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Detecting Change in the
Bering Sea Ecosystem
Sergei Rodionov1, James E. Overland2, Nicholas A.
Bond1
1JISAO, University of Washington, Seattle, WA.
2PMEL, NOAA, Seattle, WA.
The SARS Method
Searching for the first regime shift
January PDO
1
5% significance level
l = 10
0.5
0 RSI
1900 1905 1910 1915 1920 1925 1930
0.6
-0.5
0.5
0.4
-1
1910
0.3
-1.5
0.2
-2 0.1
0
-2.5
SARS – Sequential Analysis of Regime Shifts
RSI – Regime Shift Index
Searching for the next regime shift
January PDO
2.5
l = 10
2
1910 1922 RSI
1.5
0.8
1914 0.7
1
0.6
0.5 0.5
0.4
0
0.3
1900 1905 1910 1915 1920 1925 1930
0.2
-0.5
0.1
-1 5% significance level
0
1912
-1.5
The North Pacific Index (Nov-Mar)
1899-2003
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
3
2
1
std
0
-1
1977 0.8
-2
-3
l = 10 1948
1924 1977 0.6
0.1
p = 0.05 1948
1924
0.4
RSI
0.2
1958
1989
2003
0
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Arctic Oscillation, 1951-2003
3
p = 0.05
2
l = 10
5
7
1
1996
1972
0
1994
-1 1989
1977
-2
-3
1951 1956 1961 1966 1971 1976 1981 1986 1991 1996 2001
www.Beringclimate.noaa.gov
Regime Shifts in Climatic Indices
0.3
p = 10 1943
l = 0.1 PDOa
0.25 PDOw
PDOs
1977
0.2
PDOw
ALPI
NPINCAR
RSI
PNA
0.15
1976
PDOa
0.1 PDOs 1998
1934 1989 NPICPC
EPI
PDOa AO
EPI PDOs
0.05 PDOs PDOw NPICPC AI
NPINCAR
0
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
The 1989 Shift
Winter (DJF) Surface Air Temperature
4
Arctic Oscillation, Winter (DJF)
l = 10
p = 0.1
2
STD
0
1989
-2
-4
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Pacific Decadal Oscillation, Winter (DJF)
2
l = 10
p = 0.1
1
0
STD
-1
1989
-2
-3
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
The North Pacific Index, Winter (NDJFM)
2
1
0
STD
-1
1989
-2 l = 10
p = 0.1
-3
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
2
NPICPC l = 10
p = 0.1
1
1998
STD
0
1990
-1
R = -0.26
-2 Data: 1950-2003
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
2
EPI R = -0.70
1
Data: 1980-2003
STD
0
1998
-1 1990
-2
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Correlations with SLP
1950-2003
East Pacific Index (AMJJ) North Pacific Index (AMJJ)
Regime Shifts in Atmospheric Indices
0.25
l = 10 1977
p = 0.1 SATw
0.2 SATa
SLPw
0.15
RSI
0.1
1929
1938 1959 1997
0.05 SATa
SATa BSPI
1969
OWS
BSPI 1989 MIX
SLPw
0
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
SATa – Annual surface air temperature, St. Paul.
SATw – Winter surface air temperature, St. Paul
SLPw – Winter SLP over the Bering Sea
BSPI – Bering Sea pressure index
OWS – Optimal wind speed for larval feeding, Mooring 2
MIX – Summer wind mixing, Mooring 2
Mean Winter (NDJFM) SLP over the Bering Sea
1016
1012
1008
1004
1000
1911 1924 1947 1977 1989 1998
996
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Mean Winter (DJFM) SAT at St. Paul
4
2
0
-2
1924 1940 1947 1977 1989 1998
-4
1915 1925 1935 1945 1955 1965 1975 1985 1995 2005
Regime Shifts in Oceanic Indices
0.2 1983
l = 10 1977
SSTPrib
p = 0.1 SSTPrib
0.16 SSTM2
ICI
0.12
RSI
1965
0.08
SSTPrib
2000
0.04 IRI
1988
SSTM2
0
1950 1960 1970 1980 1990 2000
SATPrib – Winter SST near the Pribilof Islands
SATM2 – Winter SST at Mooring 2
ICI – Ice Cover Index
IRI – Ice Retreat Index
Ice Cover Index and Surface Temperature at Mooring 2
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
2
1 ICI
0
Std
-1 1978
-2 l = 10
p = 0.1
-3
276
1977 1988
Degrees Kelvin
272
268
264
260 Temperature
256
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Regime Shifts in Biological Indices
0.16 l = 10
1981
p = 0.1 1992
0.12 1977
1984
1966
RSI
0.08
0.04
0
1950 1960 1970 1980 1990 2000
Time Series of Fish Stocks
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
2 Herring 1989
year-class l = 10
Std
0 p = 0.1
strength 1977
-2
80
Bristol Bay sockeye salmon runs
60
Millions
1997
40 l = 10
20 p = 0.1
1979
0
80
Pollock
60
Billions
recruitment
40 1985 2001 l=5
at age 1
20 p = 0.1
1978 1989
0
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Conclusions
• Characteristics of the SARS method:
– Automatic detection of regime shifts,
– Improved performance at the ends of time series,
– Can be tuned up to detect regimes of different scales,
– Can handle the incoming data regardless of whether
they are presented in the form of anomalies or
absolute values,
– Works well with the time series containing a trend,
– Can be applied to a large set of variables.
Conclusions (continued)
• An application of SARS to the Bering Sea
ecosystem demonstrated that
– The shift of 1977 was the strongest one in the last 50
years;
– A number of indices experienced a regime shift
around 1989 (AO, PDOw, temperature at Mooring 2,
herring), 1998 (PDOs, salmon), or both (NPICPC, EPI,
winter SLP, flathead sole);
– The regime of 1989-1997 was characterized by a
relative winter cooling and reduced cyclonic activity;
– Regime shifts in biological indices are not
concentrated around certain, dominant years. The
RSI values are rather evenly distributed between
1977 and 1992.
