Five initialization and ensemble generation methods are investigated with respect to their impact on the prediction skill of the German decadal prediction system "Mittelfristige Klimaprognose" (MiKlip). Among the tested methods, three tackle aspects of model-consistent initialization using the ensemble Kalman filter (EnKF), the filtered anomaly initialization (FAI) and the initialization method by partially coupled spin-up (MODINI). The remaining two methods alter the ensemble generation: the ensemble dispersion filter (EDF) corrects each ensemble member with the ensemble mean during model integration. And the bred vectors (BV) perturb the climate state using the fastest growing modes. The new methods are compared against the latest MiKlip system in the low-resolution configuration (Preop-LR), which uses lagging the climate state by a few days for ensemble generation and nudging toward ocean and atmosphere reanalyses for initialization.
The spatial coverage for 3 variables is the following:
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Description
The spatial coverage for 3 variables is the following:
for tas_miklip_test_suite_hindcasts
lon : 0 to 358.125 by 1.875 degrees_east circular
lat : 88.5722 to -88.5722 degrees_north
surface : levels=1
for hc700_miklip_test_suite_hindcasts
lon : -179.987 to 179.993 degrees_east
lat : -83.9655 to 89.7266 degrees_north
surface : levels=1
for msftmyz_miklip_test_suite_hindcasts
lon : 0 degrees_east
lat : -89.5 to 89.5 by 1 degrees_north
depth_below_sea : levels=41