Frontiers in Decadal WATER S CIENCE AND TECHNOLOGY BOARD Climate - - PowerPoint PPT Presentation

WATER S CIENCE AND TECHNOLOGY BOARD

Frontiers in Decadal Climate Variability:

Proceedings of a Workshop

Gerald A. Meehl

National Center for Atmospheric Research (NCAR) Organizing Committee Chair

Monday, July 25th, 2pm EDT

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 a 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:
• 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 Robert Hallberg (OSB), NOAA David Halpern (OSB), NASA Jet Propulsion Laboratory

Workshop Goals

• 1. Examine our understanding of the processes governing

decadal-scale variability in key climate parameters,

• bservational 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

• bserved 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”,

• r “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). And the flip side of hiatus periods are accelerated warming periods.

Mid-1970s shift

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.

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) Early-2000s slowdown Big hiatus

NOAA press release on Karl et al Science paper published in Science Express on June 3, 2015:

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 previous positive IPO period from 1972-2001 (Fyfe et al., 2016, Nature Clim. Chg).

(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

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.

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

(Meehl et al., 2014, Nature Climate Change)

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)

Modern coral d18O 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 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

• ccurs 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 relationship between warm AMO and dry N. America

1992-2011

• bserved trends

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

Specified trend

• f 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 Observed 1992-2011 trends

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 (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

• f 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

(Meehl et al., 2016, Nature Communications)

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)

Predicted rate of global warming from 2013 initial year greater than during early-2000s slowdown and greater than uninitialized:

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
• bservations to improve the models; important

toward developing prediction capability, also important to distinguish forced and internal change through fingerprinting

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

Frontiers and Research Opportunities

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.