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The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2).

Authors :
HAI LIN
MERRYFIELD, WILLIAM J.
MUNCASTER, RYAN
SMITH, GREGORY C.
MARKOVIC, MARKO
DUPONT, FRÉDÉRIC
ROY, FRANÇOIS
LEMIEUX, JEAN-FRANÇOIS
DIRKSON, ARLAN
KHARIN, VIATCHESLAV V.
WOO-SUNG LEE
CHARRON, MARTIN
ERFAN, AMIN
Source :
Weather & Forecasting. Aug2020, Vol. 35 Issue 4, p1317-1343. 27p.
Publication Year :
2020

Abstract

The second version of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2) was implemented operationally at Environment and Climate Change Canada (ECCC) in July 2019. Like its predecessors, CanSIPSv2 applies a multimodel ensemble approach with two coupled atmosphere–ocean models, CanCM4i and GEM-NEMO. While CanCM4i is a climate model, which is upgraded from CanCM4 of the previous CanSIPSv1 with improved sea ice initialization, GEM-NEMO is a newly developed numerical weather prediction (NWP)-based global atmosphere–ocean coupled model. In this paper, CanSIPSv2 is introduced, and its performance is assessed based on the reforecast of 30 years from 1981 to 2010, with 10 ensemble members of 12-month integrations for each model. Ensemble seasonal forecast skill of 2-m air temperature, 500-hPa geopotential height, precipitation rate, sea surface temperature, and sea ice concentration is assessed. Verification is also performed for the Niño-3.4, the Pacific–North American pattern (PNA), the North Atlantic Oscillation (NAO), and the Madden–Julian oscillation (MJO) indices. It is found that CanSIPSv2 outperforms the previous CanSIPSv1 system in many aspects. Atmospheric teleconnections associated with the El Niño–Southern Oscillation (ENSO) are reasonably well captured by the two CanSIPSv2 models, and a large part of the seasonal forecast skill in boreal winter can be attributed to the ENSO impact. The two models are also able to simulate the Northern Hemisphere teleconnection associated with the tropical MJO, which likely provides another source of skill on the subseasonal to seasonal time scale. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
08828156
Volume :
35
Issue :
4
Database :
Academic Search Index
Journal :
Weather & Forecasting
Publication Type :
Academic Journal
Accession number :
145390291
Full Text :
https://doi.org/10.1175/WAF-D-19-0259.1