Bias correction techniques for meteorological data of A2 scenario climate model output in Chao Phraya River Basin of Thailand

Abstract

Abstract:Statistical and dynamic methods were used in the downscaling process from Global Climate Model (GCM) to Regional Climate Model (RCM). We selected the European Centre for Medium-Range Weather Forecasts model, Hamburg version 4 (ECHAM4) with 300 × 300 km resolution for A2 scenario. We focused on SE Asia domain located between 20°S to 30°N and 80°E to 135°E for 1960-2099 with wind components, temperature, geo-potential height, and specific humidity as data input in Providing Regional Climates for Impacts Studies (PRECIS) RCM analysis. The downscaling process output was 50 km resolution for 1971-2010 and precipitation, temperature, wind, relative humidity, radiation from 8 meteorological stations in Chao Phaya River Basin; Lampang, Suphanburi, Nan, Sisamrong, Takfa, Chainat, Uthong and Bangna selected and used for bias correction. Three methods, namely 1) adjusting the mean based on RCM, 2) adjusting the mean based on observation, and 3) quantile-based mapping were used. Methods were compared using observed climatic data, RCM outputs of calibration period, and RCM outputs from the validation period. RSME was found to be lower for method 2 compared to other methods implying a relatively superior technique for improving the model. As such method 2 was used to correct the PRECIS products during [2001][2002][2003][2004][2005][2006][2007][2008][2009]. These products are useful in the studies of impact of climate change and for early warning systems in Thailand.

Climate change projections for the Worldwide Bioclimatic Classification System in the Iberian Peninsula until 2070

“…Bias correction of climate variables is a widely used approach to postprocess climate model simulations and has been under debate since this correction may not preserve some of its characteristics (such as, temporal structure of the time series). Several studies present different approaches: direct forcing, delta change, hybrid method, and quantile mapping bias adjustment method are some examples (Themeßl et al ., 2012; Maraun, 2013; Baimoung et al ., 2014; Mourato et al ., 2014; Maraun et al ., 2017; Casanueva et al ., 2018; Hertig et al ., 2018; Ivanov et al ., 2018; Viceto et al ., 2019). Choosing the suitable approach is a balance between a reliable analysis and its possible side effects; however, to correct systematic distributional biases, it is often considered more adequate than using raw simulated data.…”

“…For both daily temperature and precipitation values, retrieved from E‐OBS, the observed 1961–1990 monthly means were used as the climate baseline. The direct forcing method (Andrade et al ., 2014; Baimoung et al ., 2014; Mourato et al ., 2014; Miao et al ., 2016; Santos et al ., 2017) was then applied for both variables and the future‐corrected RCM datasets were attained by in which T cor and P cor are the daily bias‐corrected data, T RCM, sce and P RCM, sce are the RCM output of the future period and are the observed 1961–1990 monthly means used as the climate baseline. The and are the mean values for the climate baseline of the RCM output.…”

Projection of future precipitation extremes across the Bangkok Metropolitan Region

“…Existing datasets include those created and distributed by the Southeast Asia START Regional Center (Chinvanno, 2009; SEA START RC, 2018) and more recently by the SEACAM Framework project (SEACAM, 2014). Many studies in Thailand (Baimoung et al, 2014; Chinvanno, 2009; Lacombe et al, 2012; Plangoen et al, 2013) have used downscaled RCM data derived from PRECIS ( Providing Regional Climates for Impacts Studies ), a regional climate model developed by the UK Met Office (Jones et al, 2004). Recent work by the Southeast Asia team of the Coordinated Regional Climate Downscaling Experiment (CORDEX) have applied Regional Climate Model version 4 (Ngo-Duc et al, 2016; SEACLID, 2018; Tangang et al, 2018).…”

Tendencias y anomalías termo-pluviométricas actuales y futuras de una cuenca hidrográfica de la Región Pampeana Argentina: un análisis orientado a reducir los impactos socio-ambientales

“…Las cuatro series de tiempo fueron corregidas aplicando una corrección Bias. La misma tiene como objetivo cuantificar las diferencias entre los datos provistos por el modelo y los registrados in situ (Baimoung et al, 2014). Para ello, se utilizó el período de referencia proporcionado por el modelo CCSM4.…”