Transcript Slide 1

Bulgarian Academy of Sciences
Institute of Meteorology & Hydrology
Using Better Climate
Prediction in the
Implementation of NAPs –
(Eastern) Europe
Vesselin Alexandrov
Arusha, 2006
UNCCD
“Section 1: Action programmes”
 Affected country Parties shall “prepare, make
public and implement national action
programmes (NAPs) as the central element of the
strategy to combat desertification and mitigate
the effects of drought”
 NAPs shall “incorporate long-term strategies
to combat desertification and mitigate the effects
of drought” and “enhance national
climatological, meteorological and hydrological
capabilities and the means to provide for drought
early warning”
UNCCD
Article 10: NAPs
3. NAPs may include, inter alia ... :
(a) establishment and/or strengthening, as
appropriate, of early warning systems …
(b) strengthening of drought preparedness and
management, including drought contingency
plans at the local, national, subregional and
regional levels, which take into consideration
seasonal to interannual climate predictions;
WMO DEFINITIONS
OF METEOROLOGICAL FORECASTING RANGES
6. Long-range forecasting (Seasonal to Interannual
Prediction (SIP)): from 30 days up to 2 years
6.1. Monthly outlook
6.2. Three month outlook: Description of averaged
weather parameters expressed as a departure from
climate values for that 90 day period
6.3. Seasonal outlook
In some countries, SIP are considered to be climate products
7. Climate forecasting: beyond 2 years
7.1. Climate variability prediction
7.2. Climate prediction: expected future climate
including the effects of natural and human influences
Global Producers of Long Range Forecasts
EASTERN EUROPE
UNCCD
Recommendations from the REPORT OF AD HOC PANEL:
EARLY WARNING SYSTEMS (2000)

Integrate early warning results with the results
of other climate prediction systems such as the
WMO Climate Information and Prediction
Services (CLIPS) and CLIVAR

Encourage the further development and
application of seasonal climate forecasting and
long-range forecasting as tools for early
warning systems
source: Mike Harisson (www.wmo.int)
CLIPS Questionnaire (Gocheva & Hechler, 2004)
 Is
SIP currently successful in specified regions and
sectors only ?
Albania, Cyprus:
do not use SIP and have not any precise opinion about SIP
Azerbaijan:
about successfulness of SIP it is difficult to say something
Latvia:
it is difficult to point out any geographic region where SIP works better
Bulgaria; Estonia, Slovenia, Cyprus:
SIP seems successful for specific regions and sectors
Croatia, Poland, Romania:
successful in ENSO-related regions with some weak predictability in midlatitudes (NAO)
Armenia, Moldova, Kazakhstan:
SIP is successful in wide geographical regions
Spatial pattern of correlation between modelled
February-April snow cover and NCEP/NESDIS
observations; a) shows the correlation for the GloSea
model (Shongwe et al., 2006)
Spatial pattern of correlation between modelled
February-April snow cover and NCEP/NESDIS
observations; b) shows the correlation for the ECMWF
S2 model (Shongwe et al., 2006)
CLIPS Questionnaire (Gocheva & Hechler, 2004)
 Does
your NMHSs provide official SIP?
Albania, Croatia, Cyprus, Estonia, Greece, Lithuania, Slovenia:
No
Bulgaria, Latvia, Serbia & Montenegro, Slovakia:
monthly
Belarus, Armenia, Azerbaijan, Poland:
monthly and seasonal
Romania:
one-month forecasts,
prognostic estimates for the next 2 months, following the forecasting
month; “seasonal supplement”, containing the anomaly notification in the
geophysical environment in past season and meteorological outlook for the
next season;
annual forecasting estimates bulletin elaborated at the beginning of each
season and containing estimates of the temperature and precipitation
anomalies for the next four seasons
Russia:
operational 1-3 month SIP regional and global predictions
Seasonal predictions (UK Met Office and IRI) on the
web page of Bulgarian weather service (info.meteo.bg)
CLIPS Questionnaire (Gocheva & Hechler, 2004)
 Does
your NMHS use SIP products from global
producers?
Croatia, Cyprus, Estonia:
No
Armenia, Azerbaijan, Belarus, Latvia etc.:
ROSHYDROMET
Slovakia, Greece:
ECMWF products
Bulgaria:
ECMWF, IRI, UK Met Office, Météo-France for monthly weather forecast
involving local weather and climate archive data downscaling
Lithuania:
IRI, World Resource Institute and Swedish Regional Climate Modelling
Programme
Poland:
ECMWF, IRI, DWD
Romania:
ECMWF, Met Office, IRI and Japan Meteorological Agency, etc.
CLIPS Questionnaire (Gocheva & Hechler, 2004)
 Do
you apply SIP in the management of
agricultural production, water resources, etc.?
Albania, Cyprus, Greece, Lithuania, Slovenia:
No
Russia, Croatia, Serbia & Montenegro, Slovakia:
partial application in some sectors, occasionally, etc.
Armenia, Belarus, Bulgaria, Kazakhstan, Latvia, Poland, Romania:
relatively broad SIP application in various sectors of the economy:
(Gocheva & Hechler, 2004)
CLIPS Questionnaire (Gocheva & Hechler, 2004)
 Has
your NMHS contracts for regular SIP provision
with a specific sector for example, agriculture?
50:50
Albania, Armenia, Belarus, Cyprus, Greece, Latvia, Lithuania,
Slovakia, Slovenia:
No
 Has
your NMHS requests for SIP from any sectors?
90% confirmed availability of user’s requests towards SIP
products
CLIPS Questionnaire (Gocheva & Hechler, 2004)
 Is
your SIP officially issued by the media?
 Do
you develop the theoretical basis of your SIP
activities by own research efforts?
 How
do you maintain the theoretical basis of your
operational SIP activities?
 Do
you apply downscaling methods for specific
sectors/applications/locations?
 What
are the predicted meteorological elements
and parameters in your national SIP practice?
Seasonal forecasting - numerical models
A modelling system for detailed
regional scenarios
the PRUDENCE method
Coupled GCM
(300km atmosphere)
SST/sea-ice
change from
coupled GCM
Observed
SST/sea-ice
150km global
atmospheric
GCM
12-50km RCM
for relevant region
RegCM3 regional climate model (source: Pal, 2005)
Positive (left) and negative (right) NAO phases and
related impacts on weather in Europe
temperature in winter
NAO
impact
source:
H. Cullen and M. Visbek
rainfall in winter
Statistical forecast for the NAO index
CECILIA project (WP2 objectives)
producing high resolution (10 km) 30year time slices over four target areas

comparing model responses with coarser
results from existing simulations to assess
the gain of a higher resolution

archiving daily data from the simulations
in a common database

improving high resolution models for
future scenarios

ENSEMBLE climate prediction objectives
run ensembles of different climate
models to sample uncertainties

measure variations in reliability
between models

produce probabilistic predictions of
climate change
 link these projections to potential
impacts: agriculture, health, energy,
insurance, ecosystems, etc.

source: Giorgi, GRL, 2006
Regional Climate Change Index
ECHAM4 A2 climate change scenarios for annual
air temperatures in Europe for the 2050s,
relative to 1961-1990
ECHAM4 A2 climate change scenarios for
annual precipitation in Europe for the 2050s,
relative to 1961-1990
Climate Change
Scenarios for the
Balkan Peninsula
20
0
-20
d ) 40
-40
-60
-80
-2
0
2
4
6
8
T em p erature ( о С )
IPCC A2
emission
scenario
10
P recip itatio n (% )
P recip itatio n (% )
c) 40
20
0
-20
-40
-60
-80
-2
0
2
4
6
8
T em p erature ( о С )
GCM simulated change of air temperature (X) and
precipitation (Y) for summer in Greece (c)
and Turkey (d) for the 2100, relative to 1961-1990
10
Model climate change scenarios (in %) for winter (left)
and summer (right) precipitation in Europe,
21st century
Changes in summer air temperature (in oC)
simulated by the HadCM3 and PCM models for the
2080s, A2 SRES scenario
Changes in summer precipitation (in %) simulated
by the HadCM3 and PCM models for the 2080s,
A2 SRES scenario
Extreme events
Summer (JJA)
DT [oC]
Ds/s
[ºC]
[%]
Models project large increases in climate variability and
extremes in Central and Eastern Europe
(source: Schär et al. 2004)
Mean
90% quantile
99% quantile
+2
+4
+6
+8
+10
+12°C
Changes in summer Tmax: 2071-2100 vs 1961-1990
HIRHAM RCM (source: Beniston, 2006)
2.a
1961-1990
2.b
1
5
2071-2100
10 20 30 40 50 60 70 80 90 100 200 days
Threshold exceedance: Tmax> 30°C: 2071-2100 vs
1961-1990, HIRHAM RCM (source: Beniston, 2006)
(JAS)
DP
D99% (n=5d)
Models project large increases in climate variability and
extremes in Central and Eastern Europe
(C. Simota, 2005)
(C. Simota, 2005)
(C. Simota, 2005)
(C. Simota, 2005)
(C. Simota, 2005)
(C. Simota, 2005)
(C. Simota, 2005)
Better climate prediction – DMCSEE?
DMCSEE : Drought Management Center for
Southeastern Europe
 to
serve as an operational centre for SEE for
drought preparedness, monitoring and management;
 to create and coordinate a subregional network of
NMHSs and other relevant institutions;
 to coordinate and provide the guidelines to
interpret and apply drought-related products;
 to prepare drought monitoring and forecast
products and make them available to relevant
institutions in participating countries; …
Daily soil moisture anomalies estimated by ECMWFERA40 (left) and JRC-MARS (right) (source: JRC)
Soil moisture
prediction:
7 days ahead
(source: JRC)
70 days ahead?