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

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

The worldwide C3S CORDEX grand ensemble: A major contribution to assess regional climate change in the IPCC AR6 Atlas

peer reviewedAbstract The collaboration between the Coordinated Regional Climate Downscaling Experiment (CORDEX) and the Earth System Grid Federation (ESGF) provides open access to an unprecedented ensemble of Regional Climate Model (RCM) simulations, across the 14 CORDEX continental-scale domains, with global coverage. These simulations have been used as a new line of evidence to assess regional climate projections in the latest contribution of the Working Group I (WGI) to the IPCC Sixth Assessment Report (AR6), particularly in the regional chapters and the Atlas. Here, we present the work done in the framework of the Copernicus Climate Change Service (C3S) to assemble a consistent worldwide CORDEX grand ensemble, aligned with the deadlines and activities of IPCC AR6. This work addressed the uneven and heterogeneous availability of CORDEX ESGF data by supporting publication in CORDEX domains with few archived simulations and performing quality control. It also addressed the lack of comprehensive documentation by compiling information from all contributing regional models, allowing for an informed use of data. In addition to presenting the worldwide CORDEX dataset, we assess here its consistency for precipitation and temperature by comparing climate change signals in regions with overlapping CORDEX domains, obtaining overall coincident regional climate change signals. The C3S CORDEX dataset has been used for the assessment of regional climate change in the IPCC AR6 (and for the interactive Atlas) and is available through the Copernicus Climate Data Store (CDS)

Evaluation of New CORDEX Simulations Using an Updated Köppen–Trewartha Climate Classification

A new ensemble of climate and climate change simulations covering all major inhabited regions with a spatial resolution of about 25 km, from the WCRP CORDEX COmmon Regional Experiment (CORE) Framework, has been established in support of the growing demands for climate services. The main objective of this study is to assess the quality of the simulated climate and its fitness for climate change projections by REMO (REMO2015), a regional climate model of Climate Service Center Germany (GERICS) and one of the RCMs used in the CORDEX-CORE Framework. The CORDEX-CORE REMO2015 simulations were driven by the ECMWF ERA-Interim reanalysis and the simulations were evaluated in terms of biases and skill scores over ten CORDEX Domains against the Climatic Research Unit (CRU) TS version 4.02, from 1981 to 2010, according to the regions defined by the Köppen–Trewartha (K–T) Climate Classification types. The REMO simulations have a relatively low mean annual temperature bias (about ± 0.5 K) with low spatial standard deviation (about ± 1.5 K) in the European, African, North and Central American, and Southeast Asian domains. The relative mean annual precipitation biases of REMO are below ± 50 % in most domains; however, spatial standard deviation varies from ± 30 % to ± 200 %. The REMO results simulated most climate types relatively well with lowest biases and highest skill score found in the boreal, temperate, and subtropical regions. In dry and polar regions, the REMO results simulated a relatively high annual biases of precipitation and temperature and low skill. Biases were traced to: missing or misrepresented processes, observational uncertainty, and uncertainties due to input boundary forcing