# Presentation: 777137

## Prediction Skill of the NAO and PNA from Daily to Seasonal Time-scales 

**Coupled forecasting – Monthly and Seasonal Time Scales**
**Models and Data**
- NCEP CFS and the 7 models in the DEMETER project
- IC: CFS                    5 Lagged IC (30-31 OCT and 1-3 NOV)   ;   21 Years (1981-2001)
-       DEMETER        9 Perturbed IC at 1 NOV                            ;   21 Years (1981-2001)
- Ensemble mean fields are considered
- Forecast lengths: 1-5 months
- Anomaly fields are calculated in two alternative ways by removing :
**A-B** The model climatology calculated under cross validation (CV) with one year withheld at a time
**C-D** An observed climatology over the 30 year period 1971-2000

**Probability density function of NAO and PNA**
**A-B**  The observed pdf’s of both the NAO and PNA indices are unimodal and quasi-Gaussian
**A-B**   Both distributions are also slightly negatively skewed. This is most clearly evident for the
-           NAO index, which has a mode slightly shifted to the positive side and a clear asymmetry in
-           the tails of the distribution, corresponding to a dominance of extreme negative events
-           compared to positive events.
**Skill dependence of index values**
**C-F**  It is expected that predictions of (predicted) flow states that depart significantly from climatology have higher skill than predictions of more common states. Given this, the question whether the overall forecast skill of the flow is dependent on the magnitude of the two indices is considered here. The AC as a function of forecast lead time and the value of the forecasted indices for the two indices and for both models is shown. There is a slight tendency for higher AC skill scores for larger magnitude of the indices, but less than expected. The reasons for the modest overall dependence are probably because (1) the AC skill is for the Northern Hemisphere extratropics as a whole and not just the regions occupied by the NAO and PNA and (2) the NAO and PNA each only explain ~10% of the variance of daily flows in the Northern Hemisphere extratropics. If regionalized skill scores are used instead, they would probably exhibit a stronger dependence. Furthermore, since the NAO and PNA are almost uncorrelated, a larger dependence would probably result from use of a combined index.

**Prediction Skill of the NAO and PNA from Daily to Seasonal Time-scales**** **
- Åke Johansson*  SAIC/Environmental Modeling Center  National Centers for Environmental Prediction, NWS/NOAA/DOC
- * On leave from the Swedish Meteorological and Hydrological Institute (SMHI), SE-601 76 Norrköping, Sweden

**Uncoupled forecasting - Short to Medium Range Time Scales**
**Models and Data**
- NCEP and ECMWF operational high-resolution deterministic medium range forecast models
- IC: Daily data at 00 UTC in NOV-MAR     ;     22 JAN 2000 – 24 MAR 2005     ;     819 days
- Forecast lengths: NCEP 1-15 days, ECMWF 1-7 days

-             **A                              B                             C                              D                              E**** **

**Conclusions**
- The skill of predicting the wintertime NAO and PNA indices in the short and medium-range is considerably higher than the skill of predicting the total field in the extratropics.
- The skill of the total flow in the extratropics increases with the magnitude of the NAO and PNA indices, but the relationship is not pronounced.
- The pdf’s of the NAO and PNA indices are negatively skewed in agreement with the distribution of skewness of the geopotential field, and the NAO is more skewed then the PNA.
- On the intraseasonal timescale, week-3 to week-6, it is found that both the NAO and PNA have lingering forecast skill at a level not found for the total flow. However, the skill is quite low.
- On the monthly and seasonal timescales the level of skill in predicting the two indices is generally quite low, with the exception of winter predictions at leads up to a few months. The PNA is found to have higher and more robust correlation scores compared to the NAO, in accordance with the case in the medium range, but opposite to the intraseasonal range.
- A comparison between 8 different coupled models reveals a large spread in the estimated correlation scores. The magnitude of this spread is similar to the spread obtained when different ensemble sizes is used. The spread is consistent with the low skill.

**Skill Evaluation**
**A-B**  Both models are substantially more skillful in predicting the PNA than the NAO
**A-B**  Both models are more skillful at predicting the two indices compared to the total flow (thin)
**A-B**  The ECMWF model (red) predicts both indices better than the NCEP model (blue), and the
-          difference is larger for the PNA compared to the NAO
**A-B**  NAO has somewhat higher persistence (black) than PNA for this 5-year winter period
-          The hypothesis that the higher predictive skill of PNA compared to NAO is a consequence of
-           higher persistence in PNA is thus not supported
**C-D**  Winters with large persistence skill are associated with higher skill in predicting the indices,
-           and vice versa. Persistence thus seems to play a role, especially for the NAO, but does not
-           explain superior skill for PNA
**E**** **     An interesting feature is the occurrence of a secondary maximum of the NAO persistence in
-          many winters. No evidence of similar behavior is noted for the PNA. Ambaum and Hoskins
-          argue that this feature may be a consequence of troposphere-stratosphere interaction in the
-          NAO region. However, for the overall AC skill it is hard to see any impact

**Coupled forecasting – Intraseasonal Time Scales**
**Models and Data**
- The operational NCEP Climate Forecast System (CFS) at higher horizontal resolution - T126
- IC: Daily data at 00 UTC, 7 NOV-15 JAN     ;     5 Years (2001-2005)     ;     350 days
- Forecast lengths: 1-45 days

**Comparison to Uncoupled NWP Models in the Short/Medium Range**
- Skill in the short/medium range of predicting NAO (blue curves) and PNA (red curves) in CFS (thick curves) vs. the operational medium range NWP models at NCEP (thin curves). As a reference the mean AC skill score (black curves) is shown.
**A**   NCEP NWP has higher AC skill than CFS
**A**   PNA has higher skill in NCEP NWP than CFS and the relative advantage increases with lead
**A**   NAO has higher skill in NCEP NWP than CFS out to day 10, thereafter lower skill
-     An unequivocal positive impact of coupling is not evident
-     The sample size is probably too small
**Daily Skill Scores on Intraseasonal Time Scales**
**B**   PNA (red curve) is more skillfully predicted than NAO (blue curve) up to 12 days
**B**   NAO is more skillfully predicted than PNA after 13 days
-      May be due to the NAO stratospheric connection
**B**   Both models have above zero skill at all leads
-      Indicates lingering forecast skill, albeit small, out to a month and more
-      Atmosphere-Ocean interaction in the coupled system may be the source of this skill
**Weekly and Monthly average skill scores**
- To increase the signal-to-noise ratio weekly and monthly averages are formed
**C**   The increase in skill compared to the mean of daily values is rather small
**C**   Largest improvements is for NAO in the week-2 to week-5 range
**C**   Improvements for PNA is only evident in week-3
-      Consistent with the fact that increases are only possible if the signal is non-negligible and
-        more or less constant
**C**   Month-1 (15-45 day average) skill scores is clearly higher than the average of daily scores
-       NAO has higher skill than PNA

-                                      **A                                                                           B**** **

**C**

**Monthly Forecasting**

**Seasonal Forecasting**

**B**

**C**

**D**

**A**

**C**

**B**

**D**

-              **A                                B                               C                                D                                E                                F**

**Month-1 Correlation Scores**
- 	**NAO                   33.0 %**
- 	**PNA                    21.3 %**