Assessment of Seasonal Winter Temperature Forecast Errors in the RegCM Model over Northern Vietnam

Hoa Vo Van,Duc Tran Anh,Hung Mai Khanh,Quan Dang Dinh,Tien Du Duc,Huyen Luong Thi Thanh,Lars Robert Hole

doi:10.3390/cli8060077

Abstract

This study verified the seasonal six-month forecasts for winter temperatures for northern Vietnam in 1998–2018 using a regional climate model (RegCM4) with the boundary conditions of the climate forecast system Version 2 (CFSv2) from the National Centers for Environmental Prediction (NCEP). First, different physical schemes (land-surface process, cumulus, and radiation parameterizations) in RegCM4 were applied to generate 12 single forecasts. Second, the simple ensemble forecasts were generated through the combinations of those different physical formulations. Three subclimate regions (R1, R2, R3) of northern Vietnam were separately tested with surface observations and a reanalysis dataset (Japanese 55-year reanalysis (JRA55)). The highest sensitivity to the mean monthly temperature forecasts was shown by the land-surface parameterizations (the biosphere−atmosphere transfer scheme (BATS) and community land model version 4.5 (CLM)). The BATS forecast groups tended to provide forecasts with lower temperatures than the actual observations, while the CLM forecast groups tended to overestimate the temperatures. The forecast errors from single forecasts could be clearly reduced with ensemble mean forecasts, but ensemble spreads were less than those root-mean-square errors (RMSEs). This indicated that the ensemble forecast was underdispersed and that the direct forecast from RegCM4 needed more postprocessing.

Highlights

IntroductionIn climate forecasting, an understanding of factors external to climate systems, such as solar activities, and improved forecasting skills for internal climate system factors, such as the teleconnections of the global atmospheric and oceanic circulations, have been played key roles in seasonal to decadal climate predictions [1,2]
In climate forecasting, an understanding of factors external to climate systems, such as solar activities, and improved forecasting skills for internal climate system factors, such as the teleconnections of the global atmospheric and oceanic circulations, have been played key roles in seasonal to decadal climate predictions [1,2].The current forecasting techniques still revolve around traditional statistical methods and using numerical forecasting models, but the use of numerical models is the most preferred [1,3]
Focusing on drought and flood cases, the results showed that for flood cases, an ensemble forecast can increase the probability of detection, and, for drought cases, it can decrease the false alarm rate; the study suggested that such approaches should be studied more in regional climate modeling

Summary

Introduction

In climate forecasting, an understanding of factors external to climate systems, such as solar activities, and improved forecasting skills for internal climate system factors, such as the teleconnections of the global atmospheric and oceanic circulations, have been played key roles in seasonal to decadal climate predictions [1,2]. The current forecasting techniques still revolve around traditional statistical methods and using numerical forecasting models, but the use of numerical models is the most preferred [1,3]. To solve the forecasting problems, numerical models require the current status of the climate system, in particular the actual climate observations, as initial conditions to initialize the model. Ocean data play an important role in this process. Advances in climate models on a global scale allow for the full description of global circulations and their interactions with initial conditions.

Methods

Results

Conclusion