TITLE here

Following Becker et al. 2014 approach ( 10.1175/JCLI-D-13-00597.1 ) 1-4 lead seasonal forecast correlation. 

The correlation is between 1st member and ensemble mean of rest of members.

TITLE here

Following Becker et al. 2014 approach ( 10.1175/JCLI-D-13-00597.1 ) 1-4 lead seasonal forecast correlation. 

The correlation is between HadISST and ensemble mean of simulation members.

TITLE here

nino3.4 prediction skill, compare with persistence prediction.

TITLE here

SST Rank histogram at global, NAtl(20-70N, 110W-40E) and WNPac(20-70N, 110E-180)
HadISST as observation data.
Rank histogram is ranking frequency of observation data with ensemble members. ie. 
Histogram all flat -> good ensemble
Large at most right bin -> model members are overestimate.
Large at most left bin  -> model members are underestimate.
Bell shape -> model simulation is too diverse.
Inverted bell shape -> model simulation is too concentrated.

TITLE here

Atlantic-3 (20W-0, 3S-3N) prediction skill, compare with persistence prediction.

TITLE here

From Becker etal 2014. ( 10.1175/JCLI-D-13-00597.1 ) 1-4 lead seasonal forecast correlation. 

The correlation is between 1st member and ensemble mean of rest of members.

TITLE here

Following Becker et al. 2014 approach ( 10.1175/JCLI-D-13-00597.1 ) 1-4 lead seasonal forecast correlation. 

The correlation is between GPCP and ensemble mean of simulation members.

TITLE here

Following Becker et al. 2014 approach ( 10.1175/JCLI-D-13-00597.1 ) 1-4 lead seasonal forecast correlation. 

The correlation is 100m OHC between EN4 4.2.1 analysis and ensemble mean of simulation members.

TITLE here

SST anomaly Rank histogram at global, NAtl(20-70N, 110W-40E) and WNPac(20-70N, 110E-180)
HadISST as observation data.
Rank histogram is ranking frequency of observation data with ensemble members. 
Histogram all flat -> good ensemble distribution.
Large at most right bin -> model members are overestimate.
Large at most left bin  -> model members are underestimate.
Bell shape -> model simulation is too diverse.
Inverted bell shape -> model simulation is too concentrated.

TITLE here

Tsfc anomaly Rank histogram at global, NAtl(20-70N, 110W-40E) and WNPac(20-70N, 110E-180)
GISS as observation data.
Rank histogram is ranking frequency of observation data with ensemble members. 
Histogram all flat -> good ensemble distribution.
Large at most right bin -> model members are overestimate.
Large at most left bin  -> model members are underestimate.
Bell shape -> model simulation is too diverse.
Inverted bell shape -> model simulation is too concentrated.

TITLE here

Following Becker et al. 2014 approach ( 10.1175/JCLI-D-13-00597.1 ) 1-4 lead seasonal forecast correlation. 

The correlation is between GISS and ensemble mean of simulation members.