San Francisco Chronicle - (Sunday)
Why nobody can predict the weather in California
Harris K. Telemacher was a Los Angeles TV weathercaster with an ocean of knowledge — he had a doctorate in humanities and quoted Shakespeare — but no real meteorological training. So, assuming California weather was predictable, he taped his televised forecasts weeks in advance. This worked until an unexpected Pacific storm deluged Southern California during one of his prerecorded forecasts.
Telemacher also was a fictional character invented by Steve Martin in the film “L.A. Story.” But the fake forecaster embodied a real-life cliché that needs retiring.
California weather has never been as predictable as a TV weathercaster gag — especially when it comes to the rain and snow of Golden State winters like this one.
No state in the lower 48 sees as much variability in its year-to-year precipitation as California. Such variability makes our weather at least as unpredictable as anything else in this volatile state. Last year, California was in the midst of the driest three-year run in recorded history when the National Oceanic and Atmospheric Administration forecasted a drierthan-average winter. Instead, we experienced one of our wettest winters ever.
Now, another winter of weather surprises has arrived, demonstrating that California desperately needs better forecasts so we can plan and protect ourselves in this era of climate change.
Seasonal forecasts are not the predictions of tomorrow’s weather delivered by Telemacher and his presentday imitators. Seasonal forecasts provide ranges of possible climate changes for the next season on the calendar. (Federal agency forecasts for winter are usually out by Halloween). Meteorologists will tell you that while it’s impossible to tell you the weather on a particular day months in advance, they should be able to predict, broadly, how wet or dry the next season should be.
But that’s always been hard to do in California. Lately, it’s become even harder because of the state’s “weather whiplash” — the seesawing we’ve seen between flood and drought.
Our inability to predict seasonal wet conditions makes it harder to manage water supplies (we need to store more in wet winters to prepare for drier years), prepare for disasters (including unpredictable floods, like the one that recently inundated San Diego) and do long-term planning for agriculture, which supplies food to the entire nation.
But improving seasonal forecasts is easier said than done. Even the most advanced meteorologists have struggled with making seasonal forecasts. Indeed, recent studies, now getting attention in California policy circles, suggest that our state and its meteorologists need a better understanding of the peculiarities of the Pacific Ocean to improve their forecasts.
Making predictions about how much rain or snow is likely to fall in California depends on predicting atmospheric patterns over the northern Pacific Ocean. To do so, meteorologists have tended to look at sea surface temperatures in the Pacific and the phenomena known as El Niño and La Niña. Warm temperatures, or “El Niño” conditions, were believed to herald rain. Cool “La Niña” conditions were thought to signal a dry winter.
But recent research found that El Niño conditions don’t explain most of the variability of our weather, according to a paper highlighted by the Public Policy Institute of California. To cite one example, tropical sea surface temperatures and conditions were very similar in 2021-22 and 202223, but the first winter was dry and the second was one of the wettest in history.
“It remains elusive how predictable the year-to-year variability of CA winter precipitation is and why it is challenging to achieve skillful seasonal prediction of CA precipitation,” the paper said.
According to the authors, to arrive at more accurate seasonal forecasts, scientists need a better understanding of the ocean’s “circulation anomalies,” which are deviations in averages and expected conditions independent of El Niño. Current climate models, the paper argued, “show nearly no skill in predicting these,” which means they have “limited predictive skill for California winter precipitation.”
The paper also argued that current climate models can’t predict patterns that stem from tropical convection (i.e. tropical clouds and thunderstorms) or the stratospheric polar vortex. This means that for better seasonal forecasts, meteorologists need a better understanding of conditions and patterns in the far-away waters of the western Pacific, Indian and Arctic oceans.
How do we achieve this?
One answer is to devote more time and resources to observing oceans, sea ice and clouds — and their impacts on precipitation. Another answer is to employ better computer capacity and artificial intelligence to build better climate models. This is a planetary problem — if you want better predictions of California precipitation, you need to improve modeling and data for the climate of the whole Earth.
But such improvements won’t happen fast. So, for at least a few more winters, we’re stuck with unreliable seasonal forecasts and unpredictable weather.
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