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Transcript
Significance of Decadal Prediction
for GHA
22-24 June 2009
World Bank_GFDRR Project
Geneva, Switzerland
Fredrick Semazzi
North Carolina State University
USA
Focus
Challenge of providing predicted decadal climate
variability & climate change information at spatial
scales appropriate for end-users
Improved Surface Temperature Prediction for the
Coming Decade from a Global Climate Model
Doug M. Smith,* Stephen Cusack, Andrew W. Colman, Chris K.
Folland, Glen R. Harris, James M. Murphy
• Previous climate model projections of climate change accounted for
external forcing from natural and anthropogenic sources but did not
attempt to predict internally generated natural variability.
• Smith et a 2007 present a new modeling system that predicts both
internal variability and externally forced changes & hence forecasts
surface temperature with substantially improved skill throughout a
decade, both globally and in many regions.
• This system predicts that internal variability will partially offset the
anthropogenic global warming signal for the next few years.
• However, climate will continue to warm, with at least half of the
years after 2009 predicted to exceed the warmest year currently on
record.
GFDRR Goal#1
Regional climate trends and
the adequacy of global and
regional climate observations
for adaptation purposes
Eastern Africa Decadal Dipole Mode-based on CMAP
2.5 degree resolution
+
1.5
1
0.5
0
-
+
-0.5
-1
-1.5
1981
1983
1985
1987
1989
AFRICAN
1991
HadCRUT
1993
1995
1997
1999
Eastern Africa Decadal Dipole
Mode-based on CMAP 2.5 degree
resolution
Eastern Africa Decadal Dipole
Mode
(based on NASA satellite data)
Increasing Rainfall
Largest drying
signal over land
is over Eastern
Africa
Decreasing Rainfall
+
-
Esp. Serengeti
Masai-Mara
Corridor
http://earthobservatory.nasa.gov/Newsroom/NewImages/images.php3?img_id=17761
IPCC GCM Projections
Decadal Dipole Mode: Is it a fingerprint
of climate change over Eastern Africa
INFORMATION REQUIRED AT
DISTRICT SCALE
Average Annual Total Rainfall (mm)
(Based on Willmott Climate Dataset)
arid
Turkana District (arid)
500
Wajir District (arid)
500
Garissa District (arid)
Kitui District (semi-arid)
500
1200
1200
Narok District (semi-arid)
• The analysis of the data provides important information at district
level about spatial distribution and amount of rainfall which is
necessary for planning climate sensitive social-economic activities
Semi-arid
• Makes distinction between arid and semi-arid climatic conditions over
Kenya
Climate Data
Over the arid and
semi-arid regions
rain-gauge data
coverage is very
marginal
Monitoring over
these regions need
to be upgraded
Willmott Climatology
Present Climate Data Quality is Acceptable for SocialEconomic Planning at District Level but Inherent Data
Uncertainties are a Limiting Factor and Need to be Reduced
Turkana District (arid)
500
Wajir District (arid)
500
Garissa District (arid)
Kitui District (semi-arid)
500
1200
1200
Narok District (semi-arid)
50
Wajir District (arid)
50
Garissa District (arid)
Kitui District (semi-arid)
Narok District (semi-arid)
50
100
50
Potential Errors
Willmott - CRU
Turkana District (arid)
Short Rains
one rain season
Onset/Withdraw
Highly erratic
one rain season
Examples of Applications
- Tourism
(Serengeti National Park)
- Hydroelectric power generation
Owen Falls Dam at source of River Nile
- Navigation Safety over Lake Victoria
Tourism
(Serengeti National Park)
Masai Mara-Serengeti
Climate Anomalies Impacts on Wildebeest Migration
Masai Mara-Serengeti
(2005 Drought)
Assessment shows that very late start
of the rains was responsible
for the 2005 disaster
Analysis also clearly detects the
severe 1998-1999 drought
Serengeti Masai-Mara Wildebeest Migration
Route and Timing is dependent on Climatic Conditions
Best Time for Tourists to see Migration is the Crossing of Grumeti River in Western Serengeti
Park/Tanzania or Crossing of Mara River into Masai Mara National Park/Kenya
[http://www.safarimappers.com/serengeti-migration.aspx
January
March
September
February
June
October
March
April
July
August
November
December
Masai Mara-Serengeti
Climate Anomalies Impacts on Wildebeest
Migration & Tourism Industry in Kenya
• Late start of the 2005 was the primary reason for the failure of the rains over Masai
Mara-Serengeti rather than early withdraw
• This is region has a clear trend due to the climate change fingerprint signal; Further
increased delay of 2 weeks in the start of the rains in Kenya, which is good possibility in the
next 10 years, would adversely disrupt/shift the timing of the wildebeest migration cycle
• For example a two week delay in the start of the rains would put the location of the
wildebeest herds deep into Tanzania. Kenya bound tourist using the climatic migration
calendar would miss the extravaganza altogether (see schematic for migration route)
• Although
it cannot be asserted that a collapse of this world-famous ecosystem is imminent
based on this assessment, the climate stress could eventually undermine it beyond point of no
return
• Therefore baseline characterization of the local climate is important for wildlife monitoring,
and in the face of climate change, accurate projections are critical to assess the fate of the
future of the tourism industry in Kenya that depend on this region.
Naroc district (Masai Mara-Serengeti) impacts
example
Climate Anomalies Impacts on Wildebeest Migration & Tourism Industry
in Kenya
• The late start of the rain season was the primary reason for the catastrophic failure of the rains over
Masai Mara-Serengeti in 2005.
• Apparently once there is a significant delay in the start of the rainfall onset the chances of recovery back
to normal seasonal rainfall is very unlikely.
• In the Masai-Mara Park tourism the movement of the rain belt is a fundamental driver of the migration of
wildlife because it dictates the growth of seasonal vegetation cover.
• The onset of the rains around March triggers the migration of the Wildebeest population from Kenya
Masai-Mara into Serengeti in Tanzania.
• A recent study (Douglas E. Musiega1, Sanga-Ngoie Kazadi, 2006) has shown that dry season migration
routes tend toward areas with better vegetation activity, i.e., those characterized by higher NDVI gradients.
• During the western trek, wetter dry seasons have the effect of delaying the wildlife movement
northwestward.
• However the variation in rainfall conditions during the rainy and dry season has no significant influence
on the southern trek route location because movement is dictated by hardship of the terrain rather
vegetation which is abundant
• During the northern trek, wetter dry seasons have the effect of delaying the tendency to move westward
• Therefore baseline characterization of the local climate is important for herds monitoring and
performance of the tourism industry in Kenya, and in the face of climate change, accurate projections will
be critical
• It will be critical to monitor the relationship between the dipole mode (possible finger print of climate
change) and the migration patterns as this could result in major shifts of migration patterns.
GFDRR Goal#2: assess the
adequacy and reliability of
available model-based climate
projections for adaptation needs
Rainfall Projections
(A2: 2071-2100 average) minus (RF: 1961-1990 average)
Oct-Dec Short Rains
Rainfall projections (A2: 2071-2100 average) minus (RF: 1961-1990
average) for the Oct-Dec short rains: (left) RegCM3 (40 km grid);
(centre) 2-member FvGCM ensemble average; (right) eight IPCC
GCM super ensemble average. Units, mm.
General agreement among, 40km resolution RCM, high resolution (approx 1 deg)
GCM, and coarse resolution(>2 deg) IPCC GCM ensemble; however differences
are critical for some end users
GFDRR Goal#3: provide qualified
indications of expected climate
change for assistance in
developing effective adaptation and
climate risk management strategies
Aerial view of Nalubaale-Kiira Dam
(Source of River Nile)
Spatial Scales Over Lake Victoria
Basin for Hydro-E & Tourism
Require models that are not only of high resolution but also
support the physics responsible for the critical mechanisms
Lake Victoria Basin Climate VariabilityAlgorithm for
(making lake level projections)
13.2
13
12.8
12.6
12.4
12.2
“no trend”
pre-1961 lake
levels
1961-62
IO Warming
97-98 El Nino
RegCM3-POM coupled
model lake surface
temperature & rainfall;
Anyah & Semazzi (2006)
12
Water Balance model
11.8
(Tate et al, 2004)
11.6
11.4
11.2
11
Lake Victoria levels; gauge at source of the River Nile & satellite
10.8
radar altimeter data from USDS/NASA/UMDmodeled
at lake level ( 1year pr edict ion)
Act ual lake level ( Dec)
10.6
http://www.pecad.fas.usda.gov/cropexplorer/global_reservoir/
1955
1960
1965
1970
1975
1980
1985
1990
1995
downscaling
Global NCEP January Temperature Anomaly
Pattern, 2006 minus average of 2001-2005
downscaling
MODIS (satellite)
RegCM3 RCM
NCEP Reanalysis
RegCM3 RCM
Surface temperature, RH & winds; RegCM3 model verification
against MODIS & NCEP (Onol & Semazzi, 2006).
Current & Projected Levels of Lake Victoria
(left) Lake Victoria observed levels (blue) compared to estimates based on our
modified version of Tate et al (2004) water balance model for Lake Victoria with
observed rainfall from six rain gauge stations (red); (center) Lake Victoria
observed levels (blue) compared to estimates based on our modified version of
Tate et al (2004) water balance model for Lake Victoria with rainfall from
RegCM3-20km resolution reference run (red); (right) lake levels projections
(2071-2100) based on rainfall input from RegCM3 (20km grid) A2 simulation.
Since the initial level of the lake for 2071 is unknown, we assume multiple initial
conditions for the hydrological model. All initial states converge to the same
projection curve after about 10 years.
Comparison of Raingauge Variability at Musoma & Bukoba
Located on Opposite Sides LV
Requires Uncoupled Models
Figure shows a surprising complete reversal in the observed decadal-scale rainfall oscillation at two
locations on opposite shores of Lake Victoria. We believe that such a dramatic switch occurring over
such a short distance O(200km) across Lake Victoria is in response to a common underlying regional
climate anomaly regime. This is compelling testimony of the need for application of higher resolution
regional climate models compared to GCMs which cannot resolve the salient characteristics of such an
important source of livelihood, for water and hydroelectric power, for over 300 million inhabitants of LVB
and the River Nile Basin.
Navigation Safety over Lake
Victoria
A new East African Community Program
STRENGTHENING METEOROLOGICAL SERVICES ON LAKE
VICTORIA TO ENHANCE SAFETY OF NAVIGATION
AND
EFFICIENT EXPLOITATION OF NATURAL RESOURCES
RegGCM3-POM simulation in December 1988 average over Lake Victoria. (a)
850mb wind at 6UT, and (b) lake surface temperature
Requires full hydrodynamics of Lake Victoria
Summary
•
Data resolution is a major problem. The 0.5 degree resolution seems to be the
minimum threshold acceptable for district scale assessment. Station data is not
the solution at this time because the coverage over the semi-arid districts is very
poor.
•
Present climate data quality is acceptable for social-economic planning at district
level & Inherent data uncertainties are a limiting factor and need to be reduced by
improved monitoring of baseline climate
•
Over the arid and semi-arid regions rain-gauge data coverage is very marginal &
monitoring over these regions need to be upgraded
•
Currently model-based climate information is available at regional scales & incompatible
for broad range of end users
•
Information about intraseasonal time scales is important for applications such as
wildlife management, such onset & withdraw of the rains
•
Require models that are not only high resolution but also support the physics of
the critical mechanisms; require full hydrodynamics of Lake Victoria for a vriety
of important applications such as, fisheries, pollution, hydroelectric power
generation & marine navigation safety