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This page is for WP 6.3 Impact modelling at seasonal-to-decadal time scales

Leader: UNILIV (Morse). Participants: UREADMM (Wheeler/Slingo), ARPA-SIM (Marletto), JRC-IPSC (Genovese), METEOSWISS (Liniger/Appenzeller), LSE (Smith), 
FAO (Gommes), WINFORMATICS (Norton), IRI (Thomson), EDF (Dubus), DWD (Biermann).

Information on the activity within the WP can be found below.

The first section gives the 60 month overview followed by the 18 month objectives and tasks. The section is completed by a list of deliverables and milestones.

Disclaimer: Some of the the information on this page is taken from DoW vn1.4 any errors or inconsistencies with other documentation needs to be discussed with the RT
coordinators and ultimately the IP management.

WP 6.3 subpages:

Useful links and information


Primary Objective

The primary objective of this WP is to integrate application models within a probabilistic ESM system and within RCM systems. This integration links the human dimension to earth 
system modelling and allows subsequent socio-economic evaluation of the ENSEMBLES EPS. A number of research and development tasks are required to allow an effective integration
of the modelling systems. Output from the integrated EPS/application model runs will be validated in WP5.5 against ERA-40 and where available gridded station data driven application 
model runs.

State of Knowledge

The current state of the art for quasi-operational impact models running for the forthcoming season is that they make use of the observed climate through the first part of the season or 
through the preceding season. As an example wheat yield predictions in Europe are made using a dynamical crop growth model which is run to a given date using climate observations 
and then an end of season yield prediction is made through a statistical model relating crop development phase, at that date, to previous known wheat yields. A similar approach can be
made for malaria prediction using the seasonal rainfall totals through the early part of the rainy season to make a prediction, using a statistical model, of the forthcoming number of malaria 
cases that follow at the end of the rainy season. In both applications the lead-time is limited and the predictions become more reliable the later in the season they are produced. Probabilistic 
information is routinely used in weather risk management models. However this is mostly confined to distributions from climatological records, with occasional input from the mean of seasonal
forecasts. The tropical yield crop model GLAM has successfully been used to simulate yields over large areas in India using observed gridded data and reanalysis. Some preliminary work
has also been undertaken using the DEMETER ensembles for the period 1987-98.

During DEMETER a small number of application models were developed or modified to use the seasonal probabilistic forecasts to drive, daily time step, impact model integrations. In 
DEMETER the range of impact models run, the regions covered and number of ensemble members utilised was limited. Questions that arose during DEMETER regarding downscaling, 
bias correction and how to interpret the probabilistic impact model output were only partially addressed and these will be more fully investigated within ENSEMBLES. In ENSEMBLES,
a number of new impact models will run addressing a much larger potential user community.

Novel Questions

ENSEMBLES will attempt to answer a number of novel research questions

i. How to maximise the integrated model system skill using techniques such as ensemble dressing,

ii How to quantification the existing skill within an integrated modelling system,

iii. The estimation of the skill required from the driving seasonal forecasts to allow a skilful seasonal impact forecasts.


Task 6.3.a: Downscaling and bias correction for ensemble hindcasts: This work is part of RT2B and the research and development will require the seasonal impacts groups to define their 
requirements and work in collaboration with RT2B partners. The nature of the downscaling requirements will not be uniform for each application model. The relative improvements of the 
various downscaling schemes will be assessed against un-downscaled ensembles for the impacts models.

Task 6.3.b: Integration of seasonal-to-decadal application models within an EPS. Initially the DEMETER datasets will be used to integrate the application models. As ENSEMBLES 
EPS (WP1.5 and WP2A.1) and RCM (WP2B.4 and WP3.5) output become available they will integrated into the modelling system. Probabilistic integrated application model runs will 
allow the 'skill in hand' of the current DEMETER EPS to be evaluated within WP5.5. The current skill-in-hand will act as the baseline from which to compare the developments produced 

Task 6.3.c: Assessment of GCM vs. RCM driven seasonal-to-decadal application models. Probabilistic high-resolution regional climate scenarios at seasonal to decadal timescales from 
WP2B.4 and very high resolution RCM-derived scenarios for non-European areas from WP3.5 will be used to drive the application models and comparison will be made with GCM driven
application models in the same regions. The results will be evaluated in WP5.5.

Task 6.3.d: Gaining the maximum skill from an EPS seasonal-to-decadal scale integration: EPS is a new technology particularly when used for driving application models. Research and 
development will be undertaken to see how a forecast PDF can be utilised to give the maximum skill in the application forecasts e.g. ensemble dressing.

Task 6.3.e: Quantification of EPS skill requirements at seasonal-to-decadal timescales for application models. The integrated application model/ EPS model system is almost certainly 
non-linear for most cases. The defining of skill requirements from the application forecasts will link to the socio-economic investigations RT7. The skill definitions from the application 
groups will start to define the level of skill required from the driving seasonal-to-decadal EPS hindcast. Required skills levels in the hindcast will differ between different applications but 
these required levels would become the benchmark for targeted forecast skill in current and future EPS.

Proposed Examples

In addition to the methodological and theoretical developments outlined in the tasks above the integration of the impacts models within the ENSEMBLES EPS will lead to a number of 
measurable outputs including as examples

Deliverables and Milestones

Activity for each deliverable and milestone will appear here with links to RT6 WPs

Notes: Deliverable Number 6.0 basic site up and running by month 3 ongoing improvements and expansion through course of ENSEMBLES

Del. No. Deliverable name WP no. Lead participant Estimated indicative person-months Nature Dissemination level Delivery date
6.0 Internal RT6 web site RT6_home page 6.0 UNILIV 3 O RE 3
6.1 Versions of the LPJ and Hadley Centre models which include interactive annual crops. Version of the LPJ model with globally applicable representation of managed forests 6.1 PIK 17 O CO 18
6.2 First-phase impact models to predict damage to human activities, the environment and tropical annual crops from climate extremes: e.g. wind storm, drought, flood and heat stress 6.1 and 6.2 UEA 33 O CO 18
6.3 Calibrated and tested crop, forest, hydrology and energy impact models; baseline data and scenarios for constructing impact response surfaces. 6.1 and 6.2 SYKE 38.5 O CO 18
6.4 Seasonal-to-decadal application models running as part of an integrated probabilistic ESM based on DEMETER hindcasts 6.3 UNILIV 76 O CO 18

18 month plan

Work Package number


Start date:

Month 1

Activity type


Participant id (person-months):

UNILIV (9), UREADMM (3), ARPA-SIM (24), JRC-IPSC (12), METEOSWISS (4), LSE (3), FAO (4), WINFORMATICS (9), IRI(3), EDF(2), DWD (2.5)


Integration of seasonal-to-decadal application modelling within the ENSEMBLES EPS.  Commencement of research and development for gaining maximising skill in the integrated modelling system.


Description of work

WP6.3: Impact modelling at seasonal to decadal timescales

Task 6.3.1: Consultation on seasonal-to-decadal applications models requirements for downscaling and RCM integrations with partners in RT2B and WP3.6

Task 6.3.2:  The integration of seasonal-to-decadal application models within a probabilistic ESM based on DEMETER hindcasts.



D6.4: Seasonal-to-decadal application models running as part of an integrated probabilistic ESM based on DEMETER hindcasts  Month 18


Milestones and expected result

M6.5   Integration of seasonal-to-decadal application models within ESM using DEMETER and where available the pilot ENSEMBLES system Month 18



Links to Six Monthly Reports

WP six monthly reports will appear here...

Maintained by Andy Morse and Caminade Cyril        Co-ordinators Andy Morse and Colin Prentice

Updated 18th September, 2008