# Presentation: 167366

## Downstream weather impacts associated with atmospheric blocking: Linkage between low-frequency variability and weather extremes 

- Marco L. Carrera, R. W. Higgins and V. E. Kousky
- Climate Prediction Center NCEP/NWS/NOAA

## Objective 

**To assess the relationship between weather extremes and atmospheric blocking.**
**Do the statistical distributions of temperature and precipitation change during blocking regimes?**

_**Motivation**_

**During the NH cool season atmospheric blocking occurs frequently over the North Pacific and North Atlantic Oceans.**
**Strong and persistent atmospheric blocks are often associated with anomalous storm tracks which can influence monthly and seasonal values of temperature and precipitation.**

## Gulf of Alaska Blocking

**High-amplitude ridging over the Gulf of Alaska has been identified as one of the principal flow regimes dominating the Pacific-North American sector during the NH cool season (Stoss and Mullen 1995; Robertson and Ghil 1999).**
**Positive height anomalies in the vicinity of Alaska have been linked to heavy precipitation events along the US West Coast (Ely et al. 1994; Lackmann and Gyakum 1999).**
**The persistence of positive 700 hPa height anomalies in northern Alaska is an important predictor of monthly mean surface temperatures over the US (Klein 1983).**

## Data

**Daily averaged 500 hPa heights from NCEP/NCAR reanalysis, 1979-2000.**
**Global gridded daily mean temperature, 2.5****o**** resolution, 1979-.**
**Historical unified US-Mexico Precipitation Dataset, 1****o**** resolution, 1948-2000.**

## Methodology: Identification of Blocking Events

_**500 hPa heights**_**:  Anomalies calculated first by removing the local seasonal cycle (Mean + 1****st**** 2 harmonics) and then applying a 10 day low-pass  filter (Lanczos 121 weights).**
**Threshold crossing procedure of Dole and Gordon (1983) applied to anomaly time-series for DJFM 1979-2000 at key point 162.5****o****W, 62.5****o****N.**
**Threshold and duration criteria used were (100m, 8 days).**
**Result: 37 events with durations ranging from 8 days to 25 days with a mean duration of 11.3 days.**
**Percentage of days belonging to a blocking events is 15.6%.**

## Interannual Variability of Gulf of Alaska Blocking

**Interannual Variability of Gulf of Alaska Blocking**

**Number of Blocked Days**

## Time-Averaged Circulation During Blocking Events

**Time-Averaged Circulation During Blocking Events**

## Onset Time

**Onset Time**

## End Time

**End Time**

## Time-Averaged over 11 Day Period Prior to Blocking

**Time-Averaged over 11 Day Period Prior to Blocking**

**Time-Averaged over Duration of Blocking Events**

**Time-Averaged over 11 Day Period After Blocking**

## Onset Time

**Onset Time**

## End Time

**End Time**

## Onset Time

**Onset Time**

## End Time

**End Time**

## Statistical Distribution of Mean Daily Temperature During Blocking Regime

**Calculate the daily anomalies of mean temperature by removing a smooth annual cycle (defined as mean daily values for each day of the year based upon the 22 year period 1979-2000). **
**Consider all DJFMs from 1979 to 2000 and calculate the upper and lower tercile percentiles of the daily anomalies.  By definition, each tercile will contain 1/3****rd**** of the daily anomalies.**
**For the subset of days during DJFM 1979-2000 belonging to the “blocking regime” calculate the number of days with daily temperature anomalies in each of the three terciles.**

## BLOCKING REGIME

**BLOCKING REGIME**

## Slide 17

## Slide 18

## At least 1 Day of Daily Area-Averaged Precipitation over Southern California > 90th Percentile  

**At least 1 Day of Daily Area-Averaged Precipitation over Southern California > 90****th**** Percentile  **

**At least 1 Day of Daily Area-Averaged Precipitation over Ohio Valley > 90****th**** Percentile**

## Onset Time

**Onset Time**

## Summary

_**Interannual Variability**_
- Gulf of Alaska blocking is sensitive to the phase of ENSO.  Reduced (increased) number of blocking days during El Niño (La Niña/Neutral) conditions.
- Difference in the mean number of blocked days between  El Niño and Neutral DJFMs is statistically significant at the 99% level.

## Summary Cont’d

**2. **_**Mean Daily Temperature Distribution During Blocking**__** **_
-         Comparison of blocking regime with the DJFM 1979-2000 mean daily temperature distribution:
- (i) in the region extending from northern British Columbia southeastwards to the southern Plains of the US a dramatic increase in the number of blocking days with daily temperature anomalies in the lower tercile, 30-40% greater than the expected number of 33.3%,
- (ii) in this same region there is increase in the occurrence of extreme cold days,
- (iii) in western Alaska an increase a dramatic increase in the number of blocking days with daily temperature anomalies in the upper tercile, up to 25% greater than expected (33.3%),
- (iv) in this same region there is an increase in the occurrence of extreme warm days.

## Summary Cont’d

_**Daily Precipitation Analysis**_
**         ****Results not as robust as for mean daily temperature.**
**Two key regions with an increased frequency of heavy precipitation events during Gulf of Alaska blocking: (I) US Southwest, (II) Ohio Valley and Southeast.**
**Stratification of  the 37 blocking events based upon heavy precipitation occurrence over Southern California and the Ohio Valley revealed;**
_**(i) Southern California**_**:   equatorward of the blocking ridge, the anomalous storm track and the associated southwesterly moisture transports (“pineapple express”) are more prominent and extend  eastward toward the US West Coast, **
_**(ii) Ohio Valley**_**:****  enhanced ridging off the US East Coast a few days after block onset is associated with anomalous southerly moisture transports from the Gulf of Mexico. **

## Future Work

**Determine the statistical significance of the frequency changes of daily mean temperature anomalies and precipitation during the blocking regime.  Bootstrapping methods selecting individual blocks of data to preserve the temporal correlation in the data.**
**Make use of a global pentad precipitation dataset to examine the precipitation structures over oceanic regions.**
**Examine the downstream weather impacts associated with blocking episodes further west over the eastern North Pacific.**