Building Asian climate change scenario by multi-regional climate models ensemble. Part I: surface air temperature

2016 ◽  
Vol 36 (13) ◽  
pp. 4241-4252 ◽  
Author(s):  
Jianping Tang ◽  
Qian Li ◽  
Shuyu Wang ◽  
Dong-Kyou Lee ◽  
Pinhong Hui ◽  
...  
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Climate Change Scenario ◽  
Climate Models ◽  
Regional Climate ◽  
Surface Air Temperature ◽  
Regional Climate Models ◽  
Change Scenario

Building Asian climate change scenario by multi-regional climate models ensemble. Part II: mean precipitation

2016 ◽  
Vol 36 (13) ◽  
pp. 4253-4264 ◽  
Author(s):  
Qian Li ◽  
Shuyu Wang ◽  
Dong-Kyou Lee ◽  
Jianping Tang ◽  
Xiaorui Niu ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Change Scenario ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Change Scenario

Evaluation of MM5 and HadGEM3-RA hindcast in the CORDEX East Asia Phase Ⅱ: near-surface air temperature

2020 ◽  
Author(s):  
Seok-Woo Shin ◽  
Dong-Hyun Cha ◽  
Taehyung Kim ◽  
Gayoung Kim ◽  
Changyoung Park ◽  
...  
Keyword(s):  
East Asia ◽  
Air Temperature ◽  
Climate Models ◽  
Extreme Values ◽  
Regional Climate ◽  
Surface Air Temperature ◽  
Regional Climate Models ◽  
Near Surface ◽  
The Mean ◽  
Cordex East Asia

<p>Extreme temperature can have a devastating impact on the ecological environment (i.e., human health and crops) and the socioeconomic system. To adapt to and cope with the rapidly changing climate, it is essential to understand the present climate and to estimate the future change in terms of temperature. In this study, we evaluate the characteristics of near-surface air temperature (SAT) simulated by two regional climate models (i.e., MM5 and HadGEM3-RA) over East Asia, focusing on the mean and extreme values. To analyze extreme climate, we used the indices for daily maximum (Tmax) and minimum (Tmin) temperatures among the developed Expert Team on Climate Change Detection and Indices (ETCCDI) indices. In the results of the CORDEX-East Asia phase Ⅰ, the mean and extreme values of SAT for DJF (JJA) tend to be colder (warmer) than observation data over the East Asian region. In those of CORDEX-East Asia phase Ⅱ, the mean and extreme values of SAT for DJF and JJA have warmer than those of the CORDEX-East Asia phase Ⅰ except for those of HadGEM3-RA for DJF. Furthermore, the Extreme Temperature Range (ETR, maximum value of Tmax - minimum value of Tmin) of CORDEX-East Asia phase Ⅰ data, which are significantly different from those of observation data, are reduced in that of CORDEX-East Asia phase Ⅱ. Consequently, the high-resolution regional climate models play a role in the improvement of the cold bias having the relatively low-resolution ones. To understand the reasons for the improved and weak points of regional climate models, we investigated the atmospheric field (i.e., flow, air mass, precipitation, and radiation) influencing near-surface air temperature. Model performances for SAT over East Asia were influenced by the expansion of the western North Pacific subtropical high and the location of convective precipitation in JJA and by the contraction of the Siberian high, the spatial distribution of snowfall and associated upwelling longwave radiation in DJF.</p>

An assessment of precipitation and surface air temperature over China by regional climate models

2016 ◽  
Vol 10 (4) ◽  
pp. 644-661 ◽  
Author(s):  
Xueyuan Wang ◽  
Jianping Tang ◽  
Xiaorui Niu ◽  
Shuyu Wang
Keyword(s):  
Air Temperature ◽  
Climate Models ◽  
Regional Climate ◽  
Surface Air Temperature ◽  
Regional Climate Models

scholarly journals An Application of ANN Ensemble for Estimating of Precipitation Using Regional Climate Models

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Dongwoo Jang
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Pearson Correlation ◽  
Regional Climate ◽  
Circulation Model ◽  
Regional Climate Models ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Precipitation Data ◽  
Simulated Precipitation

Climate change scenarios are used for predicting future precipitation. More detailed regional climate change scenarios are being used through dynamic downscale based on global circulation model results. There is a global tendency to utilize simulated precipitation data from downscaled regional climate models (RCMs) suitable for each country. In Korea, there are studies for improving the accuracy of climate change scenario precipitation forecasts compared with observed precipitation. In this study, the precipitation of five regional climate models and actual observed precipitation provided in Korea are applied to ANN (artificial neural network), which suggests ways to improve prediction accuracy for precipitation. The ANN ensemble of RCMs simulates the actual observed precipitation more accurately than the individual RCM. In particular, it is more effective inland than in coastal areas, where precipitation patterns are complex. Pearson correlation coefficient of ANN is high as 0.04 compared with MRA. It is expected that more detailed analysis will be possible if it is applied not only to four cities but also to other regions in Korea. If observed precipitation data are collected in sufficient quantity, the applicability of the ANN model will widen.

Kriging Approach to Quantile Delta Mapping (QDM) for Spatial Downscaling of Climate Change Scenario

2020 ◽  
Author(s):  
Yong-Tak Kim ◽  
Carlos H R Lima ◽  
Hyun-Han Kwon
Keyword(s):  
Climate Change ◽  
Spatial Variability ◽  
Bias Correction ◽  
Climate Change Scenario ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Change Scenario ◽  
Proposed Model ◽  
Spatial Downscaling

<p>Rainfall simulation by climate model is generally provided at coarse grids and bias correction is routinely needed for the hydrological applications. This study aims to explore an alternative approach to downscale daily rainfall simulated by the regional climate model (RCM) at any desired grid resolution along with bias correction using a Kriging model, which better represents spatial dependencies of distribution parameters across the watershed. The Kringing model also aims to reproduce the spatial variability observed in the ground rainfall gauge. The proposed model is validated through the entire weather stations in South Korea and climate change scenarios simulated by the five different RCMs informed by two GCMs. The results confirmed that the proposed spatial downscaling model could reproduce the observed rainfall statistics and spatial variability of rainfall. The proposed model further applied to the climate change scenario. A discussion of the potential uses of the mode is offered.</p><p>KEYWORDS: Climate Change Scenario, Global Climate Models, Regional Climate Models, Statistical Downscaling, Spatial-Temporal Bias</p><p> </p><p>Acknowledgement</p><p>This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI(KMI2018-01215)</p>

Reproduction and projection of sea level around Korean Peninsula using regional climate ocean model with dynamical downscaling method

2020 ◽  
Author(s):  
Kwang-Young Jeong ◽  
Eunil Lee ◽  
Do-Seong Byun ◽  
Gwang-Ho Seo ◽  
Hwa-Young Lee ◽  
...  
Keyword(s):  
Climate Change ◽  
Sea Level Rise ◽  
Sea Level ◽  
Korean Peninsula ◽  
Climate Change Scenario ◽  
Climate Models ◽  
Regional Climate ◽  
Ocean Model ◽  
Global Ocean ◽  
Change Scenario

<p>Recently, the rate of sea level rise in accelerating with time, and many studies have reported that sea level will increase rapidly in the near future. Also, various global ocean climate models are used to predict sea level rise due to global warming. However, most global ocean climate models have low resolutions, so it is hard to explain detailed the ocean phenomena such as sea level and currents around Korean Peninsula. This study aims to past 30-year reproduce and future 100-year predict for rising trend of sea level using Regional Climate Ocean Model (RCOM) with ROMS according to IPCC climate change scenario (RCP 4.5).</p><p>The RCOM with high resolution of 1/20° horizontally and 40 layers vertically has been established for reproduction and long term forecast of sea-level rise in the Northwest Pacific, including marginal seas around Korea. Dynamic downscaling processes using result of the global climate models were applied to the open boundary conditions of our RCOM. To prepare the optimal boundary data for RCOM, the CMIP5 climate model was evaluated to select 4 climate models: IPSL-CM5A-LR, and -MR, NorESM1-M, MPI-ESM-LR.</p><p>Based on the RCOM results of 4 experiments, the rate of sea level rise for IPCC climate change scenario (RCP4.5) around Korean peninsula were 2.52, 2.21, 3.11, 3.36 mm/yr for the last 30 years (1976~2005), and 5.17, 4.99, 5.62, 5.42 mm/yr for the next 100 years (2006~2100), respectively. Ensemble mean value of next 100 years for 4 model results was 5.30 mm/yr. The sea level rise of 4 models for RCP 4.5 were 48, 48, 58, 48 cm for next 100 years, respectively, and ensemble mean value of 4 models was 50 cm during 2006~2100.</p><p>Future studies will focus on predicting the next 100 years of sea level change based on IPCC climate change scenario (RCP2.6, 8.5).</p><p> </p>

scholarly journals Water Budgets of Managed Forests in Northeast Germany under Climate Change—Results from a Model Study on Forest Monitoring Sites

2021 ◽  
Vol 11 (5) ◽  
pp. 2403
Author(s):  
Daniel Ziche ◽  
Winfried Riek ◽  
Alexander Russ ◽  
Rainer Hentschel ◽  
Jan Martin
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Regional Climate ◽  
Coupled Model ◽  
Regional Climate Models ◽  
Realistic Model ◽  
Forest Monitoring ◽  
Climate Change Scenarios ◽  
Historical Time ◽  
Time Period

To develop measures to reduce the vulnerability of forests to drought, it is necessary to estimate specific water balances in sites and to estimate their development with climate change scenarios. We quantified the water balance of seven forest monitoring sites in northeast Germany for the historical time period 1961–2019, and for climate change projections for the time period 2010–2100. We used the LWF-BROOK90 hydrological model forced with historical data, and bias-adjusted data from two models of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) downscaled with regional climate models under the representative concentration pathways (RCPs) 2.6 and 8.5. Site-specific monitoring data were used to give a realistic model input and to calibrate and validate the model. The results revealed significant trends (evapotranspiration, dry days (actual/potential transpiration < 0.7)) toward drier conditions within the historical time period and demonstrate the extreme conditions of 2018 and 2019. Under RCP8.5, both models simulate an increase in evapotranspiration and dry days. The response of precipitation to climate change is ambiguous, with increasing precipitation with one model. Under RCP2.6, both models do not reveal an increase in drought in 2071–2100 compared to 1990–2019. The current temperature increase fits RCP8.5 simulations, suggesting that this scenario is more realistic than RCP2.6.

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