An Arctic Ice/Ocean Coupled Model with Wave Interactions

2013 ◽  
Author(s):  
Vernon A. Squire
Keyword(s):  
Coupled Model ◽  
Wave Interactions ◽  
Arctic Ice

scholarly journals Landfast ice thickness in the Canadian Arctic Archipelago from observations and models

2016 ◽  
Vol 10 (4) ◽  
pp. 1463-1475 ◽  
Author(s):  
Stephen E. L. Howell ◽  
Frédéric Laliberté ◽  
Ron Kwok ◽  
Chris Derksen ◽  
Joshua King
Keyword(s):  
Canadian Arctic ◽  
Coupled Model ◽  
Ocean Modeling ◽  
Ice Thickness ◽  
Canadian Arctic Archipelago ◽  
Coupled Models ◽  
Ocean Reanalysis ◽  
Landfast Ice ◽  
Thickness Variability ◽  
Arctic Ice

Abstract. Observed and modelled landfast ice thickness variability and trends spanning more than 5 decades within the Canadian Arctic Archipelago (CAA) are summarized. The observed sites (Cambridge Bay, Resolute, Eureka and Alert) represent some of the Arctic's longest records of landfast ice thickness. Observed end-of-winter (maximum) trends of landfast ice thickness (1957–2014) were statistically significant at Cambridge Bay (−4.31 ± 1.4 cm decade−1), Eureka (−4.65 ± 1.7 cm decade−1) and Alert (−4.44  ± 1.6 cm −1) but not at Resolute. Over the 50+-year record, the ice thinned by  ∼ 0.24–0.26 m at Cambridge Bay, Eureka and Alert with essentially negligible change at Resolute. Although statistically significant warming in spring and fall was present at all sites, only low correlations between temperature and maximum ice thickness were present; snow depth was found to be more strongly associated with the negative ice thickness trends. Comparison with multi-model simulations from Coupled Model Intercomparison project phase 5 (CMIP5), Ocean Reanalysis Intercomparison (ORA-IP) and Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) show that although a subset of current generation models have a "reasonable" climatological representation of landfast ice thickness and distribution within the CAA, trends are unrealistic and far exceed observations by up to 2 orders of magnitude. ORA-IP models were found to have positive correlations between temperature and ice thickness over the CAA, a feature that is inconsistent with both observations and coupled models from CMIP5.

scholarly journals 5-Day-Wave Interactions with Tropical Precipitation in CMIP5 Models

2016 ◽  
Vol 29 (23) ◽  
pp. 8611-8624 ◽  
Author(s):  
Malcolm J. King ◽  
Matthew C. Wheeler ◽  
Todd P. Lane
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Gulf Of Guinea ◽  
Tropical Andes ◽  
Wave Interactions ◽  
The Andes ◽  
Trmm Precipitation ◽  
Precipitation Anomalies

Abstract The 5-day Rossby–Haurwitz wave is unlike other large-scale wave modes that interact with tropical rainfall in that associated rainfall presents as a modulation of localized areas of rainfall instead of propagating with the wave. This form of wave-modulated convective organization in climate models has received little attention. This study investigates the simulation of interactions between the 5-day wave and tropical convection in 30 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and compares these with the interaction diagnosed from ERA-Interim and TRMM precipitation data. Models simulate the dry dynamics of the 5-day wave well, with realistic coherences between upper- and lower-tropospheric winds, as well as magnitudes and geographic distribution of wave wind anomalies being close to observations. The models consistently display significant coherences between 5-day-wave zonal winds and precipitation but perform less well at simulating the spatial distribution and magnitude of precipitation anomalies. For example, a third of the models do not reproduce significant observed anomalies near the Andes, and the best-performing model simulates only 38% of the observed variance over the tropical Andes and 24% of the observed variance over the Gulf of Guinea. Models with higher resolution perform better in simulating the magnitude of the Andean rainfall anomalies, but there is no similar relationship over the Gulf of Guinea. The evidence therefore suggests that the simulated interaction is mostly one way only, with the wave dynamics forcing the precipitation variations on the 5-day time scale.

scholarly journals Assimilating Copernicus SST Data into a Pan-Arctic Ice–Ocean Coupled Model with a Local SEIK Filter

2017 ◽  
Vol 34 (9) ◽  
pp. 1985-1999 ◽  
Author(s):  
Xi Liang ◽  
Qinghua Yang ◽  
Lars Nerger ◽  
Svetlana N. Losa ◽  
Biao Zhao ◽  
...  
Keyword(s):  
Sea Ice ◽  
Canadian Arctic ◽  
Coupled Model ◽  
Arctic Basin ◽  
Beaufort Sea ◽  
Canadian Arctic Archipelago ◽  
Negative Effects ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Arctic Ice

AbstractSea surface temperature (SST) data from the Copernicus Marine Environment Monitoring Service are assimilated into a pan-Arctic ice–ocean coupled model using the ensemble-based local singular evolutive interpolated Kalman (LSEIK) filter. This study found that the SST deviation between model hindcasts and independent SST observations is reduced by the assimilation. Compared with model results without data assimilation, the deviation between the model hindcasts and independent SST observations has decreased by up to 0.2°C at the end of summer. The strongest SST improvements are located in the Greenland Sea, the Beaufort Sea, and the Canadian Arctic Archipelago. The SST assimilation also changes the sea ice concentration (SIC). Improvements of the ice concentrations are found in the Canadian Arctic Archipelago, the Beaufort Sea, and the central Arctic basin, while negative effects occur in the west area of the eastern Siberian Sea and the Laptev Sea. Also, sea ice thickness (SIT) benefits from ensemble SST assimilation. A comparison with upward-looking sonar observations reveals that hindcasts of SIT are improved in the Beaufort Sea by assimilating reliable SST observations into light ice areas. This study illustrates the advantages of assimilating SST observations into an ice–ocean coupled model system and suggests that SST assimilation can improve SIT hindcasts regionally during the melting season.

scholarly journals Landfast ice thickness in the Canadian Arctic Archipelago from Observations and Models

2016 ◽  
Author(s):  
Stephen E. L. Howell ◽  
Frédéric Laliberté ◽  
Ron Kwok ◽  
Chris Derksen ◽  
Joshua King
Keyword(s):  
Canadian Arctic ◽  
Coupled Model ◽  
Ocean Modeling ◽  
Ice Thickness ◽  
Canadian Arctic Archipelago ◽  
Coupled Models ◽  
Ocean Reanalysis ◽  
Landfast Ice ◽  
Thickness Variability ◽  
Arctic Ice

Abstract. Observed and modelled landfast ice thickness variability and trends spanning more than five decades within the Canadian Arctic Archipelago (CAA) are summarized. The observed sites (Cambridge Bay, Resolute, Eureka and Alert) represent some of the Arctic's longest records of landfast ice thickness. Observed end-of-winter (maximum) trends of landfast ice thickness (1957–2014) were statistically significant at Cambridge Bay (−4.31 ± 1.4 cm decade-1), Eureka (−4.65 ± 1.7 cm decade-1) and Alert (−4.44 ± 1.6 cm decade-1) but not at Resolute. Over the 50+ year record, the ice thinned by ~ 0.24–0.26 m at Cambridge Bay, Eureka and Alert with essentially negligible change at Resolute. Although statistically significant warming in spring and fall was present at all sites, only low correlations between temperature and maximum ice thickness were present; snow depth was found to be more strongly associated with the negative ice thickness trends. Comparison with multi-model simulations from Coupled Model Intercomparison project phase 5 (CMIP5), Ocean Reanalysis Intercomparison (ORA-IP) and Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) show that although a subset of current generation models have a "reasonable" climatological representation of landfast ice thickness and distribution within the CAA, trends are unrealistic and far exceed observations by up to two magnitudes. ORA-IP models were found to have positive correlations between temperature and ice thickness over the CAA, a feature that is inconsistent with both observations and coupled models from CMIP5.

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