EUCLIPSE First Period Report

WP4 : Sensitivity Experiments and Hypothesis Testing
In this WP we will integrate results from other work-packages to develop numerical
experiments designed to both test our developing understanding and identify
observables that can help further constrain cloud feedbacks. The work proposed in
this package is broken into three tasks:
1. Evaluate unusual behavior
2. Developing and Testing Parameterization Improvements
3. Establishing Observational Metrics
This WP officially started only in Month 13 and since many of the tasks depend on the
results of the other WP’s it is in a preparing stage. A meeting in the autumn of 2011 is
planned to discuss the practical implementation of the tasks in WP4 in relation to the
other 3 WP’s. As a preparation of the use from numerical weather prediction for the
evaluation of fast processes in climate models, ECMWF has started in setting up a
testing framework to evaluate climate models in weather prediction mode. A report
can be found below
A study identifying the utility of NWP based methods for identifying and narrowing
sources of divergent behaviour in cloud-feedbacks in models (Month 36)
Deliverable D4.3
Daniel Klocke, Mark Rodwell
ECMWF
The work for the EUCLIPSE project at ECMWF started on June 1 st 2011. The aim is to
utilize and compare different techniques from numerical weather prediction for the
evaluation of fast processes in climate models. Such processes include, for example,
clouds and convection.
Two methods are proposed in the literature to evaluate climate models in weather
prediction mode. The diagnostic of initial physical tendencies (Rodwell and Palmer,
2007) allows evaluation closest to the process level. However, this technique requires
data assimilation capabilities of the used model, which are not always available for
climate models that are used to solve boundary condition problems. Alternatively
transpose-AMIP experiments were proposed by Phillips et al., (2004), where a short
forecast with a climate model is initialized with an analysis produced by an ‘alien’
model. The advantage is that no data assimilation capabilities are required. However,
the analysis from the alien model is not entirely consistent with the physics of the
evaluated climate model and this can lead to spurious tendencies at the beginning of
each forecast. This could potentially lead to wrongly diagnosed model error. Therefore
errors in the transpose-AMIP framework are diagnosed after a few forecast days in the
hope that the initial shock has decayed sufficiently. At these longer lead times, there
is more scope for processes to interact and for local errors to be overwhelmed by
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