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Climate Data Assimilation

Contacts, for more information:

Due to insufficient observations and an incomplete understanding of physical processes, climate models always contain some biases, and they may produce climate features and variability which are different from the real world. For understanding climate variability and predictability on seasonal-interannual to decadal scales, GFDL scientists use coupled model dynamics to extract observational information from the earth observing system and reconstruct the historical and present states of the earth climate system.

Climate Data Assimilation optimally integrates pieces of observational information and produces a balanced and coherent climate estimate and prediction initialization by maintaining the instantaneous flux exchanges among the coupled components. Optimizing model parameters by using observations through coupled data assimilation is expected to mitigate model biases and enhance model predictability.

GFDL’s Climate Data Assimilation (CDA) uses global [comprehensive] climate models to interpret a broad array of Earth observations, in order to generate detailed, accurate, and physically-consistent estimates of the state of the global ocean, atmosphere, land, and sea ice. GFDL continues to advance the state-of-the-art in CDA — using its climate reanalyses to evaluate next-generation models, initialize and evaluate climate predictions, and to inform scientific research on climate variability and change.

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Recent publications

  • Zhang, S., Zhao, M. et al., 2014: Retrieval of Tropical Cyclone Statistics with a High-Resolution Coupled Model and Data. Geophysical Research Letters, 41(2), DOI:10.1002/2013GL058879.
  • Zhang, S., You-Soon Chang, X. Yang and A. Rosati, 2013: Balanced and Coherent Climate Estimation by Combining Data with a Biased Coupled Model, Journal of Climate
  • Yang, X., A. Rosati, et al., 2013: A predictable AMO-Like Pattern in the GFDL Fully Coupled Ensemble Initialization and Decadal Forecasting System, Journal of Climate
  • Chang, Y, S. Zhang, et al., 2013: An assessment of oceanic variability for 1960-2010 from the GFDL ensemble coupled data assimilation.Climate Dynamics
  • Zhang, S., M. Winton, et al., 2013: Impact of Enthalpy-Based Ensemble Filtering Sea-Ice Data Assimilation on Decadal Predictions: Simulation with a Conceptual Pycnocline Prediction Model. Journal of Climate
  • Wu, X., Zhang, S., et al., 2013: A study of impact of the geographic dependence of observing system on parameter estimation with an intermediate coupled model. Climate Dynamics
  • Han, G., X. Wu, S. Zhang, and Z. Liu, 2013: Error Covariance Estimation for Coupled Data Assimilation Using a Lorenz Atmosphere and a Simple Pycnocline Ocean Model. J. Climate
  • Liu, Z., S. Wu, et al., 2013: Ensemble data assimilation in a simple coupled climate model: The role of ocean-atmosphere interaction.Advances in Atmospheric Sciences, 30(5), doi: 10.1007/s00376-013-2268-z.