Predicting European soil-moisture status (scPDSI) using seasonal circulation indices
Introduction
The availability of sets of characteristic atmospheric circulation patterns for the EMULATE
region (70°-25° N by 70° W-50° E), for the period 1850-2002, via EMULATE deliverables
D3, D5 and D6, permits their correlation with measures of weather and weather-related
phenomena within a similar geographical domain. This opens the prospect for the prediction
of weather/weather-related phenomena which would be favoured by certain antecedent
behaviour of the circulation. Summer soil-moisture status, as expressed by the self calibrating
Palmer Drought Severity Index (scPDSI) technique (van der Schrier et al., 2006), can be seen
as an integration of the weather conditions of the preceding winter and spring and the
“current” summer. The study seeks to determine how much of the summer scPDSI values can
be explained by winter and spring circulation fields.
Seasonal average values for a number of typical circulation patterns, which have been
identified by three different techniques, have been correlated with summer-seasonal indices of
soil moisture status, for the six most prominent soil-moisture patterns identified within the
region (70°-35° N by 10°W-60°E), for the period 1901-2002 (details in van der Schrier et al.,
2006). Individual correlation coefficients, for the common period 1901-2002 (between the
circulation indices for specific seasons and the intensity of individual soil-moisture patterns),
range from zero to ~ |0.4|. When the strongest circulation predictors are grouped (for
individual seasons) and multiple regression is performed, multiple correlation coefficients as
high as 0.56 have been achieved.
Method and analyses
Circulation pattern indices have been created (see EMULATE deliverables D5 and D6) by
two basic methodologies to produce clusters/patterns which allow a daily assessment of the
circulation type within our large European region. The methods are:


Simulated Annealing clustering
Varimax-rotated t-mode PCA (Principal Component Analysis)
For more detail of the clustering techniques and all data files, including the individual
daily/seasonal pattern amplitude series, see (EMULATE deliverables D5 and D6 report):
http://www.cru.uea.ac.uk/cru/projects/emulate/emslp3_pattern_classification/emslp3_pattern_classification/
The daily pattern amplitude series for Simulated Annealing-clustering have been produced in
two different ways. These are:


Correlation indices
Euclidian distance indices
Here we average the daily indices to a monthly or seasonal level and the latter has been done
for the current analyses. There are, therefore, three different seasonal circulation index values
– two for Simulated Annealing and one for PCA.
Patterns/clusters differ between seasons. For example, in the current work, there are nine
winter, eleven spring and six summer patterns/clusters for both methods of classification.
1
The summer soil-moisture status patterns have been produced using the self calibrating
Palmer Drought Severity Index (scPDSI, van der Schrier et al., 2006). This is effectively a
measure of European soil-moisture that fairly represents the relative degree of drought across
different climatic regions. Droughts in southern Europe are more severe than those in the
north, but the scPDSI equates these so that there are equal numbers in each region.
Characteristic summer soil-moisture patterns have been identified (using Empirical
Orthogonal Teleconnection) and, for the current work, the first six of these have been used for
the correlations with the different circulation index series. Table 2 indicates the geographical
locations of the six scPDSI patterns used here.
Correlation matrices have been produced which show the strength of relationships between all
scPDSI index series and all circulation index series, for all methods of circulation index
derivation for the preceding winter and spring seasons and the current summer (see Tables 1a1c). Based upon the strength of these correlation relationships, multiple regressions have
been undertaken, using combinations of the circulation patterns that show significant (95%)
individual correlation coefficients – on a season-by-season basis (see Table 3). This
procedure optimizes the skill in predicting summer soil-moisture status from antecedent
circulation behaviour within the EMULATE region. Figs. 1a and 1b show the best (from a
predictive perspective) combinations of soil moisture index and seasonal circulation indices,
during the period 1901-2002.
When the scPDSI dataset (on a 0.5° x 0.5° spatial resolution) values for the whole of the
European window (70°-35° N by 10°W-60°E) were averaged for the summer season, the
resulting index was correlated with all combinations of circulation indices. The strength of
correlation relationships with the different circulation pattern indices were generally lower
and less robust than that seen with the scPDSI index series.
Conclusions
Summer soil moisture status (as measured by scPDSI) shows reasonable degrees of
predictability using indices of antecedent behaviour of atmospheric circulation, for some of
the typical patterns of scPDSI. Notable here is scPDSI pattern III which dominates with
respect to the highest and most frequent occurrence of significant regression/multiple
regression coefficients. This situation is probably favoured by pattern III being mainly
associated with western regions of the scPDSI geographical domain. The circulation
patterns/indices are dominated by the Atlantic part of the EMULATE geographical region and
weather conditions in western Europe tend to be dominated by Atlantic atmospheric
circulation.
It should be noted that, whilst we have here presented the different derivations of the
circulation series (PCA, Annealing, Annealing/Euclidian distance) as having equal merit,
there are some subtle differences that should be borne in mind. For example, the lower order
PCA patterns are very rare (low frequency of occurrence) and this probably makes them less
useful as predictors of weather-related phenomena like soil-moisture status – when compared
to the more equal (in terms of frequency) distribution associated with the Simulated
Annealing clusters.
References
van der Schrier, G., Briffa, K. R., Jones, P.D. and Osborn, T. J., 2006: Summer moisture
availability across Europe. J. Climate, in press.
2
Table 1a: The individual correlations between all scPDSI indices and all winter circulation indices
(by all methods of index derivation). Coefficients in bold indicate 95% statistical significance.
WINTER
PDSI pattns. Circ. pattns.
I
1
I
2
I
3
I
4
I
5
I
6
I
7
I
8
I
9
II
1
II
2
II
3
II
4
II
5
II
6
II
7
II
8
II
9
III
1
III
2
III
3
III
4
III
5
III
6
III
7
III
8
III
9
IV
1
IV
2
IV
3
IV
4
IV
5
IV
6
IV
7
IV
8
IV
9
V
1
V
2
V
3
V
4
V
5
V
6
V
7
V
8
V
9
VI
1
VI
2
VI
3
VI
4
VI
5
VI
6
VI
7
VI
8
VI
9
Euclid
-0.080
-0.127
0.024
-0.117
0.010
-0.165
-0.273
0.016
-0.044
-0.104
-0.062
-0.169
0.158
-0.078
0.233
0.125
0.237
0.263
-0.048
-0.289
-0.229
0.413
0.179
0.106
0.185
0.155
0.314
-0.102
0.022
0.062
-0.236
-0.050
-0.190
-0.132
-0.130
-0.181
-0.126
-0.058
-0.106
0.077
-0.024
0.057
0.124
0.114
0.131
-0.198
0.054
-0.143
0.003
-0.220
0.183
0.120
0.117
0.151
Correlation coeffs.
Annealing
-0.052
-0.126
0.071
-0.080
0.059
-0.166
-0.215
0.239
-0.045
-0.162
-0.108
-0.234
-0.083
-0.197
0.034
-0.109
-0.039
0.144
-0.116
-0.267
-0.229
0.082
-0.033
-0.032
-0.031
-0.060
0.251
-0.051
-0.008
0.027
-0.120
-0.024
-0.120
-0.072
-0.083
-0.191
-0.159
-0.093
-0.161
-0.129
-0.147
-0.065
-0.064
-0.127
-0.039
-0.139
0.064
-0.181
-0.135
-0.239
0.096
0.004
-0.081
0.025
3
PCA
0.011
-0.154
-0.011
0.224
-0.166
-0.220
-0.082
-0.322
0.278
-0.190
-0.072
0.160
-0.174
-0.080
0.184
-0.174
0.094
-0.079
-0.078
-0.130
0.279
-0.275
0.245
0.223
-0.368
0.097
-0.048
-0.038
-0.071
-0.184
0.033
-0.124
0.004
-0.069
-0.062
-0.107
-0.165
-0.091
-0.038
-0.130
0.103
0.019
-0.108
0.106
-0.025
-0.205
0.035
0.007
-0.093
0.123
0.067
0.196
0.177
-0.106
Table 1b: The individual correlations between all scPDSI indices and all spring circulation indices
(by all methods of index derivation). Coefficients in bold indicate 95% statistical significance.
SPRING
PDSI pattns. Circ. pattns.
I
1
I
2
I
3
I
4
I
5
I
6
I
7
I
8
I
9
I
10
I
11
II
1
II
2
II
3
II
4
II
5
II
6
II
7
II
8
II
9
II
10
II
11
III
1
III
2
III
3
III
4
III
5
III
6
III
7
III
8
III
9
III
10
III
11
IV
1
IV
2
IV
3
IV
4
IV
5
IV
6
IV
7
IV
8
IV
9
IV
10
IV
11
V
1
V
2
V
3
V
4
V
5
V
6
V
7
V
8
V
9
V
10
V
11
VI
1
VI
2
VI
3
VI
4
VI
5
VI
6
VI
7
VI
8
VI
9
VI
10
VI
11
Euclid
-0.286
-0.091
-0.211
-0.242
-0.124
-0.043
-0.005
0.001
-0.160
-0.159
0.048
0.051
-0.029
-0.080
0.059
0.178
-0.035
-0.124
0.040
-0.051
0.028
0.078
-0.170
0.197
-0.235
0.237
0.233
-0.149
-0.009
0.416
-0.410
-0.155
0.289
-0.235
0.064
-0.179
-0.187
-0.066
0.022
-0.085
0.114
-0.226
-0.332
0.169
-0.132
-0.006
-0.246
-0.168
0.096
0.046
-0.184
0.069
-0.200
-0.237
0.194
0.001
0.171
0.023
0.147
0.095
0.035
0.009
0.214
-0.153
-0.159
0.137
Correlation coeffs.
Annealing
PCA
-0.161
0.134
0.082
0.023
0.032
-0.127
-0.087
0.025
-0.027
0.041
0.167
-0.151
0.128
-0.135
0.104
-0.196
0.059
-0.008
0.014
0.072
0.131
-0.073
0.038
-0.173
-0.060
-0.122
-0.177
-0.036
-0.026
0.080
0.228
0.170
-0.106
0.306
-0.188
0.047
-0.032
-0.213
-0.161
-0.097
-0.090
-0.193
0.088
0.053
-0.286
-0.310
0.114
-0.094
-0.294
-0.306
0.117
0.425
0.283
0.186
-0.281
0.380
-0.184
0.040
0.307
-0.027
-0.417
-0.057
-0.162
-0.238
0.224
0.161
-0.281
0.025
0.196
-0.239
-0.069
-0.254
-0.228
0.173
-0.097
-0.014
0.091
0.077
-0.071
-0.015
0.051
0.084
-0.152
0.091
-0.289
-0.072
0.169
-0.052
-0.025
-0.054
0.111
-0.254
-0.171
-0.048
-0.231
0.114
0.154
0.217
0.101
0.169
-0.177
-0.295
-0.048
-0.040
-0.146
-0.045
-0.223
-0.134
0.223
0.160
0.012
0.015
0.226
-0.247
-0.016
0.063
-0.106
0.171
-0.037
0.002
0.055
0.216
-0.131
-0.217
-0.001
0.233
-0.137
-0.112
-0.261
-0.250
0.064
0.062
4
Table 1c: The individual correlations between all scPDSI indices and all summer circulation indices
(by all methods of index derivation). Coefficients in bold indicate 95% statistical significance.
SUMMER
Correlation coeffs.
PDSI pattns. Circ. pattns
Euclid
Annealing
PCA
I
1
-0.093
-0.180
-0.081
I
2
0.047
-0.028
-0.187
I
3
0.127
0.056
0.161
I
4
-0.102
-0.173
0.143
I
5
0.079
0.122
0.211
I
6
0.092
0.058
0.049
II
1
-0.195
-0.064
-0.103
II
2
0.012
0.010
0.018
II
3
-0.095
-0.063
0.044
II
4
-0.004
0.034
-0.069
II
5
-0.177
-0.065
-0.101
II
6
-0.165
-0.142
0.029
III
1
-0.061
-0.015
0.054
III
2
-0.094
0.031
-0.069
III
3
0.179
0.166
0.120
III
4
-0.200
-0.096
0.206
III
5
-0.088
0.057
0.080
III
6
0.160
0.185
0.141
IV
1
-0.103
-0.123
0.012
IV
2
0.172
0.057
-0.139
IV
3
0.005
-0.004
0.079
IV
4
0.062
-0.054
-0.150
IV
5
0.042
-0.130
-0.028
IV
6
-0.085
-0.130
0.068
V
1
-0.237
-0.253
-0.115
V
2
-0.117
-0.115
-0.292
V
3
0.170
0.041
0.093
V
4
-0.284
-0.296
0.151
V
5
0.056
0.030
0.231
V
6
0.187
0.087
-0.001
VI
1
-0.056
-0.198
-0.209
VI
2
-0.026
-0.148
-0.143
VI
3
0.105
-0.098
-0.033
VI
4
-0.079
-0.165
0.065
VI
5
0.136
-0.068
0.043
VI
6
0.088
-0.066
0.063
Table 2: The six scPDSI patterns and their principal region of associated drought effect.
PDSI pattern
I
II
III
IV
V
V1
Principal centres of drought for each pattern
North of the Caspian Sea (ca. 50° N, 50 ° E)
West of the Black Sea (ca. 45°N, 20° E)
SE UK and adjacent areas to the E and SE ( ca.50° N, 5°E)
West of the Caspian Sea (ca. 42° N, 55°E)
North western Russia (ca. 57°N, 40° E)
South western France (ca. 43°N, 3° E)
5
Table 3: The multiple correlation coefficients from multiple regression analyses. Multiple regression
includes all circulation patterns (for each method of index derivation) which show 95%-significant
individual correlations. Coefficients in non-bold italics indicate the point of “diminishing returns”.
Multiple regressions (as in the attached Figs.) include the members within the grey shaded areas only.
scPDSI-pattern
I
II
III
IV
V
VI
1
0.253
-
2
-
3
-
I
II
III
IV
V
VI
0.286
0.281
-
0.226
0.365
-
I
II
III
IV
V
VI
-
0.267
-
0.234
0.282
-
I
II
III
IV
V
VI
0.237
-
-
-
I
II
III
IV
V
VI
0.286
0.235
-
-
0.287
0.235
0.246
-
I
II
III
IV
V
VI
0.233
-
0.289
-
I
II
III
IV
V
VI
0.209
I
II
III
IV
V
VI
I
II
III
IV
V
VI
Anneal - summer
4
5
0.297
-
patterns (1-6)
6
7
-
8
9
10
11
0.504
-
0.518
-
0.363
0.243
0.279
0.524
0.259
-
0.298
-
0.360
-
Euclid - spring patterns (1-11)
0.315
0.551
0.551
-
0.553
0.214
0.559
0.245
0.247
-
0.351
0.271
-
0.566
-
0.291
-
Euclid - winter patterns (1-9)
0.273
0.433
0.236
0.220
-
0.267
-
0.289
0.443
-
0.292
-
-
PCA - summer patterns (1-6)
0.211
0.206
0.305
-
0.310
-
0.239
0.254
0.247
0.375
0.311
-
PCA - spring
0.475
0.275
-
patterns (1-11)
0.306
0.476
0.362
0.274
0.355
0.403
0.384
-
0.477
0.447
-
0.205
-
0.280
-
PCA - winter patterns (1-9)
0.224
0.257
0.342
0.399
0.402
0.524
-
0.363
-
0.430
-
Anneal - spring patterns (1-11)
0.228
0.397
0.450
0.356
0.231
Anneal - winter patterns (1-9)
0.215
0.239
Euclid - summer
0.200
0.310
-
6
patterns (1-6)
-
Fig. 1a: Predicted (from multiple regression) and observed time-series for scPDSI pattern III.
Predictions are for all derivations of circulation indices for the preceding spring (left) and winter
(right) seasons. From top to bottom, the circulation patterns are derived from Simulated Annealing,
Euclidian Distance and PCA.
7
Fig. 1b: Further comparison of predicted (from multiple regression) and observed scPDSI time-series
that indicate reasonable predictive skill. Left, two spring PCA patterns are used to predict scPDSI
pattern II and, right, five spring PCA are used to predict scPDSI pattern VI.
8