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- Description:
- As strong cooling agents in the climate system, marine low-level clouds are an important component of the climate system. Demonstrating how marine low-level clouds respond to anomalies in the atmospheric general circulation in the present climate has the potential to be illustrative of how low clouds might change in a future climate. We examine how thermodynamic factors that control low cloud occurrence change during an ENSO cycle and then how low clouds observed by the CloudSat and CALIPSO satellites vary. In addition to the well-known decrease in marine low clouds in the Northeast Pacific during El Niño onset in June, July and August (JJA), we also find significant increases in the low cloud occurrence on the flanks of the anomalously warm water in the Equatorial Central Pacific during December, January and February (DJF). These low cloud changes are linked to measurable changes in the Earth’s energy budget with net warming of the Earth system during JJA and cooling of the Earth system during DJF. This is the python code to create the figures for the paper about the above research.
- Keyword:
- CloudSat , CALIPSO, El Niño–Southern Oscillation (ENSO), and marine
- Subject:
- ENSO
- Creator:
- Gombert, Peter M., Strong, Courtenay, and Mace, Gerald G. (Jay)
- Owner:
- Sally Benson
- Based Near Label Tesim:
- Salt Lake City, Utah, United States
- Language:
- Python and English
- Date Uploaded:
- 06/18/2024
- Date Modified:
- 09/09/2024
- Date Created:
- 2023-06-01 to 2024-06-01 (collected) and 2007-2018 (created)
- License:
- CC BY NC - Allows others to use and share your data non-commercially and with attribution.
- Resource Type:
- Software or Program Code
- Identifier:
- https://doi.org/10.7278/S5d-64j3-1n2n
-
- Description:
- We apply Bayesian inference to instrument calibration and experimental-data uncertainty analysis for the specific application of measuring radiative intensity with a narrow-angle radiometer. We develop a physics-based instrument model that describes temporally varying radiative intensity, the indirectly measured quantity of interest, as a function of scenario and model parameters. We identify a set of five uncertain parameters, find their probability distributions (the posterior or inverse problem) given the calibration data by applying Bayes’ Theorem, and employ a local linearization to marginalize the nuisance parameters resulting from errors-in-variables. We then apply the instrument model to a new scenario that is the intended use of the instrument, a 1.5 MW coal-fired furnace. Unlike standard error propagation, this Bayesian method infers values for the five uncertain parameters by sampling from the posterior distribution and then computing the intensity with quantifiable uncertainty at the point of a new, in-situ furnace measurement (the posterior predictive or forward problem). Given the instrument-model context of this analysis, the propagated uncertainty provides a significant proportion of the measurement error for each in-situ furnace measurement. With this approach, we produce uncertainties at each temporal measurement of the radiative intensity in the furnace, successfully identifying temporal variations that were otherwise indistinguishable from measurement uncertainty.
- Subject:
- Validation and Simulation
- Creator:
- Scheib, Kaitlyn, Spinti, Jennifer P., Smith, Sean T., Harding, N. Stanley, Smith, Philip J., and Draper, Teri S.
- Owner:
- Philip Smith
- Based Near Label Tesim:
- Salt Lake City, Utah, United States
- Language:
- English
- Date Uploaded:
- 12/01/2020
- Date Modified:
- 01/28/2022
- Date Created:
- November 2020
- Resource Type:
- Software or Program Code
- Identifier:
- https://doi.org/10.7278/S50D6AFQ84VP