Simulation of the Asian Summer Monsoons of 1987 and 1988 with a Regional Model Nested in a Global GCM

Ji, Yindong; Vernekar, Anandu D.

doi:10.1175/1520-0442(1997)010<1965:sotasm>2.0.co;2

Cited by 107 publications

(54 citation statements)

References 28 publications

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“…Given the constraints imposed by boundary conditions, it has been more-or-less assumed that internal model variability is insignificant in comparison to that of the large-scale forcing. The first ensemble studies based on analyses with multiple RCMs were published in the 1990s (Ji and Vernekar 1997;Takle et al 1999), and convincing examples demonstrating the importance of internal variability have been published by Weisse et al (2000) and Rinke and Dethloff (2000). More systematically, Christensen et al (2001) compared the level of internal variability generated in RCMs to that of a GCM and showed that, although the internal variability of the RCMs was by comparison substantially reduced, it was not negligible.…”

Section: Origin Of Dynamical Downscaling Ensemblementioning

confidence: 99%

“…experiments The DDS modeling community was late in recognizing the chaotic nature of regional climate dynamics (Ji and Vernekar 1997;Weisse et al 2000). Already in the 1970s, global modelers understood (Chervin and Schneider 1976) that running the same model with slightly different conditions (such as slightly different initial states) would generate a different simulation trajectory.…”

Section: Origin Of Dynamical Downscaling Ensemblementioning

confidence: 99%

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Reconsidering the Quality and Utility of Downscaling

Takayabu¹,

Kanamaru

Dairaku

et al. 2016

Journal of the Meteorological Society of Japan

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Dynamical downscaling (DDS) is performed using regional climate models (RCMs) with global atmospheric states as the input, but there is no consensus among researchers on how to define and estimate the resolvable scale of the various climatic variables obtained by DDS. Sources of RCM uncertainties, including both internal model and intermodel variability, have been assessed by performing ensemble simulations and model intercomparisons, sometimes under the controversial assumption that model bias is independent of the climatic state. Compared with low-resolution global climate simulations, DDS can add value in several ways. For example, because they consider high-resolution topographic data, RCMs can often capture mesoscale phenomena and can better represent climate dynamics. Another downscaling method, empirical statistical downscaling (ESD), is complementary to DDS because it is based on a different philosophy (i.e., sources of information) and on a mostly different set of assumptions. More collaboration and communication should be encouraged among those who develop models, those who use models and perform downscaling, those who use downscaling data, and those who make decisions

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Section: Origin Of Dynamical Downscaling Ensemblementioning

confidence: 99%

Section: Origin Of Dynamical Downscaling Ensemblementioning

confidence: 99%

Reconsidering the Quality and Utility of Downscaling

Takayabu¹,

Kanamaru

Dairaku

et al. 2016

Journal of the Meteorological Society of Japan

View full text Add to dashboard Cite

show abstract

“…domain size/location, initialisation time), substantial variability between the individual realisations is well known (e.g. Ji and Vernekar 1997;Rinke and Dethloff 2000;Weisse et al 2000), demonstrating the need for ensemble RCM simulations with a large number of realisations. Many publications demonstrate that RCMs are able to realistically simulate climate in comparison with raw or gridded observations or reanalysis.…”

Section: Added Value Of Dynamical Downscalingmentioning

confidence: 99%

Projected Change—Models and Methodology

Wibig

Maraun

Benestad

et al. 2015

Regional Climate Studies

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General (global) circulation models (GCMs) are a useful tool for studying how climate may change in the future. Although GCMs have high temporal resolution, their spatial resolution is low. To simulate the future climate of the Baltic Sea region, it is necessary to downscale GCM data. This chapter describes the two conceptually different ways of downscaling: regional climate models (RCMs) nested in GCMs and using empirical and/or statistical relations between large-scale variables from GCMs and small-scale variables. There are many uncertainties in climate models, including uncertainty related to future land use and atmospheric greenhouse gas concentrations, limits on the amount of input data and their accuracy, and the chaotic nature of weather. The skill of methods for describing regional climate futures is also limited by natural climate variability. For the Baltic Sea area, the lack of an oceanic component in RCMs and poor representation of forcing by aerosols and changes in land use are major limitations. IntroductionThe development of general circulation models (GCMs) has created a useful tool for projecting how climate may change in the future. Such models describe the climate at a set of grid points, regularly distributed in space and time and with the same density over land and ocean. Their temporal resolution is relatively high, but their spatial resolution is limited by computing power. Many important processes, such as cloud formation, convection, and precipitation, occur at spatial scales much smaller than the distance between grid points. This means that these so-called sub-grid processes are not explicitly simulated by the models, but must be approximated with simplifying algorithms referred to as parameterisations. The low spatial resolution also means that the topography, coastline, and processes at the land-air, ocean-air, and land-ocean boundaries are coarsely represented in GCMs.

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“…If, however, a GCM were to be combined with a regional atmospheric model (RAM), with the expectation of providing more realistic simulations, some GCM biases could possibly be corrected. This notion is encouraged by Ji and Vernekar (1997), who nested a RAM in a GCM and successfully simulated the Indian summer monsoon in both the warm and cold events of ENSO.…”

Section: Introductionmentioning

confidence: 99%

GCM Bias of the Western Pacific Summer Monsoon and Its Correction by Two-Way Nesting System

Inatsu

Satake

Kimoto

et al. 2012

Journal of the Meteorological Society of Japan

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Accurate simulation of summertime convection associated with the Asian monsoon trough over the subtropical western Pacific is important but di‰cult to achieve in many general circulation models (GCMs). This study reports a case in which bias could be reduced by introducing a higher-resolution regional atmospheric model (RAM), two-way nested in an atmospheric GCM over the western Pacific. Additional partial-coupling experiments revealed that GCM bias correction was insensitive to the coupling domain. The two-way nesting e¤ect was similar to one phase of a leading mode of natural variability in the system. This is indicative that the twoway nesting model provides more realistic tropical heating that e¤ectively excites a correct phase of the intrinsic dynamical mode to reduce GCM bias.

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Simulation of the Asian Summer Monsoons of 1987 and 1988 with a Regional Model Nested in a Global GCM

Cited by 107 publications

References 28 publications

Reconsidering the Quality and Utility of Downscaling

Reconsidering the Quality and Utility of Downscaling

Projected Change—Models and Methodology

GCM Bias of the Western Pacific Summer Monsoon and Its Correction by Two-Way Nesting System

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