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A modified thermodynamic sea ice model suitable for large-scale climate simulations is described. Originated from the Winton's three-layer model framework, this new model includes several improvements in the vertical thermodynamics: (1) the number of ice layers increases from two to three; (2) the snow heat capacity is included; (3) a vertically varying salinity profile is implemented; and (4) a temperature- and salinity-dependent heat conductivity parameterization scheme is introduced. A non-iterative fully implicit time-stepping scheme similar to Winton's model is used to calculate the temperature of ice and snow. Results from a series of one-dimensional experiments show that equilibrium ice thickness in the modified model is increased by 45 cm when compared with the original Winton's model. All modifications mentioned above contribute to this change of ice thickness, among which the increase of ice layer has the most significant effect. Experiments using the Modular Ocean Model version 4 (MOM4) coupled with the modified model show an improved sea ice simulation which includes an increase in both the sea ice volume and thickness over the entire Arctic region, confirming the above founding. However, contrary model behavior exhibits when the snow heat capacity is considered that warrants further investigation. • A modified thermodynamic sea ice model is described. • The number of ice layer is increased and the snow heat capacity is considered. • Implementation of a vertically varying salinity profile in sea ice model. • Implementation of a heat conductivity parameterization scheme in sea ice model. • Ice thickness simulation is improved due to modifications in sea ice thermodynamic. [ABSTRACT FROM AUTHOR]

A Deterministic Time-lagged Ensemble Forecast using a Probabilistic Threshold (DEFPT) method is suggested for improving summer 6–15 day categorical precipitation prediction in China from the Beijing Climate Center Atmospheric General Circulation Model version 2.1 (BCC_AGCM2.1). It is based on a time-lagged ensemble system that consists of 13 ensemble members separated sequentially at 6 hour intervals lagging the last three days. The DEFPT is not intended to predict the probability of rainfall, but rather to forecast rainfall (yes/no) occurrence for different categories of precipitation at any model grid box. A given categorical precipitation is forecasted to occur at one gridbox only when the ensemble probability for that categorical precipitation exceeds a certain threshold. This method is useful for providing an estimate of whether precipitation events will occur to decision-makers based on probabilistic forecasts during days 6–15. A large number of hindcast experiments for 1996–2005 summers reveal that this threshold can be best (and empirically) set as 5/13 and 4/13 respectively for the 6–15 day prediction of 1 + mm (i.e., above 1 mm per day) and 5 + mm rainfall events, using the Relative Operating Characteristic (ROC) curve, the Equitable Threat Score (ETS), the Hanssen and Kuipers (HK) score, and frequency bias (BIA) to achieve best prediction performance. With this set of thresholds, the DEFPT shows skill improvement over the corresponding single deterministic forecast using one initial value and the Time-Lagged Average Forecast (LAF) ensemble method. Similar improvements by the DEFPT are also found for the prediction of several other categories of precipitation between 1 + mm and 10 + mm per day. Application of DEFPT to larger ensemble size and BCC_AGCM version 2.2 with a higher horizontal resolution also demonstrates the effectiveness of the DEFPT for 6–15 day categorical precipitation forecasts. [ABSTRACT FROM AUTHOR]

Abstract As the amount of environmental data expands exponentially worldwide, researchers are challenged to efficiently analyze data maintained in multiple data centers. Because distributed data access, server-side analysis, multinode collaboration, and extensible analytic functions are still research gaps in this field, this paper introduces a collaborative analysis framework for gridded environmental data, i.e. CAFE. Multiple CAFE nodes can collaborate to perform complex data analysis. Analytic functions are performed near where data are stored. A web-based user interface allows researchers to search for data of interest, submit analytic tasks, check the status of tasks, visualize the analysis results, and download the resulting data files. CAFE facilitates overall research efficiency by dramatically lowering the amount of data that must be transmitted from data centers to researchers for analysis. The results of this study may lead to the further development of collaborative computing paradigm for environmental data analysis. Highlights • CAFE enables data discovery, visualization, and analysis through a web-based portal. • CAFE always execute data analysis near where data are stored. • CAFE enables multiple nodes to collaboratively fulfill analysis task automatically. • CAFE can alleviate large data transfer from data centers to researchers. • CAFE can wrap executable analytic scripts as analytic functions for users to select. [ABSTRACT FROM AUTHOR]