Development of Statistically Downscaled Regional Climate Model based on Representative Concentration Pathways for Ipoh, Subang and KLIA Sepang in Peninsular Malaysia

Abstract Climate change is one of the challenging global issues that our world is facing and it is intensely debated on the international agenda. It is a fact that climate change has brought about many disastrous events on a global scale which affect our livelihoods. Climate models are commonly used by researchers to study the magnitude of the changing climate and to simulate future climate projections. Most climate models are developed based on various interactions among the Earth’s climate components such as the land surface, oceans, atmosphere and sea-ice. In this study, the second-generation Canadian Earth System Model (CanESM2) was statistically downscaled to develop a regional climate model (RCM) based on three representative concentration pathways (RCPs): RCP2.6, RCP4.5 and RCP8.5. The RCM will be used to simulate the average minimum and maximum temperatures and average precipitation for Ipoh, Subang and KLIA Sepang in Peninsular Malaysia for the years 2006 to 2100. The simulated data were bias corrected using the historical observation data of monthly average minimum and maximum temperatures and monthly average rainfall retrieved from the Malaysian Meteorological Department (MMD). The different trends of the simulated data for all the three locations based on the RCP2.6, RCP4.5 and RCP8.5 were evaluated for future climate projection.

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Regionalization of Climate Change Simulations over the Eastern Mediterranean

Journal of Climate ◽

10.1175/2008jcli1807.1 ◽

2009 ◽

Vol 22 (8) ◽

pp. 1944-1961 ◽

Cited By ~ 75

Author(s):

Bariş Önol ◽

Fredrick H. M. Semazzi

Keyword(s):

Climate Change ◽

Regional Climate Model ◽

Temperature Increase ◽

Climate Model ◽

Eastern Mediterranean ◽

Regional Climate ◽

Future Climate ◽

Climate Change Projections ◽

Model Simulations ◽

The Future

Abstract In this study, the potential role of global warming in modulating the future climate over the eastern Mediterranean (EM) region has been investigated. The primary vehicle of this investigation is the Abdus Salam International Centre for Theoretical Physics Regional Climate Model version 3 (ICTP-RegCM3), which was used to downscale the present and future climate scenario simulations generated by the NASA’s finite-volume GCM (fvGCM). The present-day (1961–90; RF) simulations and the future climate change projections (2071–2100; A2) are based on the Intergovernmental Panel on Climate Change (IPCC) greenhouse gas (GHG) emissions. During the Northern Hemispheric winter season, the general increase in precipitation over the northern sector of the EM region is present both in the fvGCM and RegCM3 model simulations. The regional model simulations reveal a significant increase (10%–50%) in winter precipitation over the Carpathian Mountains and along the east coast of the Black Sea, over the Kackar Mountains, and over the Caucasus Mountains. The large decrease in precipitation over the southeastern Turkey region that recharges the Euphrates and Tigris River basins could become a major source of concern for the countries downstream of this region. The model results also indicate that the autumn rains, which are primarily confined over Turkey for the current climate, will expand into Syria and Iraq in the future, which is consistent with the corresponding changes in the circulation pattern. The climate change over EM tends to manifest itself in terms of the modulation of North Atlantic Oscillation. During summer, temperature increase is as large as 7°C over the Balkan countries while changes for the rest of the region are in the range of 3°–4°C. Overall the temperature increase in summer is much greater than the corresponding changes during winter. Presentation of the climate change projections in terms of individual country averages is highly advantageous for the practical interpretation of the results. The consistence of the country averages for the RF RegCM3 projections with the corresponding averaged station data is compelling evidence of the added value of regional climate model downscaling.

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Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

10.21203/rs.3.rs-725819/v1 ◽

2021 ◽

Author(s):

Antoine Doury ◽

Samuel Somot ◽

Sébastien Gadat ◽

Aurélien Ribes ◽

Lola Corre

Keyword(s):

Climate Change ◽

Network Architecture ◽

Large Scale ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Future Climate ◽

Near Surface ◽

The Way

Abstract Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost.We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

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Evaluation of SRE Scenarios for Penang, Selangor and Johor in Peninsular Malaysia using PRECIS Regional Climate Model (RCM)

E3S Web of Conferences ◽

10.1051/e3sconf/20186505020 ◽

2018 ◽

Vol 65 ◽

pp. 05020

Author(s):

Kah Seng Chin ◽

Kok Weng Tan

Keyword(s):

Bias Correction ◽

Climate Models ◽

Regional Climate ◽

Peninsular Malaysia ◽

Observation Data ◽

Future Projections ◽

Monthly Average ◽

Average Maximum ◽

Maximum Temperatures ◽

Historical Observation

Climate change is unambiguous as there is much evidence from around the world showing that changes have already occurred. This phenomenon is in response to an array of human activities, notably the release of greenhouse gases; an understanding of the rate, mode and scale of this change is now of literally vital importance to society. Researchers utilize climate models to study the dynamics of our changing climate and also to make future projections. Climate models are basic representation of many interactions within the Earth’s climate which includes the atmosphere, land surface, oceans and ice. These models are typically quantitative in nature and range from simple depictions of the climate to very complex ones. In this present study, downscaled PRECIS regional climate models (RCMs) were used to project the average minimum and average maximum temperatures and average precipitation for Penang, Selangor and Johor in Peninsular Malaysia. The RCM projections for these three states were developed based on ECHAM4 A2 and ECHAM5 A1B scenarios for the years 1980 to 2069 and ECHAM4 B2 scenario for the years 2010 to 2069. Bias correction will be applied to the simulated historical data to remove common systematic model errors. Historical observation data of monthly average minimum and maximum temperatures and monthly average rainfall from the Malaysian Meteorological Department (MMD) will be used in the bias correction. Finally, a RCM scenario which matches with the historical observation data of the three states for future projections will be recommended.

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Future Changes in Surface Runoff over Korea Projected by a Regional Climate Model under A1B Scenario

Advances in Meteorology ◽

10.1155/2014/753790 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 9

Author(s):

Ji-Woo Lee ◽

Suryun Ham ◽

Song-You Hong ◽

Kei Yoshimura ◽

Minsu Joh

Keyword(s):

Climate Change ◽

Regional Climate Model ◽

Surface Runoff ◽

Climate Model ◽

Hydrological Cycle ◽

Regional Climate ◽

Future Climate ◽

Climate Simulation ◽

Intergovernmental Panel ◽

The Future

This study assesses future change of surface runoff due to climate change over Korea using a regional climate model (RCM), namely, the Global/Regional Integrated Model System (GRIMs), Regional Model Program (RMP). The RMP is forced by future climate scenario, namely, A1B of Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). The RMP satisfactorily reproduces the observed seasonal mean and variation of surface runoff for the current climate simulation. The distribution of monsoonal precipitation-related runoff is adequately captured by the RMP. In the future (2040–2070) simulation, it is shown that the increasing trend of temperature has significant impacts on the intra-annual runoff variation. The variability of runoff is increased in summer; moreover, the strengthened possibility of extreme occurrence is detected in the future climate. This study indicates that future climate projection, including surface runoff and its variability over Korea, can be adequately addressed on the RMP testbed. Furthermore, this study reflects that global warming affects local hydrological cycle by changing major water budget components. This study adduces that the importance of runoff should not be overlooked in regional climate studies, and more elaborate presentation of fresh-water cycle is needed to close hydrological circulation in RCMs.

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Climate change projections for CORDEX-Africa with COSMO-CLM regional climate model and differences with the driving global climate models

Climate Dynamics ◽

10.1007/s00382-015-2664-4 ◽

2015 ◽

Vol 46 (5-6) ◽

pp. 1599-1625 ◽

Cited By ~ 78

Author(s):

Alessandro Dosio ◽

Hans-Jürgen Panitz

Keyword(s):

Climate Change ◽

Regional Climate Model ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Regional Climate ◽

Global Climate Models ◽

Climate Change Projections

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Uncertainties in climate change projections and regional downscaling: implications for water resources management

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-1821-2010 ◽

2010 ◽

Vol 7 (2) ◽

pp. 1821-1848 ◽

Cited By ~ 11

Author(s):

W. Buytaert ◽

M. Vuille ◽

A. Dewulf ◽

R. Urrutia ◽

A. Karmalkar ◽

...

Keyword(s):

Climate Change ◽

Water Resources ◽

Regional Climate Model ◽

Water Resources Management ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Climate Change Projections ◽

Resources Management

Abstract. Climate change is expected to have a large impact on water resources worldwide. A major problem in assessing the potential impact of a changing climate on these resources is the difference in spatial scale between available climate change projections and water resources management. Regional climate models (RCMs) are often used for the spatial disaggregation of the outputs of global circulation models. However, RCMs are time-intensive to run and typically only a small number of model runs is available for a certain region of interest. This paper investigates the value of the improved representation of local climate processes by a regional climate model for water resources management in the tropical Andes of Ecuador. This region has a complex hydrology and its water resources are under pressure. Compared to the IPCC AR4 model ensemble, the regional climate model PRECIS does indeed capture local gradients better than global models, but locally the model is prone to large discrepancies between observed and modelled precipitation. It is concluded that a further increase in resolution is necessary to represent local gradients properly. Furthermore, to assess the uncertainty in downscaling, an ensemble of regional climate models should be implemented. Finally, translating the climate variables to streamflow using a hydrological model constitutes a smaller but not negligible source of uncertainty.

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