Solar has had remarkable success making clean energy cheap. But in California its increasingly a victim of its own success. In a major new report we find solar value in CA fell 37% since 2014, and explore race between value deflation and cost declines: thebreakthrough.org/articles/quant… 1/
California leads the world in solar installation. In 2019 it generated 19.2% of all of its electricity from solar, with 13% from utility scale solar and the remainder from distributed rooftop solar: 2/
Solar is intermittent, but predictably so. It always generates electricity when the sun is shining, and in sunny California does not experience that much day-to-day variability. Heres what California Independent System Operator (CAISO) gen looks like in a typical spring week: 3/
However, there are major variations between summer and winter in California. Solar produces approximately twice as much electricity in the summer months as in the winter months (even though demand is only 20% lower in the winter): 4/
When more solar is available, it is primarily used to displace imports from other states (which are mostly natural gas generation) and in-state natural gas generation. Nuclear, geothermal, wind, and hydro output does not change that much with more solar on the grid: 5/
However, high levels of solar generation drive down wholesale prices. When 50% or more of California's electricity generation comings from solar, prices are often zero or negative. Here are hourly wholesale electricity prices in 2019 as a function of solar penetration. 6/
This creates a phenomenon known as value deflation. The more solar generation you have, the lower wholesale prices become when solar is producing power, and the worse the economics for solar become. Here is the average monthly price paid to different generation types over time 7/
Back in 2014, the average wholesale price paid to solar was around $50 per MWh, similar to non-solar generation on the grid. Today the value of solar has fallen by 37% compared to non-solar generation. 8/
We built a model to examine how the value of solar might change going forward under current grid conditions, essentially asking what would happen if solar was 13%, 20%, 30%, 40%, or 50% of utility scale generation in 2019: 9/
The model does a good job of reproducing the monthly pattern of observed value deflation (black line), which is largest in lower-demand spring months and lowest in the summer when cooling demand tends to well-align with solar generation. 10/
As solar penetration increases, spring and fall months experience rapid value deflation, with the relative value of solar falling by around 70% at 20% solar, 90% at 30% solar, and 95% at 40%+ solar. However, summer and winter months continue to see lower value deflation. 11/
We can also see how the annual value of solar (relative to other source) changes as a function of solar penetration (though note that this is utility-scale only; add 7% to these numbers for total solar generation): 12/
This value deflation sounds bad – and is a real challenge – but there is a silver lining. Solar's cost has been falling rapidly, and has kept up with value deflation to-date. This race between value deflation and cost declines will determine if solar can keep growing. 13/
Modeling done by the CEC for California's SB100 decarbonization goals anticipates that around 60% of electricity generation will come from solar by 2045. 14/
Based on different learning rates – e.g. how much solar costs will fall when global installation doubles – we model how the race between cost declines and value deflation might evolve in the future: 15/
If solar continues the 30% learning rate that characterized the 2010-2020 period, it may keep pace with value deflation despite California's aggressive deployment. Under the 18% learning rate over the full 1970-2020 period, however, a gap would emerge in coming years. 16/
This bakes in current solar subsidies – which effectively reduce the actual cost of solar by 40%. If we look at levelized costs of energy there is a much larger gap between solar's cost and wholesale price/value: 17/
Subsidies are there for a reason – they reflect the non-market benefits of solar such as its lack of CO2 emissions in a world that does not have a carbon price. These subsidies end up amounting to a quite reasonable $37 per ton CO2-eq. 18/
Solar value deflation is not a fait accompli. While it cannot be fully eliminated, it can be mitigated through storage (e.g. batteries), expanded transmission, and demand response. We look at how the picture might change if we can reduce value deflation by 15%, 30%, or 50%: 19/
Solar has huge potential, but also ongoing challenges. For a somewhat clearer discussion of value deflation and our new results, @jtemple has an excellent story over at @techreview: technologyreview.com/2021/07/14/102… 20/
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With all the July model runs now in, it is very likely that 2026 will see the largest El Niño event since records began in the late 1800s – and potentially by a truly mind-blowing margin. The median estimate is now 3.6C, roughly 0.8C hotter than the prior record (2.75C).
The 2026/2027 El Niño event has already grown faster than any prior events (at least on an ONI basis). Here its observed and projected future evolution compared to the strongest prior El Niño events in recorded history.
Currently 13 out of 14 dynamical models expect a record setting event based on the Niño 3.4 region sea surface temperature anomalies (ONI), with overall odds of a record sitting at 91% across all 667 model ensemble members.
Heat waves are driven by weather patterns but occur on the backdrop of a rapidly warming world. Without climate change the current European heat wave would have been ~3.2 °C (5.8 °F) cooler.
Heat impacts are non-linear, so this higher severity can lead to much greater suffering
Europe has been warming at a much faster rate than the world as a whole: roughly twice as fast as the global average, and 40% faster than the global land average.
This warming has been fastest in the winter months – driven in part by greater absorption of sunlight with less winter snow cover – but has been rapid year-round:
Today the @WMO released projections of where temperatures may end up over the next five years (baed on 13 different models and 250 ensemble members).
Their estimates for 2026 and 2027 are quite close to my (updated) ones:
My 2026 uncertainties are narrower as I'm using the first four months of data for the year to constrain my estimate. I also have a more up-to-date El Nino forecast than the WMO models (which are initialized considerably earlier and don't reflect the likely development of a very strong event.
@WMO I've also updated my estimates using data through April and the latest El Nino forecasts, which slightly bumped up the 2026 and 2027 central values compared to my last estimate that only used data through March: theclimatebrink.com/p/higher-warmi…
The arc of the scenario universe is long, but it bends inevitably toward more realistic emissions.
A new paper outlining the emissions scenarios we will be using in the upcoming IPCC AR7 report notes that "the CMIP6 high emission levels (quantified by SSP5-8.5) have become implausible".
It outlines a yet-to-be-released high emissions scenario notably lower than the one (SSP5-8.5) used in the prior IPCC 6th Assessment Report:
This is a change that a number of us in the community have long advocated, going back to Justin Ritchie's work in 2017.gmd.copernicus.org/articles/19/26…
And in 2020 Glen Peters and I published a piece in Nature arguing that high emissions scenarios were no longer "business as usual", and that more realistic emissions make for better climate policy: nature.com/articles/d4158…
El Niño is coming, and it is shaping up to be a big one.
Over at The Climate Brink I've put together a compilation of the latest forecasts by different modeling groups. They suggest that we might see an event comparable in strength to what we saw in 2016.
This is based on a collection of 11 different models (and 455 individual ensemble members) all updated since the start of March. I've put an interactive version of the data up on the Climate Dashboard here: dashboard.theclimatebrink.com/#enso
While there remains a big spread in models (and some models only run through August), more than half the runs show a strong (>1.5C Nino3.4) event developing by August and a very strong event (>2C) by the end of the year.
As a rare climate scientist working in Silicon Valley, I've been drinking from the AI firehose a lot more than my peers. I thought it would be helpful to lay out my experiences of both the promise and pitfalls of using AI to accelerate scientific research.
As a bit of background, I've been working with these tools since late 2022, and seen firsthand how they have dramatically improved over time. I’ve also worked with frontier AI labs to evaluate how well LLMs answer climate questions, and to help enable AI tools to support scientific collaboration.
So what do AI tools do well for scientific work? In short, coding.
Scientists are generally not software engineers. Much of their coding is self-taught, and many struggle with writing code quickly, producing well-documented reproducible code, and fixing errors.