implications of climate change on long lead forecasting ...• probabilistic forecasts –...

Implications of Climate Change on Long Lead Forecasting and

Global Agriculture

Ray Motha

The Challenge: Food Security Robert Watson

Source: http://www.coaps.fsu.edu/lib/climatoons/toon38.shtml

ENSO Teleconnections

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Data from the Climate Prediction Center (NWS)Data updated thru October 2004

Observed Monthly Sea Surface Temperatures Central Equatorial Pacific Ocean (Region Niño-3.4)


Obs

erv e

d Te

mpe

rat u

res

(De g

r ee s

C)

Dec-99 Jun-00 Dec-00 Jun-01 Dec-01 Jun-02 Dec-02 Jun-03 Dec-03 Jun-04 Dec-04Mar-00 Sep-00 Mar-01 Sep-01 Mar-02 Sep-02 Mar-03 Sep-03 Mar-04 Sep-04

World Agricultural Outlook BoardJoint Agricultural Weather Facility

Dotted black line is the normal Sea Surface Temperature (1971-2000).

2000 2001 2002 2003 2004

Normal Seasonal Variation of SSTs

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Obs

erv e

d Te

mpe

rat u

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(De g

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Thick green line is the actual Sea Surface Temperature (SST).Dotted black line is the normal Sea Surface Temperature (1971-2000).

2000 2001 2002 2003 2004

Overlay Actual SST’s




Obs

erv e

d Te

mpe

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(De g

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Thick green line is the actual Sea Surface Temperature (SST).Dotted black line is the normal Sea Surface Temperature (1971-2000).Red areas are above normal Sea Surface Temperatures.Blue areas are below normal Sea Surface Temperatures.

2000 2001 2002 2003 2004

La Niña

La Niña

El NiñoNeutral

Neutral

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Monthly Sea Surface Temperature Departures from NormalCentral Equatorial Pacific Ocean (Region Niño-3.4)

7071

7273

7475

7677

7879

8081

8283

8485

8687

8889

9091

9293

9495

9697

9899

0001

-3

-2

-1

0

1

2

3

4

Tem

pera

ture

Dep

artu

re fr

om N

orm

al (D

egre

es C

) Warm Episodes (El Niño)

Cold Episodes (La Niña)

Circles denote individual events

Data from Climate Prediction Center (NWS)Data updated thru February 2001World Agricultural Outlook Board

Joint Agricultural Weather Facility

Example of a Strong ElExample of a Strong El--NiñoNiño

Example of a Strong LaExample of a Strong La--NiñaNiña

TYPICAL RAINFALL IMPACTS FROM EL NIÑO(BASED ON STATISTICAL CORRELATIONS)

(0) = YEAR OF EL NIÑO (+) = YEAR FOLLOWING EL NIÑO

WET DRY

APR(0)-OCT(0)

OCT(0)-MAR(+)

INDETERMINATE

JUL(0)-OCT(0)

JUL(0)-MAR(+)

NOV(0)-APR(+)

NOV(0)-FEB(+)

NOV(0)-MAY(+)

OCT(0)-APR(+)

JUN(0)-SEP(0)

OCT(0)-DEC(0) MAY(0)-APR(+)

NOV(0)-MAY(+)

APR(0)-MAR(+)

JUN(0)-NOV(0)

SEP(0)-MAR(+)

MAR(0)-FEB(+)MAY(0)-OCT(0)

Prepared by the Joint Agricultural Weather FacilitySource: Ropelewski and Halpert, 1987. Monthly Weather Review, (115) p. 1606-1626.

Defining El Niño / La Niña Crop Weather Impacts

World Agricultural Outlook Board

Defining El Niño / La Niña Crop Weather Impacts


(0) = YEAR OF LA NIÑA(+) = YEAR FOLLOWING LA NIÑA

WET DRY

Prepared by the Joint Agricultural Weather FacilitySource: Ropelewski and Halpert, 1989. Journal of Climate, (2) p. 268-284.

OCT(0)-APR(+)

INDETERMINATE

MAR(+)

JUN(0)-DEC(0)

NOV(0)-APR(+)

NOV(0)-MAR(+)

JUN(0)-SEP(0)

OCT(0)-DEC(0) APR(0)-JUN(+)

NOV(0)-APR(+)

SEP(0)-MAR(+)

JUN(0)-DEC(0)

SEP(0)-JAN(+)

MAR(0)-FEB(+)AUG(0)-DEC(0)

JUN(0)-

TYPICAL RAINFALL IMPACTS FROM LA NIÑA(BASED ON STATISTICAL CORRELATIONS)

Moderate to strong:

El Niño

La Niña

Assessing El Niño / La Niña Crop Weather Impacts


Impacts are rarely the same between events.

CPC Forecast System Schematic

Applied Research, Diagnostics and Forecast ToolsCollaborators: EMC, TPC, CDC, GFDL, IR I, Scripps, COLA, U. W ash.

Inter-Annual Variability

- ENSO

Decadal Variability

- PDO- AO/NAO- Global Warming

Intra-seasonal Variability

- Tropical MJO- Blocking- AO/NAO/NPO/PNA

SeasonalExtended Range

Climate Prediction Center Forecast System Schematic

HighFrequency:Interannual

Low-Frequency:

Trend

U.S.Threats Assessment

6-10 Day

Week Two

Monthly

International Threats

Dynamical/statistical models

- Real-Time Diagnostics- Model Simulations- Ensembles- Verification

W eather/clim ate links

- Composites- Teleconnections- Extreme events- Tropical storms- Drought/Floods- Climate/W eather Monitoring

Forecast Process SchematicDynamical model forecasts/multi-

model ensembles

Recent observations Historical observations..

Verifications/Statistical tools Downscaling, Analogs, Composites

WEB PAGES/AUTOMATED DATABASES

Peer-reviews of the forecast tools and of the penultimate forecast via web/telephone conference with partners and through local discussions (map

discussions,sanity check, conference calls, etc…)

Forecaster-created or automated products

Dissemination to public

Part 1. Long-Lead Seasonal Forecasts

Background: A Variety of Forecasts

Weather• mostly regional, short-lived events• Deterministic forecasts• protection of life Seasonal climate• Global, seasons in advance• Probabilistic forecasts – deviations from normal seasons• Mitigation – energy, food, water, health, etc sectorsClimate change scenarios• Global, visions of the future• Includes chemistry and biology modeling• Projections of possible future changes to regional climatology• Understanding unintended consequences & adaptation• Solving the “carbon” problem

Mid-1970s

Atmosphere

Mid-1980s

Atmosphere

Land Surface

Early 1990s

Atmosphere

Land Surface

Ocean & Sea Ice

Late 1990s

Atmosphere

Land Surface

Ocean & Sea Ice

SulphateAerosol

Present Day

Atmosphere

Land Surface

Ocean & Sea Ice

SulphateAerosol

Non-sulphateAerosol

Carbon Cycle

Early 2000s?

Atmosphere

Land Surface

Ocean & Sea Ice

SulphateAerosol

Non-sulphateAerosol

Carbon Cycle

DynamicVegetation

AtmosphericChemistry

Weather

Climate Change

ClimateVariability

Overview of Weather and Climate Models and the Required Observations

Source: http://www.fsl.noaa.gov/~osborn/CG_Figure_48.gif.html

Mother Earth -- Our Home

It is has water, oxygen and a hospitable climate

Food production needs to double to meet the needs of an additional 3 billion people in the next 30 years

Climate change is projected to decrease agricultural productivity in the tropics and sub-tropics for almost any amount of warming Robert Watson

The Challenge: Water Security Robert Watson

Water Services

One third of the world’s population is now subject to water scarcity

Population facing water scarcity will more than double over the next 30 years Robert Watson

Climate change is projected to decrease water availability in many arid- and semi-arid regions

The Challenge: Sustainable Forestry Robert Watson

The Challenge: Sustainable Management of an Ever-Changing Planet

Health ImpactsWeather-related MortalityInfectious DiseasesAir Quality-Respiratory Illnesses

Agriculture ImpactsCrop yieldsIrrigation demands

Water Resource ImpactsChanges in water supplyWater qualityIncreased competition for water

Impacts on Coastal AreasErosion of beachesInundate coastal landsCosts to defend coastal communities

Forest ImpactsChange in forest compositionShift geographic range of forestsForest Health and Productivity

Species and Natural AreasShift in ecological zonesLoss of habitat and species

Potential Climate Change ImpactsPotential Climate Change Impacts

Climate Changes

Sea Level Rise

Temperature

Precipitation

Warmer, glacial melt,oceans rise

Sea ice melts

Drier, yields decrease,desertification

Longer growing season,increased production

More heat waves,tropical diseases

More drought/floods,decreased production

Modest warming,higher production

More stormsurges/erosion

Coastalflooding

Intensecyclones

Frequent floods/drought,increased disease

Potential Impacts of Global Warming

Map is an interpretation of concerns expressed by the Intergovernmental Panel on Climate Change(sponsered by the United Nations).

Office of the Chief EconomistWorld Agricultural Outlook Board

Change in Average Annual TemperatureClimate Models - Differing Forecasts

Observed 20th century Canadian Model 21st century

Hadley Model 21st century• U.S. temperatures 20th century

• most areas - warmed• southeast - cooled slightly

• Forecasts for 21st century• warmer throughout• Hadley - 3 to 7o F• Canadian - 5 to 15o F

Source: Maps and findings from the Intergovernmental Panel of Climate Change (sponsored by U.N.)

Annual U.S. TemperaturesLower 48 States (1970-2000, 31 Years)

18951900

19051910

19151920

19251930

19351940

19451950

19551960

19651970

19751980

19851990

19952000

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Deg

rees

F

1940-1969Trend = -0.022 1970-2000

Trend = 0.0511920-1939Trend = 0.045

1895-1919Trend = -0.012

Climate Models - Differing ForecastsChange in Average Annual Precipitation

Observed 20th century Canadian Model 21st century

Hadley Model 21st century• U.S. precipitation 20th century

• most areas - wetter• locally drier

• Forecasts for 21st century• Canadian - much wetter west,

drier east, parts of Plains• Hadley - wetter throughout

Source: Maps and findings from the Intergovernmental Panel of Climate Change (sponsored by U.N.)

Possible benefits and drawbacks of climate change on agriculture

Source: Scientific American, March, 1994.

Source: http://www.thepsychicspot.com/fun_global_warming.htm

The Grand Challenges for 21st Century

• Issues associated with global population growth

• Climate change/variability and natural disasters

• Potential impacts of global warming

• New products to meet emerging societal needs

• Sustainable agriculture

• Adaptation strategies

Agricultural Weather

• While focusing on sustainable agriculture, farmers have to cope with variable weather throughout the growing season, extreme events during the season, and changing climate patterns.

• Agriculture has learned to adapt to climate variability and climate change, but past changes have been relatively transitional.

New Directions in Agricultural Weather

• Long-lead seasonal forecasting is improving.• Climate change/variability and natural disasters

are key issues for agricultural decision-makers.• Potential impacts vary by region and by season

with the most vulnerable agricultural systems at the greatest risk.

• Risk management planning should be developed (or adopted) by region with adaptation strategies and mitigation plans to cope with extreme events.

Disaster ManagementShift from Crisis to Risk Management

• Risk Management:• Based on preparedness and mitigation.• Preparedness is designed to increase the

level of pre-disaster readiness to respond to an event.

• Mitigation refers to activities designed to reduce the impact of a disaster prior to its occurrence (land use planning).

Drought Research

• What instigates drought?• What prolongs drought?• What ends drought?• Drought forecast?

To forecast drought, you need to know what “causes”drought. Much of the atmospheric variability may be random, but feedback between the atmosphere,the land, and oceans influences weather and climate for weeks and months.

USGS Streamflow

CPC Daily Soil Model

Satellite Veg Health

30-day Precip. USDA Soil Ratings

Principal Drought Monitor InputsPrincipal Drought Monitor Inputs

Palmer Drought Index

http://tgsv5.nws.noaa.gov/oh/hic/nho/

Agroclimatic Risk Management Plan

• Vulnerability Analyses• Impact Assessments• Mitigation Planning• Adaptation Strategies

Adaptation Strategies

1. Adaptation measures are assessed in a developmental context.

2. Adaptation to short-term climate variability and extreme events are explicitly included as a step toward reducing vulnerability to longer-term climate change

3. Adaptation occurs at all levels, ranging from local to national and international levels.

Agricultural Weather and Climate Policy

• Develop an agricultural weather and climate policy with preparedness as its foundation (concept similar to Australia’s Drought Plan or U.S. National Drought Policy).

• Outline a course of action that includes a preparedness initiative to help reduce the economic hardships caused by extreme climate events.


• Recommending a paradigm shift in policy from “Response” to “Readiness”.

• Goal: Reduce the impacts of climate variability and change on the agricultural sector.

• Objective: Preparedness must become the cornerstone of an agricultural weather and climate policy.


• Preparedness is the key to a proactive policy.


• GOAL 1:• Incorporate planning, implementation of

plans and proactive mitigation measures, risk management, resource stewardship, environmental considerations, and public education as the key elements of an effective agricultural weather and climate policy.


• GOAL 2:• Improve collaboration among scientists

and managers to enhance the effectiveness of observation networks, monitoring, prediction, information delivery, and applied research, and, to foster public understanding of and preparedness for climate variability and change.

Summary

• Developing an agricultural weather and climate policy that addresses climate issues for policy makers and scientists would aid risk management, conservation of natural resources, and mitigation of climate variability/change.

• A win-win scenario!

Source: http://www.english.uiuc.edu/baron/cartoons/global.htm

THANK YOU

implications of climate change on long lead forecasting ...• probabilistic forecasts –...

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