Horizontal Validation results comparing model output against observations.

Experiment Information

Area: AMM7 Experiment: NetSW_LW Validation Type: Horizontal Validation Variables: SALT_SURFACE, TEMP_SURFACE

Overview

MetricSALT_SURFACE / CCI-SSSTEMP_SURFACE / OSTIATEMP_SURFACE / CCI-SST
RMSE1.00770.94150.9283
Bias0.28580.42080.4006
Corr0.60030.96270.9621
N points195,106,513151,821,276197,655,640

Salinity (surface) — annual statistics timeseries (RMSE, bias, correlation) Salinity (surface) — annual statistics timeseries (RMSE, bias, correlation)

Temperature (surface) — annual statistics timeseries (RMSE, bias, correlation) Temperature (surface) — annual statistics timeseries (RMSE, bias, correlation)

Horizontal Validation

Statistics

SALT_SURFACE

MetricCCI-SSS
RMSE1.0077
Bias+0.2858
MAE0.4488
Corr0.6003
Model mean35.1821
Obs mean34.9009
View Full Statistics Report
################################################################################
SALT_SURFACE Validation Statistics
################################################################################
  Format:     1.1
  Created:    2026-05-06 11:28:55
  Author:       RT
  Project:      OceanICU
  Institute:    BB
  Area:         AMM7
  Experiment:   NetSW_LW
################################################################################

================================================================================
Period: 2016-2023  |  Model: pyGETM
================================================================================
Analysed: 2026-05-06  11:28:55

--- CCI-SSS ---
  RMSE:          1.0077
  Bias:         +0.2858
  MAE:           0.4488
  Correlation:   0.6003
  N points:      195,106,513
  Model mean:   35.1821
  Obs mean:     34.9009
  Model std:    0.9352
  Obs std:      1.1772

  Error distribution:
    Min:         -32.4221
    5th pct:      -0.2771
    25th pct:     +0.0077
    Median:       +0.1546
    75th pct:     +0.4123
    95th pct:     +1.3204
    Max:         +16.3265

  Monthly breakdown:
  Month      RMSE      Bias      MAE    Corr
  --------------------------------------------
  Jan      0.8432   +0.2394   0.3926  0.6232
  Feb      0.9136   +0.2317   0.4012  0.5853
  Mar      1.0157   +0.2551   0.4435  0.5626
  Apr      1.0651   +0.2812   0.4709  0.5669
  May      1.0869   +0.2993   0.4697  0.6017
  Jun      1.1483   +0.3362   0.4991  0.6016
  Jul      1.1188   +0.3210   0.4819  0.6039
  Aug      1.0879   +0.3178   0.4772  0.5964
  Sep      1.0561   +0.3132   0.4666  0.5980
  Oct      0.9856   +0.2984   0.4439  0.6115
  Nov      0.9353   +0.2941   0.4415  0.6017
  Dec      0.8550   +0.2402   0.3961  0.6195
  --------------------------------------------
  All      1.0077   +0.2858   0.4488  0.6003


================================================================================
Period: 2016  |  Model: pyGETM
================================================================================
Analysed: 2026-05-06  11:28:55

--- CCI-SSS ---
  RMSE:          0.9594
  Bias:         +0.3248
  MAE:           0.4503
  Correlation:   0.6330
  N points:      24,460,506
  Model mean:   35.2094
  Obs mean:     34.8875
  Model std:    0.8429
  Obs std:      1.1664

  Error distribution:
    Min:         -25.5946
    5th pct:      -0.2305
    25th pct:     +0.0541
    Median:       +0.1935
    75th pct:     +0.4096
    95th pct:     +1.3452
    Max:         +16.3265


================================================================================
Period: 2017  |  Model: pyGETM
================================================================================
Analysed: 2026-05-06  11:28:55

--- CCI-SSS ---
  RMSE:          1.0479
  Bias:         +0.2937
  MAE:           0.4354
  Correlation:   0.5488
  N points:      24,395,369
  Model mean:   35.2175
  Obs mean:     34.9268
  Model std:    0.8948
  Obs std:      1.1686

  Error distribution:
    Min:         -28.3040
    5th pct:      -0.2082
    25th pct:     +0.0260
    Median:       +0.1407
    75th pct:     +0.3729
    95th pct:     +1.3619
    Max:         +16.2470


================================================================================
Period: 2018  |  Model: pyGETM
================================================================================
Analysed: 2026-05-06  11:28:55

--- CCI-SSS ---
  RMSE:          0.9953
  Bias:         +0.2466
  MAE:           0.4174
  Correlation:   0.5908
  N points:      24,338,489
  Model mean:   35.1827
  Obs mean:     34.9407
  Model std:    0.9119
  Obs std:      1.1622

  Error distribution:
    Min:         -29.8040
    5th pct:      -0.2883
    25th pct:     -0.0009
    Median:       +0.1349
    75th pct:     +0.3634
    95th pct:     +1.1597
    Max:         +15.5663


================================================================================
Period: 2019  |  Model: pyGETM
================================================================================
Analysed: 2026-05-06  11:28:55

--- CCI-SSS ---
  RMSE:          1.0044
  Bias:         +0.2971
  MAE:           0.4474
  Correlation:   0.5851
  N points:      24,377,458
  Model mean:   35.1933
  Obs mean:     34.8998
  Model std:    0.8936
  Obs std:      1.1549

  Error distribution:
    Min:         -29.1627
    5th pct:      -0.2490
    25th pct:     +0.0209
    Median:       +0.1611
    75th pct:     +0.4125
    95th pct:     +1.3083
    Max:         +14.6261


================================================================================
Period: 2020  |  Model: pyGETM
================================================================================
Analysed: 2026-05-06  11:28:55

--- CCI-SSS ---
  RMSE:          1.0255
  Bias:         +0.3018
  MAE:           0.4643
  Correlation:   0.5967
  N points:      24,422,636
  Model mean:   35.1828
  Obs mean:     34.8879
  Model std:    0.9311
  Obs std:      1.1916

  Error distribution:
    Min:         -29.0291
    5th pct:      -0.2635
    25th pct:     -0.0010
    Median:       +0.1472
    75th pct:     +0.4307
    95th pct:     +1.4081
    Max:         +14.5636


================================================================================
Period: 2021  |  Model: pyGETM
================================================================================
Analysed: 2026-05-06  11:28:55

--- CCI-SSS ---
  RMSE:          1.0262
  Bias:         +0.2771
  MAE:           0.4437
  Correlation:   0.6026
  N points:      24,370,685
  Model mean:   35.1523
  Obs mean:     34.8807
  Model std:    0.9750
  Obs std:      1.2061

  Error distribution:
    Min:         -27.7744
    5th pct:      -0.2809
    25th pct:     +0.0145
    Median:       +0.1563
    75th pct:     +0.4129
    95th pct:     +1.2479
    Max:         +15.5230


================================================================================
Period: 2022  |  Model: pyGETM
================================================================================
Analysed: 2026-05-06  11:28:55

--- CCI-SSS ---
  RMSE:          1.0024
  Bias:         +0.2781
  MAE:           0.4645
  Correlation:   0.6167
  N points:      24,370,685
  Model mean:   35.1668
  Obs mean:     34.8939
  Model std:    1.0255
  Obs std:      1.1594

  Error distribution:
    Min:         -32.4221
    5th pct:      -0.3130
    25th pct:     -0.0127
    Median:       +0.1576
    75th pct:     +0.4558
    95th pct:     +1.2994
    Max:         +12.8125


================================================================================
Period: 2023  |  Model: pyGETM
================================================================================
Analysed: 2026-05-06  11:28:55

--- CCI-SSS ---
  RMSE:          1.0010
  Bias:         +0.2671
  MAE:           0.4675
  Correlation:   0.6295
  N points:      24,370,685
  Model mean:   35.1518
  Obs mean:     34.8901
  Model std:    0.9913
  Obs std:      1.2056

  Error distribution:
    Min:         -29.6282
    5th pct:      -0.3723
    25th pct:     -0.0433
    Median:       +0.1417
    75th pct:     +0.4433
    95th pct:     +1.3474
    Max:         +15.5347

📄 Download Statistics Report (txt) · 📄 YAML

TEMP_SURFACE

MetricOSTIACCI-SST
RMSE0.94150.9283
Bias+0.4208+0.4006
MAE0.73980.7288
Corr0.96270.9621
Model mean12.291512.4765
Obs mean11.870712.0759
View Full Statistics Report
################################################################################
TEMP_SURFACE Validation Statistics
################################################################################
  Format:     1.1
  Created:    2026-05-06 10:52:36
  Author:       RT
  Project:      OceanICU
  Institute:    BB
  Area:         AMM7
  Experiment:   NetSW_LW
################################################################################

================================================================================
Period: 2016-2023  |  Model: pyGETM
================================================================================
Analysed: 2026-05-06  10:52:36

--- OSTIA ---
  RMSE:          0.9415
  Bias:         +0.4208
  MAE:           0.7398
  Correlation:   0.9627
  N points:      151,821,276
  Model mean:   12.2915
  Obs mean:     11.8707
  Model std:    3.9080
  Obs std:      3.8133

  Error distribution:
    Min:         -12.9817
    5th pct:      -0.9061
    25th pct:     -0.0742
    Median:       +0.4324
    75th pct:     +0.9404
    95th pct:     +1.7018
    Max:         +9.2566

  Monthly breakdown:
  Month      RMSE      Bias      MAE    Corr
  --------------------------------------------
  Jan      1.0564   +0.4409   0.8227  0.9485
  Feb      1.0318   +0.4161   0.7851  0.9525
  Mar      0.9651   +0.3636   0.7317  0.9572
  Apr      0.8632   +0.2957   0.6639  0.9652
  May      0.8248   +0.3558   0.6514  0.9702
  Jun      1.0194   +0.5312   0.8090  0.9593
  Jul      1.0607   +0.5550   0.8423  0.9580
  Aug      0.9591   +0.4249   0.7414  0.9617
  Sep      0.7970   +0.2961   0.6193  0.9741
  Oct      0.7889   +0.3338   0.6222  0.9745
  Nov      0.9222   +0.4741   0.7408  0.9647
  Dec      1.0633   +0.5592   0.8480  0.9544
  --------------------------------------------
  All      0.9415   +0.4208   0.7398  0.9627

--- CCI-SST ---
  RMSE:          0.9283
  Bias:         +0.4006
  MAE:           0.7288
  Correlation:   0.9621
  N points:      197,655,640
  Model mean:   12.4765
  Obs mean:     12.0759
  Model std:    3.7919
  Obs std:      3.7509

  Error distribution:
    Min:         -14.3565
    5th pct:      -0.9097
    25th pct:     -0.1167
    Median:       +0.4034
    75th pct:     +0.9249
    95th pct:     +1.6919
    Max:         +9.3532

  Monthly breakdown:
  Month      RMSE      Bias      MAE    Corr
  --------------------------------------------
  Jan      1.0161   +0.4097   0.7839  0.9490
  Feb      0.9931   +0.4081   0.7525  0.9531
  Mar      0.9465   +0.3793   0.7199  0.9568
  Apr      0.8466   +0.3059   0.6541  0.9640
  May      0.8181   +0.3624   0.6459  0.9686
  Jun      0.9992   +0.5039   0.7926  0.9582
  Jul      1.0285   +0.5264   0.8177  0.9591
  Aug      0.9547   +0.4141   0.7398  0.9626
  Sep      0.8154   +0.2718   0.6339  0.9730
  Oct      0.8119   +0.2914   0.6401  0.9724
  Nov      0.9148   +0.4188   0.7304  0.9625
  Dec      1.0519   +0.5125   0.8328  0.9511
  --------------------------------------------
  All      0.9283   +0.4006   0.7288  0.9621


================================================================================
Period: 2016  |  Model: pyGETM
================================================================================
Analysed: 2026-05-06  10:52:36

--- OSTIA ---
  RMSE:          0.8488
  Bias:         +0.1464
  MAE:           0.6590
  Correlation:   0.9602
  N points:      19,016,628
  Model mean:   12.0200
  Obs mean:     11.8735
  Model std:    3.8818
  Obs std:      3.7392

  Error distribution:
    Min:         -10.8903
    5th pct:      -1.1494
    25th pct:     -0.3265
    Median:       +0.1762
    75th pct:     +0.6929
    95th pct:     +1.4225
    Max:         +6.8282

--- CCI-SST ---
  RMSE:          0.8102
  Bias:         +0.1454
  MAE:           0.6280
  Correlation:   0.9633
  N points:      24,757,704
  Model mean:   12.1967
  Obs mean:     12.0513
  Model std:    3.7655
  Obs std:      3.7266

  Error distribution:
    Min:         -10.8550
    5th pct:      -1.1084
    25th pct:     -0.3038
    Median:       +0.1764
    75th pct:     +0.6670
    95th pct:     +1.3590
    Max:         +7.4316


================================================================================
Period: 2017  |  Model: pyGETM
================================================================================
Analysed: 2026-05-06  10:52:36

--- OSTIA ---
  RMSE:          0.9122
  Bias:         +0.4188
  MAE:           0.7204
  Correlation:   0.9628
  N points:      18,964,670
  Model mean:   12.3162
  Obs mean:     11.8974
  Model std:    3.7273
  Obs std:      3.6558

  Error distribution:
    Min:         -6.7119
    5th pct:      -0.8847
    25th pct:     -0.0655
    Median:       +0.4394
    75th pct:     +0.9462
    95th pct:     +1.7174
    Max:         +9.2566

--- CCI-SST ---
  RMSE:          0.8836
  Bias:         +0.3842
  MAE:           0.6967
  Correlation:   0.9640
  N points:      24,690,059
  Model mean:   12.5105
  Obs mean:     12.1263
  Model std:    3.6457
  Obs std:      3.6290

  Error distribution:
    Min:         -7.9452
    5th pct:      -0.9117
    25th pct:     -0.1069
    Median:       +0.3939
    75th pct:     +0.9053
    95th pct:     +1.6614
    Max:         +7.1168


================================================================================
Period: 2018  |  Model: pyGETM
================================================================================
Analysed: 2026-05-06  10:52:36

--- OSTIA ---
  RMSE:          1.0061
  Bias:         +0.5127
  MAE:           0.7935
  Correlation:   0.9615
  N points:      18,964,670
  Model mean:   12.1566
  Obs mean:     11.6439
  Model std:    4.0553
  Obs std:      3.9844

  Error distribution:
    Min:         -12.2141
    5th pct:      -0.8627
    25th pct:     -0.0095
    Median:       +0.5263
    75th pct:     +1.0548
    95th pct:     +1.8536
    Max:         +8.8416

--- CCI-SST ---
  RMSE:          0.9916
  Bias:         +0.5336
  MAE:           0.7786
  Correlation:   0.9631
  N points:      24,690,050
  Model mean:   12.3377
  Obs mean:     11.8041
  Model std:    3.9064
  Obs std:      3.8476

  Error distribution:
    Min:         -12.0296
    5th pct:      -0.7528
    25th pct:     +0.0207
    Median:       +0.5329
    75th pct:     +1.0581
    95th pct:     +1.8693
    Max:         +7.4533


================================================================================
Period: 2019  |  Model: pyGETM
================================================================================
Analysed: 2026-05-06  10:52:36

--- OSTIA ---
  RMSE:          0.9338
  Bias:         +0.4929
  MAE:           0.7384
  Correlation:   0.9661
  N points:      18,964,670
  Model mean:   12.2560
  Obs mean:     11.7631
  Model std:    3.7895
  Obs std:      3.6817

  Error distribution:
    Min:         -8.0217
    5th pct:      -0.7613
    25th pct:     +0.0106
    Median:       +0.5021
    75th pct:     +1.0138
    95th pct:     +1.7466
    Max:         +7.6184

--- CCI-SST ---
  RMSE:          0.9417
  Bias:         +0.5068
  MAE:           0.7449
  Correlation:   0.9640
  N points:      24,690,060
  Model mean:   12.4589
  Obs mean:     11.9521
  Model std:    3.6799
  Obs std:      3.6177

  Error distribution:
    Min:         -9.7678
    5th pct:      -0.7272
    25th pct:     -0.0094
    Median:       +0.5087
    75th pct:     +1.0421
    95th pct:     +1.7834
    Max:         +6.9756


================================================================================
Period: 2020  |  Model: pyGETM
================================================================================
Analysed: 2026-05-06  10:52:36

--- OSTIA ---
  RMSE:          0.9412
  Bias:         +0.4488
  MAE:           0.7341
  Correlation:   0.9642
  N points:      19,016,628
  Model mean:   12.2282
  Obs mean:     11.7794
  Model std:    3.7892
  Obs std:      3.6783

  Error distribution:
    Min:         -9.9017
    5th pct:      -0.8316
    25th pct:     -0.0329
    Median:       +0.4633
    75th pct:     +0.9674
    95th pct:     +1.7206
    Max:         +7.9620

--- CCI-SST ---
  RMSE:          0.9430
  Bias:         +0.4880
  MAE:           0.7398
  Correlation:   0.9644
  N points:      24,757,704
  Model mean:   12.4062
  Obs mean:     11.9182
  Model std:    3.6910
  Obs std:      3.6279

  Error distribution:
    Min:         -14.3298
    5th pct:      -0.7581
    25th pct:     -0.0095
    Median:       +0.4921
    75th pct:     +1.0011
    95th pct:     +1.7472
    Max:         +7.7940


================================================================================
Period: 2021  |  Model: pyGETM
================================================================================
Analysed: 2026-05-06  10:52:36

--- OSTIA ---
  RMSE:          1.0497
  Bias:         +0.5446
  MAE:           0.8199
  Correlation:   0.9561
  N points:      18,964,670
  Model mean:   12.2797
  Obs mean:     11.7351
  Model std:    3.9846
  Obs std:      3.8776

  Error distribution:
    Min:         -11.8210
    5th pct:      -0.8373
    25th pct:     +0.0189
    Median:       +0.5428
    75th pct:     +1.1011
    95th pct:     +1.9431
    Max:         +9.0611

--- CCI-SST ---
  RMSE:          1.0277
  Bias:         +0.4997
  MAE:           0.7983
  Correlation:   0.9553
  N points:      24,689,971
  Model mean:   12.4782
  Obs mean:     11.9786
  Model std:    3.8665
  Obs std:      3.8267

  Error distribution:
    Min:         -11.6745
    5th pct:      -0.8838
    25th pct:     -0.0684
    Median:       +0.4860
    75th pct:     +1.0571
    95th pct:     +1.9357
    Max:         +8.5444


================================================================================
Period: 2022  |  Model: pyGETM
================================================================================
Analysed: 2026-05-06  10:52:36

--- OSTIA ---
  RMSE:          0.8771
  Bias:         +0.4041
  MAE:           0.6910
  Correlation:   0.9691
  N points:      18,964,670
  Model mean:   12.4558
  Obs mean:     12.0517
  Model std:    3.9214
  Obs std:      3.8585

  Error distribution:
    Min:         -6.8852
    5th pct:      -0.8434
    25th pct:     -0.0608
    Median:       +0.4250
    75th pct:     +0.9047
    95th pct:     +1.6157
    Max:         +7.2476

--- CCI-SST ---
  RMSE:          0.8663
  Bias:         +0.3347
  MAE:           0.6819
  Correlation:   0.9656
  N points:      24,690,060
  Model mean:   12.6361
  Obs mean:     12.3015
  Model std:    3.7990
  Obs std:      3.7716

  Error distribution:
    Min:         -7.7442
    5th pct:      -0.9426
    25th pct:     -0.1763
    Median:       +0.3444
    75th pct:     +0.8622
    95th pct:     +1.6026
    Max:         +7.6917


================================================================================
Period: 2023  |  Model: pyGETM
================================================================================
Analysed: 2026-05-06  10:52:36

--- OSTIA ---
  RMSE:          0.9612
  Bias:         +0.3987
  MAE:           0.7625
  Correlation:   0.9623
  N points:      18,964,670
  Model mean:   12.6208
  Obs mean:     12.2221
  Model std:    4.0710
  Obs std:      3.9821

  Error distribution:
    Min:         -12.9817
    5th pct:      -1.0507
    25th pct:     -0.1016
    Median:       +0.4453
    75th pct:     +0.9589
    95th pct:     +1.7080
    Max:         +7.5824

--- CCI-SST ---
  RMSE:          0.9602
  Bias:         +0.3125
  MAE:           0.7622
  Correlation:   0.9575
  N points:      24,690,032
  Model mean:   12.7882
  Obs mean:     12.4757
  Model std:    3.9403
  Obs std:      3.9041

  Error distribution:
    Min:         -14.3565
    5th pct:      -1.1693
    25th pct:     -0.2715
    Median:       +0.3480
    75th pct:     +0.9166
    95th pct:     +1.7121
    Max:         +9.3532

📄 Download Statistics Report (txt) · 📄 YAML

Plots

Full Period

SALT_SURFACE

Salinity (surface) — monthly by year Salinity (surface) — monthly by year

Salinity (surface) — monthly statistics Salinity (surface) — monthly statistics

CCI-SSS

Salinity (surface) — CCI-SSS comparison Salinity (surface) — CCI-SSS comparison

Salinity (surface) — CCI-SSS monthly maps Salinity (surface) — CCI-SSS monthly maps

Salinity (surface) — CCI-SSS monthly taylor Salinity (surface) — CCI-SSS monthly taylor

Salinity (surface) — CCI-SSS pdf annual Salinity (surface) — CCI-SSS pdf annual

Salinity (surface) — CCI-SSS pdf monthly Salinity (surface) — CCI-SSS pdf monthly

Salinity (surface) — CCI-SSS spatial stats Salinity (surface) — CCI-SSS spatial stats

TEMP_SURFACE

Temperature (surface) — monthly by year CCI-SST Temperature (surface) — monthly by year CCI-SST

Temperature (surface) — monthly by year OSTIA Temperature (surface) — monthly by year OSTIA

Temperature (surface) — monthly statistics Temperature (surface) — monthly statistics

CCI-SST

Temperature (surface) — CCI-SST comparison Temperature (surface) — CCI-SST comparison

Temperature (surface) — CCI-SST monthly maps Temperature (surface) — CCI-SST monthly maps

Temperature (surface) — CCI-SST monthly taylor Temperature (surface) — CCI-SST monthly taylor

Temperature (surface) — CCI-SST pdf annual Temperature (surface) — CCI-SST pdf annual

Temperature (surface) — CCI-SST pdf monthly Temperature (surface) — CCI-SST pdf monthly

Temperature (surface) — CCI-SST spatial stats Temperature (surface) — CCI-SST spatial stats

OSTIA

Temperature (surface) — OSTIA comparison Temperature (surface) — OSTIA comparison

Temperature (surface) — OSTIA monthly maps Temperature (surface) — OSTIA monthly maps

Temperature (surface) — OSTIA monthly taylor Temperature (surface) — OSTIA monthly taylor

Temperature (surface) — OSTIA pdf annual Temperature (surface) — OSTIA pdf annual

Temperature (surface) — OSTIA pdf monthly Temperature (surface) — OSTIA pdf monthly

Temperature (surface) — OSTIA spatial stats Temperature (surface) — OSTIA spatial stats

Taylor diagram Taylor diagram

Gridded 3D Validation

Plots

Full Period

Temperature — monthly 3D Taylor diagram Temperature — monthly 3D Taylor diagram

ICES Point Profiles

Statistics

PSAL

MetricICES point observations
RMSE1.0444
Bias+0.2130
MAE0.3195
Corr0.6838
Model mean34.9935
Obs mean34.7805
View Full Statistics Report
################################################################################
PSAL Profile Validation Statistics
################################################################################
  Format:     1.1
  Created:    2026-05-06 12:42:23
  Area:         AMM7
  Experiment:   NetSW_LW
################################################################################

================================================================================
Period: 2016-2023  |  Model: pyGETM
================================================================================
Analysed: 2026-05-07  07:20:21

--- ICES point observations ---
  RMSE:          1.0444
  Bias:         +0.2130
  MAE:           0.3195
  Correlation:   0.6838
  N points:      4,479,562
  N profiles:    32,134
  Model mean:   34.9935
  Obs mean:     34.7805
  Model std:    0.7693
  Obs std:      1.3806

  Error distribution:
    Min:         -34.4617
    5th pct:      -0.2762
    25th pct:     -0.0024
    Median:       +0.1045
    75th pct:     +0.2258
    95th pct:     +0.6707
    Max:         +34.4926

  Statistics by depth:
  Depth               N        Bias        RMSE      Corr
  ------------------------------------------------------
  0-10m         288,164     +1.2519      3.6106    0.5622
  10-25m        420,745     +0.4517      1.4190    0.6832
  25-50m        567,989     +0.1706      0.5162    0.7665
  50-100m       763,824     +0.1251      0.2922    0.7967
  100-200m      681,882     +0.0789      0.1777    0.8136
  200-500m      782,467     +0.1002      0.1425    0.8359
  500-1000m     586,611     +0.0630      0.1345    0.8868

📄 Download Statistics Report (txt) · 📄 YAML

TEMP

MetricICES point observations
RMSE1.1929
Bias+0.3071
MAE0.8469
Corr0.9593
Model mean7.9451
Obs mean7.6380
View Full Statistics Report
################################################################################
TEMP Profile Validation Statistics
################################################################################
  Format:     1.1
  Created:    2026-05-06 12:39:49
  Area:         AMM7
  Experiment:   NetSW_LW
################################################################################

================================================================================
Period: 2016-2023  |  Model: pyGETM
================================================================================
Analysed: 2026-05-07  07:18:25

--- ICES point observations ---
  RMSE:          1.1929
  Bias:         +0.3071
  MAE:           0.8469
  Correlation:   0.9593
  N points:      4,488,796
  N profiles:    28,989
  Model mean:   7.9451
  Obs mean:     7.6380
  Model std:    4.0341
  Obs std:      4.0496

  Error distribution:
    Min:         -8.8166
    5th pct:      -1.4073
    25th pct:     -0.2566
    Median:       +0.2660
    75th pct:     +0.8738
    95th pct:     +2.2069
    Max:         +9.7904

  Statistics by depth:
  Depth               N        Bias        RMSE      Corr
  ------------------------------------------------------
  0-10m         286,658     +0.3697      1.2433    0.9625
  10-25m        422,418     +0.1945      1.2322    0.9463
  25-50m        570,518     +0.1912      1.2322    0.8883
  50-100m       766,720     +0.4023      1.0218    0.8609
  100-200m      684,363     +0.3649      0.9221    0.8528
  200-500m      783,657     +0.2531      1.4109    0.8521
  500-1000m     586,581     +0.2439      1.2771    0.9576

📄 Download Statistics Report (txt) · 📄 YAML

Plots

Practical Salinity — statistics vs depth profile Practical Salinity — statistics vs depth profile

Temperature — statistics vs depth profile Temperature — statistics vs depth profile

Argo Float Profiles

Statistics

PSAL

MetricARGO floats
RMSE4.3341
Bias+0.6635
MAE0.7589
Corr0.1747
Model mean35.3552
Obs mean34.6917
View Full Statistics Report
################################################################################
PSAL Profile Validation Statistics
################################################################################
  Format:     1.1
  Created:    2026-05-06 16:30:15
  Area:         AMM7
  Experiment:   NetSW_LW
################################################################################

================================================================================
Period: 2016-2023  |  Model: pyGETM
================================================================================
Analysed: 2026-05-06  16:30:15

--- ARGO floats ---
  RMSE:          4.3341
  Bias:         +0.6635
  MAE:           0.7589
  Correlation:   0.1747
  N points:      4,905,728
  N profiles:    18,473
  Model mean:   35.3552
  Obs mean:     34.6917
  Model std:    0.3072
  Obs std:      4.3260

  Error distribution:
    Min:         -26.4433
    5th pct:      -0.2524
    25th pct:     -0.0142
    Median:       +0.0582
    75th pct:     +0.1835
    95th pct:     +0.4617
    Max:         +36.8896

  Statistics by depth:
  Depth               N        Bias        RMSE      Corr
  ------------------------------------------------------
  0-10m          97,572     +0.5858      3.2640    0.1936
  10-25m        146,016     +0.7126      3.5961    0.1783
  25-50m        232,397     +0.7863      3.7338    0.1202
  50-100m       425,227     +0.8042      3.8500    0.1092
  100-200m      495,254     +0.7264      4.4642    0.1659
  200-500m     1,075,494     +0.7130      4.6170    0.1999
  500-1000m    1,117,739     +0.6050      4.5116    0.2516

📄 Download Statistics Report (txt) · 📄 YAML

TEMP

MetricARGO floats
RMSE1.3198
Bias+0.2620
MAE0.9788
Corr0.9609
Model mean8.3462
Obs mean8.0842
View Full Statistics Report
################################################################################
TEMP Profile Validation Statistics
################################################################################
  Format:     1.1
  Created:    2026-05-06 16:27:55
  Area:         AMM7
  Experiment:   NetSW_LW
################################################################################

================================================================================
Period: 2016-2023  |  Model: pyGETM
================================================================================
Analysed: 2026-05-06  16:27:55

--- ARGO floats ---
  RMSE:          1.3198
  Bias:         +0.2620
  MAE:           0.9788
  Correlation:   0.9609
  N points:      4,906,063
  N profiles:    18,473
  Model mean:   8.3462
  Obs mean:     8.0842
  Model std:    4.6737
  Obs std:      4.4683

  Error distribution:
    Min:         -49.6503
    5th pct:      -1.5851
    25th pct:     -0.5087
    Median:       +0.0598
    75th pct:     +1.0409
    95th pct:     +2.4750
    Max:         +20.4119

  Statistics by depth:
  Depth               N        Bias        RMSE      Corr
  ------------------------------------------------------
  0-10m          97,583     +0.4209      1.0560    0.9720
  10-25m        146,026     +0.3737      1.1224    0.9636
  25-50m        232,410     +0.7220      1.5659    0.9118
  50-100m       425,244     +0.9842      1.7337    0.8992
  100-200m      495,283     +0.5425      1.5783    0.9089
  200-500m     1,075,630     +0.1778      1.4003    0.9334
  500-1000m    1,117,875     -0.3191      1.0309    0.9688

📄 Download Statistics Report (txt) · 📄 YAML

Plots

Amm7 Argo Argo Overview Amm7 Argo Argo Overview

AMM7_ARGO — Hovmöller diagram AMM7_ARGO — Hovmöller diagram

Amm7 Argo Overview Amm7 Argo Overview

Practical Salinity — statistics vs depth profile Practical Salinity — statistics vs depth profile

Temperature — statistics vs depth profile Temperature — statistics vs depth profile

Observations

ARGO Observations — 2016-2023

ARGO Station Map ARGO Station Map

ARGO Observation Density ARGO Observation Density

ARGO Hovmoller ARGO Hovmoller

ICES Observations — 2016-2022

ICES Station Map ICES Station Map

ICES Observation Density ICES Observation Density

ICES Alk Hovmoller ICES Alk Hovmoller

ICES Amon Hovmoller ICES Amon Hovmoller

ICES Doxy Hovmoller ICES Doxy Hovmoller

ICES Ntra Hovmoller ICES Ntra Hovmoller

ICES Ph Hovmoller ICES Ph Hovmoller

ICES Phos Hovmoller ICES Phos Hovmoller

ICES PSAL Hovmoller ICES PSAL Hovmoller

ICES Slca Hovmoller ICES Slca Hovmoller

ICES TEMP Hovmoller ICES TEMP Hovmoller

ICES Observations — 2016-2023

ICES Station Map ICES Station Map

ICES Observation Density ICES Observation Density

ICES Alk Hovmoller ICES Alk Hovmoller

ICES Amon Hovmoller ICES Amon Hovmoller

ICES Cphl Hovmoller ICES Cphl Hovmoller

ICES Doxy Hovmoller ICES Doxy Hovmoller

ICES Ntra Hovmoller ICES Ntra Hovmoller

ICES Ph Hovmoller ICES Ph Hovmoller

ICES Phos Hovmoller ICES Phos Hovmoller

ICES PSAL Hovmoller ICES PSAL Hovmoller

ICES Slca Hovmoller ICES Slca Hovmoller

ICES TEMP Hovmoller ICES TEMP Hovmoller

Methodology

Horizontal Validation

Model surface fields compared against gridded satellite products and in-situ observations.

Statistics: RMSE, Bias, MAE, Pearson correlation.
Spatial statistics show the error distribution across the domain.

Gridded 3D Validation

Model vertical profiles compared against gridded 3D climatologies.
Statistics: RMSE and Bias at each depth level.

ICES Point Profiles

Quality-controlled hydrographic profiles from the ICES data portal (temperature, salinity, biogeochemical variables).

Argo Float Profiles

Autonomous profiling floats providing temperature and salinity profiles to 2000 m depth (Ifremer ERDDAP).

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