She et al. 
Operational Oceanography and Earth System Science 
Frontiers In Earth Science | www.frontlersln.org 
7 
February 2020 | Volume 8 | Article 7 
reanalysis production in RCO-SCOBI and NEMO-SCOBI (Axell 
and Liu, 2016; Liu et al., 2017). Currently the Baltic Sea data 
assimilation collaboration in CMEMS focuses on developing 
physical and biogeochemical assimilation systems by using the 
PDAF both for operational NRT forecast and also for reanalyses. 
Assimilation schemes for SST, T/S, sea ice and nutrients are 
relatively mature. New schemes are developed for assimilating sea 
level and satellite ocean-color data (Tuomi et al., 2018). 
Model Quality and Validation 
Before 2009, the operational model validation and quality 
assessment were mainly done at national level with different 
methods and quality standards. Common cal/val methodology 
had been developed and applied in the MyOcean projects 
(2009-2015), including cal/val metrics definition and error 
statistics calculation and presentation for new model version, 
NRT validation and reanalysis quality assessment. The cal/val 
has been part of the BAL MFC operational activities since 
2015. Before release of each new BAL MFC model version— 
both forecasting and reanalysis systems, a correspondent QUID 
(Quality Information Document) report has to be released 
to demonstrate the quality of the new version products 
(e.g., Golbeck et al., 2017). The NRT validation for the 
BAL MFC forecast products (ocean-ice-wave-biogeochemical 
parameters) is shown online at the BOOS website. The 
cal/val method and toolbox developed in the BAL MFC 
is now further extended to a BOOS model quality and 
validation cooperation. 
Multi-Model Ensemble (MME) Forecasting 
Based on NRT exchange of both model and observational data 
via the BOOS ftp network, a MME forecast system has been 
developed for sea level, SST, sea surface salinity (SSS), T/S and 
currents (Golbeck et al., 2015). By weighting the individual 
forecast related to its forecasting error, a weighted MME method 
is used to generate the MME forecast. The results, shown online 
at the BOOS website, demonstrate superior quality of the MME 
forecast to the individual ones. The MME is a joint achievement 
of ROOSs and MFCs in the Baltic and North Sea. Currently 
the BOOS MME Working Group aims at extending the current 
MME system for national forecasting use. 
Major Challenges 
Future direction of the operational modeling in the Baltic Sea 
is seamless modeling in spatial, temporal, and state variable 
dimensions (WMO, 2015; She and Murawski, 2018). In spatial 
scales, the modeling capacity will be extended from basin scale to 
local coastal-estuary scale and from mesoscale to sub-mesoscale. 
In temporal scales, the synoptic and climate scales will be 
resolved by the same operational modeling framework. For 
state variables, future operational modeling capacity (including 
forecast, reanalysis, and scenario-based projections) will be 
extended to cover sedimentation, movements of pollutants 
(either as particles or Eulerian tracers) and biological parameters. 
In this dimension, operational ecological modeling will be 
developed, different modeling sectors will be coupled, e.g., 
hydrological-ocean coupling, wave-ocean and wave-ice coupling, 
and ocean-optical coupling. 
There still exist many knowledge gaps toward the 
establishment of the seamless operational service. Monitoring 
and accurate modeling of water and biogeochemical mass 
transport caused by coastal-estuary interaction, inter-sub-basin 
exchange and meso- and submeso-scale eddies is still a challenge. 
Capacities for precisely predicting currents, upwelling, extreme 
sea level and waves in icing waters, skin temperature, algae 
bloom, and oxygen depletion are yet to be improved. 
BALTEX/BALTIC EARTH MARINE 
RESEARCH 
In the following, a few selected research highlights from 
BALTEX/Baltic Earth are presented, documenting the progress 
in physical oceanography of the Baltic Sea during 2003-2014 
(Omstedt et al., 2004, 2014; see also BACC Author Team, 2008; 
BACC II Author Team, 2015) and after. One of the main 
motivations for the foundation of BALTEX in the 1990s was 
the exchange of data between eastern and western Baltic Sea 
countries. Due to the Iron Curtain after World War II, the 
exchange of scientific information was limited. Hence, after the 
rise of the Iron Curtain in the 1990s BALTEX aimed to enhance 
international collaboration between the Baltic Sea countries and 
to increase the exchange of especially observational data. 
Process Understanding 
With the help of project-oriented research data and process 
modeling, our knowledge about oceanographic processes and 
the interactions of the ocean with the other components of the 
Earth system such as atmosphere, land surface and sediments 
has considerably increased since the start of BALTEX (Omstedt 
et al., 2004, 2014). For instance, the importance of surface waves 
in air-sea interaction of heat, momentum, and matter is better 
understood, and Stokes drift and Langmuir circulation have been 
identified as likely playing an important role in surface water 
mixing explaining the underestimation of mixed layer depth in 
many Baltic Sea models (Reissmann et al., 2009). 
Research on water exchange between the Baltic Sea and North 
Sea and saltwater inflows into the Baltic has a long tradition. 
Today we know that, on average, half of the total amount of 
salt imported into the Baltic is supplied by barotropic inflows 
of highly saline water (Mohrholz, 2018). In particular, the well- 
observed, exceptionally strong Major Baltic Inflow (MBI) of 
2014 (Mohrholz et al., 2015) enabled unprecedented, detailed 
studies of the dynamics of saltwater inflow events and of their 
implications for the ecosystem (e.g., Grawe et al., 2015; Schmale 
et al., 2016; Holtermann et al., 2017; Bergen et al., 2018). In a 
long-term average MBIs contribute 20 to 25% to the total salt 
import into the Baltic (Mohrholz, 2018), beside this they are 
the solely mechanism for deep water ventilation of the central 
Baltic (Meier et al., 2006). Despite the decrease of nutrient supply 
after the 1980s, recently observed oxygen consumption rates 
are higher than ever observed (Meier et al., 2018b). According 
to model results, oxygen consumption in the water column