Ocean  Dynamics

Ö  Springer

resulting  positive  and  negative  transport  values  along  a
transect  are  summarized  yielding  the  total  inflow  and
outflow.  The  net  transport  is  given  by  summing  up  inflow ­
  and  outflow.  The  transport  data  of  all  contributing
models  are  displayed  in  charts  and  vertical  profiles  on
the  NOOS  and  BOOS  websites  (www.noos.ee/index.
php?id=151,  www.boos.org/index.php?id=24,  accessed
24  October  2014).
Based  on  data  from  this  ongoing  project,  a  MME  of
vertically  integrated  and  surface  water  transport  is  developed ­
  to  provide  information  about  model  uncertainty.
Daily  data  across  the  defined  transects  are  provided  by
up  to  six  models  for  NOOS  transects  and  by  up  to  four
models  for  BOOS  transects  (see  Fig.  7  for  transect  locations ­
  and  numbering).  The  ensemble  mean  and  standard
deviation  of  the  model  data  are  calculated  and  displayed
on  daily  maps.  An  additional  statistical  parameter,  the
coefficient  of  variation  (CV),  helps  to  compare  the  dispersion ­
  between  the  data  (i.e.,  Brown  (1998)).  The  CV  is
the  ratio  of  the  standard  deviation  (T std )  to  the  absolute
ensemble  mean  of  transports  (T mea „)\
CV  =  J std  ,  with  T mean  =  and
|  mean  \  72
Tstd  =  \J—j-  (Tj-TmeanY
A  low  CV  index  means  low  variability  between  the
models.  If  the  standard  deviation  is  larger  than  the  mean
transport,  the  CV  index  is  higher  than  1.  For  this  study,  the
CV  index  is  subdivided  into  three  categories:  category  1
(CV<1),  category  2  (1<CV<3),  and  category  3  (CV>3),
where  a  CV  above  3  is  often  associated  with  high  variability
or  even  outliers  (Brown  1998).
2.3  Spatio-temporal  statistics
For  the  statistical  evaluations,  only  complete  data  sets
were  included,  thus  only  those  days  and  regions  where
all  model  data  are  available.  The  amount  of  complete
data  sets  varies  with  region  and  parameter  and  is  also
due  to  the  late  inclusion  of  some  forecasts.  Accordingly,
the  study  period  varies  between  the  parameters:  For  SST
and  SSS,  the  period  is  01.01.2014-31.12.2014,  SSC  are
evaluated  for  the  time  period  01.05.2014-31.05.2015,
and  TRA  is  studied  for  the  period  01.04.2013-31.05.2015.

2.3.1  Comparison  of  sea  surface  temperature  forecasts
to  satellite  observations
Sea  surface  temperature  of  the  MME  mean,  the  MME  median ­
  (MME  products),  and  the  individual  forecasts  are

compared  to  remote  sensing  (satellite)  data.  It  should  be
mentioned  that  satellite  SST  measures  skin  temperature,
while  the  SST  used  for  the  MME  is  the  5-m  mean  of  the
upper  model  layers.  Due  to  the  diverse  performance  of
satellite  observations,  several  products  are  selected  for
the  comparison:  For  the  North  Sea,  the  daily  level  3
MyOcean  SST  nighttime  satellite  data  is  used,  which  is
from  the  mono  sensor  AVHRR.  For  the  Baltic  Sea,  the
comparison  is  carried  out  by  using  the  daily  level  3
MyOcean  SST  nighttime  satellite  product,  which  is  provided ­
  by  various  sensors:  AATSR,  AVHRR,  AVHRR  GAC,
SEVIRI,  GOES  Imager,  MODIS,  and  TMI.  It  has  to  be
noted  that  satellite  data  is  affected  by  cloud  cover.  In  comparison ­
  to  the  Baltic  Sea,  less  satellite  data  are  used  for  the
North  Sea,  where  the  satellite  products  are  from  mono
sensor.
Due  to  the  limitation  of  the  spatial  coverage  of  SST
satellite  data  in  the  North  Sea  and  the  Baltic  Sea,  the
comparison  is  carried  out  on  a  monthly  basis.  The  SST
01-h  forecast  is  selected  for  comparison,  since  it  is  closest
to  the  nighttime  satellite  data.  Satellite  data  at  0  h  UTC
are  interpolated  to  the  reference  grids  of  the  MME  products. ­
  The  bias  between  the  individual  SST  01-h  forecast
and  the  satellite  data  (forecast-satellite  data)  is  averaged
over  each  month  at  each  grid  point.  In  addition  the  rootmean-square
  deviation  (RMSD)  of  each  forecast  is  calculated ­
  for  each  month  at  each  grid  point.  Moreover,  the
number  of  days  with  available  satellite  data  is  divided
by  the  length  of  the  month  giving  the  available  satellite
data  (%)  for  each  grid  cell.  It  has  to  be  noted  that  only
grid  points  are  taken  into  account,  where  the  satellite  data
are  available  for  more  than  7  days  per  month.  The  monthly ­
  mean  values  for  bias,  RMSD,  and  available  satellite
data  are  further  spatially  averaged.  Annual  means  of  bias
and  RMSD  are  compared  respectively.  The  comparison  is
done  for  the  time  period  January-December  2014  using
the  MATLAB  package  CalVal-toolbox  (Lagemaa  et  al.
2013;  Jandt  et  al.  2014).  Results  are  presented  in
Sect.  4.1.

2.3.2  Seasonal  changes  of  sea  surface  salinity
For  SSS,  the  differences  among  the  individual  forecasts
are  evaluated  for  the  time  period  January-December
2014.  Therefore,  the  temporal  mean  of  the  MME  mean
and  the  standard  deviation  between  the  forecasts  is  calculated ­
  at  each  grid  point.  In  addition,  the  daily  spatial
mean  for  each  region  is  calculated  for  each  forecast
and  the  MME  products.  The  ensemble  spread,
expressed  as  the  ensemble  standard  deviation,  is  taken
into  account  for  the  comparison.  Results  are  presented
in  Sect.  4.2.