ECO covers the coordination of the Module E; it integrates the individual evaluation efforts and organises scientific exchanges with the other modules and WPs in MiKlip II. Stakeholder/end-user interaction required for the orientation of the evaluation system to end-user needs will be ensured in cooperation with module D, involving individual work packages as needed.

Beside the coordination of Module E, ECO has following scientific contributions:

As bias and drift correction become more relevant for all validation activities, ECO will implement a drift correction framework based on generalised linear models (GLMs). The framework includes a drift correction which is initialisation time dependent. It shall be developed and implemented into the Central Evaluation System (CES). Together with WP E-6 DROUGHTCLIP, the GLM-based framework will be extended to skewed and positive variables, to be particularly suited for precipitation (WP E-2 DAPAGLOCO, WP E-5 PROMISA), humidity (WP E-1 MOSQUITO) or wind. Furthermore, calibration of probabilistic forecasts to increase reliability is an important issue in MiKlip II. Different calibration approaches will be developed in Module E within WP E-8 PROVESIMAC and WP E-9 CALIBRATION. Together with bias and drift correction, the calibration of the forecast will contribute to general post-processing methodologies which will be coordinated by ECO. Predictive skill is assumed not only to vary with lead time but also with season and/or with the phase of low-frequency climate modes, like Atlantic multidecadal variability (AMV), Atlantic meridional overturning circulation (AMOC), and Pacific decadal oscillation (PDO). Skill estimation stratified along these influences will be a valuable contribution to the general skill estimation but also for processoriented validation. Therefore ECO will implement CES plug-ins for stratified verification.

The verification of forecast skill has been stratified along different variability modes on decadal time scales, e.g. "El Nino/Southern Oscillation" (ENSO) and "Atlantic Multidecadal Variability" (AMV). By means of a decomposition of a verification measure, it was possible to assign skill contributions of different phases of variability to total forecast skill.

Goals

• focusing end-user needs concerning the evaluation system for the transferof predictions towards a user-oriented operational system

• development of bias and drift correction frameworks and coordination of calibration approaches of probabilistic forecasts

• implementing frameworks for stratified verification

Progress so far

Within the framework of ECO, a drift adjustment plug-in for the Central Evaluation System (CES) has been implemented, which is flexible in the choice of the correction method. Model drift can be estimated and adjusted parametrically by means of generalised linear models, or non-parametrically.

The sensitivity of the prediction skill and improvement of the parametric bias adjustment has been tested concerning cross validation (CV) with different block length.