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Standardized and Scalable Assay to Study Perfused 3D Angiogenic Sprouting of iPSC-derived Endothelial Cells In Vitro

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

Raju Pathak¹, Sandeep Sahany^2,3, Saroj K Mishra¹

¹Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India.

Scientific Reports

|October 16, 2020

Summary

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Seasonal dependent suitability of physical parameterizations to simulate precipitation over the Himalayan headwater.

Scientific reports·2023

Sensitivity analysis of NCAR CAM5 cloud schemes identified key parameters influencing precipitation and climate simulations. Tuning these parameters, including threshold-relative-humidity-for-stratiform-low-clouds and auto-conversion-size-threshold-for-ice-to-snow, is crucial for reducing climate model uncertainty.

Area of Science:

Atmospheric Science
Climate Modeling
Uncertainty Quantification

Background:

Cloud parameterization schemes in climate models like NCAR CAM5 are complex, involving numerous parameters.
Accurate simulation of clouds is vital for predicting climate change and its impacts.

Purpose of the Study:

To conduct a sensitivity analysis of 17 parameters in the NCAR CAM5 cloud parameterization schemes.
To identify the most influential parameters affecting climate simulations using uncertainty quantification techniques.

Main Methods:

Utilized the LLNL PSUADE software for sensitivity analysis.
Employed the Morris One-At-a-Time (MOAT) method to assess parameter influence.

Main Results:

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Standardized and Scalable Assay to Study Perfused 3D Angiogenic Sprouting of iPSC-derived Endothelial Cells In Vitro

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Published on: November 6, 2019

Simulations of precipitation, cloud fractions, and radiative/heat fluxes are highly sensitive to threshold-relative-humidity-for-stratiform-low-clouds ([Formula: see text]) and auto-conversion-size-threshold-for-ice-to-snow ([Formula: see text]).

Parameter sensitivity exhibits seasonal and regional dependence, impacting monsoon and storm track simulations.

Somali jet strength and tropical easterly jet in the South Asian Summer Monsoon (SASM) show dependence on [Formula: see text] and [Formula: see text].

SASM withdrawal timing over India is monotonically delayed with increased [Formula: see text].

Conclusions:

The parameters [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] are identified as the most sensitive cloud parameters.
Prioritizing these parameters in model tuning is essential for reducing uncertainties in climate simulations.