Abstract
The updated Coupled Arctic Prediction System (CAPS) is evaluated, which is built on new versions of Weather Research and Forecasting model (WRF), the Regional Ocean Modeling System (ROMS), the Community Ice CodE (CICE), and a data assimilation based on the Local Error Subspace Transform Kalman Filter. A set of Pan-Arctic prediction experiments with improved/changed physical parameterizations in WRF, ROMS and CICE as well as different configurations are performed for the year 2018 to assess their impacts on the predictive skill of Arctic sea ice at seasonal timescale. The key improvements of WRF, including cumulus, boundary layer, and land surface schemes, result in improved simulation in near surface air temperature and downward radiation. The major changes of ROMS, including tracer advection and vertical mixing schemes, lead to improved evolution of the predicted total ice extent (particularly correcting the late ice recovery issue in the previous CAPS), and reduced biases in sea surface temperature. The changes of CICE, that include improved ice thermodynamics and assimilation of new sea ice thickness product, have noticeable influences on the predicted ice thickness and the timing of ice recovery. Results from the prediction experiments suggest that the updated CAPS can better predict the evolution of total ice extent compared with its predecessor, though the predictions still have certain biases at the regional scale. We further show that the CAPS can remain skillful beyond the melting season, which may have potential values for stakeholders making decisions for socioeconomical activities in the Arctic.
Highlights
Over the past few decades, the extent of Arctic sea ice has decreased rapidly and entered a thinner/younger regime associated with global climate change (e.g., Kwok, 2018; Serreze and Meier, 2019)
This paper presents and evaluates the updated Coupled Arctic Prediction System (CAPS)
The CAPS consists of the Weather Research and Forecasting Model (WRF), ROMS, and Community Ice CodE (CICE) models under the framework of the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) system, as well as data assimilation system based on the localized error subspace transform ensemble Kalman filter to assimilate satellite-observed sea ice observations
Summary
Over the past few decades, the extent of Arctic sea ice has decreased rapidly and entered a thinner/younger regime associated with global climate change (e.g., Kwok, 2018; Serreze and Meier, 2019). The drastic changes in the properties of Arctic sea ice have captured attentions of a wide range of stakeholders, such as trans-Arctic shipping, natural resource exploration, and activities of coastal communities relying on sea ice (e.g., Newton et al, 2016) This leads to increasing demands on skillful Arctic sea ice prediction, at seasonal timescale (e.g., Jung et al, 2016; Liu et al, 2019; Stroeve et al, 2014). With recent improvements in the model components of CAPS, this paper gives a description of the updated CAPS, and presents the assessment of seasonal Arctic sea ice predictions associated with improved/changed physical parameterizations. As described in Y20, to enhance our ability to predict seasonal Arctic sea ice as well as climate, we have developed CAPS by coupling the Community Ice CodE (CICE) with the Weather Research and Forecasting Model (WRF) and the Regional Ocean Modeling System (ROMS) based on the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST). Major changes in physics parameterization and the model infrastructure in the WRF, ROMS, and CICE models are described below
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