scholarly journals Simulation of Rainfall over Bangladesh Using Regional Climate Model (RegCM4.7)

Jalawaayu ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 1-19
Author(s):  
Muhammad Tanjilur Rahman ◽  
Md. Nazmul Ahasan ◽  
Md. Abdul Mannan ◽  
Madan Sigdel ◽  
Dibas Shrestha ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Rainfall Simulation ◽  
Theoretical Physics ◽  
Convective Precipitation ◽  
Annual Rainfall ◽  
Linear Scaling ◽  
Simulated Rainfall

Regional climate model is a scientific tool to monitor present climate change and to provide reliable estimation of future climate projection. In this study, the Regional Climate Model version 4.7 (RegCM4.7) developed by International Centre for Theoretical Physics (ICTP) has been adopted to simulate rainfall scenario of Bangladesh. The study examines model performance of rainfall simulation through the period of 1991-2018 with ERA-Interim75 data of 75 km horizontal resolution as lateral boundaries, downscaled at 25km resolution using the mixed convective precipitation scheme; MIT-Emanuel scheme over land and Grell scheme with Fritsch-Chappell closure over ocean. The simulated rainfall has been compared both at spatial and temporal scales (monthly, seasonal and annual) with observed data collected from Bangladesh Meteorological Department (BMD) and Climate Research Unit (CRU). Simulated annual rainfall showed that the model overestimated in most of the years. Overestimation has been observed in the monsoon and underestimation in pre-monsoon and post-monsoon seasons. Spatial distribution of simulated rainfall depicts overestimation in the southeast coastal region and underestimation in the northwest and northeast border regions of Bangladesh. Better estimation of rainfall has been found in the central and eastern parts of the country. The simulated annual rainfall has been validated through the Linear Scaling bias correction method for the years of 2016, 2017, and 2018 considering the rainfall of 1991-2015 as reference. The bias correction with linear scaling method gives fairly satisfactory results and it can be considered in the future projection of rainfall over Bangladesh.

A Regional Climate Model Laboratory for Understanding Coastal Climate

2021 ◽  
Author(s):  
Travis O'Brien ◽  
Thomas Burkle ◽  
Michael Krauter ◽  
Thomas Trapp
Keyword(s):  
Steady State ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Cloud Fraction ◽  
Theoretical Physics ◽  
Coastal Regions ◽  
Local Climate ◽  
Coastal Climate ◽  
Mean Climate

<p>Midlatitude western coastal regions are recognized as being important for the global energy cycle, marine and terrestrial biodiversity, and regional economies.  These coastal regions exhibit a rich range of weather and climate phenomena, including persistent stratocumulus clouds, sea-breeze circulations, coastally-trapped Kelvin waves, and wind-driven upwelling. During the summer season, when impacts from transient synoptic systems are relatively reduced, the local climate is governed by a complex set of interactions among the atmosphere, land, and ocean.  This complexity has so far inhibited basic understanding of the drivers of western coastal climate, climate variability, and climate change.</p><p>As a way of simplifying the system, we have developed a hierarchical regional climate model experimental framework focused on the western United States. We modify the International Centre for Theoretical Physics RegCM4 to use steady-state initial, lateral, and top-of-model boundary conditions: average July insolation (no diurnal cycle) and average meteorological state (winds, temperature, humidity, surface pressure).  This July <em>Base State</em> simulation rapidly reaches a steady state solution that closely resembles the observed mean climate and the mean climate achieved using RegCM4 in a standard reanalysis-driven configuration.  It is particularly notable that the near-coastal stratocumulus field is spatially similar to the satellite-observed stratocumulus field during arbitrary July days: including gaps in stratocumulus coverage downwind of capes. We run similar <em>Base State</em> simulations for the other calendar months and find that these simulations mimic the annual cycle.  This suggests that the summer coastal stratocumulus field results from the steady-state response of the marine boundary layer to summertime climatological forcing; if true for the real world, this would imply that stratocumulus cloud fraction, within a given month, is temporally modulated by deviations from the summer base state (e.g., transient synoptic disturbances that interrupt the cloud field).  We describe modifications to this simplified experimental framework aimed at understanding the factors that govern stratocumulus cloud fraction and its variability.</p>

Assessment of simulated rainfall and temperature from the regional climate model REMO and future changes over Central Africa

2016 ◽  
Vol 48 (11-12) ◽  
pp. 3685-3705 ◽  
Author(s):  
Thierry C. Fotso-Nguemo ◽  
Derbetini A. Vondou ◽  
Clément Tchawoua ◽  
Andreas Haensler
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Central Africa ◽  
Simulated Rainfall

scholarly journals Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue

2013 ◽  
Vol 26 (6) ◽  
pp. 2137-2143 ◽  
Author(s):  
Douglas Maraun
Keyword(s):  
Regional Climate Model ◽  
Bias Correction ◽  
Climate Model ◽  
Daily Precipitation ◽  
Temporal Structure ◽  
Regional Climate ◽  
Quantile Mapping ◽  
Model Simulations ◽  
Scale Mismatch ◽  
Climate Model Simulations

Abstract Quantile mapping is routinely applied to correct biases of regional climate model simulations compared to observational data. If the observations are of similar resolution as the regional climate model, quantile mapping is a feasible approach. However, if the observations are of much higher resolution, quantile mapping also attempts to bridge this scale mismatch. Here, it is shown for daily precipitation that such quantile mapping–based downscaling is not feasible but introduces similar problems as inflation of perfect prognosis (“prog”) downscaling: the spatial and temporal structure of the corrected time series is misrepresented, the drizzle effect for area means is overcorrected, area-mean extremes are overestimated, and trends are affected. To overcome these problems, stochastic bias correction is required.

Climate Change Impacts on Jordan River Flow: Downscaling Application from a Regional Climate Model

2010 ◽  
Vol 11 (4) ◽  
pp. 860-879 ◽  
Author(s):  
Rana Samuels ◽  
Alon Rimmer ◽  
Andreas Hartmann ◽  
Simon Krichak ◽  
Pinhas Alpert
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Climate Model ◽  
Local Level ◽  
Regional Climate ◽  
Resource Planning ◽  
Annual Rainfall ◽  
Future Climate Change ◽  
Jordan River ◽  
Response Models

Abstract The integration of climate change projections into hydrological and other response models used for water resource planning and management is challenging given the varying spatial resolutions of the different models. In general, climate models are generated at spatial ranges of hundreds of kilometers, while hydrological models are generally watershed specific and based on input at the station or local level. This paper focuses on techniques applied to downscale large-scale climate model simulations to the spatial scale required by local response models (hydrological, agricultural, soil). Specifically, results were extracted from a regional climate model (RegCM) simulation focused on the Middle East, which was downscaled to a scale appropriate for input into a local watershed model [the Hydrological Model for Karst Environment (HYMKE)] calibrated for the upper Jordan River catchment. With this application, the authors evaluated the effect of future climate change on the amount and form of precipitation (rain or snow) and its effect on streamflow in the Jordan River and its tributaries—the major water resources in the region. They found that the expected changes in the form of precipitation are nearly insignificant in terms of changing the timing of streamflow. Additionally, the results suggest a future increase in evaporation and decrease in average annual rainfall, supporting expected changes based on global models in this region.

scholarly journals High-resolution Med-CORDEX regional climate model simulations for hydrological impact studies: a first evaluation in Morocco

2013 ◽  
Vol 10 (5) ◽  
pp. 5687-5737 ◽  
Author(s):  
Y. Tramblay ◽  
D. Ruelland ◽  
S. Somot ◽  
R. Bouaicha ◽  
E. Servat
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Regional Climate Model ◽  
Bias Correction ◽  
Hydrological Model ◽  
Climate Model ◽  
Regional Climate ◽  
Impact Studies ◽  
Hydrological Impact ◽  
Split Sample

Abstract. In the framework of the international CORDEX program, new regional climate model (RCM) simulations at high spatial resolutions are becoming available for the Mediterranean region (Med-CORDEX initiative). This study provides the first evaluation for hydrological impact studies of these high-resolution simulations. Different approaches are compared to analyze the climate change impacts on the hydrology of a catchment located in North Morocco, using a high-resolution RCM (ALADIN-Climate) from the Med-CORDEX initiative at two different spatial resolutions (50 km and 12 km) and for two different Radiative Concentration Pathway scenarios (RCP4.5 and RCP8.5). The main issues addressed in the present study are: (i) what is the impact of increased RCM resolution on present-climate hydrological simulations and on future projections? (ii) Are the bias-correction of the RCM model and the parameters of the hydrological model stationary and transferable to different climatic conditions? (iii) What is the climate and hydrological change signal based on the new Radiative Concentration Pathways scenarios (RCP4.5 and RCP8.5)? Results indicate that high resolution simulations at 12 km better reproduce the seasonal patterns, the seasonal distributions and the extreme events of precipitation. The parameters of the hydrological model, calibrated to reproduce runoff at the monthly time step over the 1984–2010 period, do not show a strong variability between dry and wet calibration periods in a differential split-sample test. However the bias correction of precipitation by quantile-matching does not give satisfactory results in validation using the same differential split-sample testing method. Therefore a quantile-perturbation method that does not rely on any stationarity assumption and produces ensembles of perturbed series of precipitation was introduced. The climate change signal under scenarios 4.5 and 8.5 indicates a decrease of respectively −30% to −57% in surface runoff for the mid-term (2041–2062), when for the same period the projections for precipitation are ranging between −15% and −19% and for temperature between +1.28°C and +1.87°C.

scholarly journals Bias Correction of High‐Resolution Regional Climate Model Precipitation Output Gives the Best Estimates of Precipitation in Himalayan Catchments

2019 ◽  
Vol 124 (24) ◽  
pp. 14220-14239 ◽  
Author(s):  
Daniel Bannister ◽  
Andrew Orr ◽  
Sanjay K. Jain ◽  
Ian P. Holman ◽  
Andrea Momblanch ◽  
...  
Keyword(s):  
High Resolution ◽  
Regional Climate Model ◽  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Model Precipitation ◽  
Best Estimates
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