⬆ 5th or 6th highest surface temps
⬆ Warmest summer on land
⬆ Warmest year for 25 countries + 1.8 billion people
⬆ Record ocean heat
⬆ Record high GHGs
⬆ Record high sea levels
⬇ Record low glacier mass
1/18
2021 was a bit cooler than the last few years due to a moderate La Nina event. La Nina tends to result in cooler temps globally, though the global response tends to lag 3-4 months after peak conditions. Here is what global temps look like since 1970 with and without ENSO removed:
The years since 2015 – 2021 included – are quite a bit warmer than any years that came before. Barring a Pinatubo-sized eruption in the next few years, its exceedingly unlikely we will ever see a year as cool as 2014 again:
Land temperatures – where we all live – are warming 40% faster than the world as a whole (which is mostly oceans). The world's land has warmed by around 1.8C already since preindustrial times:
Areas home to 1.8 billion people saw their warmest year on record during 2021, with 25 countries – including China, South Korea, Bangladesh and Nigeria – setting all-time annual temperature records. No parts of the world set cold records for the year.
2021 saw the warmest summer (N Hem summer – June, July, August) on record for the world's land areas:
These high temperatures – and the long-term warming trend – contributed to a number of extreme events both in the summer and across the year: carbonbrief.org/guest-post-rev…
Checking in on climate model projections (from the CMIP5 models that provide future projections after 2005), temperatures are pretty well-in-line with what models think they should be:
Note that I'm not featuring a comparison with CMIP6 models; they are less well suited to a multimodel mean approach given a subset of high-sensitivity outliers. The new assessed warming ranges (which downweight too-warm models) in the AR6 only start in 2015 making comparison hard
In the lower troposhere we saw 2021 as the 6th warmest (RSS) or 8th warmest (UAH) year on record. Note that the troposhere tends to see a larger influence of La Nina and El Nino events than the surface.
The stratosphere continues to see cooling temperatures. This is a clear fingerprint of climate change from greenhouse gases, which warm the lower part of the atmosphere by trapping heat while cooling the upper atmosphere as less heat escapes.
We saw record high sea levels in 2021. Global sea levels have risen by around 0.2 metres (200mm) since 1900, and there is evidence of accelerating sea level rise over the post-1993 period when high-quality satellite altimetry data is available.
The figure below shows the change in global average glacier mass from 1950 through to the end of 2020 (2021 values are not yet available). We see consistent loss of ice mass associated with warming temperatures:
Greenhouse gas concentrations reached a new high in 2021, driven by human emissions from fossil fuels, land use and agriculture. Methane concentrations in particular have seen a sharp rise over the past decade after a plateau in the 2000s.
Arctic sea ice was at the low end of the historical (1979-2010) range for most of 2021, but saw few new all-time daily low records set outside of brief periods in February and July. The summer minimum extent was the 12th lowest since records began in the late 1970s.
Finally, we can use current conditions (and El Nino/La Nina forecasts) to estimate where temperatures will end up in 2022. Four different groups (including a new @CarbonBrief estimate) have projections for 2022, and the differ a fair bit!
Our projection and that of the @metoffice has 2022 looking pretty similar to 2021, driven down a bit by the current "double dip" La Nina event. @BerkeleyEarth has it in the middle, while @ClimateOfGavin has 2022 threatening to top 2016 and 2020 as warmest year on record. 18/18
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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.