On the Dependence of Cloud Feedbacks on Physical Parameterizations in WRF Aquaplanet Simulations

Cesana, G; Sušelj, K.; Brient, Florent

doi:10.1002/2017gl074820

Cited by 14 publications

(13 citation statements)

References 40 publications

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“…Regional and local scale precipitation remains challenging to simulate well in large scale dynamical climate models (e.g., Cesana et al, 2017; Tapiador et al, 2019). Accordingly, impacts researchers benefit from using SD‐generated precipitation projections as input to their work, as long as the sensitivities of precipitation‐related variables connected to their needs are better documented.…”

Section: Introductionmentioning

confidence: 99%

Statistically downscaled precipitation sensitivity to gridded observation data and downscaling technique

Wootten

Dixon

Adams‐Smith

et al. 2020

Intl Journal of Climatology

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Future climate projections illuminate our understanding of the climate system and generate data products often used in climate impact assessments. Statistical downscaling (SD) is commonly used to address biases in global climate models (GCM) and to translate large‐scale projected changes to the higher spatial resolutions desired for regional and local scale studies. However, downscaled climate projections are sensitive to method configuration and input data source choices made during the downscaling process that can affect a projection's ultimate suitability for particular impact assessments. Quantifying how changes in inputs or parameters affect SD‐generated projections of precipitation is critical for improving these datasets and their use by impacts researchers. Through analysis of a systematically designed set of 18 statistically downscaled future daily precipitation projections for the south‐central United States, this study aims to improve the guidance available to impacts researchers. Two statistical processing techniques are examined: a ratio delta downscaling technique and an equi‐ratio quantile mapping method. The projections are generated using as input results from three GCMs forced with representative concentration pathway (RCP) 8.5 and three gridded observation‐based data products. Sensitivity analyses identify differences in the values of precipitation variables among the projections and the underlying reasons for the differences.Results indicate that differences in how observational station data are converted to gridded daily observational products can markedly affect statistically downscaled future projections of wet‐day frequency, intensity of precipitation extremes, and the length of multi‐day wet and dry periods. The choice of downscaling technique also can affect the climate change signal for variables of interest, in some cases causing change signals to reverse sign. Hence, this study provides illustrations and explanations for some downscaled precipitation projection differences that users may encounter, as well as evidence of symptoms that can affect user decisions.

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Section: Introductionmentioning

confidence: 99%

Statistically downscaled precipitation sensitivity to gridded observation data and downscaling technique

Wootten

Dixon

Adams‐Smith

et al. 2020

Intl Journal of Climatology

View full text Add to dashboard Cite

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“…We perform simulations using the global WRF model (ARW, Version 3) (Skamarock et al, 2008) in aquaplanet setup (Hoskins et al, 1999). This setup has also been utilized in Bhattacharya et al (2017), as well as in Cesana et al (2017). The horizontal resolution is 1 31 and we use a stretched vertical mesh with 40 levels up to the top of the atmosphere.…”

Section: Setupmentioning

confidence: 99%

“…Here we investigate how different boundary layer and convection parameterizations interact with each other in a global model and how they produce different mean-state climates. As suggested by a number of recent studies (Cesana et al, 2017;Medeiros et al, 2008Medeiros et al, , 2015Medeiros et al, , 2016Stevens & Bony, 2013), aquaplanet simulations provide an attractive framework for these investigations, as they retain the dynamics and physics of fully realistic simulations, but eliminate complexities arising from land surface, topography, and other zonal asymmetries. In addition, lack of long timescale processes associated with the ocean circulation and land implies that a shorter spin-up is required to reach equilibrium of the generated climate (Williamson et al, 2012), making it computationally more feasible to perform numerous simulations.…”

Section: Introductionmentioning

confidence: 99%

Parameterization Interactions in Global Aquaplanet Simulations

Bhattacharya

Bordoni

Sušelj

et al. 2018

J Adv Model Earth Syst

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Global climate simulations rely on parameterizations of physical processes that have scales smaller than the resolved ones. In the atmosphere, these parameterizations represent moist convection, boundary layer turbulence and convection, cloud microphysics, longwave and shortwave radiation, and the interaction with the land and ocean surface. These parameterizations can generate different climates involving a wide range of interactions among parameterizations and between the parameterizations and the resolved dynamics. To gain a simplified understanding of a subset of these interactions, we perform aquaplanet simulations with the global version of the Weather Research and Forecasting (WRF) model employing a range (in terms of properties) of moist convection and boundary layer (BL) parameterizations. Significant differences are noted in the simulated precipitation amounts, its partitioning between convective and large‐scale precipitation, as well as in the radiative impacts. These differences arise from the way the subcloud physics interacts with convection, both directly and through various pathways involving the large‐scale dynamics and the boundary layer, convection, and clouds. A detailed analysis of the profiles of the different tendencies (from the different physical processes) for both potential temperature and water vapor is performed. While different combinations of convection and boundary layer parameterizations can lead to different climates, a key conclusion of this study is that similar climates can be simulated with model versions that are different in terms of the partitioning of the tendencies: the vertically distributed energy and water balances in the tropics can be obtained with significantly different profiles of large‐scale, convection, and cloud microphysics tendencies.

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“…The simulation of the WRF is constructed by many different parameterizations for each physical scheme, such as radiation, microphysics (MP), convection or cumulus (CU), and planetary boundary layer (PBL). Therefore, thousands of combinations can be generated (Cesana et al ., 2017). Previous studies have shown that different combined physical parameterizations have different performances in regional climate simulations (Jankov et al ., 2005; Misenis and Zhang, 2010; Osuri et al ., 2012).…”

Section: Introductionmentioning

confidence: 99%

Uncertainties in simulating central Asia: Sensitivity to physical parameterizations using Weather Research and Forecasting model

Wang

Feng

Luo

et al. 2020

Intl Journal of Climatology

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The Weather Research and Forecasting model (WRF) comprises a number of parameterization schemes, and therefore the different combined physical parameterization schemes should be discussed before determining optimal configurations. The regional climate of central Asia is simulated from 2003 to 2008 using the WRFv4.0.1 with nine physical combinations (microphysics [MP], cumulus [CU], planetary boundary layer [PBL]) on a 25‐km horizontal grid. The goal is to identify a reasonable configuration assemble of physical parameterization schemes for long‐term simulations through the sensitivity experiments of regional climate respond to microphysics, convection, and boundary layer processes. Compared to the observation data, all the groups mostly simulate the daily and seasonal variations of precipitation but with a shift from wet bias to dry bias based on the daily precipitation categories of the Tropical Rainfall Measuring Mission (TRMM). The large‐scale precipitation caused by the microphysics schemes is 3–4 times the amount of precipitation caused by the subscale precipitation generated by the cumulus schemes. The simulated difference of the vertical temperature shows results similar to those of the surface air temperature. The microphysics schemes play the dominant role among the three physical options, and the Thompson scheme addresses better precipitation and surface temperature simulations in contrast to the WDM6 scheme. By combining with the bias, correlation and root‐mean‐square error (RMSE) between the reproduced temperature and precipitation, it can be concluded that the best optimal physical schemes combination is the Thompson–Tiedtke–YSU, which can be used for long‐term simulations.

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On the Dependence of Cloud Feedbacks on Physical Parameterizations in WRF Aquaplanet Simulations

Cited by 14 publications

References 40 publications

Statistically downscaled precipitation sensitivity to gridded observation data and downscaling technique

Statistically downscaled precipitation sensitivity to gridded observation data and downscaling technique

Parameterization Interactions in Global Aquaplanet Simulations

Uncertainties in simulating central Asia: Sensitivity to physical parameterizations using Weather Research and Forecasting model

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