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A new CAM6 + DART reanalysis with surface forcing from CAM6 to other CESM models
Kevin Raeder1, Timothy J Hoar2, Mohamad El Gharamti2
1National Center for Atmospheric Research, CISL/DAReS, Boulder, CO, 80305, USA. raeder@ucar.edu.
Scientific Reports
|August 13, 2021
Summary
A new dataset from an ensemble Kalman filter reanalysis is now available, offering high-frequency atmospheric data from 2011-2019. This resource supports Earth system model research and machine learning applications.
Area of Science:
- Earth System Science
- Atmospheric Science
- Climate Modeling
Background:
- Earth system models (ESMs) require accurate atmospheric forcing data.
- Data assimilation techniques improve the accuracy of climate model reanalyses.
- Previous reanalysis datasets may lack the resolution or ensemble size for certain applications.
Purpose of the Study:
- To archive and release a comprehensive ensemble Kalman filter reanalysis dataset.
- To provide high-frequency, multiyear atmospheric data for Earth system modeling.
- To facilitate hindcast studies and machine learning applications.
Main Methods:
- Utilized the Community Earth System Model (CESM) with a CAM6 configuration.
- Employed the Data Assimilation Research Testbed (DART) with millions of daily observations.
- Generated an 80-member ensemble reanalysis covering the period 2011-2019.
Main Results:
- Archived a ~120 Terabyte dataset including atmospheric forcing, model restarts, and analysis data.
- Data includes sub-daily atmospheric forcing, weekly CAM6 restarts, and 6-hourly hindcasts.
- The dataset features 6-hourly land model plant growth variables and gridded atmospheric analyses.
Conclusions:
- The dataset offers a unique combination of large ensemble size, high frequency, and multiyear coverage.
- Enables robust statistical analysis for climate model evaluation and improvement.
- Serves as a valuable training dataset for machine learning in Earth system science.
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