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	ASO2017	SON2017	OND2017	NDJ2017/18	DJF2017/18
JFM2018	FMA2018	MAM2018	AMJ2018	MJJ2018	JJA2018	JAS2018	ASO2018	SON2018	OND2018	NDJ2018/19	DJF2018/19
JFM2019	FMA2019	MAM2019	AMJ2019	MJJ2019	JJA2019	JAS2019	ASO2019	SON2019	OND2019	NDJ2019/20	DJF2019/20
JFM2020	FMA2020	MAM2020	AMJ2020	MJJ2020	JJA2020	JAS2020	ASO2020	SON2020	OND2020	NDJ2020/21	DJF2020/21
JFM2021	FMA2021	MAM2021	AMJ2021	MJJ2021	JJA2021	JAS2021	ASO2021	SON2021	OND2021	NDJ2021/22	DJF2021/22
JFM2022	FMA2022	MAM2022	AMJ2022	MJJ2022	JJA2022	JAS2022	ASO2022	SON2022	OND2022	NDJ2022/23	DJF2022/23
JFM2023	FMA2023	MAM2023	AMJ2023	MJJ2023	JJA2023	JAS2023	ASO2023	SON2023	OND2023	NDJ2023/24	DJF2023/24
JFM2024	FMA2024	MAM2024	AMJ2024	MJJ2024	JJA2024	JAS2024	ASO2024

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.