Description of MRCM
MIT Regional Climate Model (MRCM) is a mesoscale numerical model, which was developed at Eltahir research group at MIT. Similar to Global Climate Model (GCM), it basically works on atmospheric governing equations with some parameterizations for cloud microphysics, cumulus convection, planetary boundary layer, land surface, and radiation processes. Characteristically, this model as a limited-area model is designed to dynamically downscales coarse-resolution global meteorological data to a higher resolution over a region of interest (i.g., the MRCM is driven by meteorological reanalysis products or GCM simulations as lateral boundary conditions). Therefore, MRCM has the advantage of higher spatial resolution than GCM and thus can be used extensively in climate studies to explain how future climate and anthropogenic land-use changes will affect society at local and regional scales (e.g., Alter et al. 2015; Pal and Eltahir 2016; Im et al. 2017; Kang and Eltahir 2018).
The dynamical component of the MRCM is rooted in Abdus Salam International Centre for Theoretical Physics (ICTP) Regional Climate Model version 3 (RegCM3; Pal et al. 2007), which is a compressible, hydrostatic, and finite difference model. Compared to the RegCM3, the major upgrade to the MRCM is coupling to a dynamic global vegetation model, Integrated Biosphere Simulator (IBIS; Foley et al. 1996). The IBIS has several advantages over Biosphere-Atmosphere Transfer Scheme (BATS) incorporated in RegCM3. The followings are considered in the IBIS: (1) vegetation dynamics, (2) the coexistence of multiple plant function types within the same grid cell, (3) sophisticated plant phenology, (4) plant competitions, (5) explicit modeling of soil and plant biogeochemistry, and (6) additional soil and snow layers (see Winter et al. 2009 for a more detailed description). MRCM coupled with the IBIS has the sufficient skill to realistically represent land surface processes (Winter et al. 2009).
In addition, a number of physics parameterizations have been updated or replaced compared to RegCM3. For example, an irrigation module is newly added to the MRCM, which not only improves the model performance but also allows users to examine the effects of landuse change (Marcella and Eltahir 2014). Then, capability of the MRCM has been further improved by implementing a new surface albedo assignment method, prescribing the satellite-derived albedo, and adjusting the associated parameters. (Marcella 2012; Marcella and Eltahir 2012; Pal and Eltahir 2016). Planetary boundary layer scheme is modified to reduce an erroneously high nocturnal PBL height and to remove unrealistic low-level cloud cover over land Gianotti (2012).
The other primary updates are associated with convective cloud and precipitation processes. The representation of the convective cloud cover was modified on the basis of a relationship between the simulated and observed convective cloud water density (Gianotti and Eltahir 2014a). This suggested cloud fraction parameterization allows realistic simulation of convective-radiative feedback completely independent of model resolution. Additionally, a new method for convective autoconversion (i.e., conversion of convective cloud liquid water into rainfall) is also incorporated into the MRCM (Gianotti and Eltahir 2014b). These updated parameterizations resulted in better simulation of the hydrological cycle, shortwave radiation, net radiation, and turbulent surface fluxes of latent and sensible heat. MRCM has been rigorously evaluated in its ability to simulate the key climatological features over various domains at the regional or local scales, such as North America (Winter et al. 2009), West Africa (Im et al. 2014), Southwest Asia (Pal and Eltahir 2016; Tuel et al. 2021), South Asia (Im et al. 2017; Choi et al. 2021), the Maritime Continent (Im et al. 2018), and East Asia (Kang and Eltahir 2018). Given the satisfactory performance of the model, the MRCM can be widely applied in regional climate change and impacts studies.
The major upgrade features included in the MRCM are summarized in the table below:
| Upgrade Features | References |
| Coupling of IBIS land surface scheme | Winter et al. (2009) |
| New surface albedo assignment | Marcella and Eltahir (2012) |
| Irrigation scheme | Im et al. (2014) |
| New convective cloud scheme | Gianotti and Eltahir (2014 a) |
| New convective rainfall autoconversion scheme | Gianotti and Eltahir (2014 b) |
| Modified boundary layer height and boundary layer cloud scheme | Gianotti (2013) |
Bias correction
Despite significant improvements in the MRCM performance, systematic biases still appear in downscaled climate simulations due to several factors: inadequate representation of physical processes and/or structural errors transferred from lateral boundary conditions. Such biases ultimately can reduce the reliability of future projections. To reduce the effect of them, our earlier research applied two kinds of bias correction methods including delta method (Pal and Eltahir 2016) and quantile mapping technique (Kang et al 2019a, 2019b). The delta method is simpler and less computationally expensive than other methods, like quantile mapping. However, it only corrects for the mean bias, not the distribution of the climate variable. On the other hand, quantile mapping techniques are a more advanced and commonly used method in climate research that adjusts both mean and variance, although this approach could artificially distort the simulated climate change signals (Cannon et al., 2015). Truthful observation data (e.g., station and reanalysis data, such as ERA-Interim and ERA5) at high spatial and temporal resolution are required to correct for the structural biases in simulated climate variables, like rainfall and temperature. In this bias-correction process, it is assumed that the biases in the current and future climates are the same.
References
Alter, R. E., Im, E. S., & Eltahir, E. A. (2015). Rainfall consistently enhanced around the Gezira Scheme in East Africa due to irrigation. Nature Geoscience, 8(10), 763-767.
Choi, Y. W., Campbell, D. J., Aldridge, J. C., & Eltahir, E. A. (2021). Near-term regional climate change over Bangladesh. Climate Dynamics, 1-19.
Foley, J. A., Prentice, I. C., Ramankutty, N., Levis, S., Pollard, D., Sitch, S., & Haxeltine, A. (1996). An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics. Global biogeochemical cycles, 10(4), 603-628.
Gianotti, R. L. (2012). Regional climate modeling over the Maritime Continent: Convective cloud and rainfall processes (Doctoral dissertation, Ph. D. dissertation, Massachusetts Institute of Technology).
Gianotti, R. L., & Eltahir, E. A. (2014a). Regional climate modeling over the Maritime Continent. Part I: New parameterization for convective cloud fraction. Journal of Climate, 27(4), 1488-1503.
Gianotti, R. L., & Eltahir, E. A. (2014b). Regional climate modeling over the Maritime Continent. Part II: New parameterization for autoconversion of convective rainfall. Journal of Climate, 27(4), 1504-1523.
Im ES, Gianotti RL, Eltahir EAB (2014) Improving the simulation of the West African Monsoon using the MIT regional climate model. J Clim 27(6):2209–2229
Im, E. S., Pal, J. S., & Eltahir, E. A. (2017). Deadly heat waves projected in the densely populated agricultural regions of South Asia. Science advances, 3(8), e1603322.
Im, E. S., & Eltahir, E. A. (2018). Simulation of the diurnal variation of rainfall over the western Maritime Continent using a regional climate model. Climate Dynamics, 51(1), 73-88.
Kang, S., & Eltahir, E. A. (2018). North China Plain threatened by deadly heatwaves due to climate change and irrigation. Nature communications, 9(1), 1-9.
Kang, S., Im, E. S., & Eltahir, E. A. (2019a). Future climate change enhances rainfall seasonality in a regional model of western Maritime Continent. Climate Dynamics, 52(1), 747-764.
Kang, S., Pal, J. S., & Eltahir, E. A. (2019b). Future heat stress during Muslim pilgrimage (Hajj) projected to exceed “extreme danger” levels. Geophysical Research Letters, 46(16), 10094-10100.
Marcella, M. P. (2012). Biosphere-Atmosphere Interactions over Semi-arid Regions: Modeling the Role of Mineral Aerosols and Irrigation in the Regional Climate Systems. Ph. D. Thesis.
Marcella, M. P., & Eltahir, E. A. (2012). Modeling the summertime climate of Southwest Asia: The role of land surface processes in shaping the climate of semiarid regions. Journal of Climate, 25(2), 704-719.
Marcella, M. P., & Eltahir, E. A. (2014). Introducing an irrigation scheme to a regional climate model: A case study over West Africa. Journal of climate, 27(15), 5708-5723.
Pal, J. S., Giorgi, F., Bi, X., et al. (2007). Regional climate modeling for the developing world: the ICTP RegCM3 and RegCNET. Bulletin of the American Meteorological Society, 88(9), 1395-1410.
Pal, J. S., & Eltahir, E. A. (2016). Future temperature in southwest Asia projected to exceed a threshold for human adaptability. Nature Climate Change, 6(2), 197-200.
Piani, C., Haerter, J. O., & Coppola, E. (2010). Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology, 99(1), 187-192.
Tuel A, Choi YW, AlRukaibi D, Eltahir EAB (2021) Extreme Storms in Southwest Asia (Northern Arabian Peninsula) under Current and Future Climates. Climate Dynamics, Accepted.
Winter, J. M., Pal, J. S., & Eltahir, E. A. (2009). Coupling of integrated biosphere simulator to regional climate model version 3. Journal of Climate, 22(10), 2743-2757.