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This study evaluated the performance of the climatological precipitation over Southeast Asia using an ensemble of 48 global climate models/earth system models from the sixth phase of Coupled Model Intercomparison Project (CMIP6) suite of experiments. Multi‐source observational data sets were used to evaluate the historical simulation data of the CMIP6 ensembles towards the semi‐year mean of precipitation during November–April (NDJFMA) and May–October (MJJASO). The results indicate that the CMIP6 models were able to reproduce the large‐scale monsoonal features. Precipitation is overestimated over most eastern ocean areas in NDJFMA and over most areas in MJJASO. In terms of the ensemble bias and inter‐model uncertainty, the diagnosis of moisture budget indicates the dominant item is the dynamic convergence and the other important items have dynamic advection and evaporation. The main types of pattern bias were examined through the inter‐model empirical orthogonal function analysis, which is correlated with the global sea surface temperature and moisture flux. The climatological evaluation yielded a higher pattern correlation and a lower root‐mean square error in NDJFMA than MJJASO. Most of the models have greater standard deviations among the spatial grids than observations, especially in NDJFMA. The models with higher resolutions showed a better performance than those with lower resolutions. A model performance index is defined for the convenient comparison, the best three models are CNRM‐CM6‐1, EC‐Earth3‐Veg and NorESM2‐MM, and the worst three are INM‐CM4‐8, AWI‐ESM‐1‐1‐LR and MCM‐UA‐1‐0. This evaluation study provided useful analytics in understanding model behaviour and processes related to rainfall mechanisms which could be further examined in high‐resolution downscaling experiments in future studies. [ABSTRACT FROM AUTHOR]

Large‐scale reforestation can potentially bring both benefits and risks to the water cycle, which needs to be better quantified under future climates to inform reforestation decisions. We identified 477 water‐insecure basins worldwide accounting for 44.6% (380.2 Mha) of the global reforestation potential. As many of these basins are in the Asia‐Pacific, we used regional coupled land‐climate modeling for the period 2041–2070 to reveal that reforestation increases evapotranspiration and precipitation for most water‐insecure regions over the Asia‐Pacific. This resulted in a statistically significant increase in water yield (p <.05) for the Loess Plateau–North China Plain, Yangtze Plain, Southeast China, and Irrawaddy regions. Precipitation feedback was influenced by the degree of initial moisture limitation affecting soil moisture response and thus evapotranspiration, as well as precipitation advection from other reforested regions and moisture transport away from the local region. Reforestation also reduces the probability of extremely dry months in most of the water‐insecure regions. However, some regions experience nonsignificant declines in net water yield due to heightened evapotranspiration outstripping increases in precipitation, or declines in soil moisture and advected precipitation. [ABSTRACT FROM AUTHOR]

The crop simulation model Decision Support System for Agrotechnology Transfer (DSSAT) was applied over the Mekong River Delta (MRD), Southern Vietnam, to assess future (2020–2050) impacts of climate change on rice production. The DSSAT model was driven using observed station data and projected climate data derived through the dynamical downscaling of three global climate models (GCMs) using the Weather Research and Forecasting (WRF) model. The WRF model was simulated at a spatial resolution of 30 km over the study region, and the large-scale driving fields for future climates were taken from the Coupled Model Inter-Comparison Project Phase 3 (CMIP3) global models ECHAM5, CCSM3, and MIROC5 under the A2 emission scenario. Rice growth during two main seasons, namely, the winter-spring (winter) and summer-autumn (summer), were selected to quantify impacts under both irrigated and rain-fed rice cultivation. The results from this climate-crop study suggest that under rain-fed conditions, winter rice yield was likely to experience nearly 24% reduction while summer rice yield was projected to decrease by about 49%. Without irrigation, the annual rice yield was projected to decrease by about 36.5%, and under irrigated conditions, climate change is likely to reduce annual irrigated rice yields by about 1.78%. Winter rice yield was likely to decrease by 4.7% while summer rice yield was projected to marginally increase by about 0.68%. Increasing temperatures and seasonal variations of precipitation are likely to significantly reduce rice yields under rain-fed condition. In addition, (1) a decrease (increase) in the number of rainy days during the dry (wet) season and (2) positive effects of elevated CO2 for rain-fed rice growth under each of the three WRF model realizations would markedly influence rice yields. With Vietnam being one of the largest exporters of rice, these findings have serious implications for the local agricultural sector. This also serves an early warning for the policymakers and stakeholders for effective planning of not only crop production but also water resource management. The findings call for prudent diversification strategy planning by those countries which import rice. [ABSTRACT FROM AUTHOR]

This study evaluates the National Aeronautics Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset that provides statistically downscaled CMIP5 historical and future climate projections (precipitation and temperature), at high spatial (25 km) and temporal (daily) resolutions. The study is performed over Southeast Asia for the historical period 1976-2005 and compared against gridded observations on daily scales. Future climate change is assessed over the future time slices, 2020-2050 and 2070-2099, under two Representative Concentration Pathways (RCP) scenarios 4.5 and 8.5 with respect to 1976-2005. The future climate projections indicate that surface temperatures over Southeast Asia are likely to increase by more than 3.5 °C by the end of the century. As to precipitation, both the mean and extreme rainfall are likely to increase but the biases in the historical simulations could contribute to larger uncertainties in the estimates of rainfall projections. Findings of the study indicate that NEX-GDDP are in good agreement with observations over the historical period only on monthly scales and that they do not capture the observed statistics on daily scales which suggests that these data need a deeper scrutiny on daily scales, especially when used for impacts studies. [ABSTRACT FROM AUTHOR]

The uncertainties in two high-resolution satellite precipitation products (TRMM 3B42 v7.0 and GSMaP v5.222) were investigated by comparing them against rain gauge observations over Singapore on sub-daily scales. The satellite-borne precipitation products are assessed in terms of seasonal, monthly and daily variations, the diurnal cycle, and extreme precipitation over a 10-year period (2000-2010). Results indicate that the uncertainties in extreme precipitation is higher in GSMaP than in TRMM, possibly due to the issues such as satellite merging algorithm, the finer spatio-temporal scale of high intensity precipitation, and the swath time of satellite. Such discrepancies between satellite-borne and gauge-based precipitations at sub-daily scale can possibly lead to distorting analysis of precipitation characteristics and/or application model results. Overall, both satellite products are unable to capture the observed extremes and provide a good agreement with observations only at coarse time scales. Also, the satellite products agree well on the late afternoon maximum and heavier rainfall of gauge-based data in winter season when the Intertropical Convergence Zone (ITCZ) is located over Singapore. However, they do not reproduce the gauge-observed diurnal cycle in summer. The disagreement in summer could be attributed to the dominant satellite overpass time (about 14:00 SGT) later than the diurnal peak time (about 09:00 SGT) of gauge precipitation. From the analyses of extreme precipitation indices, it is inferred that both satellite datasets tend to overestimate the light rain and frequency but underestimate high intensity precipitation and the length of dry spells. This study on quantification of their uncertainty is useful in many aspects especially that these satellite products stand scrutiny over places where there are no good ground data to be compared against. This has serious implications on climate studies as in model evaluations and in particular, climate model simulated future projections, when information on precipitation extremes need to be reliable as they are highly crucial for adaptation and mitigation. [ABSTRACT FROM AUTHOR]

We present preliminary analyses of the historical (1986-2005) climate simulations of a ten-member subset of the Coupled Model Inter-comparison Project Phase 5 (CMIP5) global climate models over Southeast Asia. The objective of this study was to evaluate the general circulation models’ performance in simulating the mean state of climate over this less-studied climate vulnerable region, with a focus on precipitation. Results indicate that most of the models are unable to reproduce the observed state of climate over Southeast Asia. Though the multi-model ensemble mean is a better representation of the observations, the uncertainties in the individual models are far high. There is no particular model that performed well in simulating the historical climate of Southeast Asia. There seems to be no significant influence of the spatial resolutions of the models on the quality of simulation, despite the view that higher resolution models fare better. The study results emphasize on careful consideration of models for impact studies and the need to improve the next generation of models in their ability to simulate regional climates better. [ABSTRACT FROM AUTHOR]

• The model bias is decomposed into the ensemble bias and the inter-model modes. • The CMIP6 models simulate well the meridional march and the annual climatology. • The long-term trends are poorly represented in CMIP6 models. • The CMIP6 models can only reproduce about 23% of interannual variability. • The best 3 models are EC-Earth3, EC-Earth3-Veg and E3SM-1-0. This study evaluated the bias in rainfall over Southeast Asia in the historical simulations of the sixth phase of Coupled Model Intercomparison Project (CMIP6). Using 48 CMIP6 models, a suite of evaluation methods was applied to examine in detail – annual meridional march, annual climatology, long-term trend, and interannual variability. For this study, multi-source observational datasets were employed for comparisons against model simulations. The biases in the climatological state were decomposed into ensemble biases and the leading modes of inter-model uncertainty. The CMIP6 models could simulate well the climatological state of meridional march and annual mean with high pattern correlation coefficients of 0.88 and 0.59, respectively. However, the performance is poor in simulating the long-term trend that exhibits a correlation coefficient of 0.05 between simulation and observation in spatial patterns. The CMIP6 models could only reproduce about 23% of interannual variability. This evaluation not only revealed the systematic biases in 48 CMIP6 models but also illustrated the biases in the different Empirical Orthogonal Function (EOF) modes of inter-model uncertainty and the relative performance ranking for a particular model. Based on the total performance index, the best 3 models are EC-Earth3, EC-Earth3-Veg, E3SM-1-0 and relatively poor performance were seen in INM-CM4-8, AWI-ESM-1-1-LR and MCM-UA-1-0. The study provides an assessment of the overall uncertainties in CMIP6 models in the historical simulations and could also be a useful feedback to the global climate modellers in optimizing the models for better performance. [ABSTRACT FROM AUTHOR]

Summary The Standardized Precipitation Index (SPI) has been computed based on the monthly precipitation for different observed and modelled datasets over the Central Highland, Vietnam during the period 1990–2005. Station data from a total of 13 stations were collected from the study region and used for benchmarking to compare gridded observation data and two regional climate models (RCMs). Various characteristics of drought across the study region were analyzed using spatial and temporal distributions, number of drought events, their frequency and their deficit. The RCMs were able to capture the SPI temporal distributions of station data fairly well. The analysis from RCMs showed close estimation for the number of drought events to station data and gridded observations. In terms of Drought Deficit and frequency, the RCMs matched the station data better than gridded observations. The drought trend was carried out using a Modified Mann–Kendall trend test which yielded no clear trends that suggested the need for longer records of data. The results also highlight uncertainties in gridded data and the need for robust station data quality and record lengths. The regional climate models proved to be appropriate tools in assessing drought over the study area as they can serve as good proxies over data sparse regions, especially in developing countries, for studying detailed climate features at sub regional and local scales. [ABSTRACT FROM AUTHOR]

The study of climate change adaptation plans for drainage infrastructure in a small country as that of Singapore, rainfall projections on the time scale of minutes and on the spatial scales of 1 km are deemed appropriate In this paper, we introduce an application of radar-based stochastic downscaling for rainfall projections at high temporal and spatial resolutions. The input for stochastic model is derived from a Regional Climate Model. The sub-hourly extreme rainfall intensity derived from stochastic model outputs was validated against observed rain-gauge data over the historical period. Considering the advantage in computational efficiency of the stochastic downscaling method, thousand scenarios of rainfall projections at very high temporal and spatial resolution were generated. The implication of this approach is that, from these stochastically downscaled time series of rainfall, it is possible to study future sub-hourly extreme rainfall intensities which would be useful to address issue of flash floods/drainage systems. [ABSTRACT FROM AUTHOR]

Understanding the spatial and temporal variability of rainfall has always been a great challenge, and the impacts of climate change further complicate this issue. The present study employs the concepts of complex networks to study the spatial connections in rainfall, with emphasis on climate change and rainfall scaling. Rainfall outputs (during 1961–1990) from a regional climate model (i.e. Weather Research and Forecasting (WRF) model that downscaled the European Centre for Medium-range Weather Forecasts, ECMWF ERA-40 reanalyses) over Southeast Asia are studied, and data corresponding to eight different temporal scales (6-hr, 12-hr, daily, 2-day, 4-day, weekly, biweekly, and monthly) are analyzed. Two network-based methods are applied to examine the connections in rainfall: clustering coefficient (a measure of the network’s local density) and degree distribution (a measure of the network’s spread). The influence of rainfall correlation threshold ( T ) on spatial connections is also investigated by considering seven different threshold levels (ranging from 0.5 to 0.8). The results indicate that: (1) rainfall networks corresponding to much coarser temporal scales exhibit properties similar to that of small-world networks, regardless of the threshold; (2) rainfall networks corresponding to much finer temporal scales may be classified as either small-world networks or scale-free networks, depending upon the threshold; and (3) rainfall spatial connections exhibit a transition phase at intermediate temporal scales, especially at high thresholds. These results suggest that the most appropriate model for studying spatial connections may often be different at different temporal scales, and that a combination of small-world and scale-free network models might be more appropriate for rainfall upscaling/downscaling across all scales, in the strict sense of scale-invariance. The results also suggest that spatial connections in the studied rainfall networks in Southeast Asia are weak, especially when more stringent conditions are imposed (i.e. when T is very high), except at the monthly scale. [ABSTRACT FROM AUTHOR]

• A model to simulate interactions between a building and its outdoor conditions. • The model co-simulates EnergyPlus, OpenFOAM, and lumped thermal models. • The model provides proper estimates of outdoor conditions at the urban microscale. • Waste heat releases from the HVAC system can be simulated from the model. • Indirect and direct effects of green roofs can be considered by the model. This paper introduces a physically-based model to simulate interactions between a building and its outdoor conditions at the urban microscale. In the model, the building is simulated with EnergyPlus while its outdoor conditions are assessed from OpenFOAM. Furthermore, the model simulates the street pavement and surrounding buildings based on the lumped thermal theory. All components of the model are quasi-dynamically co-simulated. Through simulations of the model, waste heat releases from a cooling system can be observed with a higher resolution than this achieved by most urban microclimate models in the literature. Unlike several methods in the literature, the model evaluates a countermeasure to urban heat islands by considering direct and indirect effects simultaneously. To validate the model, measurements were collected during a field experiment in an university campus. Comparing measurements to estimates, the model seems to properly approximate the outdoor temperature and the air motion. However, a discrepancy is observed between estimates and measurements of the surface temperature. The discrepancy could be minimised if a better consideration of the net-longwave radiation was possible when co-simulating EnergyPlus. After some improvements, the model could become a support tool to mitigate urban heat islands and climate change in different regions of the world. [ABSTRACT FROM AUTHOR]