scholarly journals Seasonal forecast skill of Arctic sea ice area in a dynamical forecast system

2013 ◽  
Vol 40 (3) ◽  
pp. 529-534 ◽  
Author(s):  
M. Sigmond ◽  
J. C. Fyfe ◽  
G. M. Flato ◽  
V. V. Kharin ◽  
W. J. Merryfield
2018 ◽  
Vol 52 (5-6) ◽  
pp. 2721-2743 ◽  
Author(s):  
Mitchell Bushuk ◽  
Rym Msadek ◽  
Michael Winton ◽  
Gabriel Vecchi ◽  
Xiaosong Yang ◽  
...  

2018 ◽  
Author(s):  
Stephanie J. Johnson ◽  
Timothy N. Stockdale ◽  
Laura Ferranti ◽  
Magdalena Alonso Balmaseda ◽  
Franco Molteni ◽  
...  

Abstract. In this paper we describe SEAS5, ECMWF’s fifth generation seasonal forecast system, which became operational in November 2017. Compared to its predecessor, System 4, SEAS5 is a substantially changed forecast system. It includes upgraded versions of the atmosphere and ocean models at higher resolutions, and adds a prognostic sea ice model. Here, we describe the configuration of SEAS5 and summarise the most noticeable results from a set of diagnostics including biases, variability, teleconnections and forecast skill. An important improvement in SEAS5 is the reduction of the Equatorial Pacific cold tongue bias, which is accompanied by a more realistic ENSO amplitude and an improvement in ENSO prediction skill over the central-west Pacific. Improvements in two-metre temperature skill are also clear over the tropical Pacific. SST biases in the northern extratropics change due to increased ocean resolution, especially in regions associated with western boundary currents. The increased ocean resolution exposes a new problem in the northwest Atlantic, where SEAS5 fails to capture decadal variability of the North Atlantic subpolar gyre, resulting in a degradation of DJF two-metre temperature prediction skill in this region. The prognostic sea ice model improves seasonal predictions of sea ice cover, although some regions and seasons suffer from biases introduced by employing a fully dynamical model rather than the simple, empirical scheme used in System 4. There are also improvements in two-metre temperature skill in the vicinity of the Arctic sea-ice edge. Cold temperature biases in the troposphere improve, but increase at the tropopause. Biases in the extratropical jets are larger than in System 4: extratropical jets are too strong, and displaced northwards in summer. In summary, development and added complexity since System 4 has ensured SEAS5 is a state-of-the-art seasonal forecast system which continues to display a particular strength in ENSO prediction.


2020 ◽  
Vol 47 (5) ◽  
Author(s):  
J. C. Acosta Navarro ◽  
P. Ortega ◽  
L. Batté ◽  
D. Smith ◽  
P. A. Bretonnière ◽  
...  

2016 ◽  
Vol 43 (24) ◽  
pp. 12,457-12,465 ◽  
Author(s):  
M. Sigmond ◽  
M. C. Reader ◽  
G. M. Flato ◽  
W. J. Merryfield ◽  
A. Tivy

2017 ◽  
Vol 30 (21) ◽  
pp. 8429-8446 ◽  
Author(s):  
Zhiqiang Chen ◽  
Jiping Liu ◽  
Mirong Song ◽  
Qinghua Yang ◽  
Shiming Xu

Here sea ice concentration derived from the Special Sensor Microwave Imager/Sounder and thickness derived from the Soil Moisture and Ocean Salinity and CryoSat-2 satellites are assimilated in the National Centers for Environmental Prediction Climate Forecast System using a localized error subspace transform ensemble Kalman filter (LESTKF). Three ensemble-based hindcasts are conducted to examine impacts of the assimilation on Arctic sea ice prediction, including CTL (without any assimilation), LESTKF-1 (with initial sea ice assimilation only), and LESTKF-E5 (with every 5-day sea ice assimilation). Assessment with the assimilated satellite products and independent sea ice thickness datasets shows that assimilating sea ice concentration and thickness leads to improved Arctic sea ice prediction. LESTKF-1 improves sea ice forecast initially. The initial improvement gradually diminishes after ~3-week integration for sea ice extent but remains quite steady through the integration for sea ice thickness. Large biases in both the ice extent and thickness in CTL are remarkably reduced through the hindcast in LESTKF-E5. Additional numerical experiments suggest that the hindcast with sea ice thickness assimilation dramatically reduces systematic bias in the predicted ice thickness compared with sea ice concentration assimilation only or without any assimilation, which also benefits the prediction of sea ice extent and concentration due to their covariability. Hence, the corrected state of sea ice thickness would aid in the forecast procedure. Increasing the number of ensemble members or extending the integration period to generate estimates of initial model states and uncertainties seems to have small impacts on sea ice prediction relative to LESTKF-E5.


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