year II, we designed and evaluated a Sub-path Enumeration and Pruning (SEP) approach to the
discovery of interesting spatiotemporal sub-paths/intervals such as those with abrupt changes. The
approach has two different design decisions, namely, the row-wise traversal and top-down traversal
strategies. They enumerate and prune intervals in semi-top-down and fully top-down manners
respectively, exploiting subset relationship among intervals. Experiments were also carried out to evaluate
the performance of the approach. We further analyzed spatial autocorrelation level of eco-climate data at
multiple resolutions. The SEP approach is applied in the analysis of spatial autocorrelation correlogram
across multiple resolutions. It is used to find resolution ranges where abrupt change in spatial
autocorrelation occurs.
Findings: In Year I, the proposed approach was used to analyze the smoothed Sahel precipitation
anomaly data. By setting the sameness degree threshold to 0.5, we discovered a few major abrupt change
intervals of precipitation. For example, the interval from 1967 to 1971 is the well-known abrupt decline of
precipitation in Sahel. We also identified several abrupt increase intervals (1903-1908, 1944-1953, 19861988 and 2008-2010), which are not widely discussed in literature. For example, rainfall in Sahel seems
to start recovering since 1986. By reducing the sameness score threshold to 0.3, we discovered a longer
interval of abrupt precipitation decrease from 1957 to 1983 which includes the two shorter periods (19681971 and 1981-1983) we previously discovered. We also generalized our method for detecting long
periods of extreme precipitation. The method discovered the long dryer period from 1985 to 1992 which
is widely discussed in research. In addition, it also discovered a long wetter period from 1920 to1960
which is not often discussed in the literature.
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The Sahel
(a)
(b)
Figure 5. (a) Map of vegetation cover (measured in Normalized Difference of Vegetation Index, NDVI)
in Africa, Aug. 1-15, 1981. (b) Abrupt transitions of vegetation detected in Africa (south to north).
ACIM was also used to analyze vegetation cover data (see Figure 5). It discovered many ecotones, as
shown in Fig. 5(b); an interesting finding is characterization of Sahel of an ecotone, indicated by the
green ellipse.
In year II, experimental and theoretical analysis show that both proposed SEP row-wise and top-down
algorithms are orders of magnitude faster than naive algorithm. The top-down design decision has better
performance than the row-wise for datasets with longer patterns while the row-wise always has lower
memory cost.
We also applied our approach to analyze spatial autocorrelation correlogram across different resolutions.
We compute the spatial autocorrelation (e.g., Moran’s I, Geary’s C) of NDVI in Africa across 40 different
resolutions The general trend observed in these datasets is that the spatial autocorrelation measured by
Moran’s I increase at very fine resolutions , reaching a peak and then slowly drops to a lower level. The
turning point of the curve shows the resolution at which autocorrelation or heterogeneity vanishes. We
also observed that the curve for February has hardly any abrupt change intervals while the August curve
has a steadily decreasing interval (as shown in Fig. 6). These results are likely due to the fact that the
vegetation in the rain forest and grassland are less irrigated by precipitation in the dry winter, which
brings down the spatial heterogeneity at large scales. By contrast, the dense rain forest and grassland in
summer (August) makes the land cover quite different at large scales, compared to the large area of
deserts in the north.
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Figure 6. Autocorrelation correlogram at multiple resolutions with the identified change/stable
intervals.
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