Tom hopson

Climate change, Implications for

Hydrologic Extremes, and What can

we do about it? Tom Hopson

climate advisor

CSU Feed the Future Innovation Lab

“I have a very strong feeling that science exists to serve human welfare. It’s wonderful to have the opportunity given us by society to do basic research, but in return, we have a very important moral responsibility to apply that research to benefiting humanity.” Dr. Walter Orr Roberts (NCAR founder)

GCRS1950

1960

1970

1980

1990

2000

2010

GFDL

ANMRC

BMRC

NCAR 1

NCAR I1

NCAR II1

CCM 0-A

CCM 0-B

ECMWF

CCM 1

CCM 2

CCM 3

CSM

CCSM 3

CCSM 4

CESM

PCM

MPI

ECHAM3

ECHAM4

ECHAM5

UCLA

GISS

CM2.0

CM2.1

GISS II

GR

E

E2

MRI

CGCM1

CGCM2

CGCM3

CSIRO

UKMO

HadCM

HadCM2

HadCM3

HadGEM

HadGEM2

CGCM

CGCM2

CanESM2

CGCM3

2

3

3.6

CCSR

MIROC

MIROC5

BCC

CSM1.1

CNRM

CM5.1

INMCM

FGOALS

ACCESS

FGCM

Outline

• Climate change overview from IPCC AR5 and

Implications for hydrologic extremes

– A word of caution about regional uncertainty

• What can we do about it?

– Meningitis forecasting for Africa

– Case study: Bangladesh flood forecasting

Large-scale constraints on Mean

and Extreme Precipitation

What have we observed – “likely” (66-100%) that

human influence has affected:

•global surface specific humidity

•global zonal mean terrestrial precipitation

•Arctic precipitation

•global-scale intensification of heavy precipitation

Large-scale constraints on Mean

Precipitation

What we expect – mean precipitation and moisture

•distribution of relative humidity remain roughly

constant and thus water vapor mixing ratio to

increase (6-10%/degC)

•mean precipitation increasing at 1–3% / degC

(tropospheric net radiative cooling rate)

•“Wet-get-wetter”, “dry-get-drier”, but large regional

variability and margins unclear due to regional

circulation shifts

Large-scale constraints on

Extreme Precipitation

What we expect – extreme precipitation

•individual storms increase 6-10% /degC (scales

with available moisture)

•high confidence much greater than mean

precipitation, but varies with time-scale, location,

season

South Asian Monsoon

Precip increases in:

•average

•variance

•5-day seasonal max

•duration

%

Yr 2100

AR5

The Precip

Challenge

80 yr. Temp. Rise

CMIP

80 yr.

Precipitation

Trend ?

Covey et al. 2003

Climate Prediction:

Sources of Uncertainty Forcing (or Scenario Uncertainty)

– GHG emission scenarios (e.g., B1, A1B, A2, RCPs)

ozone, sulfate aerosols, land use, black carbon …

Response (or Model Uncertainty)

– Model sensitivity (different physics, parameterizations, resolution …) – 20+ different climate models in both IPCC AR4 and AR5

Internal (Natural) Variability

– Atmosphere and Ocean – Coupled atmosphere-ocean interactions – Multiple simulations

Jim Hurrell

(now using “Representative

Concentration Pathways”,

or RCPs, in AR5)

Projected Global SAT (IPCC AR4)

(Hawkins and Sutton 2009)

Natural Variability

Can be large & slow

(multiple decades)

} }

}

Forcing (or Scenario) Uncertainty

Response (or Model) Uncertainty

20th Century Simulations

Jim Hurrell

NCAR

Time

Forced Climate Change

The Challenge: Assessing Climate Change in the Presence

of Unforced Multi-decadal Variability

10 years

Jim Hurrell

Natural Variability over the Atlantic

All significant modes of natural variability

Variations and significant impacts from seasonal to multi-decadal time scales

Major Phenomena

• North Atlantic Oscillation (NAO)

• Atlantic Meridional Overturning Circulation (AMOC)

• Tropical Atlantic Variability

(Hurrell et al. 2006)

Jim Hurrell

NCAR

Unforced Multi-Decadal Variability

North Atlantic

SST

Jim Hurrell

1850 control

40 CESM Integrations

20th Century 21st Century

(2000-2060)

A1B GHG

40

different atmospheric initial states

same ocean, sea ice, land initial states

The NCAR Large Ensemble Project:

Year 541

Uncertainty Arising from Natural Variability

http://www.cesm.ucar.edu/working_groups/Climate/

i.e., spread is not

predictable

Jim Hurrell

http://www.cesm.ucar.edu/working_groups/

http://www.cesm.ucar.edu/working_groups/Variability/index.html

NCAR

Projected European Precipitation

Uncertainty due to Natural Variability

DJF 2010-2060

Wettest

Run 3

Driest

Run 9

Forced }

}

Jim Hurrell

Outline



– A word of caution about regional uncertainty


– Meningitis forecasting for Africa

– Case study: Bangladesh flood forecasting

Humidity and Meningitis

in the Sahel

Abudulai Adams-Forgor, Anaïs

Columbini, Vanja Dukic, Mary Hayden,

Abraham Hodgson, Thomas Hopson,

Benjamin Lamptey, Jeff Lazo, Kristen

McCormack, Roberto Mera, Raj Pandya,

Jennie Rice, Fred Semazzi, Madeleine

Thomson, Sylwia Trazka, Tom Warner,

Tom Yoksas

NC STATE UNIVERSITY

19

Goal: improve WHO efficiency

of vaccine dissemination due

to humidity-reduced risk link

Context: Managing Meningitis in

the Sahel

• Meningitis is endemic in the Sahel in countries with a distinct wet-dry season – e.g. 1996-1997 epidemic resulted in 250,000 cases, 25,000

fatalities

• Infectious disease due to bacterium – Neisseria meningitidis – Dominant serogroup A

– 5-10% fatality rate

– 10-20% of survivors have permanent impacts, e.g. hearing loss, brain damage, leaning disabilities

• A reactive (polysaccharide) vaccine strategy had currently been used to manage epidemics – Doesn’t prevent transmission of the disease by the

individual vaccinated => no “herd immunity” – Only lasts two-to-three years – No immune response in children under two – Limited suppy => effective allocation strategies

Using Thorpex Tigge 1- to 2-week forecasts from 7 centers: JMA, KMA, ECMWF

NCEP, CMA, CMC, MeteoFrance, UK Met

Recent findings on impact of dust:

C. Perez et al. 2013 for Niger

A. F. Abdusalam et al. 2013 for N. Nigeria

Humidity

Dust

Temperature

Wind

Time Indoors

Irritation of

the mucosal

membrane

Greater

Interpersonal

Contact

Greater

transmission

opportunity

Impaired

mucosal

defenses Upper

respiratory

infection

New Nm.

strains

Migrating

individuals

Biologic

change

Environmental

Factors

Social Impacts

Biologic Impacts

Physiological Impacts

Meningitis:

Inflammation of the

protective membranes

covering the brain and

spinal cord. In Africa,

often caused by bacteria

Person-to-person

transmission through

respiratory and throat

secretions – between 10-

25% of population may

carry bacteria at any time;

higher during epidemics

Effects:

can be life-threatening,

but also lead to brain

damage and deafness.

Tens of thousands of

people

Possible causes:

Relative humidity improves prediction

• Knowing the RH two weeks ago improves accuracy in predicting an epidemic by ~25%1

• Coupled with a two week forecast, this indicates an improved ability to anticipate a roll-off in epidemic 4 weeks in advance

1It turns out other variables (air temp, winds, NE winds) also help,

but less than relative humidity

Without RH

Using RH

Archive Centre

Current Data Provider

NCAR NCEP

CMC

UKMO

ECMWF MeteoFrance

JMA KMA

CMA

BoM CPTEC

IDD/LDM

HTTP

FTP

Unidata IDD/LDM

Internet Data Distribution / Local Data Manager

Commodity internet application to send and receive data

NCDC

Unique Datasets/Software Created

Thorpex-Tigge

Countries provided forecasts

6/ 18/ 13 7:52 AMmap of africa - Google Maps

Page 1 of 1https:/ / maps.google.com/ maps?q= map+ of+ africa&ie= UTF8&hq= &hne…93,73.828125&t= m&z= 4&vpsrc= 6&ei= xGXAUZbACeTuwQGYioCQDA&pw= 2

Address

National Center for Atmospheric Research (NCAR)

Relative humidity

across the Meningitis

Belt forecast 2 week

in advance …

… converted to

probability of an

epidemic alert

occurring

across the Belt

4 weeks in advance

Within dashed region,

risk has doubled

(above background levels)

Relative Humidity linkage

Locations where end-of-season

forecasts would have saved vaccine

Using “Perfect” Forecasts

- 18 epidemics identified

Using Climatological Information

-- 3 epidemics identified

• WHO districts where end-of-season forecasts predict no need for follow-up

vaccination campaign due to changing weather conditions

• ~2.6 million doses of vaccine used elsewhere more effectively

• Expense ~ US$ 1 million

Outline



• A word of caution about regional uncertainty


• Meningitis forecasting for Africa

• Case study: Bangladesh flood forecasting

Operational Flood Forecasting for Bangladesh:

Tom Hopson, RAL-NCAR

Peter Webster, Georgia Tech

A. R. Subbiah and R. Selvaraju, Asian Disaster

Preparedness Centre

Climate Forecast Applications for Bangladesh (CFAB):

USAID/CARE/ECMWF/NASA/NOAA

Bangladesh Stakeholders: Bangladesh Meteorological Department, Flood Forecasting and Warning Center,

Bangladesh Water Development Board, Department of Agriculture Extension, Disaster Management Bureau,

Institute of Water Modeling, Center for Environmental and Geographic Information Services, CARE-

Bangladesh

Context: CFAB Project PI Peter Webster, Georgia Tech ([email protected])

Partners: USAID, CARE, ECMWF, Bangladesh’s Meteorology Dept and Flood

Forecasting Warning Centre (FFWC), NASA-TRMM, NOAA-CMORPH

Purpose: provide flood forecasts of the Ganges and Brahmaputra rivers for

Bangladesh, operational 2003-ongoing

(World Food Program)

Damaging Floods:

large peak or extended duration

Affect agriculture: early floods in May, late floods in September

Recent severe flooding: 1974, 1987, 1988, 1997, 1998, 2000, 2004, and 2007

1998: 60% of country inundated for 3 months, 1000 killed, 40 million homeless, 10-20% total food production

2004: Brahmaputra floods killed 500 people, displaced 30 million, 40% of capitol city Dhaka under water

2007: Brahmaputra floods displaced over 20 million

River Flooding

Forecast Trigger: ECMWF forecast files

Updated TRMM-

CMORPH-CPC

precipitation estimates

Updated distributed

model parameters

Updated outlet

discharge estimates

Above-critical-level

forecast probabilities

transferred to Bangladesh

Lumped Model Hindcast/Forecast

Discharge Generation

Distributed Model Hindcast/Forecast

Discharge Generation

Multi-Model Hindcast/Forecast Discharge Generation

Discharge Forecast PDF Generation

Calibrate model

Statistically correct

downscaled forecasts

Generate forecasts Generate hindcasts Generate forecasts Generate hindcasts

Update soil moisture

states and in-stream flows

Generate hindcasts

Calibrate AR error model

Calibrate multi-model

Generate forecasts Generate hindcasts

Generate model error PDF

Convolute multi-model forecast

PDF with model error PDF

E O

F

M

Q P

B D

L

F

C

Generate forecasts

Five Pilot Sites chosen in

2006 consultation

workshops based on

biophysical, social criteria:

Rajpur Union

-- 16 sq km

-- 16,000 pop.

Uria Union

-- 23 sq km

-- 14,000 pop.

Kaijuri Union

-- 45 sq km

-- 53,000 pop.

Gazirtek Union

-- 32 sq km

-- 23,000 pop.

Bhekra Union

-- 11 sq km

-- 9,000 pop.

Average Damage (Tk.) per Household in Pilot Union

7,255

28,745

60,99364,000

4058

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

Uria Gazirtek Kaijuri Rajpur Bekra

Union

Av

era

ge

Da

ma

ge

(T

k)

pe

r

Ho

us

eh

old

2007 Brahmaputra Ensemble Forecasts and

Danger Level Probabilities

7-10 day Ensemble Forecasts 7-10 day Danger Levels

7 day 8 day

9 day 10 day

7 day 8 day

9 day 10 day

Community level decision

responses for 2007 flood

forecasts (Low lands)

“… on 25th July we started communicating the information to as many

people as possible … especially those people living in river islands

(“chars”)...”

“On the 28th and 29th, meetings were organized in villages near Rangpur

… they perceived that the river water level would fall, but our forecasts

showed a rising trend…[with] significant chance of overflow and

breaches [of weak] embankments ... We engaged … an evacuation plan

urgently”

“We communicated the forecast to another pilot union … on July 26th …

to mobilize resources for evacuation ... All the six villages in the union

were later flooded to a height of 4-6 feet on July 29th… about 35% of the

people in the union were evacuated in advance.”

“The communities in Rajpur Union … were able to use the forecast for …

mobilizing food, safe drinking water for a week to 10 days, protecting

their … rice seedlings, fishing nets, and … fish pods.”

Summary

• Climate change impacts on the hydrologic cycle

have already been observed, most strongly in the

Arctic

• Globally, mean precipitation is expected to

increase 1-3%/degC, while the strength of

extreme storms increase 6-10%/degC

• However, much local and regional variation so

use care in estimating impacts (use multiple

models and ensembles)

• However, ensemble (probabilistic) weather

forecasts continue to improve and can be used to

mitigate some of the impacts

Climate Prediction:

Sources of Uncertainty Forcing (or Scenario Uncertainty)

– GHG emission scenarios (e.g., B1, A1B, A2, RCPs)

ozone, sulfate aerosols, land use, black carbon …

Response (or Model Uncertainty)

– Model sensitivity (different physics, parameterizations, resolution …) – 20+ different climate models in both IPCC AR4 and AR5

Internal (Natural) Variability

– Atmosphere and Ocean – Coupled atmosphere-ocean interactions – Multiple simulations

Projected Global SAT (IPCC AR4)

(Hawkins and Sutton 2009)

Natural Variability

Can be large & slow

(multiple decades)

} }

}

Forcing (or Scenario) Uncertainty

Response (or Model) Uncertainty

20th Century Simulations

NCAR

Linear Trend of Surface Temperature

1901 – 2012 ( C over period)

Mixture of internal variability and forced climate change

IPCC (2013)

°

NCAR

Change in Winter Sea Level Pressure (1980-2009)

(hPa) Dec-Mar

Pressure Falls

Pressure Rises

Decadal Climate Variability

1900-2009

1900-2009

Natural Variability over the Atlantic

All significant modes of natural variability

Variations and significant impacts from seasonal to multi-decadal time scales

Major Phenomena

• North Atlantic Oscillation (NAO)

• Atlantic Meridional Overturning Circulation (AMOC)

• Tropical Atlantic Variability


NCAR

Unforced Multi-Decadal Variability

North Atlantic

SST

NCAR

Time

Forced Climate Change

The Challenge: Assessing Climate Change in the Presence

of Unforced Multi-decadal Variability

10 years

NCAR

The Community Earth System Model www.cesm.ucar.edu

60

Figures 1

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3

4

5 6

Figure 1. Schematic of the different component models in CESM1. 7

8


Community Climate System Model (CCSM)

60

Figures 1

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4

5 6


8

60

Figures 1

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5 6


8

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Figures 1

2

3

4

5 6


8

60

Figures 1

2

3

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Figures 1

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NCAR

The Community Earth System Model

• CESM is used to:

– Explore Earth’s climate history and

processes responsible for variability

and change

– Estimate future of environment for

policy formulation

Modeling the Earth System

www.cesm.ucar.edu

• Developed by NCAR NSF, DOE, Universities, National Laboratories

• Fully documented, frequently and freely

distributed, fully supported releases

• Capacity Building (e.g., tutorials and workshops)

• CESM: a set of different geophysical

component models that exchange

boundary data via a coupler

• Code base developed over 20+ yrs:

runs on multiple platforms, resolutions

and model configurations




40 member ensemble from 2000-2061

http://www.cesm.ucar.edu/working_groups/Climate/experiments/ccsm3.0/

Deser, Phillips, Bourdette, Teng (2012): Climate Dynamics

(also articles by Branstator, Teng, Meehl and others)

30+ member ensemble from 1920-2080

http://www.cesm.ucar.edu/experiments/cesm1.1/LE/

Kay et al. (2014): Bulletin of the American Meteorological Society



http://www.cesm.ucar.edu/working_groups/Climate/experiments/ccsm3.0/

http://www.cesm.ucar.edu/experiments/cesm1.1/LE/

1850 control

40 CESM Integrations

20th Century 21st Century

(2000-2060)

A1B GHG

40

different atmospheric initial states

same ocean, sea ice, land initial states


Year 541



i.e., spread is not

predictable



Winter Surface Air Temperature Trends 2010-2060

Projected European Climate


Hurrell, Deser and Phillips (2014), Geophysical Research Letters

NCAR

2010 2010-2060 Trends (°C/51 yrs)

NCAR

Projected European SAT


DJF 2010-2060

Forced

Warmest

Run 26

Coolest

Run 13

• Forced and unforced amplitudes similar over Europe

• Unforced component has large spatial scales

Natural (Total – Forced)

Warmest

Coolest

NCAR

Projected European SAT


DJF 2010-2060

Forced

Warmest

Run 26

Coolest

Run 13

NCAR

Projected European Precipitation


DJF 2010-2060

Wettest

Run 3

Driest

Run 9

Forced }

}

NCAR

Dominant Source of Noise: Dynamics

SLP and Air Temperature Trends 2010-60

NCAR

Uncertainty due to NAO

% chance warming

Projected European SAT (DJF)

2010-2035 2010-2060

# of ensemble members with trend > 0

total # of ensemble members

NCAR

Uncertainty due to NAO

% chance increase

Projected European Precipitation (DJF)

2010-2035 2010-2060

# of ensemble members with trend > 0

total # of ensemble members

NCAR

Projected European SLP and SAT (DJF)

Total Natural Forced = +

Run 1

0

Run 2

6

Two of 40 realizations

Dominant Source of Noise: Dynamics

NCAR


Projected European SLP and Precipitation (DJF)

• Natural component can be larger than forced

• Natural component has large spatial scales

Two of 40 realizations

Total Natural Forced = +

Run 1

0

Run 2

6

NCAR

2*

Leading EOF (67%) of 40 SLP Trend Maps

(Dominant pattern of SLP trend uncertainty)

Regressions on SLP PC1

SLP and Surface Air T (DJF)

SLP and Precipitation (DJF)

North Atlantic Oscillation

NCAR

2*

Range of Outcomes

(Due to uncertainty introduced by NAO)

Forced + NAO Forced – NAO

Tom hopson

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