Spring sunshine in the Southwest may be predictable months ahead, new study finds

The breakneck expansion of solar and wind power across the United States has transformed the nation’s electricity landscape, but it has also created a problem that few consumers ever think about: the grid now depends on the weather weeks and months in advance. A new study published in Communications Earth & Environment by Xiaosong Yang of NOAA’s Geophysical Fluid Dynamics Laboratory and colleagues addresses that challenge head-on, showing that a state-of-the-art seasonal forecast system can skillfully predict springtime solar energy resources over the southwestern United States, one of the sunniest and most valuable renewable energy regions in the country.

Seasonal forecasting sits in an awkward middle ground between weather prediction and climate projection. Weather forecasts lose most of their useful skill after about two weeks, because the atmosphere is chaotic and small errors grow rapidly. Climate projections, meanwhile, look decades ahead and are shaped mainly by greenhouse gas emissions scenarios. In between lies a window of months in which slow variations in the ocean and land surface — patterns that evolve over seasons rather than days — can nudge the atmosphere in predictable ways. The El Niño-Southern Oscillation, the periodic warming and cooling of the tropical Pacific, is the best-known of these memory sources, and it is precisely the kind of signal that a well-designed seasonal model can exploit.

What makes the new work distinctive is the way it dissects where spring solar predictability actually comes from. Rather than treating forecast skill as a single number, the researchers identify two leading sources of solar predictability. The first and dominant component is linked directly to primary El Niño-Southern Oscillation conditions, meaning the canonical state of the tropical Pacific during the season in question. When the Pacific is in a pronounced El Niño or La Niña phase, it reshapes atmospheric circulation patterns far beyond the tropics, altering cloudiness and hence the amount of sunlight reaching solar panels in the American Southwest.

The second component is subtler and, in many respects, more scientifically interesting. It is associated with the diversity of El Niño events themselves — not all El Niños look alike, and their different flavors have different atmospheric consequences — together with variability in the tropical Atlantic. El Niño diversity has become a major topic in climate science precisely because conventional indices can mask important differences between events centered in the eastern versus central Pacific. By separating this secondary component, the study shows that the spatial structure of forecast skill across the United States, and the physical drivers behind it, can be traced to identifiable and interpretable climate phenomena rather than appearing as an opaque statistical byproduct.

The practical implications of that interpretability are considerable. A grid operator who knows that a forecast of above- or below-normal spring sunshine is tied to a specific, monitored pattern in the tropical Pacific has some basis for judging how much confidence to place in it, and for watching whether that pattern is strengthening or weakening as the season approaches. A forecast that is merely a correlation with no physical story behind it offers no such guidance. In this sense, the study provides what the authors describe as a physically interpretable foundation for integrating seasonal forecasts into wind-solar energy planning.

The team did not stop at regional averages. They evaluated their forecasts at the level of individual states, a scale that matters enormously for real-world energy decisions because electricity markets, transmission planning, and renewable portfolio standards are largely organized along state lines. The results show robust solar prediction skill in high-resource states — those with large installed solar capacity and abundant insolation. For states whose economies and grids increasingly hinge on the sun, the finding suggests that seasonal outlooks could move from an academic curiosity toward a genuine operational tool, complementing the day-ahead and week-ahead forecasts that already guide dispatch decisions.

Perhaps the most consequential part of the research concerns what happens when wind and solar resources fluctuate together. Because solar and wind generation are both weather-driven, they can decline simultaneously, producing what energy analysts call energy droughts, or surge together, producing surpluses that stress storage and transmission. The historical co-fluctuations of wind and solar output are not random; they are shaped by the same large-scale climate patterns that govern seasonal averages. The study’s forecast system captures this compound behavior of wind-solar variability, meaning it can in principle anticipate seasons in which the two resources rise and fall in tandem — the situations that pose the greatest risk to a grid increasingly reliant on variable renewables.

Compound risk management has become a central theme in climate adaptation literature, and the energy sector is one of its most demanding arenas. A single-resource shortfall can often be covered by another generator or by storage, but a compound event — a spring in which weak winds and cloudy skies coincide across a broad region — can exhaust those buffers. By demonstrating that a seasonal model can reproduce the historical co-fluctuations associated with such events, the research opens a path toward seasonal-scale warnings of compound energy risk, giving planners months rather than days to arrange reserve capacity, adjust maintenance schedules, or coordinate fuel purchases.

The study arrives at a moment when the stakes for forecast-informed planning are rising quickly. The United States has added solar and wind capacity at a record pace in recent years, and the technical challenges of balancing a weather-dependent grid scale up with every gigawatt installed. Batteries help smooth out hour-to-hour swings, but they are not yet an economical solution to a season-long resource deficit. Hydro resources, which traditionally provide flexible seasonal storage in the western grid, are themselves climate-sensitive. In that context, even a modest seasonal signal — for example, an enhanced likelihood of a cloudy spring in the Southwest during certain Pacific states — carries real economic and engineering value if it can be trusted.

Yang and his colleagues, all based at NOAA’s Geophysical Fluid Dynamics Laboratory in Princeton apart from co-author Colleen McHugh, worked with a forecast system of the kind that underpins operational seasonal prediction worldwide: coupled ocean-atmosphere models initialized from observed conditions and run forward in ensembles, so that the spread among ensemble members provides a measure of forecast uncertainty. The work was supported through base funding from NOAA to the Geophysical Fluid Dynamics Laboratory, and the authors report no competing interests. The article was received in April 2026, accepted in September, and published in open-access form on October 3, 2026.

The broader significance of the paper lies in its framing of renewable energy forecasting as a climate science problem. Much of the current effort in renewable meteorology focuses on short horizons — nowcasting cloud fields, predicting wind ramps over hours, refining day-ahead dispatch models. The new results argue that the seasonal horizon deserves equal attention, and that the tools to fill it already exist. Two decades of investment in El Niño prediction, tropical Atlantic monitoring, and coupled model development have quietly produced a capability that the energy transition now needs.

There are, inevitably, limits to what any seasonal forecast can deliver. Skill varies by region, by season, and by the state of the climate system in a given year; years without a strong Pacific or Atlantic signal will remain difficult to anticipate. The study itself makes clear that predictability is structured, not uniform: it is the two identified components, tied to El Niño conditions and their diversity plus tropical Atlantic variability, that together explain the spatial pattern of skill. That honesty about the boundaries of predictability is part of what makes the work usable — operators can know when a forecast is likely to be informative and when it is likely to be a coin flip.

For the rapidly growing community of grid planners, commodity analysts, and climate scientists now working at the intersection of weather and energy, the message of the study is encouraging: the atmosphere’s slow ocean memory, harnessed by modern seasonal prediction systems, can illuminate spring solar resources in one of the nation’s most important solar regions months in advance, and can capture the joint behavior of wind and sun that determines how risky a given season will be for a renewable-powered grid. As the energy transition accelerates, forecasts of this kind — physically grounded, regionally validated, and transparent about their sources of skill — are likely to become as familiar to grid operators as today’s seven-day weather outlook is to the rest of us.

Subject of Research: Seasonal predictability of spring solar energy resources and compound wind-solar variability in the United States

Article Title: Seasonal predictability of spring solar resources and wind-solar variability across the United States

Article References: Yang, X., Delworth, T. L., Jia, L., Johnson, N. C., McHugh, C. E., Cooke, W. F., & Wittenberg, A. T. (2026). Seasonal predictability of spring solar resources and wind-solar variability across the United States. Communications Earth & Environment. https://doi.org/10.1038/s43247-026-04114-6

Image Credits: AI Generated

DOI: 10.1038/s43247-026-04114-6

Keywords: seasonal forecasting, solar energy, wind energy, El Niño-Southern Oscillation, renewable energy grid, energy droughts, tropical Atlantic variability, southwestern United States, climate prediction, compound variability, NOAA Geophysical Fluid Dynamics Laboratory, Communications Earth & Environment