The Assessment of Surface Air Temperature and Precipitation Simulated by Regional Climate Model REMO over China

The ability of German regional climate Model (REMO) to simulate the near surface air temperature and total precipitation over China in 1989-2008 were assessed with the use of Taylor diagrams and bias analysis. Comparing the simulated near surface air temperature with a 20-year observational dataset from China, the spatial correlation coefficient was relatively high (0.94). However, the spatial correlation coefficient for total precipitation was relatively low (about 0.42). The near surface air temperature simulated by REMO was higher than the observed values in most part of China, showing a bias range within 4 C. Significant cold bias of about -4 to -2 C occurred over most of the Qinghai-Tibetan Plateau. In terms of total precipitation, the simulated values were higher than the observed ones, with biases evenly distributed. The annual mean bias in most part of China was within 300 mm. Except for the Qinghai-Tibetan Plateau, South China and Southwest China, REMO accurately reflected the distribution of near surface air temperature and total precipitation. REMO represented the temperature and total precipitation well in North China and Northeast China. REMO simulations were quite close to observations for near surface air temperature in summer and total precipitation in winter. REMO still needs to be improved in complex terrain areas

Added value of the regionally coupled model ROM in the East Asian summer monsoon modeling

The performance of the regional atmosphere-ocean coupled model ROM (REMO-OASIS-MPIOM) is compared with its atmospheric component REMO in simulating the East Asian summer monsoon (EASM) during the time period 1980–2012 with the following results being obtained. (1) The REMO model in the standalone configuration with the prescribed sea surface conditions produces stronger low-level westerlies associated with the South Asian summer monsoon, an eastward shift of the western Pacific subtropical high (WPSH) and a wetter lower troposphere, which jointly lead to moisture pathways characterized by stronger westerlies with convergence eastward to the western North Pacific (WNP). As a consequence, the simulated precipitation in REMO is stronger over the ocean and weaker over the East Asian continent than in the observational datasets. (2) Compared with the REMO results, lower sea surface temperatures (SSTs) feature the ROM simulation with enhanced air-sea exchanges from the intensified low-level winds over the subtropical WNP, generating an anomalous low-level anticyclone and hence improving simulations of the low-level westerlies and WPSH. With lower SSTs, ROM produces less evaporation over the ocean, inducing a drier lower troposphere. As a result, the precipitation simulated by ROM is improved over the East Asian continent but with dry biases over the WNP. (3) Both models perform fairly well for the upper level circulation. In general, compared with the standalone REMO model, ROM improves simulations of the circulation associated with the moisture transport in the lower- to mid-troposphere and reproduces the observed EASM characteristics, demonstrating the advantages of the regionally coupled model ROM in regions where air-sea interactions are highly relevant for the East Asian climate

On the role of horizontal resolution over the Tibetan Plateau in the REMO regional climate model

A number of studies have shown that added value is obtained by increasing the horizontal resolution of a regional climate model to capture additional fine-scale weather processes. However, the mechanisms leading to this added value are different over areas with complicated orographic features, such as the Tibetan Plateau (TP). To determine the role that horizontal resolution plays over the TP, a detailed comparison was made between the results from the REMO regional climate model at resolutions of 25 and 50 km for the period 1980–2007. The model was driven at the lateral boundaries by the European Centre for Medium-Range Weather Forecasts Interim Reanalysis data. The experiments differ only in representation of topography, all other land parameters (e.g., vegetation characteristics, soil texture) are the same. The results show that the high-resolution topography affects the regional air circulation near the ground surface around the edge of the TP, which leads to a redistribution of the transport of atmospheric water vapor, especially over the Brahmaputra and Irrawaddy valleys—the main water vapor paths for the southern TP—increasing the amount of atmospheric water vapor transported onto the TP by about 5. This, in turn, significantly decreases the temperature at 2 m by > 1.5 °C in winter in the high-resolution simulation of the southern TP. The impact of topography on the 2 m temperature over the TP is therefore by influencing the transport of atmospheric water vapor in the main water vapor paths. © 2018 Springer-Verlag GmbH Germany, part of Springer Natur

Seasonal temperature response over the Indochina Peninsula to a worst-case high-emission forcing: a study with the regionally coupled model ROM

Changes of surface air temperature (SAT) over the Indochina Peninsula (ICP) under the Representative Concentration Pathway (RCP) 8.5 scenario are projected for wet and dry seasons in the short-term (2020–2049) and long-term (2070–2099) future of the twenty-first century. A first analysis on projections of the SAT by the state-of-the-art regionally coupled atmosphere-ocean model ROM, including exchanges of momentum, heat, and water fluxes between the atmosphere (Regional Model) and ocean (Max Planck Institute Ocean Model) models, shows the following results: (i) In both seasons, the highest SAT occurs over the southern coastal area while the lowest over the northern mountains. The highest warming magnitudes are located in the northwestern part of the ICP. The regionally averaged SAT over the ICP increases by 2.61 °C in the wet season from short- to long-term future, which is slightly faster than that of 2.50 °C in the dry season. (ii) During the short-term future, largest SAT trends occur over the southeast and northwest ICP in wet and dry seasons, respectively. On regional average, the wet season is characterized by a significant warming rate of 0.22 °C decade−1, while it is non-significant with 0.11 °C decade−1for the dry season. For the long-term future, the rapid warming is strengthened significantly over whole ICP, with trends of 0.51 °C decade−1and 0.42 °C decade−1in wet and dry seasons,respectively. (iii) In the long-term future, more conspicuous warming is noted, especially in the wet season, due to the increased downward longwave radiation. Higher CO2concentrations enhancing the greenhouse effect can be attributed to the water vapor–greenhouse feedback, which, affecting atmospheric humidity and counter radiation, leads to the rising SAT

The Regional Climate Change Hyper‐Matrix Framework

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95340/1/eost16593.pd

Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22 degrees resolution over the CORDEX Central Asia domain

To allow for climate impact studies on human and natural systems, high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. The CORDEX Central Asia domain is one of these regions, and this article describes the evaluation for two regional climate models (RCMs), REMO and ALARO-0, that were run for the first time at a horizontal resolution of 0.22 degrees (25 km) over this region. The output of the ERA-Interim-driven RCMs is compared with different observational datasets over the 1980-2017 period. REMO scores better for temperature, whereas the ALARO-0 model prevails for precipitation. Studying specific subregions provides deeper insight into the strengths and weaknesses of both RCMs over the CAS-CORDEX domain. For example, ALARO-0 has difficulties in simulating the temperature over the northern part of the domain, particularly when snow cover is present, while REMO poorly simulates the annual cycle of precipitation over the Tibetan Plateau. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temper-ature range. This study aims to evaluate whether REMO and ALARO-0 provide reliable climate information over the CAS-CORDEX domain for impact modeling and environmental assessment applications. Depending on the evaluated season and variable, it is demonstrated that the produced climate data can be used in several subregions, e.g., temperature and precipitation over western Central Asia in autumn. At the same time, a bias adjustment is required for regions where significant biases have been identified

Wheat yield estimation from NDVI and regional climate models in Latvia

Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics

Future changes in annual precipitation extremes over Southeast Asia under global warming of 2°C

THIS ARTICLE PROVIDES detailed information on projected changes in annual precipitation extremes over Southeast Asia under global warming of 2°C based on the multi-model simulations of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment Southeast Asia (SEACLID/CORDEX-SEA). Four indices of extreme precipitation are considered: annual total precipitation (PRCPTOT), consecutive dry days (CDD), frequency of rainfall exceeding 50 mm/day (R50mm), and intensity of extreme precipitation (RX1day). The ensemble mean of 10 simulations showed reasonable performance in simulating observed characteristics of extreme precipitation during the historical period of 1986–2005. The year 2041 was taken as the year when global mean temperature reaches 2°C above pre-industrial levels under unmitigated climate change scenario based on Karmalkar and Bradley (2017). Results indicate that the most prominent changes during the period of 2031–2051 were largely significant. Robust increases in CDD imply impending drier conditions over Indonesia, while increases in RX1day suggest more intense rainfall events over most of Indochina under 2°C global warming scenario. Furthermore, northern Myanmar is projected to experience increases in CDD, R50mm and RX1day, suggesting that the area may face more serious repercussions than other areas in Southeast Asia