basc decadal variability webinar slides
Frontiers in Decadal
Climate Variability:
WATER SCIENCE AND TECHNOLOGY BOARD
Proceedings of a Workshop
Monday, July 25th, 2pm EDT
Gerald A. Meehl
National Center for Atmospheric Research (NCAR)
Organizing Committee Chair
Today’s webinar discusses the recently released
Frontiers in Decadal Climate Variability: Proceedings of
a Workshop.
Proceedings:
• chronicle the presentations and
discussions at a workshop,
symposium, or other convening
event
• statements and opinions contained
are those of the participants and are
not necessarily endorsed by other
participants, the planning
committee, or the National
Academies of Sciences, Engineering,
and Medicine
• peer reviewed
Not a report:
• evidence-based consensus of an
authoring committee of experts
• typically include findings,
conclusions, and recommendations
based on information gathered by
the committee and committee
deliberations
• peer reviewed and approved by
the National Academies of
Sciences, Engineering, and
Medicine
For information about other products and activities of the Academies, please visit
nationalacademies.org/whatwedo.
The Workshop:
• Topic: Decadal climate variability and the role of
the ocean in variability of the GMST trend
• Organized jointly by the Academies’ Board on
Atmospheric Sciences and Climate (BASC) & the
Ocean Studies Board (OSB)
• Planning Committee Membership:
Gerald A. (Jerry) Meehl, Chair
(BASC), NCAR
Kevin Arrigo (OSB), Stanford
Shuyi S. Chen (BASC), University of
Miami
Lisa Goddard (BASC), Columbia
University
Robert Hallberg (OSB), NOAA
David Halpern (OSB), NASA Jet
Propulsion Laboratory
• Workshop held September 3-4, 2015 at NAS
Jonsson Center in Woods Hole, MA
Workshop Goals
1. Examine our understanding of the processes governing
decadal-scale variability in key climate parameters,
observational evidence of decadal variability and potential
forcings, and model-based experiments to explore possible
factors affecting decadal variations;
2. Identify key science, observing, and modeling gaps;
3. Consider the utility and accuracy of various observations for
tracking long-term climate variability, anticipating the onset and
end of hiatus regimes, and closing the long-term heat budget;
4. Consider the utility of hiatus regimes as a metric for evaluating
performance of long-term climate models; and
5. Consider how best to communicate current understanding of
climate variability, including potential causes and consequences,
to non-expert audiences.
Workshop Participants
•
•
•
•
•
•
•
•
•
•
•
•
•
Kevin Arrigo, Stanford University
•
Antonietta Capotondi, Cooperative Institute for •
Research in Environmental Sciences
•
(CIRES)/National Oceanic and Atmospheric
Administration (NOAA)
•
Shuyi S. Chen, University of Miami
Kim Cobb, Georgia Institute of Technology
•
Gokhan Danabasoglu, National Center for
•
Atmospheric Research (NCAR)
Tom Delworth, Geophysical Fluid Dynamics
•
Laboratory (GFDL)
•
Baylor Fox-Kemper, Brown University
•
John Fyfe, Canadian Centre for Climate Modelling •
and Analysis
•
Lisa Goddard, International Research Institute for
•
Climate and Society (IRI)
•
Robert Hallberg, NOAA
David Halpern, National Aeronautics and Space •
Administration Jet Propulsion Laboratory (NASA
•
JPL)
Susan Hassol, Climate Communication
Patrick Heimbach, University of Texas at Austin •
Brian Kahn, Climate Central
Tom Knutson, GFDL
Yochanan Kushnir, Lamont Doherty Earth
Observatory (LDEO)
James Overland, NOAA Pacific Marine
Environmental Laboratory (PMEL)
Michael Mann, Pennsylvania State University
John Marshall, Massachusetts Institute of
Technology (MIT)
Gerald A. Meehl, NCAR
Matthew Menne, NOAA
Veronica Nieves, NASA JPL
Susan Solomon, MIT
Diane Thompson, Boston University
Mingfang Ting, LDEO
Jim Todd, NOAA
Caroline Ummenhofer, Woods Hole
Oceanographic Institution
Shang-Ping Xie, Scripps Institution of
Oceanography
Huai-min Zhang, NOAA
Acknowledgements
• Thank you to:
– Planning committee (especially Jerry!) and staff
– NASA, NOAA, NSF, and DOE for their support
– Reviewers:
•
•
•
•
Lisa Goddard, Columbia University
Philip Jones, University of East Anglia
Veronica Nieves, NASA Jet Propulsion Laboratory
Gavin Schmidt, NASA Goddard Institute for Space
Studies
Frontiers in Decadal Climate Variability
Gerald A. Meehl
National Center for Atmospheric Research
Biological and Energy Research
Regional and Global Climate Modeling Program
Decadal climate variability science problems:
1. What are the relative contributions of internally generated
decadal timescale variability and externally forced response to the
observed time evolution of global climate on decadal timescales?
2. What are the processes and mechanisms in the climate system
that produce internally generated climate variability?
3. Can these processes and mechanisms, if properly initialized,
provide increased prediction skill of the time evolution of regional
climate in the near-term, over and above that from the externally
forced response?
The workshop focused on 1 and 2
Workshop report prepared by NRC staff
(thanks to Amanda Purcell and Nancy Huddleston)
Attention on decadal climate variability was brought into
focus by the reduced rate of global surface warming in the
early 21st century.
This has been variously referred to as a “hiatus”, “pause”,
or “slowdown”.
Slowdown periods have occurred before in observations
and models and are a naturally-occurring part of climate
variability in combination with contributions from external
forcings (Easterling and Wehner, 2009, GRL).
Mid-1970s shift
And the flip side of hiatus periods are accelerated warming
periods.
Interpretation of trends related to decadal climate
variability must use a process-based approach.
There is evidence that the phase of the
Interdecadal Pacific Oscillation (IPO) influences
global surface temperature trends.
If the IPO is the process-based decadal climate
variability framework, global temperature trends
can be compared for different IPO phases to see if
they are different.
Following Zhang, Wallace and Battisti (1997, J. Climate) the Interdecadal Pacific
Oscillation (IPO, Power et al., 1999) defined for entire Pacific; the Pacific Decadal
Oscillation PDO (Mantua et al 1997, BAMS) is defined for the North Pacific but patterns
are comparable (sometimes both referred to as “PDV” – Pacific Decadal Variability)
Climate model simulations indicate IPO is internally generated
Observations
Big hiatus
Unforced model control run (CCSM4)
Early-2000s slowdown
Mid-70s
Shift
The observed IPO pattern resembles
internally-generated decadal pattern from
an unforced model control run (pattern
correlation= +0.63)
(Meehl et al., 2009, J. Climate; Meehl and
Arblaster, 2011, J. Climate)
NOAA press release on Karl et al Science paper published in Science Express on
June 3, 2015:
The early-2000s slowdown (2001-2014, negative phase of the Interdecadal Pacific
Oscillation, IPO) is characterized by a trend that is significantly less than the
previous positive IPO period from 1972-2001 (Fyfe et al., 2016, Nature Clim. Chg).
Recent slow down in global surface temperature increase
We understand what produces slowdown decades in the model
(opposite for accelerated warming decades):
• relatively greater trends of ocean heat content below 300m
• surface temperature trends indicate negative phase of the IPO
• 3 ocean mixing processes: subtropical cells in Pacific, Southern Ocean Antarctic
Bottom Water formation; Atlantic Meridional Overturning Circulation
(Meehl et al., 2011, Nature Climate Change: Meehl et al., 2013, J. Climate)
Global warming does not stop during slowdown decades—heat
content of the climate system continues to increase but we don’t
see as much warming if the heat goes into the subsurface ocean
during negative IPO.
(Meehl et al., 2011, Nature Climate Change: Meehl et al., 2013, J. Climate)
Forcing from volcanic eruptions and stratospheric water vapor also
could be playing a role in the early-2000s slowdown.
Solomon et al., 2010, Science: maybe 25% of the early-2000s
slowdown was due to decreased stratospheric water vapor since
2000; and ~30% of the accelerated warming from 1980-2000 due
to increased stratospheric water vapor
Santer et al., 2014 Nat. Geo.; 2015 GRL: perhaps at least 15% of the
slowdown was due to stratospheric aerosols from several moderate
sized volcanoes
Maher et al., 2015, GRL: models show a lagged La Niña-like
response the third year after a composite large tropical volcanic
eruption associated with global cooling
Some CMIP5 uninitialized
models actually simulated the
slowdown
Tend to be characterized by a negative
phase of the IPO.
Internally generated variability in
those model simulations happened to
sync with observed internally
generated variability.
Total: 262 possible simulations
2000-2012 slowdown: 21
2000-2014 slowdown: 9
2000-2015 slowdown: 6
2000-2016 slowdown: 6
2000-2017 slowdown: 1
2000-2018: 1
(Meehl et al., 2014, Nature Climate Change)
Slowdown as observed from 2000-2013:
10 members out of 262 possible realizations
But it gets complicated when various ocean observations or ocean
reanalysis products are analyzed:
• Slowdown caused by redistribution from Pacific to 200-300m layer in Indian
Ocean (Nieves et al., 2015, Science), or from Pacific to upper 700 m of Indian
Ocean (Lee et al., 2015, Nature Geo.)
• Slowdown caused by mixing of heat into subsurface ocean across multiple
basins (Drijfhout et al., 2014, GRL)
• Slowdown caused by mixing of heat into the North Atlantic (Chen and Tung,
2014, Science)
• Ocean heat content during the slowdown is increasing mainly in the Southern
Ocean from 700 to 1400m (Roemmich et al., 2015, Nature Clim. Chg.)
• Observed upper ocean heat content biased low (Durack et al., 2014, Nature
Clim. Chg.)
• No significant signal of deep ocean warming inferred from sea level rise (Llovel,
Willis et al, 2014, Nature Clim. Chg.)
Frontiers and research opportunities: maintain and expand current observational
network, synthesize existing records for further analyses, use other sources of data
(e.g. paleoclimate proxies)
Paleoclimate proxies from coral reefs:
Modern coral d18O records
from Christmas Island track
very closely to SSTs.
Westerly winds associated with El Niño
events are correlated with spikes in
coral Mn/Ca and also spikes in coral
d18O indicating fresher and warmer
water associated with El Niño.
Fossil coral records can be
analyzed to produce tropical
Pacific temperature
reconstructions farther back in
time.
Another prominent source of
decadal timescale variability
occurs in the Atlantic north of
the equator, called the
“Atlantic Multi-decadal
Oscillation” (AMO)
The AMO could be driven by the
meridional overturning circulation
in the Atlantic (AMOC)
An index of the AMO can be constructed
by removing the long-term trend from
smoothed area-averaged SSTs from
equator-60N in the Atlantic
Atlantic Multidecadal Oscillation (AMO) has been
shown to affect the frequency and severity of
droughts across North America
Observed relat ionship bet ween warm AMO and dry N. America
Decadal variability from the AMO could be driving the IPO in the Pacific
1992-2011
observed trends
Observed 1992-2011 trends
Specified Atlantic SSTs
Specified trend
of positive
Atlantic SSTs
drives negative
IPO Pacific SST
pattern
(McGregor et al, Nature Climate Change, 2014) (also Chikamoto et al., 2015, Nature Comms.)
But “pacemaker” experiments with the GFDL model (specifying
tropical Pacific SSTs in the coupled model) suggest that the IPO could
be driving the AMO.
Kosaka and Xie Pacific pacemaker runs
IPO-AMO
AMO leads IPO
IPO leads AMO
years
(Meehl et al., 2016, Nature Climate Change, in press)
Why do we care?
The new field of decadal climate prediction seeks to
use climate models initialized with observations to
predict the time evolution of the statistics of regional
climate over the near term (i.e. the next 10 years) by
predicting the interplay between internal variability
and response to increasing GHGs
Can decadal climate variability processes and
mechanisms, if properly initialized, provide
increased prediction skill of the time evolution of
regional climate in the near-term?
Climate model prediction
initialized in 2013
indicates a positive phase
of the IPO for 3-7 year
average 2015-2019
This is quite different
from persistence (20082012 persisted to 20152019)
And is different from
uninitialized projection
for 2015-2019
(Meehl et al., 2016, Nature
Communications)
Predicted rate of global warming from 2013 initial year greater
than during early-2000s slowdown and greater than uninitialized:
Observed 2001-2014:
+0.08±0.05°C/decade
Predicted 2013-2022:
+0.22±0.13°C/decade
Uninitialized 2013-2022:
+0.14±0.12°C/decade
(Meehl et al., 2016, Nature Communications)
Larger increasing trends of Antarctic sea ice since 2000 associated
with negative IPO phase, deeper Amundsen Sea Low, stronger
northward surface winds in the Pacific sector
Multi-model ensemble mean shows Antarctic sea ice decreases
But ten of the model ensemble members simulate the 2000-2014
global surface warming slowdown and also simulate negative IPO
phase with increasing Antarctic sea ice
Antarctic sea ice anomalies traced to SST and precipitation
anomalies in eastern equatorial Pacific with negative IPO phase in
specified convective heating anomaly climate model experiment
(Meehl et al., July 4, 2016, Nature Geoscience; also Turner et al., July 21, 2016, Nature)
Frontiers and Research Opportunities
Metrics for climate change:
• Global mean surface temperature (still important),
combined with sea level rise, ocean heat content,
top of atmosphere heat balance could be best
Confronting models with observations:
• Verification of model performance from
observations to improve the models; important
toward developing prediction capability, also
important to distinguish forced and internal change
through fingerprinting
Frontiers and Research Opportunities
Knowledge gaps:
• Many mechanisms were examined that might be
driving decadal variability, but what is driving the
mechanisms themselves? (e.g., IPO, AMO)
• How heat trapped in the ocean will be transported
in the next decade or two and how that might affect
global temperatures in the future
Way Forward:
• Mechanistic understanding -> assessment of
understanding -> prediction and attribution
capabilities
Summary: Naturally-occurring decade-to-decade variability of global surface
temperature is superimposed on a steadily increasing long term trend from increasing
GHGs, and there is compelling evidence that the tropical Pacific can drive global
decadal climate variability, with possible connections to Atlantic decadal variability.
Global warming (warming of entire climate system, atmosphere, ocean, land,
cryosphere) has not stopped, but the rate of global surface temperature increase
slowed from 2001-2014 during the negative phase of the IPO compared to the 1972–
2001 period with positive phase of the IPO.
Evidence from models indicates that during periods of global warming slowdown, the
excess heat is mixed into the subsurface ocean in the subtropical Pacific, high latitude
Southern Ocean, and North Atlantic; but evidence from ocean observations so far is
not definitive with regards to location, processes, and depth.
An initialized climate model prediction made in 2013 shows a shift to positive phase of
the IPO in 2014 and larger rates of global surface temperature increase averaged over
2013-2022.
The IPO has been shown to have made a major contribution to the expansion of
Antarctic sea ice from 2000-2014.
Questions?
Read/Download Proceedings at www.nap.edu.
Find 4-page Brief, webinar recording and slides, sign up for notifications at
http://dels.nas.edu/basc and
http://dels.nas.edu/osb.
Climate Variability:
WATER SCIENCE AND TECHNOLOGY BOARD
Proceedings of a Workshop
Monday, July 25th, 2pm EDT
Gerald A. Meehl
National Center for Atmospheric Research (NCAR)
Organizing Committee Chair
Today’s webinar discusses the recently released
Frontiers in Decadal Climate Variability: Proceedings of
a Workshop.
Proceedings:
• chronicle the presentations and
discussions at a workshop,
symposium, or other convening
event
• statements and opinions contained
are those of the participants and are
not necessarily endorsed by other
participants, the planning
committee, or the National
Academies of Sciences, Engineering,
and Medicine
• peer reviewed
Not a report:
• evidence-based consensus of an
authoring committee of experts
• typically include findings,
conclusions, and recommendations
based on information gathered by
the committee and committee
deliberations
• peer reviewed and approved by
the National Academies of
Sciences, Engineering, and
Medicine
For information about other products and activities of the Academies, please visit
nationalacademies.org/whatwedo.
The Workshop:
• Topic: Decadal climate variability and the role of
the ocean in variability of the GMST trend
• Organized jointly by the Academies’ Board on
Atmospheric Sciences and Climate (BASC) & the
Ocean Studies Board (OSB)
• Planning Committee Membership:
Gerald A. (Jerry) Meehl, Chair
(BASC), NCAR
Kevin Arrigo (OSB), Stanford
Shuyi S. Chen (BASC), University of
Miami
Lisa Goddard (BASC), Columbia
University
Robert Hallberg (OSB), NOAA
David Halpern (OSB), NASA Jet
Propulsion Laboratory
• Workshop held September 3-4, 2015 at NAS
Jonsson Center in Woods Hole, MA
Workshop Goals
1. Examine our understanding of the processes governing
decadal-scale variability in key climate parameters,
observational evidence of decadal variability and potential
forcings, and model-based experiments to explore possible
factors affecting decadal variations;
2. Identify key science, observing, and modeling gaps;
3. Consider the utility and accuracy of various observations for
tracking long-term climate variability, anticipating the onset and
end of hiatus regimes, and closing the long-term heat budget;
4. Consider the utility of hiatus regimes as a metric for evaluating
performance of long-term climate models; and
5. Consider how best to communicate current understanding of
climate variability, including potential causes and consequences,
to non-expert audiences.
Workshop Participants
•
•
•
•
•
•
•
•
•
•
•
•
•
Kevin Arrigo, Stanford University
•
Antonietta Capotondi, Cooperative Institute for •
Research in Environmental Sciences
•
(CIRES)/National Oceanic and Atmospheric
Administration (NOAA)
•
Shuyi S. Chen, University of Miami
Kim Cobb, Georgia Institute of Technology
•
Gokhan Danabasoglu, National Center for
•
Atmospheric Research (NCAR)
Tom Delworth, Geophysical Fluid Dynamics
•
Laboratory (GFDL)
•
Baylor Fox-Kemper, Brown University
•
John Fyfe, Canadian Centre for Climate Modelling •
and Analysis
•
Lisa Goddard, International Research Institute for
•
Climate and Society (IRI)
•
Robert Hallberg, NOAA
David Halpern, National Aeronautics and Space •
Administration Jet Propulsion Laboratory (NASA
•
JPL)
Susan Hassol, Climate Communication
Patrick Heimbach, University of Texas at Austin •
Brian Kahn, Climate Central
Tom Knutson, GFDL
Yochanan Kushnir, Lamont Doherty Earth
Observatory (LDEO)
James Overland, NOAA Pacific Marine
Environmental Laboratory (PMEL)
Michael Mann, Pennsylvania State University
John Marshall, Massachusetts Institute of
Technology (MIT)
Gerald A. Meehl, NCAR
Matthew Menne, NOAA
Veronica Nieves, NASA JPL
Susan Solomon, MIT
Diane Thompson, Boston University
Mingfang Ting, LDEO
Jim Todd, NOAA
Caroline Ummenhofer, Woods Hole
Oceanographic Institution
Shang-Ping Xie, Scripps Institution of
Oceanography
Huai-min Zhang, NOAA
Acknowledgements
• Thank you to:
– Planning committee (especially Jerry!) and staff
– NASA, NOAA, NSF, and DOE for their support
– Reviewers:
•
•
•
•
Lisa Goddard, Columbia University
Philip Jones, University of East Anglia
Veronica Nieves, NASA Jet Propulsion Laboratory
Gavin Schmidt, NASA Goddard Institute for Space
Studies
Frontiers in Decadal Climate Variability
Gerald A. Meehl
National Center for Atmospheric Research
Biological and Energy Research
Regional and Global Climate Modeling Program
Decadal climate variability science problems:
1. What are the relative contributions of internally generated
decadal timescale variability and externally forced response to the
observed time evolution of global climate on decadal timescales?
2. What are the processes and mechanisms in the climate system
that produce internally generated climate variability?
3. Can these processes and mechanisms, if properly initialized,
provide increased prediction skill of the time evolution of regional
climate in the near-term, over and above that from the externally
forced response?
The workshop focused on 1 and 2
Workshop report prepared by NRC staff
(thanks to Amanda Purcell and Nancy Huddleston)
Attention on decadal climate variability was brought into
focus by the reduced rate of global surface warming in the
early 21st century.
This has been variously referred to as a “hiatus”, “pause”,
or “slowdown”.
Slowdown periods have occurred before in observations
and models and are a naturally-occurring part of climate
variability in combination with contributions from external
forcings (Easterling and Wehner, 2009, GRL).
Mid-1970s shift
And the flip side of hiatus periods are accelerated warming
periods.
Interpretation of trends related to decadal climate
variability must use a process-based approach.
There is evidence that the phase of the
Interdecadal Pacific Oscillation (IPO) influences
global surface temperature trends.
If the IPO is the process-based decadal climate
variability framework, global temperature trends
can be compared for different IPO phases to see if
they are different.
Following Zhang, Wallace and Battisti (1997, J. Climate) the Interdecadal Pacific
Oscillation (IPO, Power et al., 1999) defined for entire Pacific; the Pacific Decadal
Oscillation PDO (Mantua et al 1997, BAMS) is defined for the North Pacific but patterns
are comparable (sometimes both referred to as “PDV” – Pacific Decadal Variability)
Climate model simulations indicate IPO is internally generated
Observations
Big hiatus
Unforced model control run (CCSM4)
Early-2000s slowdown
Mid-70s
Shift
The observed IPO pattern resembles
internally-generated decadal pattern from
an unforced model control run (pattern
correlation= +0.63)
(Meehl et al., 2009, J. Climate; Meehl and
Arblaster, 2011, J. Climate)
NOAA press release on Karl et al Science paper published in Science Express on
June 3, 2015:
The early-2000s slowdown (2001-2014, negative phase of the Interdecadal Pacific
Oscillation, IPO) is characterized by a trend that is significantly less than the
previous positive IPO period from 1972-2001 (Fyfe et al., 2016, Nature Clim. Chg).
Recent slow down in global surface temperature increase
We understand what produces slowdown decades in the model
(opposite for accelerated warming decades):
• relatively greater trends of ocean heat content below 300m
• surface temperature trends indicate negative phase of the IPO
• 3 ocean mixing processes: subtropical cells in Pacific, Southern Ocean Antarctic
Bottom Water formation; Atlantic Meridional Overturning Circulation
(Meehl et al., 2011, Nature Climate Change: Meehl et al., 2013, J. Climate)
Global warming does not stop during slowdown decades—heat
content of the climate system continues to increase but we don’t
see as much warming if the heat goes into the subsurface ocean
during negative IPO.
(Meehl et al., 2011, Nature Climate Change: Meehl et al., 2013, J. Climate)
Forcing from volcanic eruptions and stratospheric water vapor also
could be playing a role in the early-2000s slowdown.
Solomon et al., 2010, Science: maybe 25% of the early-2000s
slowdown was due to decreased stratospheric water vapor since
2000; and ~30% of the accelerated warming from 1980-2000 due
to increased stratospheric water vapor
Santer et al., 2014 Nat. Geo.; 2015 GRL: perhaps at least 15% of the
slowdown was due to stratospheric aerosols from several moderate
sized volcanoes
Maher et al., 2015, GRL: models show a lagged La Niña-like
response the third year after a composite large tropical volcanic
eruption associated with global cooling
Some CMIP5 uninitialized
models actually simulated the
slowdown
Tend to be characterized by a negative
phase of the IPO.
Internally generated variability in
those model simulations happened to
sync with observed internally
generated variability.
Total: 262 possible simulations
2000-2012 slowdown: 21
2000-2014 slowdown: 9
2000-2015 slowdown: 6
2000-2016 slowdown: 6
2000-2017 slowdown: 1
2000-2018: 1
(Meehl et al., 2014, Nature Climate Change)
Slowdown as observed from 2000-2013:
10 members out of 262 possible realizations
But it gets complicated when various ocean observations or ocean
reanalysis products are analyzed:
• Slowdown caused by redistribution from Pacific to 200-300m layer in Indian
Ocean (Nieves et al., 2015, Science), or from Pacific to upper 700 m of Indian
Ocean (Lee et al., 2015, Nature Geo.)
• Slowdown caused by mixing of heat into subsurface ocean across multiple
basins (Drijfhout et al., 2014, GRL)
• Slowdown caused by mixing of heat into the North Atlantic (Chen and Tung,
2014, Science)
• Ocean heat content during the slowdown is increasing mainly in the Southern
Ocean from 700 to 1400m (Roemmich et al., 2015, Nature Clim. Chg.)
• Observed upper ocean heat content biased low (Durack et al., 2014, Nature
Clim. Chg.)
• No significant signal of deep ocean warming inferred from sea level rise (Llovel,
Willis et al, 2014, Nature Clim. Chg.)
Frontiers and research opportunities: maintain and expand current observational
network, synthesize existing records for further analyses, use other sources of data
(e.g. paleoclimate proxies)
Paleoclimate proxies from coral reefs:
Modern coral d18O records
from Christmas Island track
very closely to SSTs.
Westerly winds associated with El Niño
events are correlated with spikes in
coral Mn/Ca and also spikes in coral
d18O indicating fresher and warmer
water associated with El Niño.
Fossil coral records can be
analyzed to produce tropical
Pacific temperature
reconstructions farther back in
time.
Another prominent source of
decadal timescale variability
occurs in the Atlantic north of
the equator, called the
“Atlantic Multi-decadal
Oscillation” (AMO)
The AMO could be driven by the
meridional overturning circulation
in the Atlantic (AMOC)
An index of the AMO can be constructed
by removing the long-term trend from
smoothed area-averaged SSTs from
equator-60N in the Atlantic
Atlantic Multidecadal Oscillation (AMO) has been
shown to affect the frequency and severity of
droughts across North America
Observed relat ionship bet ween warm AMO and dry N. America
Decadal variability from the AMO could be driving the IPO in the Pacific
1992-2011
observed trends
Observed 1992-2011 trends
Specified Atlantic SSTs
Specified trend
of positive
Atlantic SSTs
drives negative
IPO Pacific SST
pattern
(McGregor et al, Nature Climate Change, 2014) (also Chikamoto et al., 2015, Nature Comms.)
But “pacemaker” experiments with the GFDL model (specifying
tropical Pacific SSTs in the coupled model) suggest that the IPO could
be driving the AMO.
Kosaka and Xie Pacific pacemaker runs
IPO-AMO
AMO leads IPO
IPO leads AMO
years
(Meehl et al., 2016, Nature Climate Change, in press)
Why do we care?
The new field of decadal climate prediction seeks to
use climate models initialized with observations to
predict the time evolution of the statistics of regional
climate over the near term (i.e. the next 10 years) by
predicting the interplay between internal variability
and response to increasing GHGs
Can decadal climate variability processes and
mechanisms, if properly initialized, provide
increased prediction skill of the time evolution of
regional climate in the near-term?
Climate model prediction
initialized in 2013
indicates a positive phase
of the IPO for 3-7 year
average 2015-2019
This is quite different
from persistence (20082012 persisted to 20152019)
And is different from
uninitialized projection
for 2015-2019
(Meehl et al., 2016, Nature
Communications)
Predicted rate of global warming from 2013 initial year greater
than during early-2000s slowdown and greater than uninitialized:
Observed 2001-2014:
+0.08±0.05°C/decade
Predicted 2013-2022:
+0.22±0.13°C/decade
Uninitialized 2013-2022:
+0.14±0.12°C/decade
(Meehl et al., 2016, Nature Communications)
Larger increasing trends of Antarctic sea ice since 2000 associated
with negative IPO phase, deeper Amundsen Sea Low, stronger
northward surface winds in the Pacific sector
Multi-model ensemble mean shows Antarctic sea ice decreases
But ten of the model ensemble members simulate the 2000-2014
global surface warming slowdown and also simulate negative IPO
phase with increasing Antarctic sea ice
Antarctic sea ice anomalies traced to SST and precipitation
anomalies in eastern equatorial Pacific with negative IPO phase in
specified convective heating anomaly climate model experiment
(Meehl et al., July 4, 2016, Nature Geoscience; also Turner et al., July 21, 2016, Nature)
Frontiers and Research Opportunities
Metrics for climate change:
• Global mean surface temperature (still important),
combined with sea level rise, ocean heat content,
top of atmosphere heat balance could be best
Confronting models with observations:
• Verification of model performance from
observations to improve the models; important
toward developing prediction capability, also
important to distinguish forced and internal change
through fingerprinting
Frontiers and Research Opportunities
Knowledge gaps:
• Many mechanisms were examined that might be
driving decadal variability, but what is driving the
mechanisms themselves? (e.g., IPO, AMO)
• How heat trapped in the ocean will be transported
in the next decade or two and how that might affect
global temperatures in the future
Way Forward:
• Mechanistic understanding -> assessment of
understanding -> prediction and attribution
capabilities
Summary: Naturally-occurring decade-to-decade variability of global surface
temperature is superimposed on a steadily increasing long term trend from increasing
GHGs, and there is compelling evidence that the tropical Pacific can drive global
decadal climate variability, with possible connections to Atlantic decadal variability.
Global warming (warming of entire climate system, atmosphere, ocean, land,
cryosphere) has not stopped, but the rate of global surface temperature increase
slowed from 2001-2014 during the negative phase of the IPO compared to the 1972–
2001 period with positive phase of the IPO.
Evidence from models indicates that during periods of global warming slowdown, the
excess heat is mixed into the subsurface ocean in the subtropical Pacific, high latitude
Southern Ocean, and North Atlantic; but evidence from ocean observations so far is
not definitive with regards to location, processes, and depth.
An initialized climate model prediction made in 2013 shows a shift to positive phase of
the IPO in 2014 and larger rates of global surface temperature increase averaged over
2013-2022.
The IPO has been shown to have made a major contribution to the expansion of
Antarctic sea ice from 2000-2014.
Questions?
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