Atmospheric teleconnections, bird
migration, and implications for
probabilistic forecasts of bird strikes
Steven B. Feldstein
Department of Meteorology, The Pennsylvania State University,
University Park, Pennsylvania, U.S.A.
Presented at Tel Aviv University, Department of Zoology, Tel Aviv,
Israel on May 12, 2010
PREDICTABILITY

Three time scales associated with atmospheric predictability

Deterministic Predictability (Weather) (Useful 3-5 day numerical model
forecasts) Numerous studies show a linkage between weather, i.e.,
storms, precipitation, fronts, etc., and bird migration. Van Belle et al.
(2007) 3-day forecast of bird
migration intensity.

Extended-Range Predictability (1 week to 1 month timescale)
Predictability mostly poor (because of chaos), except perhaps when
teleconnction patterns are excited. Linkage to bird migration associated
with storms that accompany teleconnection patterns (Elkins (2008).

Monthly and Seasonal Predictability (Climate, > 1month, average of
weather) (Closely linked to seasonal cycle and boundary forcing) (Useful
ensemble forecasts.) Linkage between El Nino/Southern Oscillation and
bird migration (Hameed et al. 2009).
El Nino/Southern Oscillation and Bird
Migration at Attu, Alaska

From 1980-2000, each year, about 100 birders
visited Attu, Island to add Asian bird species to the
North American bird lists Island, Alaska
Quic kT ime™ and a
T IFF (Uncompress ed) decompress or
are needed to s ee this pi cture.
to add Asian birds to their North American bird
QuickT ime™ and a
T IFF (Uncompressed) decompressor
are needed to see t his picture.
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Common Sandpiper
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AIPD = Asian Individuals Per Day (North American species excluded
from total) (Hameed et al. 2009)
Long-toed Stint
Rustic Bunting
El Nino/Southern Oscillation
ENSO, storms, and bird migration
ENSO affects
bird migration
through its
influence on the
latitude of the
jet and the
storms that
follow the jet, i.e.
ENSO alters the
environment
through which
storms propagate.
The dominant Northern Hemisphere teleconnection patterns
North Atlantic Oscillation
Pacific/North American pattern
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Teleconnections evolve on a 7-10 day time scale (longer than weather time scale).
They alter storm path and storm intensity.
Climate Prediction Center
Siberian Vagrants and the PNA
Teleconnection Pattern


Records of all observations (locations and dates in California)
of Dusky Warbler, White Wagtail, Red-throated Pipit and other
Siberian vagrants
Composite PNA index
Dusky Warbler
(PNA=- 0.42, n=15, p<0.1)
White Wagtail
(PNA=-0.26, n=22, p<0.02)
Red-throated Pipit (PNA=-0.38, n=79, p<0.01)
(Steven Feldstein, Peter Pyle, Steve Mlodinow, Richard Erickson, Jim Tietz)
Anomalous wind vectors associated with Dusky Warbler records in California
Lag -3 Days
Lag -1 Days
Lag -2 Days
Lag 0 Days
Nearctic Vagrants and the North Atlantic Oscillation
(Elkins 2008, British Birds)
Circumglobal Teleconnection Pattern
EOF1
Wave packets associated with SL precip
wet
dry
Time-averaged
V 300 over
persistent event
(lag -6 to lag +9 days)
Correlation with EOF1 =0.83
Correlation with EOF1 =-0.72
300-hPa geopotential height evolution - Middle Eastern precipitation
-6 days
-4 days
-2 days
Composite
analysis
Evolution of
300-hPa
height field
determines the
wind, T, T,
P, P, and
rainfall,
variables
which
influence bird
migration.
0 days
+5 days
+2 days
+7days
Wave packet
first seen
+4 days over NE
Pacific.
Wave field
persists for 2
weeks. This
slow
evolution
+9 days may allow
for a 7-day
weather
forecast for
Israel?
Feldstein and Dayan (2008)
Israeli Migrants and the Circumglobal Teleconnection
Pattern (CTP)?

QUESTIONS: Does the CTP influence bird
migration over Israel?
 Can the CTP be used to forecast the bird
migration intensity or bird strike frequency with a
1-7 day lead time (fall season)?
 Beyond 3-4 days, is a forecast of bird migration
intensity with a probabilistic model based upon the
CTP better than that based upon a deterministic
linear regression model (e.g., Van Belle et al. 2007)?
16 North Pacific sea level pressure cluster patterns
Example of
cluster
analysis
Tropical Convection Associated with
the Madden-Julian Oscillation (MJO)
Phase 1




Dominant intraseaonal
oscillation in the tropics
Phase 2
MJO cycle: 30-60 days
Phase 3
Shading OLR
Phase 4
Time between phases ~
6 days
Phase 5
Phase 6
Time between Phases ~
6 days
Phase 7
Phase 8
From Wheeler and Hendon (2004)
20۫°E
180۫°
From Wheeler and Hendon (2004)
60۫°W
Frequency of occurrence for each cluster pattern and MJO phase
1-7 day Forecast of Anomalous Bird Migration intensity in Israel
Phase Number = location in
Israel
Lag = 1 to 7 days (Feldstein
and Dayan 2008)
Pattern Number = cluster pattern
Color denotes anomalous
bird migration intensity
determined from composites
of the daily bird migration
intensity for each pattern
number
1-7 Day Probabilistic Bird Migration Intensity Forecast

Cluster analysis with 300-hPa meridional wind
(1-7 day variability (CTP) dominated by the upper tropospheric flow and it
also determines the lower tropospheric flow where birds are observed)
(Cluster patterns represent slowly-evolving component of flow, small number
of patterns with large spatial scales)



Analysis performed at separate locations (airports, radar
stations, etc.) in Israel
Analysis can be performed separately for soaring (raptors)
and powered flight (shorebirds) migrants
Conditional probabilities based on the accumulation of
migrants during previous days (bad weather)
Seasonal Forecast of Anomalous Bird Migration
Intensity in Israel
mean is the anomalous seasonal meridional wind
is cluster pattern c
is the frequency of cluster pattern c
Obtain forecast of seasonal mean meridional wind
Determine the cluster pattern which has the largest projection onto
Estimate seasonal mean bird migration intensity (above average, average, below
average) in terms of the most frequently occurring cluster pattern.
Combined Probabilistic/Deterministic Bird
Migration Intensity Forecast
F
F = (w1F1 + w2F2)/(w1 + w2)
F1 = probabilistic forecast based on cluster patterns
F2 = linear regression forecast based on deterministic
weather forecast model (e. g., Van Belle et al. 2007).
W = P(B|A)
A= forecasted bird migration intensity
B= observed bird migration intensity
Presumably F1 (F2 ) forecast is better for longer (shorter) lead times.
Presumably
Bayesian forecast also possible using multimodel ensemble approach
Conclusions
Bird migration related to (a) weather, (b) teleconnections (PNA, NAO),
and (c) climate (ENSO)
Forecast of bird migration intensity based upon cluster analysis of the 300-hPa
meridional wind
Using weights, can combine probabilistic forecast with deterministic linear
regression forecast
The technique can be extended to seasonal bird migration intensity forecasts.