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WP5.5: Evaluation of seasonal-to-decadal scale impact-models forced with downscaled ERA-40, hindcasts and gridded observational datasets.
Andy Morse, University of [email protected]
This presentation contains slides from RT5 WP5.5 partners
Andy Morse and Anne Jones, University of Liverpool, Mark Liniger and Christof Appenzeller, MeteoSwiss, Andrew Challinor, Tom Osborne, Julia Slingo and Tim Wheeler, University of Reading,Laurent DUBUS, Clarisse FIL-TARDIEU and Sylvie PAREY, EDFGiampiero Genovese, Fabio Micale, Pierre Cantelaube , Jean-Michel Terres, EU-JRCVittorio Marletto, ARPASimon Mason and Madeleine Thomson, IRI, Columbia University
Presentation Structure
1.Introduction
2. DoW 60 and 18 month
3. Information from Partners
WP6.3 vs. WP 5.5?
WP6.3 production of model runs, issues connected to downscaling and bias correction, use of impacts models
within ensemble prediction system.
WP5.5 is the validation of both the reference forecast data (e.g. ERA-40) and the EPS hindcasts and the validation of the impacts model against reference forecasts, forecasts from other gridded data and real observations e.g. crop yields
Multi-tier validation
Primary Objective
O5.e: Evaluation of the impacts models driven by downscaled reanalysis, gridded and probabilistic hindcasts over seasonal-to-decadal scales through the use of application specific verification data sets.
Scientific/Technical Questions
What’s the quality of impact models at seasonal time scales?
DoW review
WP5.5: Evaluation of seasonal-to-decadal scale impact-models forced with downscaled ERA-40, hindcasts and gridded observational datasets.
Leader: UNILIV (Morse). Participants: UREADMM (Wheeler/Slingo), ARPA-SIM (Marletto), JRC-IPSC (Genovese/Micale), METEOSWISS (Liniger/Appenzeller), LSE (Smith), IRI (Thomson/Mason), EDF (Dubus/ Fil-Tardieu/Parey), DWD (Biermann {Becker}).
WHO (Menne), FAO (Gommes), WINFORMATICS (Norton)
Key Objectives: assess skill-in-hand for a number of impact models
1. Run models appropriately downscaled ERA-40 data and gridded datasets developed in WP5.1
2. Run models with fully downscaled and bias corrected probabilistic ensemble hindcasts at seasonal to-decadal scales from WP6.3.
Models include agriculture models (crop yield models for Europe and the tropics, agri-environmental impact models), infectious tropical disease (malaria and meningitis) and energy demand.
WP5.5 Objectives 60 months
Major milestone 5.5 by month 60
Evaluation of forecast skill of seasonal-to-decadal scale impacts-models when driven with ENSEMBLES EPS.
Expected results and achievements: Assessment of the skill-in-hand for a number of impact models run with downscaled ERA-40 data, gridded observational datasets and fully downscaled and bias corrected probabilistic ensemble hindcasts at seasonal-to-decadal scales.
Expected deliverable: Evaluation report on the forecast skill of seasonal-to-decadal scale impacts-models.
from Morse, Doblas-Reyes, Hoshen, Hagedorn and Palmer (2005) Tellus (in press)
Tiers of Validation
Task 5.5.a: Assess ERA-40 reanalysis key impacts driver variables - parts of Europe, India and Africa. Gridded data from stations and satellites. Tier-0
Task 5.5.b: Evaluate forecast quality key variables probabilistic hindcasts - at appropriate impacts scales - links WP5.3. Key question - capture of seasonal cycles. Links to downscaling and bias correction work from other WPs – and links WP 6.3. Tier-1
Task 5.5.c: Skill of the impacts models driven by corrected (d/s b/c) probabilistic hincasts, compared with reference forecasts from
a. downscaled ERA-40, b. other gridded data sets. Tier 2
Task 5.5.d: Impact model verified against real observations driven by a. ERA-40 reanalysis, b. other gridded data sets and c. probabilistic seasonal hindcasts. Tier-3
WP5.5 Tasks 60 months
Management Issues
Attrition of partners – withdrawal three partners Two due to UN/EU contractual issues One (SME) due to commercial considerations
Use of 6 month report
Communication -monthly or bi-monthly newsletter?
18 Month Plan
Participant id (person-months):
UNILIV (2), UREADMM (9), ARPA-SIM (7), JRC-IPSC (2), METEOSWISS (1), LSE (2), IRI (1), EDF (1), DWD (1),
WHO (3), FAO (1), WINFORMATICS (0).
Objectives
Evaluation of the impacts models driven by downscaled reanalysis, gridded and probabilistic hindcasts over seasonal-to-decadal scales through the use of application
specific verification data sets.
Tasks 18 months
Headlines:
Limited runs used to develop validation systems
Health applications workshop
either evaluation of the state of the art or on setting the agenda for future research
Task 5.5.1: Seasonal application models tested ERA-40 data and selected models run DEMETER hindcasts development of validation systems.
Task 5.5.2: A workshop on use of seasonal probabilistic forecasting for health applications
Deliverables
D5.10: Workshop report on Lessons learned from seasonal forecasting: health protection (month 18)
Milestones and expected result : None in first 18 months
Information from Partners
LiverpoolARPAJRCReadingEDFMeteoswiss
other partners
Temperature
Malaria Model: Temperature dependence
Mosquito survival after Martens (1995)
At T = 25°C sporogonic cycle length = 15.9 days
2.9% survive to infectious stage
Analysis and diagram from Anne Jones
Malaria Model comparison new dynamic and existing rules based models
MARA
Slide 15 of 14
Prevalence = proportion of human population infected with malaria
Mapping Malaria Risk in Africa
Precipitation LT AM UT
Feb 2-4 (MAM) -0.094 -0.009 -0.020
Feb 4-6 (MJJ) -0.012 -0.039 -0.049
Temperature LT AM UT
Feb 2-4 (MAM) 0.080 0.148 0.230
Feb 4-6 (MJJ) 0.104 0.210 0.314
Prevalence LT AM UT
Feb 2-4 (MAM) 0.396 0.461 0.046
Feb 4-6 (MJJ) 0.167 0.289 0.178
Brier Skill Scores Feb 2-4 and 4-6
LT is the lower tercile event, AM the above the median event and UT the upper tercile event
63 ensemble members against a reference forecast made with ERA-40.
Liverpool – malaria model output
Data 1987 to 2002 grid points 17.5S 22.5E, 17.5S 25.0E, 17.5S 27.5E, 17.5S 30.0EAfter Morse et al. (2005) Tellus, in press
Wofost/CRITERIA
Demeter ds output (P, T)
Observations (P, T)
J
JMA
MJ
1
2
3
0
500
1000
1500
2000
2500
3000
3500
4000
4500
01/01/93 31/01/93 02/03/93 01/04/93 01/05/93 31/05/93
reference runs
4000
5000
6000
7000
8000
9000
1986 1988 1990
0.05
0.1
0.25
0.5
0.75
0.9
0.95
4000
5000
6000
7000
8000
9000
1986 1988 1990
0.05
0.1
0.25
0.5
0.75
0.9
0.95
4000
5000
6000
7000
8000
9000
1986 1988 1990
0.05
0.1
0.25
0.5
0.75
0.9
0.95
Demeter evaluation runs
General layout of the hindcast evaluation tests
N(y-1)………J0
Vittorio Marletto, ARPA
Figure xB. Box and whiskers distributions of predicted potential wheat yields (kg/ha) simulated using downscaled DEMETER daily hindcasts for the years 1977-1987 in a location near Modena. In the top graph the crop model was provided with observed weather data up to the 31st of March and supplemented with DEMETER downscaled hindcasts up to harvest date (end of June in Northern Italy). The above procedure was repeated but with observed data up to the 30th of April (centre) and to the 31st of May (bottom). DEMETER data refer to four models (CNRM, UKMO, SCWF, SMPI), 9 members and 2 downscaling replicates obtained by DMI using a weather generator. Potential wheat yields simulated by the Wofost/Criteria model with observed weather data only are also provided for comparison.
Vittorio Marletto, ARPA
JRC crop modelling results• Average (1995-1998) European weighted percentage error wheat yields hybrid JRC modelling system and DEMETER full growing season crop model estimation.
• JRC hybrid WOFOST based crop model run observed meteorological data (to date) with crop yield statistical estimation crop growth indicators.
• DEMETER ensemble driven WOFOST forecast February start date 180 days 63 members. Results Portugal excluded systematic bias. Results weighted on proportional contribution each member state.
MODEL RUN WEIGHTED YIELD ERROR (%)
± STANDARD ERROR
JRC February 7.1 ± 0.9
JRC April 7.7 ± 0.5
JRC June 7.0 ± 0.6
JRC August 5.4 ± 0.5
DEMETER (Feb. start) 6.0 ± 0.4
• Percentage error obtained DEMETER end of February lies between average error end June & August JRC operational system.
• Demonstrates ability DEMETER make forecast earlier in season than current methods.
After Cantelaube P., Terres J.M. (2005) Tellus submitted
Ensembles WP5.5. Simulation of the yield of groundnuts across India using the GLAM crop model and the ECMWF ERA40 reanalysis
from Challinor et al., in pressAndrew Challinor, Tom
Osborne,
Julia Slingo, Tim Wheeler
Correlations between observed yield (left), or modelled yield (right),
and ERA40 rainfall May-Nov. Significance is shown by dots
RT5 : EDF/R&D
Laurent DUBUSClarisse FIL-TARDIEU
Sylvie PAREY
EDF/R&D topics of interest•Weather forecasts (RT5 contribution)
Main aim : production facilities management at time scales
•Daily to weekly
•Monthly to annual
•Decadal
Needs and contribution
•Spatial and temporal downscaling methods
•Area of interest : France and Europe
•Tests on electricity demand forecasting
• Climate change (Interest for RT4 results, participation?)
Impact on mean climate
•Electricity production and consumption
Impact on meteorological extremes
•Dimensioning
•Crisis management
MeteoSwiss activities for WP5.5:Temperature based application models
Studer, S., Appenzeller, C., Defila, C, 2005:Clim. Change, in press.Liniger, Appenzeller 2005
Spr
ing
phas
e (r
ed/b
lue)
Example:Modeling swiss main variability in spring onset using growing degree days
days
daily TTGDD 0,max
DWD
Kai Biermann, DWD and René Jursa, Institute of Solar Energy Technology (ISET) e.V. Kassel
DEMETER ensemble members and wind power predictions linked by linear regression model
LSE - Lenny Smith
Other Partners
Key linkages
From WP 6.3 for
Timely and easy access to data – already in discussion RT1, RT2a, RT2b, RT3 and RT5
• Effective use of downscaling tools – already in discussion with RT2B, RT3 and RT2a
• RT5 for validation – with WP6.3 and WP5.5 directly linked
• Interest in RT4 findings
• RT7 economic impacts
WP 5.5 specific WP 5.1 gridded data Europe, WP 5.3 forecast quality and WP5.2 systematic errors, WP 5.4 extreme events, RT7 economic impacts.
Possible links WP 4.3 extreme weather conditions, WP 4.2 Regional Climate Change – based on known impacts model thresholds etc.
Questions?
Malaria Modelbackground to disease
• Malaria kills more than 2,000,000 people per
year
• 90% deaths sub-Saharan Africa
-mostly children
• Mechanisms of the disease known for over
100 years
• Anopheline mosquitoes and parasite
Plasmodium spp. with P. falciparum most
dangerous and cause of African epidemics
Malaria Model malaria life cycle
Slide 30 of 14
sporogonic cycle:
temperature dependent
biting/laying:
temperature dependent
larval stage:
rainfall dependent
Cost-loss Ratios and Potential Economic Value – malaria transmission simulation
Season MAM: Lead 2 to 4 months (Morse et al.2005 Tellus, in press)
Data 1987 to 2002 grid points 17.5S 22.5E, 17.5S 25.0E, 17.5S 27.5E, 17.5S 30.0E
Upper tercile prevalence
Infectious Disease and Epidemics
• Many infectious diseases, in the tropics, have a strong seasonal cycle related to the seasonal
climatic cycles
• Climatically anomalous years can lead to epidemics
• Time between trigger threshold to epidemic peak often too short to take effective
intervention – need for skilful and timely seasonal climate forecast
Epidemic Cycle
0
20
40
60
80
100
120
140
97
45
97
48
97
51
98
02
98
06
98
09
98
12
98
15
98
18
98
21
98
24
98
27
98
30
98
33
98
41
98
44
98
47
Reporting week
Nu
mb
er o
f ca
ses
Vaccine
Threshold
Effect
Selected Recent Papers
Molesworth, A.M., Cuevas, L.E., Connor, S.J., Morse A.P., Thomson, M.C. (2003).Environmental risk and meningitis epidemics in Africa, Emerging Infectious Diseases, 9 (10), 1287-1293.
Hoshen, M.B., Morse, A.P. (2004) A weather-driven model of malaria transmission, Malaria Journal, 3:32 (6th September 2004) doi:10.1186/1475-2875-3-32 (14 pages)
Morse, A.P., Doblas-Reyes, F., Hoshen, M.B., Hagedorn, R.and Palmer, T.N. (2005) A forecast quality assessment of an end-to-end probabilistic multi-model seasonal forecast system using a malaria model, Tellus A, ( in press)
Sensitivity TestingInput Data
• Varied driving data to investigate sensitivity of model to magnitude and timing of seasons
• Interesting results were obtained by progressively lagging the temperature time series with respect to the rainfall time series
• For southern Africa very sensitive to temperature
• Relative timing is importantRainfall and temperature climatology for S28 grid point
a) peak prevalence and b) mosquito population as a function of lag for grid point S28
Temperature (degC)
10.0015.0020.0025.0030.0035.0040.00
1 61 121
181
241
301
361
421
481
541
601
661
721
781
841
901
961
1021
1081
1141
1201
1261
1321
1381
Dekadal Rainfall (mm)
0.0020.0040.0060.0080.00
100.00
1 61 121
181
241
301
361
421
481
541
601
661
721
781
841
901
961
1021
1081
1141
1201
1261
1321
1381
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0 30 60 90 120 150 180 210 240 270 300 330 360
Lag (days)
Pea
k P
reva
len
ceP
ea
k P
reva
len
ce
Lag (days)a)
25000
27000
29000
31000
33000
35000
37000
39000
41000
0 30 60 90 120 150 180 210 240 270 300 330 360
Lag (days)
Max
MP
ea
k M
osq
uito
Po
pu
latio
n
Lag (days)b)
Analysis and diagrams from Anne Jones
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
5 10 15 20 25 30 35 40
Temperature (deg C)
Su
rviv
al P
rob
abil
ity
P
40% 60% 80% 100% Martens Lindsay-Birley
Processing and diagram from Anne Jones
Data source Bayoh, M. N., 2001 unpublished Ph.D.
Adult mosquito daily survival as function of temperature
The LagExample – W12 grid point climatology
Prevalence
0.0000
0.2000
0.4000
0.6000
0.8000
1.0000
361 421 481 541 601 661 721
No. Infectious Mosquitoes
0.0
0.5
1.0
1.5
2.0
361 421 481 541 601 661 721
Hu
nd
red
s
Population ratio M/N
0
1
2
3
4
5
361 421 481 541 601 661 721
Th
ou
sa
nd
s
Dekadal Rainfall (mm)
0
20
40
60
80
100
361 421 481 541 601 661 721
Daily Incidence
0
0.05
0.1
361 421 481 541 601 661 721
Daily Incidence
0
0.05
0.1
361 421 481 541 601 661 721
Mosquito infection probability=1
(Unlimited human reservoir)-> immunity /chronic infection component in model (infectious until next rainy season)
???
West Africa – malaria is rainfall driven
• mosquito population takes a long time to get infected from the small human reservoir
• limited secondary infections
Analysis and diagram from Anne Jones
Precipitation LT AM UT
Feb 2-4 (MAM) -0.094 -0.009 -0.020
Feb 4-6 (MJJ) -0.012 -0.039 -0.049
Temperature LT AM UT
Feb 2-4 (MAM) 0.080 0.148 0.230
Feb 4-6 (MJJ) 0.104 0.210 0.314
Prevalence LT AM UT
Feb 2-4 (MAM) 0.396 0.461 0.046
Feb 4-6 (MJJ) 0.167 0.289 0.178
Brier Skill Scores Feb 2-4 and 4-6 LT is the lower tercile event, AM the above the median event and UT the upper tercile event
After Morse et al. 2005
DEMETER Seasonal Forecasting System
• EU FP5 DEMETER – multi-model ensemble system www.ecmwf.int/research/demeter
• Seven modelling groups running AOGCMs in full forecast mode, 4 start dates per year running out to 6 months, hindcasts 1959 to 2000
• Data available from data.ecmwf.int/data/
• EU FP6 ENSEMBLES www.ensembles-eu.org DEMETER - hindcast biases
WP5.5 Evaluation of seasonal-to-decadal scale impact-models forced with downscaled ERA-40, hindcasts and gridded observational datasets. UNILIV (Morse), WHO (Menne), UREADMM (Slingo), ARPA-SIM (Marletto), JRC-IPSC (Genovese), METEOSWISS (Appenzeller), LSE (Smith), FAO (Gommes), IRI (Thomson), WINFORMATICS (Norton), EDF (Dubus), DWD (Becker).
First 18 months:Seasonal application models will be tested with ERA-40 data and (selected models) with DEMETER forecasts to commence development of validation systems (requires downscaled ERA-40 and DEMETER data and bias corrected DEMETER data) working on Tier-2 (ERA-40 reference forecast) and Tier-3 (full validation) validation systems.
Workshop on the use of seasonal probabilistic forecasting for health applications either 1. evaluation of the state of the art or 2. on setting the agenda for future research
Beyond:For fields of interest at temporal and spatial scales of interest to impacts modellers- the validation of ERA-40 data against other gridded data as available, Tier-1 validation of DEMETER (downscaled) variables ERA-40 and other gridded data sets, impacts models driven with ENSEMBLES seasonal-to-decadal forecasts on Tier-2 (reference forecast) validation and Tier-3 (real observations –e.g. crop yield) validations
ARPA crop modelling results
• Wheat yields 1977-1987, Modena, Italy.
• 72 ensembles (4 models (x9) x2) downscaling replicates
• WOFOST based crop model observed data to 31st March and onwards with DEMETER hindcasts to harvest date (end June)
• Box (IQR) whiskers (10th & 90th percentiles)
• Observed weather simulation (control) solid triangle
• Climatology based run hollow circle.After Marletto et al. (2005) Tellus submitted