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Regional climate simulations from the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment – Southeast Asia (SEA) indicated model biases in temperature and rainfall over SEA. Given the influence of sea surface temperature (SST) variability on SEA climate, this study examines SST representation in climate models to investigate its potential contribution to the resulting model biases over the Philippines. Observed SST over SEA is first characterized by its spatial patterns and temporal variability. An analysis of the SST representation over SEA and its potential influence on modelled climate over the Philippines in Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) is then conducted, followed by an assessment of the potential influence of SST representation in CMIP5 GCMs on downscaled regional climate output. Our results show that GCMs with well represented SSTs (i.e., low bias, well captured variability, and pattern) can produce climate simulations well over the Philippines. Whether or not the GCMs with poor SST representation can perform well is inconclusive. During boreal winter (summer), climate variables with high (low) spatial correlation with model SST get poor (better) spatial correlation with observed climate. Over west of the Philippines, where model SST seasonal variability is captured well, models also adequately simulate climate variables. Results suggest that the negative temperature biases, and positive precipitation and wind speed biases, in both GCMs and downscaled simulations, are associated with negative model SST biases. These findings give a better understanding on how SST potentially influences modelled climatology over the Philippines. [ABSTRACT FROM AUTHOR]

An analysis of the reliability of a phenomenological model reveals that the model is capable of describing the profile of complex systems that are extended spatially. The reliability of the model depends on the method of measurement and is independent of all other factors. Errors in the model vary for fractals of different dimensions. The analysis provides information for investigating two dimensional objects.

This study uses six regional climate model (RCM) experiments from the Coordinated Regional Climate Downscaling Experiment—Southeast Asia (CORDEX‐SEA) and their driving global climate models (GCM) to investigate the model performance and the time of emergence (ToE) of temperature and precipitation over Vietnam and its seven sub‐climatic regions. A simple delta (multiplicative) bias correction (BC) method has been applied to the model temperature (precipitation). Results show clear added values of the BC downscaled products compared to their driving GCMs, particularly for temperature. The projected trends of RCM temperature during 2006–2100 under two representative concentration pathway (RCP) RCP4.5 and RCP8.5 scenarios are similar to those of their driving GCMs. Consequently, the RCM‐based ToE and GCM‐based ToE are generally consistent for temperature, occurring in the beginning of the 21st century. The earliest and latest ToE occur in summer and winter, respectively. The seasonal differentiation for temperature‐ToEs is stronger in the northern areas compared to the southern areas of Vietnam. The ToE difference between two RCPs increases from the south to the north. As for precipitation, ToE occurs quite late, in the latter half of the 21st century at the earliest. Results from ToE estimations also indicate an opposite precipitation trend between the GCMs and RCMs. The largest difference is in summer over the southern region of Vietnam. The opposite trend between the GCM and RCM precipitation under a similar increasing temperature implies the uncertainty in assessing the impact of warming on hydrological cycles in Vietnam. [ABSTRACT FROM AUTHOR]

This study identified the analogue locations of five big cities and the future appearance of novel climate in Southeast Asia (SEA) at the end of the 21st century under the Representative Concentration Pathways 8.5 (RCP8.5) and 4.5 (RCP4.5) scenarios. A modified version of an existing formulation to estimate climate distance is introduced, using the monthly means of temperature and precipitation from six regional climate experiments and from six global climate models (GCMs). Results showed that regional downscaling allowed a more accurate representation of temperature but displayed a higher variability in rainfall over SEA compared to the GCM performance. The ensemble mean (ENS) experiment had a relatively better performance compared to each individual experiment in representing the monthly time series of temperature and precipitation. The common tendency of climatic relocation towards warmer regions for the five big cities in SEA (Hanoi, Bangkok, Manila, Kuala Lumpur and Jakarta) was prominent with the regional ENS experiment. At the end of the 21st century, the ratio of novel climate areas over SEA, mainly located in low elevation, coastal, equatorial regions, and islands, was less than 2% under RCP4.5, but increased to 24 and 21% under RCP8.5 for the ensemble regional and global experiments, respectively. [ABSTRACT FROM AUTHOR]

Gridded rainfall products could augment the shortage of available rainfall data in archipelagic countries like the Philippines, where weather stations are still sparsely distributed especially over its remote and lessdeveloped islands. However, these products need to be validated first using ground measurements to determine their ability to represent properties of local rainfall. This study compares four high-resolution, gridded datasets--APHRODITEv1101, CHIRPSv2, TRMM 3B42v7, and PERSIANN-CDR--with respect to 49 synoptic weather stations over the Philippines from 1998 to 2005. The performance of these datasets was assessed in terms of bias, distribution, and different statistical error metrics and skill scores across time scales and climate types. Results show that all the datasets were able to capture the basic climatology and to varying extents, spatial patterns of Philippine rainfall. TRMM 3B42v7 has the least overall average monthly bias and most closely resembles the rainfall distribution observed at weather stations, especially dry days and torrential rain days for the whole Philippines.APHRODITEv1101 performs best in terms of errormetrics and skill scores but displays consistent underestimates. CHIRPSv2, on the other hand, best captures the seasonal rainfall peaks in the different climate types in the Philippines but is prone to larger errors. Last, PERSIANN-CDR shows generally poormetrics and rainfall distributions, in comparison to the other datasets. These key findings are used to identify possible research applications in the Philippines that are best suited for each dataset. [ABSTRACT FROM AUTHOR]

This paper highlights detailed projected changes in rainfall over Thailand for the early (2011–2040), middle (2041–2070) and late (2071–2099) periods of the 21st century under the representative concentration pathways (RCP) 4.5 and RCP 8.5 using the high‐resolution multi‐model simulations of the Coordinated Regional Climate Downscaling Experiment (CORDEX) Southeast Asia. The ensemble mean is calculated based on seven members consisting of six general circulation models (GCMs) and three regional climate models (RCMs). Generally, the ensemble mean precipitation agrees reasonably well with observations, best represented by the Global Precipitation Climatology Center (GPCC) data, over Thailand during the historical period (1976–2005). However, inter‐model variations can be large among ensemble members especially during dry months (December to March) for northern‐central‐eastern parts, and throughout the year for the southern parts of Thailand. Similarly for future projection periods, inter‐model variations in the sign and magnitude of changes exist. The ensemble means of projected changes in rainfall for both RCPs during dry months show distinct contrast between the northern‐central‐eastern parts and the southern parts of Thailand with generally wetter and drier conditions, respectively. The magnitude of change can be as high as 15% of the historical period, which varies depending on the sub‐region, season, projection period, and RCP scenario. In contrast, generally drier conditions are projected during the wet season (June to September) throughout the country for both RCPs where the rainfall reduction can be as high as 10% in some areas. However, the magnitude of projected rainfall changes of some individual models can be much larger than the ensemble means, exceeding 40% in some cases. These projected changes are related to the changes in regional circulations associated with the winter and summer monsoons, which are projected to weaken. The drier (wetter) condition is associated with the enhanced subsidence (rising motion). [ABSTRACT FROM AUTHOR]

The Quantile Mapping (QM) bias correction (BC) technique was applied for the first time to address biases in the simulated precipitation over Vietnam from the Regional Climate Model (RegCM) driven by five Coupled Model Intercomparison Project Phase 5 (CMIP5) Global Climate Model (GCM) products. The QM process was implemented for the period 1986-2005, and subsequently applied to the mid-future period 2046-2065 under both Representative Concentration Pathway (RCP) 4.5 and RCP 8.5. Comparison with the original model outputs during the independent validation period shows a large bias reduction from 45% to 3% over Vietnam and significant improvements in representing precipitation indices (PI) after applying the QM technique. Moreover, the ensemble average of the BC products generally performed better than an individual BC member in capturing the spatial distribution of the PIs. A drier condition with a longer rainfall break, and shorter consecutive rainfall events are anticipated over Northern and Central Vietnam during their respective wet seasons in the mid-future. Furthermore, this study showed that the QM method minimally modified the future changes in PIs over most of Vietnam; thus, these corrected projections could be used in climate impacts and adaptation studies. [ABSTRACT FROM AUTHOR]

ABSTRACT In this study, simulations over Southeast Asia (15°S-40°N, 80°-145°E) at 36 km resolution were conducted for the period 1989-2007 using the Regional Climate Model version 4.3 ( RegCM4.3) under the framework of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment - Southeast Asia (or SEACLID/ CORDEX- SEA) project. Forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim), 18 experiments were carried out using different combinations of cumulus parameterization and ocean flux schemes. Twelve extreme indices for both rainfall and temperature were estimated from the model output. A statistical omega index was used to measure the degree of similarity among the 18 experiments in phase and shape. The results showed relatively high similarities among the experiments over mainland Asia compared to those over the Maritime Continent for both seasonal and inter-annual variability. The extreme rainfall indices had a lower omega compared to that of temperature. Observed daily rainfall and temperature data at 52 meteorological stations over the SEA region were used to validate the simulated extreme indices. The results showed that extreme temperature indices were generally underestimated across the region. Systematic biases for each simulated rainfall index were also identified. A score ranking system was established to compare the relative performance of the 18 experiments over the 52 selected stations objectively. It was shown that the experiments with the Massachusetts Institute of Technology ( MIT)-Emanuel scheme performed relatively better than the other convective schemes. The combination of the MIT-Emanuel convective scheme with the Biosphere-Atmosphere Transfer scheme ( BATS1e) ocean flux scheme produced the best performance. [ABSTRACT FROM AUTHOR]