Evaluation of precipitation over an oceanic region of Japan in convection-permitting regional climate model simulations

Murata, Akihiko; Sasaki, Hirokazu; Kawase, Hiroaki; Nosaka, Masaya

doi:10.1007/s00382-016-3172-x

Cited by 20 publications

(17 citation statements)

References 61 publications

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“…Convection-permitting scale for a numerical model is a broad definition indicating any horizontal grid spacing from ~5 km down to a few hundred metres. Depending on the region of interest, previous convection-permitting model (CPM) climate studies have chosen a range of different grid spacings, for example, the United States and Africa at 4 km (Prein et al, 2017;Stratton et al, 2018), Japan at 2 km (Murata et al, 2017a), Alpine region at 2.2 km (Ban et al, 2015), southern Germany at 2.8 km (Fosser et al, 2017) and United Kingdom at 1.5 km (Kendon et al, 2014;Clark et al, 2016;Chan et al, 2018). In all cases, CPMs are found to provide a better representation of sub-daily precipitation statistics and convective processes on both climate and numerical weather prediction (NWP) timescales (Done et al, 2004;Richard et al, 2007;Lean et al, 2008;Weisman et al, 2008;Weusthoff et al, 2010;Kendon et al, 2012;Prein et al, 2013;Ban et al, 2014;Chan et al, 2014;Schwartz, 2014;Fosser et al, 2015;Clark et al, 2016;Liu et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Optimal configuration and resolution for the first convection‐permitting ensemble of climate projections over the United Kingdom

Fosser

Kendon

Chan

et al. 2019

Intl Journal of Climatology

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Convection‐permitting models (CPMs) provide a better representation of sub‐daily precipitation statistics and convective processes on both climate and weather forecasting timescales. This study aims to establish what horizontal resolution and physical process settings are suitable for the first ensemble of convection‐permitting climate projections for the United Kingdom. For this purpose, 12‐year‐long simulations were run using three different convection‐permitting resolutions (grid spacing of 4 km, 2.2 km and 1.5 km) each with several configurations. The focus is on the ability to represent sub‐daily precipitation due to its potential for high impacts on society through flooding. The analysis shows that the use of a finer grid spacing within the convection‐permitting regime improves the representation of sub‐daily precipitation but the added value decreases as the grid becomes increasingly finer, while the computational costs substantially increase. Changes in the representation of physical processes can have as much impact as the grid spacing and can lead to similar differences in the diurnal cycle as between convection‐parametrized models and CPMs. Although the 4km model shows some key deficiencies that make it unreliable for climate projection, the 2.2km model performs as well as the 1.5km model for most of the metrics examined with considerable benefits in terms of computer cost. The optimal configuration and resolution identified here is being used for an ensemble of simulations run within the UK Climate Projections 2018 (UKCP18) project, which allows, for the first time, an estimate of uncertainty in future changes at convection‐permitting scale and providing more reliable climate change projections at local and hourly scales.

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Section: Introductionmentioning

confidence: 99%

Optimal configuration and resolution for the first convection‐permitting ensemble of climate projections over the United Kingdom

Fosser

Kendon

Chan

et al. 2019

Intl Journal of Climatology

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“…The signs of improved precipitation extremes in long‐term (>5 years) CPRMs are prevalent in literature, with different percentile based metrics: Using percentile maps of daily and hourly precipitation over Europe (Ban et al, 2014, 2021; Berthou et al, 2020; Leutwyler et al, 2017; Lind et al, 2016), Africa (Berthou, Rowell, et al, 2019), northern (Diro & Sushama, 2019) and western (Li, Li, et al, 2019) Canada, and southern U.S. (Sun et al, 2016), using probability density functions or cumulative distributions of daily and hourly precipitation intensities (Ban et al, 2014; Brisson, Van Weverberg, et al, 2016; Diro & Sushama, 2019; Fosser et al, 2015; Kendon et al, 2012; Knist et al, 2020a; Kouadio et al, 2020; Leutwyler et al, 2017; Li, Li, et al, 2019; Lind et al, 2016; Murata, Sasaki, Kawase, & Nosaka, 2017; Piazza et al, 2019; Pieri et al, 2015; Reder et al, 2020; Sun et al, 2016; Vergara‐Temprado et al, 2020; Wang et al, 2018), using the contribution of each precipitation bin to the total precipitation (Berthou et al, 2020; Berthou, Rowell, et al, 2019; Dai, Rasmussen, Liu, et al, 2020; Finney et al, 2019; Lind et al, 2020; Stratton et al, 2018; Vergara‐Temprado et al, 2020) or quantile–quantile plots (Gutjahr et al, 2016; Karki et al, 2017; Wang et al, 2018). The use of percentiles differs between studies, in that there is no standard practice on whether all time steps or only wet intervals above some lower limit are included.…”

Section: Evidence Of Added Value In Cprcm Hindcast Simulationsmentioning

confidence: 99%

“…Modeling studies at convection‐permitting scales that span climate‐relevant periods (≥10 years) include the British Isles (Chan et al, 2013; Kendon et al, 2012, 2014, 2017; Fosser, Kendon, Chan, et al, 2020; Fosser Kendon, Stephenson, et al, 2020), Svalbard (Dobler, 2019; Dobler et al, 2020), Iceland (Nawri et al, 2014), Japanese Islands (Murata, Sasaki, Kawase, & Nosaka, 2017; Murata, Sasaki, Kawase, Nosaka, Aoyagi, et al, 2017), Hawaii (Zhang et al, 2012; Zhang, Wang, et al, 2016a, 2016b; Argüeso & Businger, 2018; Xue et al, 2020), Puerto Rico (Bhardwaj et al, 2018), Canary Islands (Expósito et al, 2015), Maritime Continent (Vincent & Lane, 2017, 2018), and New Caledonia (Dutheil et al, 2021). Other have focused on shorter periods, such as La Réunion (Morel et al, 2014), Cyprus (Zittis et al, 2017), Hainan (Zhu et al, 2017), Puerto Rico (Wootten et al, 2016), Fiji (Dayal et al, 2020, 2021), and Corsica (Barthlott et al, 2014).…”

Section: Cprcm Benefits For Impact Studiesmentioning

confidence: 99%

“…Other have focused on shorter periods, such as La Réunion (Morel et al, 2014), Cyprus (Zittis et al, 2017), Hainan (Zhu et al, 2017), Puerto Rico (Wootten et al, 2016), Fiji (Dayal et al, 2020, 2021), and Corsica (Barthlott et al, 2014). Overall, studies using CPRCMs have largely focused on the present climate to evaluate the model (Morel et al, 2014; Zhang et al, 2012; Zhang, Wang, et al, 2016a), determine the benefits of switching off convective schemes (Love et al, 2011; Kendon et al, 2014; Wootten et al, 2016; Murata, Sasaki, Kawase, & Nosaka, 2017; Zittis et al, 2017; Fosser, Kendon, Chan, et al, 2020; Argüeso et al, 2020) or investigate atmospheric processes such as the Madden‐Julian Oscillation (Birch et al, 2016; Vincent & Lane, 2016, 2018), gravity waves propagation (Ruppert Jr. et al, 2020), offshore convection propagation (Coppin & Bellon, 2019a, 2019b) and land‐sea breezes (Wei et al, 2020). Some studies have used idealized configurations to investigate the role of islands and their topography on rainfall (Coppin & Bellon, 2019a, 2019b; Tan et al, 2020).…”

Section: Cprcm Benefits For Impact Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Convection‐permitting modeling with regional climate models: Latest developments and next steps

Lucas‐Picher

Argüeso

Brisson

et al. 2021

WIREs Climate Change

134

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Approximately 10 years ago, convection-permitting regional climate models (CPRCMs) emerged as a promising computationally affordable tool to produce fine resolution (1-4 km) decadal-long climate simulations with explicitly resolved deep convection. This explicit representation is expected to reduce climate projection uncertainty related to deep convection parameterizations found in most climate models. A recent surge in CPRCM decadal simulations over larger domains, sometimes covering continents, has led to important insights into CPRCM advantages and limitations. Furthermore, new observational gridded datasets with fine spatial and temporal ($1 km; $1 h) resolutions have leveraged additional knowledge through evaluations of the added value of CPRCMs. With an improved coordination in the frame of ongoing international initiatives, the production of ensembles of CPRCM simulations is expected to provide more robust climate projections and a better identification of their associated uncertainties. This review paper presents an overview of the methodology to produce CPRCM simulations and the latest research on the related added value in current and future climates. Impact studies that are already taking advantage of these new CPRCM simulations are highlighted. This review paper ends by proposing next steps that could be accomplished to continue exploiting the full potential of CPRCMs.

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“…We use NHRCM02 as a convection-permitting RCM. The specification of NHRCM02 is described in Murata et al (2017).…”

Section: Numerical Modelmentioning

confidence: 99%

Projection of Future Climate Change over Japan in Ensemble Simulations Using a Convection-Permitting Regional Climate Model with Urban Canopy

Murata¹,

Sasaki²,

Kawase³

et al. 2017

SOLA

Self Cite

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This study investigates future changes in temperature and precipitation extremes over Japan by the end of the 21st century using ensemble simulations under the Representative Concentration Pathway 8.5 scenario projected by a convection-permitting regional climate model with a grid spacing of 2 km.For all ensemble members, the projected future climate indicates robust increases in the 99th percentile of hourly temperature over all regions of Japan. In contrast, the 99th percentile of hourly precipitation increases over the northern and part of the eastern regions of Japan, particularly on the Sea of Japan side of northern Japan in July. A couple of local-scale areas in Hokkaido are identified as a cause of significant increases in rainfall over this region.Increases in horizontal convergence near the surface and in the middle troposphere are responsible for increased heavy precipitation over a local-scale area of Hokkaido in July. The enhanced convergence near the surface can be attributed to strengthened westerly wind, whereas that in the middle layer can be explained by vertically unstable layers.(Citation: Murata, A., H. Sasaki, H. Kawase, M. Nosaka, T. Aoyagi, M. Oh'izumi, N. Seino, F. Shido, K. Hibino, K. Ishihara, H. Murai, S. Yasui, S. Wakamatsu, and I. Takayabu, 2017: Projection of future climate change over Japan in ensemble simulations using a convection-permitting regional climate model with urban canopy. SOLA, 13, 219−223,

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Evaluation of precipitation over an oceanic region of Japan in convection-permitting regional climate model simulations

Cited by 20 publications

References 61 publications

Optimal configuration and resolution for the first convection‐permitting ensemble of climate projections over the United Kingdom

Optimal configuration and resolution for the first convection‐permitting ensemble of climate projections over the United Kingdom

Convection‐permitting modeling with regional climate models: Latest developments and next steps

Projection of Future Climate Change over Japan in Ensemble Simulations Using a Convection-Permitting Regional Climate Model with Urban Canopy

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