Ocean Dynamics 
Ö Springer 
of EPS. A single-model EPS uses one model with perturbed 
initial, boundary, and/or forcing conditions and provides a 
more skillful indication of how likely an event occurs com 
pared to single forecasts (Toth and Kalnay 1993; Molteni et al. 
1996). But, this approach assumes that the model itself is well 
verified and that uncertainty arises only from errors in the 
applied conditions. Aside from the high computational effort, 
another disadvantage of this method is the difficulty in 
attaining a sufficient spread of the ensemble and thereby miss 
ing the full range of uncertainty (Eloutekamer et al. 1996). 
Also, systematic biases or errors in model parameterizations 
can impact the skill of EPS (Molteni et al. 1996). Another 
method is to combine single-model ensembles from different 
models, creating a multi-model multi-analysis ensemble 
(MMAE) that has more skill than any one single-model EPS 
also due to an increased ensemble spread (Evans et al. 2000; 
Richardson 2001; Mylne et al. 2002). A third approach is the 
construction of a so-called poor-man’s ensemble system 
(PEPS) using independent forecast models from different op 
erational centers. An advantage of PEPS is that the model 
uncertainty can be sampled through the variety of model res 
olutions, model numerics and physical formulations, initiali 
zation methods, boundary data, and forcing data (Ebert 2001). 
Since the PEPS members typically are operational model runs, 
this approach has little added computational cost. Compared 
to a single-model EPS with perturbed initial conditions, PEPS 
is not prone to systematic biases and often has the advantage 
of higher spatial resolution in the individual member models 
(Ebert 2001). In several studies, PEPS has been compared to 
single-model EPS for short-range forecast, with the result that 
the skillful PEPS is shown to be highly competitive with the 
EPS (Atger 1999; Ziehmann 2000; Ebert 2001; Buizza et al. 
2003; Arribas et al. 2005). However, the main disadvantages 
of the PEPS are the low ensemble size and the fact that one 
model with low forecast skill might have a strong negative 
impact on the whole ensemble system (Ebert 2001). 
Ziehmann (2000) also found a low number of contributing 
independent models to be a limiting factor for operational 
use. Nevertheless, the equally weighted four-member PEPS 
outperformed larger ensembles in some key aspects. A 
modified and improved approach was conducted by 
Krishnamurti et al. (1999) who developed a PEPS by ap 
plying a multiple regression technique on each forecast in 
order to determine the optimal weight of the models. The 
so-called super-ensemble outperformed other models to 
which it was compared. 
First approaches to PEPS-type ensemble systems have also 
been developed for ocean forecasting. In 2000, partners of the 
Northwest European Shelf Operational Oceanographic Sys 
tem (NOOS, www.noos.ee, accessed 24 October 2014) 
established an exchange of surge forecasts as well as water 
level measurements in order to support the national water 
level forecasting services in the NOOS area. Later, in 2007, 
a weighting method, Bayesian model averaging (BMA), was 
applied on the Multi functional Access Tool for Operational 
Ocean data Services (MATROOS) system to gain more infor 
mation about model uncertainty (Becker 2007; Ebel and 
Becker 2010). The Ensemble Suige Forecast (ENSURF) sys 
tem was fiirther developed by Pérez et al. (2012), by applying 
BMA to independent operational sea level forecasts in the region 
of the Ireland-Biscay-Iberia Regional Operational Oceanograph 
ic System (IBI-ROOS) and the western Mediterranean coast. 
Recently, the Group for High Resolution Sea Surface Tempera 
ture (GHRSST) developed a Multi-Product Ensemble (GMPE) 
for the global ocean by using various individual level 4 SST 
analyses and calculating the ensemble median and standard de 
viations. A comparison to independent Argo data demonstrated 
that the GMPE median yields a more accurate estimate of SST 
than the individual analyses (Martin et al. 2012). Weisheimer 
et al. (2009) used five equally weighted coupled atmosphere- 
ocean circulation models to study Pacific SST by comparison 
with a previous-generation ensemble, DEMETER (Palmer et al. 
2004; Doblas-Reyes et al. 2005), yielding a higher skill for the 
new multi-model ensemble. More weighted 3D multi-model 
ensembles have been developed for SST forecasts by applying 
BMA or a Kalman Filter over a learning period for determin 
ing the optimal weights between the models (Logutov and 
Robinson 2005; Rafiery et al. 2005; Lenartz et al. 2010; 
Mourre et al. 2012). The super-ensembles have been validated 
against in situ data of CTD, gliders, drifter, and scan fish. 
The main goal of this paper is to present a new multi-model 
approach for the North Sea and the Baltic Sea, which is used 
to illustrate uncertainties between operational ocean forecast 
ing products. The new PEPS, hereafter referred to as multi 
model ensemble (MME), uses outputs from existing opera 
tional ocean forecasting models as provided by the modeling 
groups, and all models have individual model codes, resolu 
tion, boundary conditions, atmospheric forcing, and methods 
for data assimilation. The uncertainties are described on a 
temporal and spatial scale by ensemble statistics and spatio- 
temporal statistics. The aim is to identify the amount, spatial, 
and temporal distribution of uncertainties for several physical 
parameters and by this to provide some added value to the 
users of the single-model forecasts. It has to be noted that 
computation of a best estimate for all parameters, which 
would need more sophisticated averaging methods, or the 
in-depth explanation of the causes of uncertainties, which 
would need frill access to the four dimensional model outputs, 
is beyond the scope of this paper. 
The development of the MME was done in the framework 
of the MyOcean project, funded by the EU research frame 
work programme (FP7) (http://www.myocean.eu/, accessed 
24 October 2014), and is now continued in the Copernicus 
Marine Environment Monitoring Service (CMEMS, http:// 
marine.copemicus.eu, accessed 30 June 2015). The MME 
was developed in the framework of two MyOcean work