transformational climate science - university of exeter · transformational climate science...
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#climate2014
Transformational
Climate Science Conference presentations from Thursday 15
May in Working Group I and II sessions
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Working
Group I
The challenge of climate change #climate2014
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Working Group 1: the challenge of climate change
Chair: Professor Stephen Belcher
Head of the Met Office Hadley Centre
Keynote: Professor Thomas Stocker
Co-chair of Working Group 1
Commentators: Dr Peter Stott
Scientific Strategic Head for Climate Monitoring and
Attribution, Met Office
Professor Corinne Le Quéré
Professor of Climate Science and Policy and Director of the
Tyndall Centre for Climate Change Research, University of
East Anglia
Professor Mat Collins
Joint Chair in Climate Change, Met Office and University of
Exeter
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The challenge of climate change
Professor Stephen Belcher
Head of the Met Office Hadley Centre
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The challenge of climate change
Professor Thomas Stocker
Co-Chair of Working Group I
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© Yann Arthus-Bertrand / Altitude
Thomas Stocker
Co-Chair IPCC Working Group I
University of Bern, Switzerland
The challenges of climate change:
The outcomes of IPCC WGI
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Key SPM Messages
19 Headlines on less than 2 Pages
7
14 Chapters & Atlas
1,100,000 Words
Summary for Policymakers
14,000 Words
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8
Observations
Understanding
Future
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Warming of the climate system
is unequivocal
IPC
C 2
013, F
ig. S
PM
.1b
Temperature Difference 1901 to 2012 based on trend (°C)
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(Lüthi et al.,2008, NOAA)
+30%
2013
The concentrations of CO2 have increased to levels
unprecedented in at least the last 800,000 years.
1000 years before present
CO
2 C
oncentr
ations (
ppm
)
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IPC
C 2
013, B
ox 3
.1, F
ig.
1, m
odifie
d
Change in E
nerg
y C
onte
nt
(10
21 J
oule
)
1980 1990 2000 2010
300
200
0
100
-100 1970
IPCC AR5 WGI: Closed energy budget
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IPCC AR5 WGI: Closed sea level budget
IPC
C 2
013, F
ig. T
S.3
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Understanding
Causes of the observed
changes
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Global mean warming since 1951 (°C)
Solar, Volcanic
Aerosols
CO2, CH4, N2O
Anthropogenic
Internal Variability
Observed
Fig. TS.10
It is extremely likely that human influence has
been the dominant cause of the observed
warming since the mid-20th century.
© I
PC
C 2
013
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atmosphere, land, ocean
extreme events
water cycle
global mean sea level
Human influence on the
climate system is clear.
sea ice, glaciers, ice sheets
Cause
Worldwide Effects
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Future
Projections of many future
climates
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IPC
C 2
013, F
ig. S
PM
.7a
Global mean surface temperature change from 1986-2005
Global surface temperature change for the end of the
21st century is likely to exceed 1.5°C relative to
1850−1900 for all scenarios except RCP2.6.
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RCP 2.6 (annual) RCP 8.5 (annual)
IPCC 2013: Atlas of Global and Regional Climate Projections
Regional Changes in Europe (2081-2100)
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Global mean warming
All CO2 emissions since 1750
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Global mean warming
All CO2 emissions since 1750
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Warming of 0.8 to 2.5°C
1000 billion tons of carbon
Any climate target implies
a limited carbon budget
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IPC
C 2
013, F
ig. S
PM
.10
790 Bill. t C
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Budget for 2°C target: 790 bill t C
CO2 emissions until 2013: −535 bill t C
CO2 emissions in 2013: 9.9 bill t C
Remaining emissions: 255 bill t C
Limiting climate change will require substantial and
sustained reductions of greenhouse gas emissions.
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Warming of the climate system is
unequivocal, [...]
Human influence on the climate
system is clear.
Limiting climate change will require
substantial and sustained reductions
of greenhouse gas emissions.
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Maintenance and upgrade of high-quality, high-density
observation networks
Accessibility and manageability of massive amounts of
numerical data from of climate model simulations
Role of climate science in the new Future Earth
programme
Climate research in the regions: capacity still limited
inspite of many years of capacity building
Challenges for climate research and assessment:
Logistic challenges:
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Increase institutional support
of scientific assessment work
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Regional modelling with a focus on the water cycle
Statistics of extreme events, quantification of the tails
Detection and Attribution of regional changes
Spatial quantification of vulnerability and exposure for a
well chosen set or variables
Coupling of economic models using approaches
compatible with the physical sciences
Challenges for climate research and assessment:
Scientific challenges (1/2):
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Coordinated model intercomparison projects (MIP)
across the topics of the three IPCC WGs
RMIP: Regional MIP
VXMIP: MIP on vulnerability and exposure
EMIP: Economic MIP
Challenges for climate research and assessment:
Scientific challenges (2/2):
Maintain a high level of
curiosity-driven research
WGI, WGII
WGII
WGI, WGIII
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www.climatechange2013.org
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© Yann Arthus-Bertrand / Altitude
www.climatechange2013.org
Full report and further information
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The challenge of climate change
Dr Peter Stott
Scientific Strategic Head for Climate
Monitoring and Attribution, Met Office
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© Yann Arthus-Bertrand / Altitude
Detection and Attribution of Climate
Change : from Global to Regional Peter Stott, Nathan Bindoff,, Krishna AchutaRao, Myles Allen, Nathan Gillett, David Gutzler, Kabumbwe
Hansingo, Gabriele Hegerl, Yongyun Hu, Suman Jain, Igor Mokhov, James Overland, Judith Perlwitz, Rachid
Sabberi, Xuebin Zhang.
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© Crown copyright Met Office
Explaining extreme climate and weather events of the previous year from a climate perspective Peter Stott, Stephanie Herring, Martin Hoerling, Tom Peterson.
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What do we mean by attribution?
• Attribution requires estimates of the expected changes in climate
due to different factors (eg increased greenhouse gas
concentrations or changing solar output) : the “fingerprints”.
• An observed change is attributed to a particular factor if observed
changes are consistent with expected changes that include the
relevant fingerprint and inconsistent with expected changes that
exclude that fingerprint.
• Attribution is defined as the process of evaluating the relative
contributions of multiple causal factors to a change or event with an
assignment of statistical confidence.
• Attribution combines statistical analysis with physical
understanding.
• [IPCC Good Practice Guidance Paper, 2010]
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1880 1920 1960 2000 1880 1920 1960 2000 1880 1920 1960 2000
Observed warming consistent with that
expected from anthropogenic factors and
inconsistent with that expected from
natural factors
The first decade of the 21st century was the warmest on record.
Fig 10.7
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Fingerprint studies quantify the contributions of
anthropgenic and natural forcings to observed warming
FAQ 10.1 Fig 1
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It is extremely likely that human influence has been the
dominant cause of the observed warming since the mid-
20th century.
Attribution studies based on different methodologies, a new generation of climate models and observations to 2010. The best estimate of the human-induced contribution to warming is similar to the observed warming.
Temperature trends over 1951-2010
OTHER ANTHROPOGENIC
NATURAL
ANTHROPOGENIC
Fig TS.10
GREENHOUSE GASES
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Likely substantial anthropogenic contribution to
increases in global upper ocean heat content
Fig 10.14
Ocean heat content
Signal to noise ratio
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It is likely that anthropogenic influences have
affected the global water cycle.
• Anthropogenic influences have contributed to:
– Observed increases in atmospheric moisture (medium confidence)
– Global-scale precipitation patterns over land (medium confidence)
– Intensification of heavy precipitation over land regions where data is sufficient
(medium confidence)
– Ocean salinity changes making fresher regions fresher over time and salty regions
saltier ( very likely)
Fig 2.30
Increased atmospheric moisture
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Occurrences of climatologically hottest days and nights in 20 years have now reduced to fewer than 10 years and 15 years respectively in many places.
Zwiers et al, 2011
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Human influence on the climate system
is clear
• Human influence has been detected in warming
of the atmosphere and the ocean, in changes in
the global water cycle, in reductions in snow and
ice, in global mean sea level rise, and in
changes in some climate extremes.
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Human influence on the climate system
is clear
• Human influence has been detected in warming
of the atmosphere and the ocean, in changes in
the global water cycle, in reductions in snow and
ice, in global mean sea level rise, and in
changes in some climate extremes.
• This evidence for human influence has grown
since AR4.
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Human influence on the climate system
is clear
• Human influence has been detected in warming
of the atmosphere and the ocean, in changes in
the global water cycle, in reductions in snow and
ice, in global mean sea level rise, and in
changes in some climate extremes.
• This evidence for human influence has grown
since AR4.
• It is extremely likely that human influence has
been the dominant cause of the observed
warming since the mid-20th century.
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What can we say about individual climate-related events?
© Crown copyright Met Office
Australia’s angry summer of 2013
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What can we say about individual climate-related events?
© Crown copyright Met Office
Wettest winter in England and Wales since 1766
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European heatwave 2003
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Attribution statements about individual events by examining the changed probability attributable to different factors
Stott et al, 2004
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European summer temperatures on track for 2003 to become the norm by 2030s
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© Crown copyright Met Office
Explaining extreme climate and weather events of the previous year from a climate perspective Tom Peterson, Peter Stott, Stephanie Herring.
• Report ground breaking in applying attribution science to recent extreme weather events.
• Climate change has made some events more likely, some less likely
• We do not see evidence for a strong human influence in all weather extremes. Natural variability also plays an important role
• Inaugural report quickly became “most read” article in BAMS
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© Crown copyright Met Office
Explaining extreme climate and weather events of the previous year from a climate perspective Tom Peterson, Martin Hoerling, Peter Stott, Stephanie Herring.
• Increase from 6 contributions last year to 19 this year
• 18 different research groups, 12 extreme events
• Some events have multiple different groups looking at them
• About half the analyses found some evidence that anthropogenic climate change was a contributing factor
• Natural climate variability a factor in all events
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Human influence on the climate system is clear
• What is our vulnerability to extremes in a changing climate?
• Operational attribution systems can provide regular assessments of attributable risk of extreme events.
• Further development of models, observations and understanding is required.
• European project, EUCLEIA, aims to develop our ability to provide reliable attribution assessments for heatwaves, floods, droughts, cold spells and storm surges.
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© Yann Arthus-Bertrand / Altitude
www.climatechange2013.org Further Information
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The challenge of climate change
Professor Corinne Le Quéré
Professor of Climate Science and Policy
and Director of the Tyndall Centre for
Climate Change Research, University of
East Anglia
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population projections (purple): Lutz et al. IIASA 2007
time (y)
world population (billion)
now
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time (y)
world population (billion)
now
functioning of the land and atmosphere
soil & vegetation CO2, O3, CH4, N2O
T & stress responses natural & managed land
population projections (purple): Lutz et al. IIASA 2007
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time (y)
world population (billion)
now
functioning of the ocean
heat balance & decadal variability ocean acidification and ecosystem impacts
population projections (purple): Lutz et al. IIASA 2007
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time (y)
world population (billion)
now
functioning of the carbon-climate-society interactions
Carbon emitted to 2013 = 535 [465 – 605] GtC Carbon budget below 2°C (50%) = <820 GtC
(based on WGI updated to 2013)
population projections (purple): Lutz et al. IIASA 2007
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time (y)
world population (billion)
functioning of the carbon-climate-society interactions
Carbon emitted to 2013 = 535 [465 – 605] GtC Carbon budget below 2°C (50%) = <820 GtC
(based on WGI updated to 2013)
population projections (purple): Lutz et al. IIASA 2007
UN COP Paris
Dec 2015
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The challenge of climate change
Professor Mat Collins
Joint Met Office Chair in Climate Change,
Met Office and University of Exeter
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© Yann Arthus-Bertrand / Altitude
Long Term Climate Change: Projections,
Commitments and Irreversibility
Mat Collins, Reto Knutti, Julie Arblaster, Jean-Louis Dufresne,
Thierry Fichefet, Pierre Friedlingstein, Xuejie Gao, Bill
Gutowski, Tim Johns, Gerhard Krinner, Mxolisi Shongwe,
Claudia Tebaldi, Andrew Weaver, Michael Wehner
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Global Mean Surface Air Temperature Change
Anomalies w.r.t 1986-2005 average
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Global Temperature Assessment
•The transient climate response is
likely in the range of 1.0°C to 2.5°C
(high confidence) and extremely
unlikely greater than 3°C
• The CMIP5 models coincide with
this range
• The RCPs are dominated by
greenhouse gas forcing by the end
of the century
• Associate the 5-95% range of
model simulations (+/- 1.64
standard deviations) with the likely
range (66-100%)
• Only valid for likely (66-100%)
• Only valid for global mean
temperature in long-term
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Global Mean Surface Air Temperature Change
5-95% ~ ‘likely’
Anomalies w.r.t 1986-2005 average likely = 66-100% probability
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Global Temperature Assessment
Global surface temperature change for the end of the 21st century is
likely to exceed 1.5°C relative to 1850 to 1900 for all RCP scenarios
except RCP2.6. It is likely to exceed 2°C for RCP6.0 and RCP8.5, and
more likely than not to exceed 2°C for RCP4.5. Warming will continue
beyond 2100 under all RCP scenarios except RCP2.6. Warming will
continue to exhibit interannual-to-decadal variability and will not be
regionally uniform (see Figures SPM.7 and SPM.8). {11.3, 12.3, 12.4,
14.8}
What about regional changes, rainfall, sea-ice etc. etc?
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In projection maps hatching indicates regions where the projected change is small
compared to internal variability (less than one standard deviation of internal
variability), and stippling indicates regions where the projected change is large
compared to internal variability (greater than two standard deviations of internal
variability), and where 90% of models agree on the sign of change. (Fig. SPM.7)
RCP8.5
Hatching: changes
are “small”
compared with
internal variability
(less than one
standard deviation of
internal variability
Stippling: changes
are “large” compared
with internal
variability, and >90%
of models agree on
sign of change
Stippling: changes
are “large” compared
with internal variability
(greater than two
standard deviations of
internal variability),
and at least 90% of
models agree on sign
of change
How large is the projected change
compared with internal variability?
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Changes Conditional on Global Mean
Temperature Rise
• High northern latitudes expected to warm most
• Land warms more than ocean surface
• More hot and fewer cold extremes
• Global mean precipitation increases but regional patterns of change not
uniform
• Contrast between wet and dry regions and seasons to increase (with
regional exceptions)
• Tropical atmospheric circulations expected to weaken, subtropics creep
polewards
• Arctic summer sea-ice to melt back – ice free conditions likely by mid
century under RCP8.5
• Permafrost and snow cover to retreat
• Atlantic Meridional Overturning Circulation (AMOC) to weaken but not
collapse
• N. Hemisphere storm track changes – low confidence
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Cryosphere
Solid lines – subset of models
Shading – min/max
Dotted – all CMIP5 models
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Annex I: Atlas of Global and Regional Climate Projections
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Annex I: Atlas of Global and Regional Climate Projections
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http://climexp.knmi.nl/plot_atlas_form.py #climate2014
Rainfall Change in
the Tropical Pacific
• Rainfall change anchored to an equatorial peak in SST warming across the Pacific
• SST change a result of oceanic processes in the west and atmospheric processes in the
east (Xie et al., 2010)
• This change in mean rainfall impacts El Nino teleconnections and increases the
frequency of ‘extreme’ El Nino events (Cai et al., 2014, Power et al., 2013)
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Challenges in making Future Projections
• There are some robust features that emerge in climate models, that we
understand and can use to make assessments of very large-scale climate
change (land-sea contrast, polar amplification, thermodynamic rainfall…)
• For some projections we are learning how to ‘calibrate’ sets of models
(e.g. Arctic summer sea-ice)
• We are beginning to understand some aspects of regional climate
variability and change better
• Models are still imperfect, observations are still incomplete and theory is
still under-developed
• We do not yet routinely try and combine information from models,
observations and understanding to make quantitative projections that may
be compared
• The above is really addressing the forced-response, the role of natural
internal variability is still very important on shorter time scales
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© Yann Arthus-Bertrand / Altitude
www.climatechange2013.org Further Information
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#climate2014
Transformational
Climate Science The future of climate change research
following the IPCC Fifth Assessment Report
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Working
Group II
The challenge and impacts of adaptation #climate2014
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Working Group II: the challenge of impacts and adaptation
Chair: Professor Neil Adger
Professor in Geography, University of Exeter
Keynote: Professor Chris Field
Co-chair of Working Group II
Commentators: Professor Richard Betts
Chair in Climate Impacts at the University of Exeter and
Head of Climate Impacts in the Met Office Hadley Centre
Professor Frans Berkhout
Professor of Environment, Society and Climate, King’s
College London and Interim Director of the Future Earth
Dr Sari Kovats
Senior Lecturer, London School of Hygiene and Tropical
Medicine
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The challenge and impacts of adaptation
Professor Neil Adger
Professor of Geography,
University of Exeter
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The challenge and impacts of adaptation
Professor Chris Field
Co-Chair of Working Group II
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The challenge and impacts of adaptation
Professor Richard Betts Chair in Climate Impacts, University of Exeter
Head of Climate Impacts, Met Office Hadley Centre
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Climate change impacts on global vegetation and hydrology: an
integrated view
Prof Richard Betts Chair in Climate Impacts, University of Exeter Head of Climate Impacts Research, Met Office Hadley Centre Lead Author, IPCC AR5 WG2 Ch4, Terrestrial and Inland Water Systems
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Projected change in streamflow at 2°C warming*: Inter-Sectoral Impacts Model
Intercomparison Project (ISI-MIP)
© Crown copyright Met Office *Relative to 1980-2010 Schewe et al (2013)
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Projected changes in vegetation by 2071-2099, RCP8.5: 3 ISI-MIP vegetation models
© Crown copyright Met Office Friend et al (2013)
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Coupled vegetation and hydrology models
© Crown copyright Met Office
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Change in runoff (streamflow) 2070-2099 vs 1981-2010 in ISI-MIP hydrology and ecosystem models
© Crown copyright Met Office
Davie et al (2013)
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Change in runoff (streamflow) 2070-2099 vs 1981-2010 in ISI-MIP hydrology and ecosystem models
© Crown copyright Met Office
Davie et al (2013)
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Change in runoff (streamflow) 2070-2099 vs 1981-2010 in ISI-MIP hydrology and ecosystem models
© Crown copyright Met Office
Davie et al (2013)
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Change in runoff (streamflow) 2070-2099 vs 1981-2010 in ISI-MIP hydrology and ecosystem models
© Crown copyright Met Office
Davie et al (2013)
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Will the land become drier or wetter?
Percentage of global land undergoing decreased runoff by 2100: MacPDM hydrology model driven by
HadGEM2-ES climate change
© Crown copyright Met Office 0
10
20
30
40
50
60
70
0 1 2 3 4 5 6 7 8 9
HadGEM2
MacPDM driven byHadGEM2 climate
RCP
8.5
Radiative forcing
RCP2.6
RCP4.5
% o
f la
nd
Source: ‘AVOID’ project
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Changes in global runoff simulated within HadGEM2-ES
© Crown copyright Met Office
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Will the land become drier or wetter?
Comparison of hydrological changes simulated within Earth System Model and ‘offline’ hydrology model
© Crown copyright Met Office 0
10
20
30
40
50
60
70
0 1 2 3 4 5 6 7 8 9
HadGEM2
MacPDM driven byHadGEM2 climate
RCP
6.0
RCP
8.5 RCP2.6
RCP4.5
% o
f la
nd
Radiative forcing
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© Crown copyright Met Office
Climate, vegetation & water cycle links
Affected by weather / climate
Affect weather / climate
Also affected by CO2 concentration
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% change in net irrigation requirements, 2070-2099 vs 1971-2000, SRES A2 for 11 major crops 19 GCMs, 1 veg model Current irrigation areas & management practice
© Crown copyright Met Office
Konzmann et al (2013)
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Estimates of transient climate response
© Crown copyright Met Office
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CO2 concentration at time of reaching 2°C in SRES A1B scenario: 58-member Earth System model ensemble (HadCM3)
© Crown copyright Met Office
Small CO2 effects on vegetation & hydrology
Large CO2 effects on vegetation & hydrology
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Summary • Projected impacts of climate change on vegetation and hydrology
depend on each other
• In recent studies, hydrological impacts smaller in ecosystem models than hydrological models
• In some cases, coupling with vegetation and/or climate models can change sign of hydrological impact
• Importance of CO2 physiological effects relative to climate depends on:
– Plant physiological processes (WG2 & WG1)
– Climate sensitivity (WG1)
– Greenhouse gas mix – CO2 and non-CO2 GHGs (WG3)
• To be examined further in EU Project HELIX
© Crown copyright Met Office
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The challenge and impacts of adaptation
Dr Sari Kovats Senior Lecturer,
London School of Hygiene and Tropical Medicine
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Climate change: impacts and adaptation
Sari Kovats, PhD Coordinating Lead Author, Chapter 23 Europe, WGII IPCC Fifth Assessment Report. LWEC Health Fellow Senior Lecturer | Department of Social and Environmental Health Research Faculty of Public Health and Policy | London School of Hygiene and Tropical Medicine
Director NIHR Health Protection Research Unit on Environmental Change and Health
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Sectors
• Infrastructure and settlements
• Food, fisheries, forestry
• Health and wellbeing
• Environmental quality and conservation
Sub-Regions
Europe chapter 23
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Climate change
Policy
Adaptation [water, food,
etc]
Mitigation policies
Regional climate changes
Health exposures
Income
Health and social impacts
Adaptation Health policies
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Climate change and
ecosystem services
Alpine Atlantic Continental Northern Southern
Sum of effects on provisioning services
˄ (6)
No (4)
˅ (11)
˄ (2)
No (4)
˅ (2)
˄ (1)
No (2)
˅ (3)
˄ (9)
No (3)
˅ (2)
No (1)
˅ (7)
Sum of effects on regulating services
˄ (9)
No (2)
˅ (11)
˄ (6)
No (4)
˅ (9)
˄ (6)
No (2)
˅ (9)
˄ (8)
No (2)
˅ (8)
˄ (4)
No (3)
˅ (14)
Sum of effects on cultural services
˄ (1)
˅ (1)
˄ (1)
˅ (1)
No (1)
˅ (1)
˄ (1)
˅ (3) ˅ (2)
(N) = number of journal papers
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Extreme weather and
multiple risks
• 2003 Heat wave, central Europe – Hottest summer in at least 500 years
– Damage to road and rail transport systems.
– Risk to nuclear power generation in France.
– Grain harvest losses of 20%
– 35,000 deaths in August in Central and Western Europe
– Decline in water quality associated with low river flows
– Air pollution episode
– Forest fires (“mega fire”)
– Damage to habitats
Source: Table 23-1: Multi-sectoral impacts of climate
extremes during the last decade in Europe
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http://www.netlore.ru/smog_v_moskve
Moscow 06/08/2010
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Climate-risk maps
• Indicators
– Exposure
– Vulnerability
• Climate-related risks
– heat stress
– Infectious diseases
• Better validation of indicators.
• Need quantification of impacts in terms of burden of disease
• Need baseline estimates of current burdens.
Eritrea - temperatures below 18 deg C in the
highlands reduce malaria transmission
Source: IRI
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Data and monitoring
• Lack of long term high quality health and demographic data in low and middle income countries
– Lack of data on poor urban populations
• Linkage with environmental exposures
• Scaling up and down
Nouna Health and Demographic
Surveillance System (HDSS),
Burkina Faso
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Social impacts
• Need more quantitative evidence
– Social impacts of disasters/extreme events
• How to scale up from small scale qualitative studies in a regional assessment.
• Loss of cultural landscapes
– Impacts on wine production
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Area/ Location
System
Adaptation measures
Limits to adaptation
measure(s)
Agriculture, Northern and Continental Europe.
Arable crops
Sowing date as
agricultural adaptation
Other constraints (e.g. frost) limit farmer behaviour
Agriculture, Northern and Continental Europe.
Arable crops
Irrigation
Groundwater availability, competition with other users.
Agriculture, Viticulture
High value
crops
Change
distribution
Legislation on cultivar and geographical region
Conservation Cultural landscapes
Alpine
meadow/
Extend habitat
No technological adaptation option.
Conservation of species richness
Movement of
species
Extend habitat
Landscape barriers and absence of climate projections in selection of conservation areas.
Forests
Movement of species and productivity reduction
Introduce new species
Not socially acceptable, Legal barriers to non-native species
Table 23-6: Examples of Limits to Adaptation in Europe
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Health effects
• Positive benefits
– Air quality
– Active transport
– Diet
• Negative effects/harms
– Housing
Biodiversity: example
Mitigation and Adaptation
synergies and trade-offs
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Funders
• Long term investment and planning
• New mechanisms of funding
• Investment in data collection
LWEC examples
• ESPA- Ecosystem Services for Poverty Alleviation
• ESEI – Environmental and Social Ecology of Human Infectious Diseases
Inter-disciplinary
research
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What we did well..
• Integration with WGI
• Intra-regional distribution of impacts
• Impacts at 2-4 degrees
• Detection and attribution in ecological systems
• Health impacts in high income countries
• Geographical inequality
What we did not do well..
• Integration with WGIII
• Inter-regional effects (globalization).
• Impacts at 4+ degrees
• Detection and attribution in human systems.
• Health impacts in low income countries
• Social inequality (distribution within populations)
AR5 -
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The challenge and impacts of adaptation
Professor Frans Berkhout
Professor of Environment, Society and
Climate, King’s College London
Interim Director, Future Earth
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Reflections on adaptation opportunities, constraints and limits
Frans Berkhout Transformational Climate Science conference
Exeter 15th May 2014
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IPCC AR5 WG2
Chapter 16: Constraints, opportunities and limits to adaptation
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Are there limits to adaptation?
• If there are no limits, why mitigate?
• Why a focus on limits?
– Focusing resources on the highly-vulnerable
– Identifying hotspots for ‘transformative adaptation’
– Underpinning mitigation goals (2degC)
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Mitigation Adaptation Residual
loss & damage
Limits to adaptation: the idea
When are losses following adaptation intolerable?
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Adaptation limits?
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Tipping points
Source: PIK Research Portal
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Crop yields and food security limits
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Limits in ecosystems: mobility of species
IPCC AR5 WG2 SPM, 2014
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A risk framing for adaptation limits
• Focus on the tolerability of climate-related risks to social objectives with growing residual damages
• Focus on risks experienced by actors and species (embedded in social- and eco-systems)
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Adaptation limits
Focus on adaptation objectives
Definition
– the point at which an actor is unable to secure valued objectives from intolerable risks through adaptive action
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Example 1: Rice in Thailand
Rice: Threshold temperature of 26℃ (at night) during flowering and pollination. 10% decline in yield for every 1℃ (with a limit at 32-35℃). Experienced in Bangkok region during 2011 rice growing season.
Objective: To produce rice
Intolerable risk: Loss of farmer livelihoods and security of supply of staple crop
Hard limit: Failure to breed rice varieties that pollinate above 32-35℃
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An adaptation limit represents a discontinuity of behaviour
At a limit there are three options for the actor:
1. Accept escalating losses
2. Change objectives (role of values and norms)
3. Transformative responses (including extinction)
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Research challenges
• Valued objectives – Would you trade wheat for rice in your diet?
• Risk tolerance – What are the costs of accepting a higher level of risk?
• Systemic risks – How do interconnected, cascading risks influence
adaptation limits
• (International) governance at adaptation limits – How are cascading climate and adaptation
emergencies to be managed?
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Thank you!
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#climate2014
Transformational
Climate Science The future of climate change research
following the IPCC Fifth Assessment Report