Twenty-eight climate indices, computed from the raw NOAA, NCEI, PSL, BOM and CPC records, correlated against each other month by month and lag by lag, then against Pennsylvania temperature and precipitation from the daily scale to the season. Every number on this page was calculated from the source files, 1950–2025, deseasonalized and linearly detrended.
Each dot is an index, grouped by the part of the climate system it lives in. Lines connect pairs whose correlation clears the threshold; thicker is stronger, red is in-phase, blue is opposite. Pick a calendar month to see how the wiring changes through the year, or a lead to see which index moves first. Tap an index to isolate its links.
The panel lists its strongest partners at the current month and lead, and its Pennsylvania signature.
An index can only carry a signal for as long as it remembers its own state. The bar spans from how quickly the index changes to how long it stays correlated with itself (e-folding time). Atmospheric patterns forget in about a week; the ocean holds on for years. That memory is the upper limit on useful forecast lead for Pennsylvania.
The correlations are symptoms. These are the physical chains most of the literature agrees on, with the numbers this dataset gives for each link.
Choose two indices. The bars show how their same-month correlation changes by calendar month (strongest, median and weakest marked). The curve shows correlation when the first index leads (right) or lags (left). The window numbers show how stable the relationship has been across sliding 30-year periods.
Same-month correlation for every pair. Tap any cell to load that pair in the explorer above.
Correlation between each index and the monthly temperature or precipitation anomaly (percent of normal) in the region chosen, for target months in the chosen season, with the index leading by the chosen number of months. Tap a bar to see that index's full lead profile and its per-standard-deviation effect.
The lead profile shows when the signal begins, peaks and fades, per season.
Daily CPC AO, NAO and PNA against Harrisburg International (USW00014711) daily mean temperature anomaly, winter or summer. Right: average Harrisburg winter temperature anomaly following each active MJO phase (RMM amplitude ≥ 1), at leads up to 25 days.
Cells: °F anomaly. Read across a row to see a phase's influence arrive and fade; read down a column to see the phase cycle.
What each index measures, how it is calculated, where the record begins, which time resolutions are published (P) or can be built (C), how it relates to the others, and where to download it.
The same pipeline that produced this page, as one Python script. Save it, then run the block below from that folder.
pip install pandas numpy scipy requests && python3 pa_teleconnection_toolkit.py
For a season ahead, add up index anomalies times their per-SD slopes only for indices that are both significant and weakly correlated with each other (for winter temperature: NAO or AO, not both; WP; PDO or NP, not both; EP/NP in autumn). Because the atmospheric patterns forget within about a week, their monthly slopes are only useful once the month's pattern is known or forecast; the oceanic ones (PDO, TNI, AMO) carry across months. Validate by leaving out one winter at a time.