Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model

Raju Pathak,Saroj K. Mishra,Sandeep Sahany

doi:10.1038/s41598-020-74441-x

Raju Pathak, Saroj K. Mishra + Show 1 more

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https://doi.org/10.1038/s41598-020-74441-x

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Journal: Scientific Reports	Publication Date: Oct 15, 2020
Citations: 8	License type: open-access

Affiliation: Indian Institute of Technology Delhi

Abstract

Using uncertainty quantification techniques, we carry out a sensitivity analysis of a large number (17) of parameters used in the NCAR CAM5 cloud parameterization schemes. The LLNL PSUADE software is used to identify the most sensitive parameters by performing sensitivity analysis. Using Morris One-At-a-Time (MOAT) method, we find that the simulations of global annual mean total precipitation, convective, large-scale precipitation, cloud fractions (total, low, mid, and high), shortwave cloud forcing, longwave cloud forcing, sensible heat flux, and latent heat flux are very sensitive to the threshold-relative-humidity-for-stratiform-low-clouds (rhminl) and the auto-conversion-size-threshold-for-ice-to-snow left( {dcs} right). The seasonal and regime specific dependence of some parameters in the simulation of precipitation is also found for the global monsoons and storm track regions. Through sensitivity analysis, we find that the Somali jet strength and the tropical easterly jet associated with the south Asian summer monsoon (SASM) show a systematic dependence on dcs and rhminl. The timing of the withdrawal of SASM over India shows a monotonic increase (delayed withdrawal) with an increase in dcs. Overall, we find that rhminl, dcs, ai, and as are the most sensitive cloud parameters and thus are of high priority in the model tuning process, in order to reduce uncertainty in the simulation of past, present, and future climate.

Highlights

Using uncertainty quantification techniques, we carry out a sensitivity analysis of a large number (17) of parameters used in the NCAR CAM5 cloud parameterization schemes
For the convective precipitation (PRECC), the region of large variance for ANN, DJF, and JJA is similar to PRECT, except over the subtropical Pacific and Atlantic region and the subtropical western Indian ocean, where the large variance occurs in large-scale precipitation (PRECL) (Fig. 1d–i)
PRECC and PRECL are the primary contributors of PRECT over the tropical and subtropical regions, respectively[8,55]

Summary

Introduction

We carry out a sensitivity analysis of a large number (17) of parameters used in the NCAR CAM5 cloud parameterization schemes. We find that rhminl , dcs , ai, and as are the most sensitive cloud parameters and are of high priority in the model tuning process, in order to reduce uncertainty in the simulation of past, present, and future climate. Tuning becomes more cumbersome when the number of uncertain parameters is too large[11] In this situation, researchers use an alternative statistical approach (known as a sensitivity analysis (SA)) to screen out the most sensitive parameters for calibrating GCMs. SA reduces the required number of iterations for parameter tuning, and the computational cost, without affecting the model performance[12,13,14,15]. We perform the qualitative SA analysis using the Morris m ethod[37,38] due to its computational efficiency This method is quite efficient in determining the few potentially important ones amongst a large number of selected parameters. A drawback of the Morris approach is that it cannot distinguish the non-linear effects of a parameter from the interaction effects between different parameters, and cannot estimate the effect of a parameter in relation to other parameters[37]

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Conclusion