Presentation human impact on droughts and hot temperatures

Introduction Preliminary results Outlook Supplement. figures

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Human influence on droughts and temperature extremes Brigitte Mueller

[email protected]


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Outline

1. Why study extremes?

2. Soil moisture impacts on hot extremes (coupling)

3. Human influence on changes in soil moisture

4. Human influence on hot future summers

1. Motivation 2. Coupling 3. Human influence on droughts 4. Hot summers


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Motivation

Heat waves impact on health and economy

Heat waves and droughts impact agricultural output

Hot extremes more relevant than mean temperatures


Year Country Estimated

death toll

(No. of people)

Estimated costs Reference

2003 Europe 70’000 13 billion Euros a,b

2010 Russia 55’736 5-12 billion Euros c,d

2012 US 123 31 billion USD e

2013 Eastern

China

40 59 billion RMB f,g

a. Robine et al., Com. Ren.Biol., 2008 . b. www.metoffice.gov.uk c. www.emdat.be d. www.dw.de e. www.ncdc.noaa.gov f. www.news.xinhuanet.com g. Hou et al., Met. Mon., 2014


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Motivation: Increase in hot extremes


Cold Average Hot

Pro

babili

ty d

ensity

Temperature distribution


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Similar to a Figure from IPCC 2007 and 2013


Cold Average Hot

Pro

babili

ty d

ensity

Temperature distribution

New climate


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Seneviratne, Donat, Mueller and Alexander (Nature Climate Change), 2014

Increase in extremely hot global land temperature vs. increase in mean temperature

Trends 1997 - 2012

Extreme T

Mean T

Tem

pera

ture

anom

aly

[K

]

K per 10 years


Txp95 over land ─ Tmean over land

Tmean ocean + land

─

─ ─


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Precipitation

Evaporation

Temperature/extreme T

Runoff

Soil moisture

Greenhouse Gases

Floods

Importance of soil moisture

Agricultural production



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Importance of soil moisture


Precipitation

Evaporation


Runoff

Soil moisture

Greenhouse Gases

Agricultural production Floods

www.newscientist.org www.oklahomafarmreports.com


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Outline







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Land water and energy balances

Seneviratne et al. (Earth-Sci. Reviews), 2010


Water balance ΔS = P – E – Rs - Rg

Energy balance ΔH = Rn – λE – SH – G

Variables S: Soil water storage P: Precipitation E: Evapotranspi- ration R: Runoff H: Ground heat storage SW: Shortwave radiation LW: Longwave radiation λE: Latent heat of vaporization * Evaporation SH: Sensible heat flux G: Ground heat flux Rn: Net radiation


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Land – atmosphere feedback

Wet soils Dry soils

Alexander (Nature Geoscience), 2011



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Land – atmosphere feedback


Seneviratne et al. (Earth-Sci. Reviews), 2010 Alexander (Nature Geoscience), 2011


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Mueller and Seneviratne (PNAS), 2012

Land-atmosphere coupling with observations

Correlation: Number of hot days and preceding drought index 1979-2010

Hatched areas significant at 10% level White: Not defined

Hot days at hottest month of each year Drought index before that month: Precipitation deficit over 3 months (standardized precipitation index SPI)


Hot day

Temperature

> 1 wet -0.99 to 0.99 near normal < -1 dry


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% of years

Probability after dry conditions After wet conditions

Occurrence of above average # of hot days

Mueller and Seneviratne (PNAS), 2012



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Mueller and Seneviratne (GRL), 2014

Evaporation bias (annual)

Land-surface impact on temperature in models

CMIP5 minus reference data

Too wet


Temperature bias (annual)

Too cold


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Mueller and Seneviratne (GRL), 2014

Evaporation bias (annual)

Land-surface impact on temperature in models

Mueller et al., (HESS), 2013

Evapotranspiration (40 datasets), 1989-1995 CMIP5 minus reference data

Too wet


Temperature bias (annual)

Too cold


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Outline







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Changes in the water cycle

Precipitation

Evaporation


Runoff

Soil moisture

Greenhouse Gases

Agricultural production Floods

www.newscientist.org www.oklahomafarmreports.com



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Seneviratne et al. (Earth-Sci. Reviews), 2010

Water balance ΔS = P – E – Rs - Rg


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Dots: Robust. Hatching: Not significant (rel. to internal climate variability)

RCP8.5 scenario (2081-2100 minus 1986-2005)

Sedláček and Knutti (Environ. Res. Lett), 2014

Future minus past


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1) Are changes in soil moisture and droughts distinguishable from internal climate variability?

→ Detection

2) Are changes due to human influence?

→ Attribution

Changes in droughts



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Central line of evidence that has supported statements such as …’most of the observed increase in global average temperature since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations’

IPCC AR5, Chapter 10, 2013

Detection and Attribution



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Figure from NOAA NCDC / CICS-NC

1. Detection

Observed variable


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1. Detection

Internal variability: Variability due to natural internal processes (ENSO, PDO etc.)

Observed variable


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Natural forcings Anthropogenic forcings

1. Detection 2. Attribution


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Attribution

Observations


- Greenhouse gases - Anthropogenic aerosols - Volcanic aerosols - Solar variability - Internal variability

Climate model

Hypotheses: Anthropogenic (ANT)? Natural (NAT)?

Pictures right top and bottom from NOAA


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1986 - 1996

XYY = X × β + ε

Adapted from Weaver and Zwiers (Nature), 2000

1946 -1956

Evaluate amplitude estimates Evaluate goodness of fit

Observations Model simulations


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1986 - 1996

XYY = X × β + ε

Adapted from Weaver and Zwiers (Nature), 2000

1946 -1956

Evaluate amplitude estimates Evaluate goodness of fit

Observations Model simulations

Signal detected in observations

0ˆ β=(XT C-1X) -1XTC-1Y


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Warm season soil moisture trends 1951-2005

Adapted from: Mueller and Zhang, submitted to Climatic Change

ALL

NAT

OBS

Obs

NAT

ALL

Soil moisture Drought area


Region: Northern mid-latitudes

Observations Natural + Anthropogenic Only natural forcing


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Scaling factors β

Adapted from: Mueller and Zhang, submitted to Climatic Change

Detected if > 0 Bars: 5-95% confidence intervals

Soil moisture Drought area

ALL NAT ALL NAT


ALL = natural and anthropogenic forcing NAT = only natural


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Outline







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Hot temperatures observed in the past showed strong negative impacts (health, economy, agriculture)

How likely does a summer as hot or hotter than the hottest in the past become in the future?

Hot future summer: Motivation


Year Country Estimated

death toll

(No. of people)

Estimated costs Ref

2003 Europe 70’000 13 billion Euros a,b

2010 Russia 55’736 5-12 billion Euros c,d

2012 US 123 31 billion USD e

2013 Eastern

China

40 59 billion RMB f,g

Heat waves, droughts


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Regions for summer land temperature



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Summer temperature Eastern Asia

CMIP5 simulations: ALL = anthropogenic and natural forcing (54 runs) NAT = only natural forcing (45 runs) RCP4.5 and 8.5 = representative concentration pathway (15 runs) Observations: CRU

Tem

pera

ture

anom

aly

[K

]



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Representative Concentration Pathways


Data obtained from http://www.pik-potsdam.de/~mmalte/rcps/

Emissions

CO2

Concentrations

0

5

10

15

20

25

30

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2000 2020 2040 2060 2080 2100

CO

2-E

mis

sio

ns (

GtC

/yr)

RCP8.5

RCP4.5

0

200

400

600

800

1000

1200

1400

2000 2020 2040 2060 2080 2100

CO

2-E

q C

on

ce

ntr

ation

(p

pm

)

RCP8.5

RCP4.5


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Summer temperature Eastern Asia

Historical hottest year 2010: 0.94 K above climatology How much more likely is a 2010 summer in the future?

Tem

pera

ture

anom

aly

[K

]



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Constraining simulations with observations


Adjust past and future simulations 1. In detection and attribution framework, calculate factor β

ALL by

which simulations need to be scaled to match best the observations

2. Adjust simulations with scaling factors βALL

Xadjusted

= XALL

*βALL

where X is the ensemble mean of the simulations.

3. Add internal variability to obtain a set of possible temperatures

Xrec

= XALL

*βALL

+ Controlsimulationsindividual

where Controlsimulations are 390 samples of internal variability.


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Exceedance probabilities

Exceedance p

robabili

ty in %

Probability of exceeding maximum historic temperature (Eastern Asia)


Results consistent with Sun et al. (Nat Clim Change), 2014 for Eastern China


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Regions for summer land temperature


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Percentage of population experiencing 1 in 2 summers hotter than the past maximum

─ under RCP4.5

─ under RCP8.5

Hottest historical summers will be the norm for more than half of the world's population by 2035

Population affected by hot summers

Mueller, Zhang and Zwiers, in preparation


Perc

ent of w

orld p

opula

tio

n [%

]


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─ under RCP4.5

─ under RCP8.5





Perc

ent of w

orld p

opula

tio

n [%

]


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─ under RCP4.5

─ under RCP8.5

Perc

ent of w

orld p

opula

tio

n [%

]


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Hot temperatures strong increase in the last 30 years

Soil moisture influences temperature extremes in large fraction of the globe

Potential for seasonal prediction

Summary I.

# hot days and drought



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Northern hemispheric land areas have become drier

Human influence on changes in soil moisture are significant

Strong increase in probabilities for future hot summers after 2020, especially without climate action

Summary II.


Soil moisture


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Local versus large scale effect on droughts, evaporation and temperature

Improving simulations of temperature selecting models with good representation of land-atmosphere coupling

Response of global water cycle to increasing greenhouse gases

Regional climate change

Open questions

[email protected]


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Local versus large scale effect on droughts, evaporation and temperature

Improving simulations of temperature selecting models with good representation of land-atmosphere coupling

Response of global water cycle to increasing greenhouse gases

Regional climate change

Thank you

[email protected]


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New info updated after presentation

Papers mentioned are now published:

Mueller, B., X. Zhang , and F.W. Zwiers (2016): Historically

hottest summers projected to be the norm for more than half of

the world's population within 20 years, Environmental Research

Letters.Climatic Change. Link

Mueller, B., and X. Zhang (2016): Causes of drying trends in

northern hemispheric land areas in reconstructed soil moisture

data, Climatic Change. Link

Supplementary

http://iopscience.iop.org/article/10.1088/1748-9326/11/4/044011;jsessionid=BB66DEFDE840068AC5382BA7F2B2E2AB.c3.iopscience.cld.iop.org?fromSearchPage=true

http://link.springer.com/article/10.1007%2Fs10584-015-1499-7


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Short explanation of detection and attribution algorithm used in the presented analyses

Supplementary


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Total least square model

Y = β*X + ϵ with X=X0 + ϵX and Y=Y0 + ϵY

β=(XT C-1X) -1XTC-1Y

C covariance of ϵ, internal variability C estimated from control simulations C Y =λCX : Assumption tested with residual consistency test


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Assumption: X

ANT = X

ALL - X

NAT

Y = βANT

*XANT

+ βNAT

*XNAT

+ ε

Two-signal detection


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Two-signal results ANT and NAT


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Internal variability

Presentation human impact on droughts and hot temperatures

Data & Analytics

Transcript of Presentation human impact on droughts and hot temperatures