Analysis and Attribution of Seasonal Climate Anomalies

In the context of the influence of initial conditions and anomalous surface boundary conditions, the ocean and atmospheric variability in climate model simulations and initialized predictions can be separated into predictable and unpredictable components. In simple terms, predictable component is the common features among the ensemble of model runs (that differentiate the ensemble from the climatology), and unpredictable component is the spread among the ensemble members. Separating predictable and unpredictable component, and relating the predictable component to external factors in initialized seasonal predictions, is what in this analysis is referred as “attribution.” Our ability to attribute is also related to understanding successes and failures of seasonal predictions, and further, their expected skill of seasonal predictions (Barnston et al. 2005).

Here we provide an analysis and attribution of seasonal mean climate anomalies that is maintained in real-time at Climate Prediction Center. In the context of initialized seasonal predictions, the analysis attempts to quantify what extent the observed atmospheric seasonal mean anomalies for the previous season can be attributed to

·         atmospheric response to the sea surface temperatures (SSTs),

·         atmospheric initial conditions from where seasonal predictions start, and

·         contribution of atmospheric internal variability.

The assessment of the atmospheric response to boundary and initial conditions is based either on the ensemble means of the simulations or initialized predictions. Although more sophisticated probabilistic approaches for attribution can also be used, a more deterministic approach is highlighted. The atmospheric general circulation model and the initialized forecasts are from the NCEP's seasonal forecast system (CFSv2). More detailed descriptions are included in the files themselves.

An important aspect of the analysis is our attempt to highlight the contribution of atmospheric internal variability on seasonal mean (particularly in high latitudes) that implies ultimately our limits of seasonal prediction (Kumar and Hoerling 1995; Kumar et al. 2013; Kumar and Chen 2015; Chen and Kumar 2016).

The analysis for the previous season will be updated around middle of current month. Please send your comments and questions to: Arun Kumar (Arun.Kumar@noaa.gov) or Mingyue Chen (Mingyue.Chen@noaa.gov) .

 

Seasonal Analysis and Attribution Files:

ASO2016

SON2016

OND2016

NDJ2016/17

DJF2016/17

JFM2017

FMA2017

MAM2017

AMJ2017

MJJ2017

JJA2017

JAS2017

 

REFERENCES

Barnston, A. G., A. Kumar, L. Goddard, and M. P. Hoerling, 2005: Improving seasonal predictions practices through attribution of climate variability. Bull. Ame. Meteor. Soc., 85, 59-72.

Kumar, A., and M. P. Hoerling, 1995: Prospects and Limitations of Seasonal Atmospheric GCM Predictions. Bull. Ame. Meteor. Soc., 76, 335-345.

Kumar, A., M. Chen, and M. P. Hoerling, and J. Eischeid, 2013: Do extreme climate events require extreme forcings? . Geophys. Res. Lett., 76, 335-345.

Kumar, A., and M. Chen, 2015: Inherent predictability, requirements on ensemble size, and complementarity. Mon. Wea. Rev., 143, 3192–3203.

Chen, M., and A. Kumar, 2016: The utility of seasonal hindcast database for the analysis of climate variability: an example. Clim. Dyn. doi:10.1007/s00382-016-3073-z.