Abstract

Here, we firstly demonstrate the potential of an advanced flow dependent data assimilation method for performing seasonal-to-decadal prediction and secondly, reassess the use of sea surface temperature (SST) for initialisation of these forecasts. We use the Norwegian Climate Prediction Model (NorCPM), which is based on the Norwegian Earth System Model (NorESM) and uses the deterministic ensemble Kalman filter to assimilate observations. NorESM is a fully coupled system based on the Community Earth System Model version 1, which includes an ocean, an atmosphere, a sea ice and a land model. A numerically efficient coarse resolution version of NorESM is used. We employ a twin experiment methodology to provide an upper estimate of predictability in our model framework (i.e. without considering model bias) of NorCPM that assimilates synthetic monthly SST data (EnKF-SST). The accuracy of EnKF-SST is compared to an unconstrained ensemble run (FREE) and ensemble predictions made with near perfect (i.e. microscopic SST perturbation) initial conditions (PERFECT). We perform 10 cycles, each consisting of a 10-yr assimilation phase, followed by a 10-yr prediction. The results indicate that EnKF-SST improves sea level, ice concentration, 2 m atmospheric temperature, precipitation and 3-D hydrography compared to FREE. Improvements for the hydrography are largest near the surface and are retained for longer periods at depth. Benefits in salinity are retained for longer periods compared to temperature. Near-surface improvements are largest in the tropics, while improvements at intermediate depths are found in regions of large-scale currents, regions of deep convection, and at the Mediterranean Sea outflow. However, the benefits are often small compared to PERFECT, in particular, at depth suggesting that more observations should be assimilated in addition to SST. The EnKF-SST system is also tested for standard ocean circulation indices and demonstrates decadal predictability for Atlantic overturning and sub-polar gyre circulations, and heat content in the Nordic Seas. The system beats persistence forecast and shows skill for heat content in the Nordic Seas that is close to PERFECT.

Highlights

  • The 5th assessment of the International Panel on Climate Change (IPCC), scheduled for 2014, will partly be dedicated to evaluate the feasibility of decadal-scale climateTellus A 2014. # 2014 F

  • The results indicate that ensemble Kalman filter (EnKF)-sea surface temperature (SST) improves sea level, ice concentration, 2 m atmospheric temperature, precipitation and 3-D hydrography compared to FREE

  • This study has two aims: (1) to reassess the use of SST data for seasonal-to-decadal prediction using a more advanced scheme compared to previous studies; (2) to demonstrate the potential use of the advanced EnKF data assimilation with a full ocean state update for such predictions

Read more

Summary

Introduction

The 5th assessment of the International Panel on Climate Change (IPCC), scheduled for 2014, will partly be dedicated to evaluate the feasibility of decadal-scale climate. Zhang et al (2009, 2010) test the predictability of their system in twin experiments and investigate the benefit of different observation networks They suggest that combined assimilation of SST and atmospheric data can control variability of AMOC during the analysis period, and that addition of Argo floats data are beneficial for controlling variability in the North Atlantic. Anthropogenic (greenhouse gases and aerosols) and natural forcing (volcanic and solar) can influence the multi-decadal variability in the Atlantic (Otteraet al., 2010; Swingedouw et al, 2012), but they are not considered here because we wish to focus on the potential predictability associated with ocean variability This differs from the CMIP5 experiment design (Taylor et al, 2012) for which predictability is tested between 1960 and 2005 using external forcing and initialisation from real observations. The skill of NorCPM is assessed with respect to our primary area of interest, the Nordic Seas

Model system
Ensemble Kalman Filter
Experimental set-up
Global state
Indices
A regional case: the Nordic Seas
Findings
Conclusion and discussions
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.