Primary Objectives :

RT5Independent

comprehensive evaluation of the

ENSEMBLES simulation-prediction

system against observations/analyses

Primary Objectives:O5.a: Production of daily gridded datasets for surface climate variables (max/min temperature, precipitation and surface air pressure) covering Europe for the greater part with a resolution high enough to capture extreme weather events and with attached information on data uncertainty;

O5.b: Identification and documentation of systematic errors in model simulations, representation of processes and assessment of key climate variability phenomena and uncertainties in ESMs and RCMs;

O5.c: Assessment of the actual and potential seasonal-to-decadal quality for the different elements of the multi-model ensemble prediction system using advanced methods to evaluate the different attributes of forecast quality (skill, resolution, reliability, etc.).

O5.d: Assessment of the amount of change in the occurrence of extremes in (gridded) observational and RCM data;

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.

1.7Meuro (11%)

RT5: Deliverables

5.0 Meeting report and RT reports. 5.0 INGV 0 R PU 12, 18

5.1 Workshop on RT5 key issues and research priorities for years 2-5 of ENSEMBLES.

5.2 INGV 1 R PU 12

5.2 Assessment of the decadal-scale variations of precipitation extremes in ERA40 by comparison to observations in the Alpine Region.

5.4 ETH 3 R PU 18

5.3 Scientific article/report and Matlab software on optimal statistical methods for combining multi-model forecasts to make probabilistic forecasts of rare extreme events.

5.3 UREADMM 3 R PU 18

5.4 Scientific article/report on the best methods for verifying probability forecasts of rare events.

5.3 UREADMM 3 R PU 18

5.5 Report on systematic errors in the ENSEMBLE models. 5.2 UREADMM 12

R PU 18

5.6 Outline assessment of decadal forecast quality in the IndoPacific sector from the initial ENSEMBLES forecasts

5.2 INGV 12

R PU 18

5.7 Assessment of the skill of seasonal NAO and PNA using multi-model seasonal integrations from DEMETER;

5.3 ECMWF 3 R PU 18

5.8 Assessment of the available station density for the gridding and daily data quality/homogeneity.

5.1 KNMI 12

R PU 18

5.9 Report on the analysis of possible gridding methods 5.1 UEA 3 R PU 18

5.10 Workshop report on "Lessons learned from seasonal forecasting: health protection"

5.5 WHO 3 R PU 18

RT5: Milestones

M5.4: Selection of "best-performing" interpolation scheme for producing the daily gridded datasets (month 18).M5.3: Early assessment of systematic errors in the ENSEMBLES models (month 18).M5.2: Prototype of an automatic system for forecast quality assessment of seasonal-to-decadal hindcasts (month 18).M5.1: Evaluation of ERA40 precipitation extremes in the Alpine region completed (month 18).D5.10: Workshop report on Lessons learned from seasonal forecasting: health protection (month 18)

WP5.1: Production of daily gridded observational datasets

(KNMI, MeteoSwiss, Climatic Research Unit, Oxford University)

First 18 months:

• Collection and evaluation of basic daily station data from various sources (see example on next slide)

• Selection of best performing interpolation scheme

Beyond:

• Producing grids for surface climate variables covering Europe, and attaching information on data uncertainty(available by month 36)

Example of input data evaluation: T-mean series, 1946-2003

http://eca.knmi.nl

ECA dataset

RT5, WP5.2 : Evaluation of processes and phenomena

Objectives : • Analyse the capability of the models to reproduce and predict the major modes of variations in the climate system

• Investigate the nature of the uncertainties due to the clouds and radiations processes

MODEL-DATA18 months : prepare tools

and preliminary report for systematic comparisons

INGV, CNRS-IPSL, MPI-MET, DMI, UREADMM

5.2.a) Tropics•ENSO, monsoon

•Intraseasonal variability

5.2.b) Extratropics•Seasonal to decadal variability

•Atlantic-Europe, THC, Storm tracks

5.2.f) Clouds and aerosols•Parametrization scheems

•Analysis of tendency errors•Nudged simulations using ERA40

5.2.c) Global Teleconnections•ENSO-global,

• Monsoon-Mediterranean•Effect of Numerical Aspects (Resolution, ..)•Intraseasonal variation in tropical heating

5.2.d) Feedacks and clouds•Decadal variation of

water vapour,clouds, radiation•Moist/convective and

dry/subsiding tropical regions•Link with surface fluxes

5.2.e) Synthesis• Report of model systematic biases

• Overall assessment of ENSEMBLES models

Example : ENSO-Indian ocean

• the 1976-1977 climate regime shift was accompanied by a remarkable change in the lead-lag relationships between Indian Ocean Sea Surface Temperature (SST) and El Niño evolution.

• It has implications for Niño predictions (S-E Indian ocean is now a precursor)

Do models reproduce this?Why? What are the major processes involved? From Terray

Example: Sensitivity of teleconnections

T30

T106

Obs

Total

Low Pass

High Pass

WP5.3 Description• WP5.3: Assessment of forecast quality.

• Participants: ECMWF, MeteoS.wiss, Met Office, CNRM, KNMI, IfM, Univ. Reading, IPSL, BMRC.

• Objective: Assessment of the actual and potential skill of the different ensemble forecast systems.

• First 18 months:

– Develop a prototype of automatic verification system for seasonal-to-decadal probabilistic predictions (M5.2).

– Formulation and verification of probabilistic rare event predictions (D5.3, D5.4) and skill assessment of extra-tropical variability modes (D5.7) based on DEMETER data.

WP5.3 Description• The prototype verification system will be based upon

the KNMI Climate Explorer and the DEMETER verification system

WP5.3 Description• Plan beyond month 18:

– Implement the verification system to assess the forecast quality of the simulations carried out within RT1/RT2A.

– Use the web-based automatic verification system to document the forecast quality of the predictions.

– Liaise with RT1 to use forecast quality information for the recommendation of best method to estimate forecast uncertainty.

– Extrapolate the skill/reliability information from the seasonal-to-decadal ensemble systems to the centennial ensemble systems.

– Design of methods to create probability predictions out of multi-model hindcasts, including verification and economic value assessment, especially from a risk management decision-making perspective.

– Liaise with RT6 to tailor design prediction skill and value assessment for the end users.

WP5.4: Evaluation of extreme events

(KNMI, Univ. Reading, Climatic Research Unit, FTS-Stuttgart, IWS-Stuttgart, ETH Zürich, Nat. Observatory Athens)

• Study of both observed and RCM data (all groups)

• Spatial pooling to improve the probability of detecting trends (2 groups)

• Reproduction of observed trends in heavy precipitation over the Alpine region by ERA-40 driven RCMs (1 group)

• Use of an objective classification of circulation types causing extreme events (2 groups)

CP 11 CP 04

Critical wet CPs classified based on discharge of Moselle Catchment

Frequency[%]

Mean Precipitation[%]

> 90%[%]

CP11 8.3 21.3 35.1

CP04 6.2 11.8 12.8

Frequency of occurrence of critical CPs and their contributions to the mean and extreme ( > 90%) precipitation in winter

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.

Marletto et al. 2005 Tellus (submitted)

WP5.2: Evaluation of processes and phenomena

(INGV, CNRS-IPSL, MPIMET, DMI, , UREADMM )

Evaluate the capability of models to reproduce and predict the major modes of variation of the climate system, with a special emphasis to tropical-extratropical teleconnection patterns

ID Task Name

4 D5.0ii meeting report and RT reports5 D5.1 workshop6

7 WP5.1 Development of datasets for Europe8 D5.8 assessment of available station density9 D5.9 report on analysis of gridding methods

10 M5.4 selection of best-performing interp. scheme11

12 WP5.2 Evaluation of processes and phenomena13 D5.5 preliminary report on systematic errors14 D5.6 outline assessment of forecast quality15 M5.3 early assessment of systematic errors16

17 WP5.3 Assessment of forecast quality18 D5.3 report and software on methods19 D5.4 report on the best methods for verifying20 D5.7 assessment of skill of seasonal NAO and PNA21 M5.2 prototype of automatic system22

23 WP5.4 Evaluation of extreme events24 D5.2 assessment of decadal-scale variations25 M5.1 evaluation of ERA40 precip. Extremes26

27 WP5.5 Evaluation of impact-models28 D5.10 workshop report

12 01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 04 05 06 07 08Year -1 Year 1 Year 2