basc decadal variability webinar slides

(1)

WATER SCIENCE AND TECHNOLOGY BOARD

Frontiers in Decadal

Climate Variability:

Proceedings of a Workshop

Gerald A. Meehl

National Center for Atmospheric Research (NCAR) Organizing Committee Chair

(2)

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.

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

Today’s webinar discusses the recently released

Frontiers in Decadal Climate Variability: Proceedings of

(3)

• 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:

• Workshop held September 3-4, 2015 at NAS

Jonsson Center in Woods Hole, MA

The Workshop:

Gerald A. (Jerry) Meehl, Chair (BASC), NCAR

Kevin Arrigo (OSB), Stanford

Shuyi S. Chen (BASC), University of Miami

Lisa Goddard (BASC), Columbia University

(4)

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)

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

(6)

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

(7)

Frontiers in Decadal Climate Variability

Gerald A. Meehl

National Center for Atmospheric Research

Biological and Energy Research

(8)

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?

(9)

Attention on decadal climate variability was brought into

focus by the reduced rate of global surface warming in the

early 21

st

_century.

(10)

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).

And the flip side of hiatus periods are accelerated warming

periods.

(11)

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

(12)

Mid-70s Shift

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

(Meehl et al., 2009, J. Climate; Meehl and Arblaster, 2011, J. Climate)

The observed IPO pattern resembles

internally-generated decadal pattern from an unforced model control run (pattern correlation= +0.63)

Observations Unforced model control run (CCSM4)

(13)

(14)

Recent slow down in global surface temperature increase

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

(15)

(16)

(17)

(Meehl et al., 2011, Nature Climate Change: Meehl et al., 2013, J. Climate)

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

(18)

(Meehl et al., 2011, Nature Climate Change: Meehl et al., 2013, J. Climate)

(19)

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

(20)

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

Slowdown as observed from 2000-2013: 10 members out of 262 possible realizations

(21)

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.)

(22)

Modern coral d18_{O records}

from Christmas Island track very closely to SSTs.

Paleoclimate proxies from coral reefs:

Westerly winds associated with El Niño events are correlated with spikes in coral Mn/Ca and also spikes in coral d18_{O indicating fresher and warmer}

water associated with El Niño.

Fossil coral records can be analyzed to produce tropical Pacific temperature

(23)

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)

(24)

Atlantic Multidecadal Oscillation (AMO) has been

shown to affect the frequency and severity of

droughts across North America

(25)

1992-2011

observed trends

(McGregor et al, Nature Climate Change, 2014) (also Chikamoto et al., 2015, Nature Comms.)

Specified trend of positive

Atlantic SSTs drives negative IPO Pacific SST pattern

Decadal variability from the AMO could be driving the IPO in the Pacific

Specified Atlantic SSTs

(26)

Kosaka and Xie Pacific pacemaker runs

years

IPO leads AMO AMO leads IPO

But “pacemaker” experiments with the GFDL model (specifying

tropical Pacific SSTs in the coupled model) suggest that

the IPO could

be driving the AMO.

IPO-AMO

(27)

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

(28)

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

(2008-2012 persisted to

2015-2019)

And is different from

uninitialized projection

for 2015-2019

(29)

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)

(30)

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

(31)

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

(32)

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

(33)

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.