FEWS NET Climate Change Data Sets and Analyses: Using observations to guide adaptation
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FEWS NET Climate Change Data Sets and Analyses: Using observations to guide adaptation. Gideon Galu Akhlalil Adoum Nancy Mutunga Everlyn Muchomba Abdulrahim Norein Emebet Kebebe Nigist Biru. Chris Funk Gary Eilerts Jim Rowland Jim Verdin Joel Michaelsen Libby White. Overview.

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Fews net climate change data sets and analyses using observations to guide adaptation

FEWS NET Climate Change Data Sets and Analyses: Using observations to guide adaptation

Gideon Galu

Akhlalil Adoum

Nancy Mutunga

Everlyn Muchomba

Abdulrahim Norein

Emebet Kebebe

Nigist Biru

Chris Funk

Gary Eilerts

Jim Rowland

Jim Verdin

Joel Michaelsen

Libby White


Overview
Overview observations to guide adaptation

Upscaling

What observed large scale climate changes do we see?

Downscaling

What observed local climate changes do we see?

Climate Trend Analyses

Kenya, Ethiopia, Uganda, Sudan, Chad, Niger, Mali, Burkina Faso, Senegal

Future analyses: guiding agricultural adaptation & development?

Up

Down


Research timeline
Research Timeline observations to guide adaptation

Climate Attribution (why?)

Declining ‘Long

Cycle’ rainsw/

Phil Steffen &

Alemu Asfaw

2002

Warming Indian Ocean

Reduces rainfall in GHA

2005

Warming Indian Ocean

Probably anthropogenic

Declining Rains

in Ethiopia and

GHA

2008

Climate change

intensifies La Nina

impacts

Declining Rains

In Kenya

Climate Trend Analyses

(what? where?)

2011

Trend mapping

Upscale 1


Declining per capita cereal production
Declining Per Capita Cereal Production observations to guide adaptation

  • http://www.springerlink.com/content/fw645377u3587404/fulltext.pdf

Upscale 2


Fews net climate change data sets and analyses using observations to guide adaptation

Principal Components 1 and 2 observations to guide adaptation

ENSO-like Variation

Warming in the western Pacific and Indian Ocean

http://www.springerlink.com/content/u0352236x6n868n2/

http://pubs.usgs.gov/of/2010/1199/pdf/ofr2010-1199.pdf

Upscale 3


Pc1 pc2 disrupt water vapor transports
PC1-PC2 disrupt water vapor transports observations to guide adaptation

PC1

Indian-WesternPacific

PC2

Central-EasternPacific

Upscale 4


Fews net climate change data sets and analyses using observations to guide adaptation

←warming→ observations to guide adaptation

La

Niña

Precipitation in new La Niña years.

Upscale 5


Pc1 and pc2 time series smoothed
PC1 and PC2 time-series (smoothed) observations to guide adaptation

Drought frequency for Central-Eastern Kenya and Southern Somalia

Upscale 6


Fews net climate change data sets and analyses using observations to guide adaptation
Drought observations to guide adaptationAND declining per capita agricultural production have contributed to the current food crisis

Drought once every six years

Drought every other year

Climate Index = PC1-PC2

Upscale 7

Rainfall and Ag data are for Kenya and Somalia


Dataset foundation observations
Dataset – Foundation - Observations observations to guide adaptation

The core FEWS NET data set is a collection of 1344 precipitation stations for the GHA and Sahel

A secondary set of air temperature observations is much less dense (179) stations

Downscale 1


Dataset the fews net climatological trend analysis fta
Dataset – The FEWS NET Climatological Trend Analysis (FTA)

Interpolat at-station change values

Builds on FCLIM techniques

Allows for explicit calculation of interpolation standard errors

Incorporates satellite data as ‘background fields’ guiding interpolations

Well-documented :ftp://chg.geog.ucsb.edu/pub/pubs/mapping_decadal_variations.pdf

The book is currently being printed

Precipitation well specified

Temperature is not

Data set is available at:

ftp://hollywood.geog.ucsb.edu/pub/FEWSNETInformingClimateChangeAdaptationSeries/FCLIM_trends/

Downscale 2


1960 2009 station trends
1960-2009 (FTA)Station Trends

M

A

M

J

J

J

A

S

Downscale 3 – FTA


Basically all stations indicate rapid warming 0 15 to 0 4 c per decade
Basically all stations indicate rapid warming (FTA)(~0.15 to 0.4°C per decade)

Downscale 4 - FTA


Observed trends
Observed Trends (FTA)

MAMJ P = Warming in IPA produces subsidence, reduced onshore moisture transports

JJAS P = Warming in IPA produces ridging across tropical Africa, reducing Congo airflows? ATL also important …

MAMJ, JJAS T = note similarity

Cause (?) related to subsidence warming and …

Downscale 5 - FTA


Standard error analysis of trend surfaces
Standard (FTA)Error analysis of trend surfaces

Sigma =

Trend divided by standard error

≈ signal to noise ratio

Downscale 6 - FTA



Air temperatures have risen sharply more than 1 standard deviation
Air temperatures have risen sharply Ugandamore than 1 standard deviation

Downscale 6 – FCLIM TS


Fews net climate change data sets and analyses using observations to guide adaptation

2010/2011 Climate Adaptation Reports Uganda

  • Kenya Completehttp://pubs.usgs.gov/fs/2010/3074/pdf/fs2010-3074.pdf

  • Sudan Complete http://pubs.usgs.gov/fs/2011/3072/pdf/FS2011-3072.pdf

  • Ethiopia Almost Complete

  • Uganda Almost Complete

  • Chad Almost Complete

  • Niger Almost Complete

  • Burkina Faso Almost Complete

  • Mali Almost Complete

  • Senegal Almost Complete

  • Mozambique – temperature affects on maize phenology Complete

  • Openfile report on Indian-Pacific trends Completehttp://pubs.usgs.gov/of/2010/1199/pdf/ofr2010-1199.pdf

Climate Trend Reports 1

Email chris@geog.ucsb.edu if interested

In seeing/commenting on almost complete reports


Fews net climate change data sets and analyses using observations to guide adaptation

Climate Trend Reports 2 Uganda

The Big Picture: Shrinking Rains

Emerging Risk Area?


Ethiopia shrinking rains across the south
Ethiopia - Shrinking Rains across the south Uganda

emergingrisk

areas?

densepop

Climate Trend Reports 3


Ethiopia rainfall transects
Ethiopia Rainfall Transects Uganda

Climate Trend Reports 4


Sudan shrinking rains and warming air temperatures
Sudan - Shrinking rains and warming Uganda air temperatures

Climate Trend Reports 5


Can we afford to not to support agricultural development
Can we afford to UgandaNOT to support agricultural development?

To date, in FY11 the USG humanitarian aid to the Horn was $604 million

In 2009/2010 a 5 million dollar Worldbank project in Kenya led to increased production of ~41,000 tons

Seed and fertilizer vouchers for 50,000 people

$122 per ton of maize

Increasing 2011 production by 20% (472,800 tons) might cost around ~$58 million dollars

Theoretical percent hungry would drop by ~45%

In FY2011 the US+WFP contributed ~141 million dollars in aid to Kenya, supplying 118,820 tons in aid.

Cost = $1,187 per ton

Guiding Agricultural Development-1


Fews net climate change data sets and analyses using observations to guide adaptation
What can the early warning community contribute to the ag/pastoral development and climate adaptation communities?

  • Targeting:

  • Where 1: Invest most in the most food insecure countries

  • Where 2: Smallholder farms in high potential areas

  • Where 3: Smallholder farms in high risk areas

  • How: Must take into account climate variability/trends

Guiding Agricultural Development-2

Food

Economy

Early

Warning

Remote

Sensing

Ag/pastoral Development


New dimensions guiding ag development for vulnerability reduction
New dimensions guiding ag development for vulnerability reduction?

Ag Exposure (rainfall uncertainty)

Ag Opportunity (kg per €)

Guiding Agricultural Development-3

vulnerable

smallholders

vulnerable

smallholders

poverty reduction

lower prices

poverty reduction

less food aid


Guiding agricultural development for ethiopia
Guiding agricultural development for Ethiopia? reduction?

Forest transition to crop Forest transition to crop+natural veg

Analysis Of MODIS landcover data shows most ag expansion

In south-central Ethiopia

Guiding Agricultural Development-4

Is western Ethiopiaclimatically secure andagriculturally under-

utilized?

Demographic inertia

leads to expansion in

stressed & drying

southern Ethiopia?


Guiding agricultural development for kenya
Guiding agricultural development for Kenya? reduction?

Mean crop per drop map for Kenya (mean yield per MAMJ rainfall )

Is south-western Kenyaclimatically secure andagriculturally under-

utilized?

Guiding Agricultural Development-5

Demographic inertia

leads to expansion in stressed & drying

Central-eastern Kenya?


Can we meet the mdg of halving the hungry
Can we meet the MDG reduction? of halving the hungry ?

~$58 million dollars?

Decreasing yields

by 20% increases

The number of hungry by 38%

44%

Guiding Agricultural Development-6

2009: 32%

22%

Increasing yields

By 20% reduces

The number of hungry by 45%


Collaborative african data archive
Collaborative African Data Archive? reduction?

  • Collaborative archive not tied to a single institution

  • Satellite based data

    • 0.05°, dekadal, continental, 1983-now

    • Rainfall – TARCAT + FCLIM

    • Vegetation – SPOT + AVHRR

    • Land Surface Temperatures (TIR)

    • PET

  • Station integration

    • Tools for national/regional agencies to integrate data

    • Agencies to develop their own trend assessments

  • Linked to early warning tools

    • SPIRITS, SERVIR, GeoWRSI, MARS Viewer, Agromet Shell

Guiding Agricultural Development-7


Fews net climate change data sets and analyses using observations to guide adaptation

Links to research reduction?

  • Recent Drought Tendencies in Ethiopia and equatorial-subtropical eastern Africa, FEWS NET Special Report 2005http://earlywarning.usgs.gov/adds/pubs/RecentDroughtTendenciesInEthiopia.pdf

  • Warming of the Indian Ocean threatens eastern and southern African food security but could be mitigated by agricultural development. Proceedings of the National Academy (2008) http://earlywarning.usgs.gov/adds/pubs/WarmingInTheIndianOceanThreatensEasternAndSouthernAfrica.pdf

  • Declining Global Per Capita Agricultural Capacity Production and Warming Oceans Threaten Food Security, Food Security (2009) http://www.springerlink.com/content/fw645377u3587404/fulltext.pdf

  • New satellite observations and rainfall forecasts help provide earlier warning of drought in Africa. The Earth Observer.http://earlywarning.usgs.gov/adds/pubs/Funk_EarthObserver_Jan_Feb09.pdf

  • Real-time Decision Support Systems: The Famine Early Warning System Network (2009) ftp://chg.geog.ucsb.edu/pub/pubs/RealtimeDSS_for_FEWS_NET_final.pdf

  • A westward extension of the warm pool leads to a westward extension of the Walker circulation, drying eastern Africa, Climate Dynamics, 2011. http://www.springerlink.com/content/u0352236x6n868n2/. IP-024695.

  • Mapping recent decadal climate variations in Eastern Africa and the Sahel, 2011 ftp://chg.geog.ucsb.edu/pub/pubs/mapping_decadal_variations.pdf

  • A Climate Trend Analysis of Kenya-August 2010, USGS Fact Sheet 2010-3074: http://pubs.usgs.gov/fs/2010/3074/pdf/fs2010-3074.pdf

  • A Climate Trend Analysis of Sudan-July 2011, USGS Fact Sheet 2011-3072. http://pubs.usgs.gov/fs/2011/3072/pdf/FS2011-3072.pdf