Assessing Predictability of Seasonal Precipitation for May-June-July in Kazakhstan

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Assessing Predictability of Seasonal
Precipitation for May-June-Julyin Kazakhstan
Tony Barnston, IRI, New York, US
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Possible sources of seasonal climate
predictability 1. Tropical sea surface
temperature (SST) anomalies such as El Nino
and La Nina, or tropical Atlantic or Indian
Ocean SST anomalies 2. Land surface anomalies
(up to 1-2 months influence) 3. Persistent
extratropical atmospheric circulation
anomalies, such as the Arctic Oscillation
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(uses CRU precipitation)
ENSO-based Teleconnections May-Jun-Jul El Nino
El Nino
Probability of above normal precipitation
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(uses CRU precipitation)
ENSO-based Teleconnections May-Jun-Jul La Nina
La Nina
Probability of above normal precipitation
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Seasonal precipitation forecasts for
May-June-July for northern Kazakhstan
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Using field of 500 hPa height as predictor of
Kazakhstan rainfall in May-Jun-Jul
Lagged in time March-April 500 hPa is used to
predict May-Jun-Jul rainfall
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Using earlier (Mar-Apr) 500 hPa height as
predictor
for MJJ rain
Cross-validation 5 years held out, middle one
predicted
skill
Distribution of Skill using Mar-Apr 500
hPa ht Correlation of precip at point X with
predictor 500 hPa ht
x
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Using observed tropical SST field as predictor of
Kazakhstan rainfall in May-Jun-Jul
Lagged in time March-April SST is used to
predict May-Jun-Jul rainfall
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Using earlier March SST as predictor
Mode 1
Mode 1
March SST Time Series
MJJ Kaz precip
Mode 2
Mode 2
March SST Time Series
MJJ Kaz precip
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Cross-validation 5 years held out, middle one
predicted
x
May-Jun-Jul
Distribution of skill using March
tropical SST
skill
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Current dynamical model climate predictions for
May-June-July 2014
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North American national multi-model ensemble
forecast For May-Jun-Jul 2014 rainfall
x
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Precipitation
May-June-July
North American National Multi-model Ensemble
Anomaly Correlation
x
skill
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European national multi-model ensemble
forecast For May-Jun-Jul 2014 rainfall
x
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North American national multi-model ensemble
forecast For May-Jun-Jul 2014 temperature
x
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Temperature
May-June-July
North American Multi-model Ensemble Anomaly
Correlation
x
skill
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European national multi-model ensemble
forecast For May-Jun-Jul 2014 temperature
x
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Precipitation Skill IRI Forecasts 1998-2013
May-June-July
0.5-month lead
x
Heidke hit skill score
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Using autocorrelations of precipitation In the 3
states in northern part of Kazakhstan, autocorrela
tions for precipitation are generally weak.
However, autocorrelations of
July ? August are at least 0.3, and gt0.4 at
some stations. Lag correlations of temperature
? precipitation are very weak during the growing
season.
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Global warming trend gives opportunity for some
skill in seasonal temperature
predictions With base period in the past,
positive temperature anomalies are often a
correct forecast.
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Time series of monthly anomaly of maximum
temperature at station
28698 (Omsk, Russia)
warming?
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Time series of annual anomaly of maximum
temperature at station 28698
(Omsk, Russia)
Warming trend is evident near Northern Kazakhstan
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Conclusions
Tropical SST anomalies during months earlier
than May-June-July have almost no relationship
with rainfall or temperature in northern
Kazakhstan in May-June-July. Upper air
geopotential height (500 hPa) in preceding
months is related only weakly to Kazakhstan
precipitation and temperature in May-June-July.
A connection with the Arctic Oscillation is
weak. Autocorrelation statistics for
precipitation in northern Kazakhstan show some
July-to-August anomaly persistence. Dynamical
model predictions for Kazakstan show very slight
skill for May-June-July precipitation.
For temperature, skill is present due to warming
trends. An upward temperature trend exists in
observations for northern Kazakhstan for the
May-June-July season.