Transcript cstar.cestm.albany.edu

Large-Scale Regime Transition and Its
Relationship to Significant Cool Season
Precipitation Events in the Northeast
Heather Archambault
Advisors: Lance Bosart and Daniel Keyser
NWS Focal Point: Rich Grumm
Department of Earth and Atmospheric Sciences,
University at Albany, State University of New York
Research Motivation
• Meteorological wisdom: increased threat of major storm
during large-scale regime change
• Past research points to a possible connection between
synoptic-scale cyclogenesis and reconfiguration of the
planetary-scale flow
• Dave Groenert (CSTAR, 2002) documented an apparent
tendency for an increased number of precipitation events in
the Northeast during phase transitions of the NAO
Presentation Overview
• Research goals
• Climatology of PNA/NAO tendency
• Time series of NAO/PNA tendency [d(INDEX)/dt]
– Correlations with NE domain-average daily precipitation
– Correlations with 1000 hPa NE Height Anomaly Index (HAI)
• Major regime change / NE precipitation correlation
• Major regime change / NE height anomaly correlation
• Future work
Focus of Research:
•
Review literature that documents individual cases of
reconfiguration of the planetary-scale flow in association
with major precipitation events in the Northeastern United
States
•
Relate previous work to modern definitions of regime
change
•
Determine and quantify an objective definition for a
significant large-scale regime change
–
Phase change of teleconnection index (PNA, NAO) greater than 2
standard deviations over a 7-day period
Focus (cont):
•
Determine whether more storms/precipitation can be
expected during regime changes as compared to
climatology in the Northeast
•
Construct composite analyses to identify characteristic
signatures of significant large-scale regime changes
•
Use composite analyses and results from case studies to
determine whether possible regime change/precipitation
relationships are associative or cause and effect
Positive PNA – Thickness Anomalies
Courtesy:
Anantha Aiyyer
& Eyad Atallah
Negative PNA – Thickness Anomalies
Courtesy:
Anantha Aiyyer
& Eyad Atallah
Positive NAO – Thickness Anomalies
Courtesy:
Anantha Aiyyer
& Eyad Atallah
Negative NAO – Thickness Anomalies
Courtesy:
Anantha Aiyyer
& Eyad Atallah
Climatology of Teleconnection Tendencies
PNA Tendencies (1948-2001, 7 Day Interval)
NAO Tendencies
Daily Index Tendency / NE Precipitation
Time Series Correlations
• Daily time series of NAO/PNA tendency were created
– Seven-day tendency: Index on Day (t + 3) – Index on Day (t - 3)
• Correlated with time series of daily NE precipitation
– Standardized domain-average precipitation values calculated from
the Unified Precipitation Dataset (UPD) from 1954 - 1998
• Seven-day NAO tendency / precipitation correlation:
– Correlation Coefficient: -0.04, R2 value: 0.0
• Seven-day PNA tendency / precipitation correlation:
– Correlation Coefficient: 0.04, R2 value: 0.0
• Conclusion: No correlation between daily teleconnection
index tendency and Northeast daily precipitation values
1000 hPa Height Anomaly Index (HAI)
•
Purpose: correlate with daily NAO/PNA tendency as an
alternative to daily precipitation values
•
1000 hPa height anomalies calculated from NCEP/NCAR
Reanalysis (1951 – 2001)
•
Height anomalies are normalized according to climatology
of the 15-day period centered around each day
•
This accounts for the higher mean/smaller standard
deviation in the height field that exists in summer vs.
winter
• 48.2 % of days had HAI < 0 (8,970 of 18,628 days)
Daily Index Tendency / NE Height Anomaly
Time Series Correlations
• Time series of daily tendency of the NAO/PNA were
correlated with time series of daily 1000 hPa Northeast
Height Anomaly Index (HAI)
• Seven-day NAO tendency / HAI correlation:
– Correlation Coefficient: -0.06, R2 value: 0.00
• Seven-day PNA tendency / HAI correlation:
– Correlation Coefficient: -0.23, R2 value: 0.05
• Conclusion: No significant correlation between daily index
tendency and daily Northeast 1000 hPa height anomaly
index
Major Regime Change / NE Precipitation
Correlations
• Based on definition of major regime change: created a
subset of days for further correlations with precipitation
• Index change during major regime transition was correlated
with daily precipitation
– Major regime transition: phase change of greater than 2 standard
deviations
Major NAO Swing / Northeast Precipitation Correlation
1954 - 1998
0.3
Correlation Coefficient
R-Squared Value
Correlation Coefficient /
R-Squared Value
0.2
0.1
0.05
0.06
0.02
0.04
0.05
0.05
0.03
0.15
0.03
0.01
0.01
0.00
0.0
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
-0.1
-0.2
-0.3
-0.4
• Overall NAO regime change / precipitation correlation:
Correlation Coefficient: -0.07, R2 value: 0.01
DEC
Major PNA Swing / Northeast Precipitation Correlation
1954 - 1998
0.6
Correlation Coefficient
R-Squared Value
0.5
Correlation Coefficient /
R-Squared Value
0.4
0.29
0.3
0.18
0.2
0.13
0.11
0.07
0.1
0.09
0.03
0.01
0.01
0.03
0.00
0.00
0.0
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
-0.1
-0.2
-0.3
• Overall PNA regime change / precipitation correlation:
Correlation Coefficient: 0.11, R2 value: 0.01
DEC
Major Regime Change /
1000 hPa NE Height Anomaly Correlations
• Index tendency for the days where a major regime change
was ongoing was correlated with daily 1000 hPa height
anomalies
Major NAO Swing / Northeast HAI Correlation
1951 - 2001
0.3
Correlation Coefficient
R-Squared Value
0.2
Correlation Coefficient /
R-Squared Value
0.13
0.10
0.1
0.03
0.00
0.06
0.05
0.07
0.00
0.00
0.01
0.01
0.00
0.0
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
-0.1
-0.2
-0.3
-0.4
• Overall NAO regime change / HAI correlation:
Correlation Coefficient: -0.15, R2 value: 0.02
OCT
NOV
DEC
Major PNA Swing / Northeast HAI Correlation
1951 - 2001
0.3
Correlation Coefficient /
R-Squared Value
Correlation Coefficient
R-Squared Value
0.19
0.2
0.08
0.1
0.04
0.08
0.05
0.01
0.00
0.03
0.00
0.00
0.00
0.00
0.0
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
-0.1
-0.2
-0.3
-0.4
-0.5
• Overall PNA regime change / HAI correlation:
Correlation Coefficient: 0.0, R2 value: 0.0
OCT
NOV
DEC
Preliminary Results
• No correlation between daily time series of index tendency
& NE precipitation / 1000 hPa height anomalies
• Major NAO swings / NE precipitation:
– Weakening jet (+NAO to –NAO) : possible enhanced precipitation
in winter
– Strengthening jet (–NAO to +NAO): possible enhanced precipitation
in summer
• For major swings in the NAO, a slight negative correlation
with 1000 hPa height anomalies can be found in the summer
months:
–
negative height anomalies are correlated with a strengthening of
the North Atlantic jet (negative to positive NAO)
– approximately 10% of height anomaly variability can be attributed
to major swings in the NAO during summer months
Preliminary Results (cont.)
• For major swings in the PNA, the largest correlation with
precipitation can be found in March (R2 = 0.29) and April
– Ridge building in west/troughing in SE: enhanced precipitation
• No overall correlation between major swings in the PNA
and 1000 hPA height anomalies
– Slight positive correlation is found in May-June
• PNA change from positive to negative corresponds with heights
lowering in western Canada/Pacific NW, ridge amplification
over SE US
– Slight negative correlation is found in July-August
• PNA change from negative to positive corresponds with
building ridge in western Canada/Pacific NW and lower heights
in the southeastern US
What’s Next
•
Determine whether a “significant” precipitation event is
more likely during a major PNA or NAO swing as
compared to climatology
–
•
Lag correlations: storm at end, beginning of regime change?
Create correlation maps using top NAO and PNA
transitions
–
Find best correlation between transitions and 1000 hPa height
anomalies/precipitation for the United States