Abstract

In this study, a regional linear Markov model is developed to assess seasonal sea ice predictability in the Arctic Pacific sector. Unlike an earlier pan-Arctic Markov model that was developed with one set of variables for all seasons, the regional model consists of four seasonal modules with different sets of predictor variables, accommodating seasonally-varying driving processes. A series of sensitivity tests are performed to evaluate the predictive skill in cross-validated experiments and to determine the best model configuration for each season. The prediction skill, as measured by the percentage of grid points with significant correlations (PGS), increased by 75 % in the Bering Sea and 16 % in the Sea of Okhotsk relative to the pan-Arctic model. The regional Markov model's skill is also superior to the skill of an anomaly persistence forecast. Sea ice concentration (SIC) trends significantly contribute to the model skill. However, the model retains skill for detrended sea ice extent predictions up to 6 month lead times in the Bering Sea and the Sea of Okhotsk. We find that surface radiative fluxes contribute to predictability in the cold season and geopotential height and winds play an indispensable role in the warm-season forecast, contrasting to the thermodynamic processes dominating the pan-Arctic predictability. The regional model can also capture the seasonal reemergence of predictability, which is missing in the pan-Arctic model.

Highlights

  • Sea ice acts as a major component of the Arctic climate system through modulating the radiative flux, heat, and momentum exchanges between the ocean and the atmosphere (Peterson et al, 2017; Porter et al, 2011; Smith et al, 2017)

  • 6) We evaluate the prediction skill measured by the Sea ice concentration (SIC) anomaly correlation coefficient (ACC), percentage of grid points with significant ACC (PGS), and root mean square error (RMSE) using crossvalidated model experiments to identify the superior model for each season

  • To determine model variables and the number of modes to be used in the model, we evaluate the prediction skill at all grid points and all seasons in a cross-validated fashion for the period 1980-2020, by calculating the ACC and RMSE between predictions and observations

Read more

Summary

Introduction

Sea ice acts as a major component of the Arctic climate system through modulating the radiative flux, heat, and momentum exchanges between the ocean and the atmosphere (Peterson et al, 2017; Porter et al, 2011; Smith et al, 2017). Yuan et al (2016) showed that a linear Markov model has skillful sea ice concentration (SIC) predictions up to 9-month lead times in many regions of the Arctic and that this statistical model consistently captured more sea ice prediction skill than NOAA/NCEP Climate Forecast System (CFSv2) and the Canadian seasonal and interannual prediction system at the seasonal time scale. In the Pacific sector of the Arctic, sea ice does not exist during the summer months in the Bering Sea and the Sea of Okhotsk, and sea ice nearly 100% covers the regions within the Arctic Basin in winter Both cases lead to no sea ice variability and no predictability. We develop a regional linear Markov model for the seasonal prediction of SIC in the Pacific sector with a focus on understanding unique sea ice driving processes in different seasons. ERA5 is produced using the version of ECMWF’s Integrated Forecast System (IFS), CY41R2, based on a hybrid incremental 4D-Var system, with 137 hybrid sigma/pressure (model) levels in the vertical direction, with the top-level at 0.01 hPa

The model
EOF analysis of Pacific SIC
Construct an optimal model for each season
Assessment of model skill
Contribution of linear trends to SIE prediction skill
Comparison with the GFDL model
Findings
Conclusions
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.