Global warming, climate change, all these things are just a dream come true for politicians. I deal with evidence and not with frightening computer models because the seeker after truth does not put his faith in any consensus. The road to the truth is long and hard, but this is the road we must follow. People who describe the unprecedented comfort and ease of modern life as a climate disaster, in my opinion have no idea what a real problem is.
A recent study by researchers from Yonsei University (led by Jaekyung Yoon and Jihyun F. Kim) and collaborators proposes that microbes capable of oxidizing iron in complete darkness and without oxygen could explain some banded iron formations (BIFs) from Snowball Earth periods.
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A recent study by researchers from Yonsei University (led by Jaekyung Yoon and Jihyun F. Kim) and collaborators proposes that microbes capable of oxidizing iron in complete darkness and without oxygen could explain some banded iron formations (BIFs) from Snowball Earth periods.
The work centers on Holocene (roughly the last ~11,700 years) sediments recovered from beneath Antarctica’s Larsen C Ice Shelf. A sediment core (GC16B) from ~324 m water depth provided a pristine, largely undisturbed archive sealed under ice since the Last Glacial Maximum.
Microbial community profiling (16S rRNA amplicon sequencing across many layers) showed distinct phases that track geological facies: more diverse communities in shallower, occasionally open-marine intervals versus chemolithoautotroph-dominated assemblages in deeper anoxic, aphotic sub- ice- shelf layers.
A keystone taxon in the sub- ice-shelf community is an uncultured member of the Thermodesulfovibrionia (phylum Nitrospirota). Metagenome -assembled genomes from these layers revealed a novel candidate order (Candidatus Mariimomonadales). The organism was provisionally named Candidatus Mariimomonas ferrooxydans.
Its genome encodes Cyc2, a fused porin– cytochrome outer -membrane protein previously linked to Fe (II) oxidation in other bacteria, along with other cytochromes, iron- sulfur proteins, and nitrate- related machinery that could support anaerobic iron oxidation coupled (directly or indirectly) to nitrate reduction.
Functional confirmation came from expressing the cyc2 gene (codon- optimized) in Escherichia coli: the protein incorporated heme, showed peroxidase activity, and significantly accelerated ferrous iron oxidation relative to controls.
Fluorescence in situ hybridization also visualized the cells in the sediment. Elevated iron- to- titanium ratios in the anoxic intervals roughly coincide with the distribution of these bacteria, consistent with in situ iron mineralization.
These dark, anoxic, oligotrophic, ice- covered sediments are presented as a modern analogue for synglacial BIFs deposited during Neoproterozoic Snowball Earth events (when ice reached low latitudes and iron- rich layers accumulated under or near ice).
Classic models emphasize photosynthetic cyanobacteria or anoxygenic photoferrotrophs that require light. The Larsen C findings supply genomic and experimental evidence that chemolithotrophic iron oxidizers can drive substantial Fe (III) precipitation without sunlight or free oxygen, potentially resolving how iron formations could accumulate under global ice cover.
The authors note this pathway may also have operated earlier in Earth’s history and has implications for interpreting iron minerals on other worlds (e.g., Mars) or icy- ocean moons.
The peer- reviewed paper is “Novel Antarctic chemolithotroph drives iron biomineralization” (Yoon et al., Microbiome, 2026).
A related preprint appeared earlier on bioRxiv under a similar title. Limitations include reliance on whole-genome amplification (due to very low DNA yields in deeper layers) and the still- open question of whether iron oxidation is directly coupled to nitrate reduction or involves abiotic steps. Overall, the work expands the known diversity of iron- cycling microbes and challenges purely phototroph- centered explanations for certain ancient iron deposits.
Novel Antarctic chemolithotroph drives iron biomineralization
Background
Iron is the most abundant redox- active metal in Earth’s crust and is tightly linked to many biogeochemical cycles, yet the organisms and mechanisms of iron oxidation are still incompletely understood. Banded iron formations (BIFs) are a major Precambrian iron- ore reservoir, but the biological contribution to their formation remains debated. Although large- scale BIF deposition largely stopped after the Proterozoic, modern Holocene sediments under fluctuating redox conditions offer analogues for studying similar iron-cycling processes.
Results
Sediments from beneath the Larsen C Ice Shelf (LCIS),Antarctica, record microbial community shifts that closely track environmental changes through the Holocene. Metagenomic analyses identified three dominant microbial phases that align with geological facies boundaries:
Phase A (shallower, occasionally open-marine conditions) showed higher taxonomic richness.
Phases B and C (deeper sub- ice-shelf sediments) were largely anoxic and dominated by diverse chemolithoautotrophic metabolisms.
A keystone taxon among the uncultured Thermodesulfovibrionia (visualized by fluorescence in situ hybridization) was designated Candidatus Mariimomonas ferrooxydans. It forms a novel clade within the phylum Nitrospirota. Metagenome- assembled genome analysis identified a putative outer- membrane Fe (II) oxidase, Cyc2, whose iron-oxidation activity was experimentally confirmed.
Conclusion
The LCIS sediments preserve a dynamic record of microbiome, environment interactions. The findings provide insights into microbial iron mineralization that parallel the biogeochemistry of the geologic past, particularly synglacial iron formation during the Neoproterozoic Snowball Earth.
Key additional points from the paper
Core GC16B (236 cm long) was recovered from ~324 m water depth in the Larsen C embayment (north-western Weddell Sea). It preserves an essentially undisturbed Holocene record sealed under ice since the Last Glacial Maximum.
Lithological units range from glacial till diamicton at the base, through parallel- and cross- laminated muds under the floating ice shelf, to sandy mud with ice-rafted debris near the top.
16S rRNA amplicon sequencing (V5- V8) of 37 layers and shotgun metagenomics (with whole-genome amplification due to very low DNA yields) revealed the community structure and metabolic potential.
Elevated iron- to- titanium ratios in the anoxic intervals roughly coincide with the distribution of the Thermodesulfovibrionia, supporting in situ microbial iron mineralization under dark, anoxic conditions.
Heterologous expression of the cyc2 gene in Escherichia coli confirmed its ability to catalyze Fe (II) oxidation.
The study positions these modern sub-ice-shelf sediments as a living analogue for how chemolithotrophic iron oxidizers (rather than solely light- dependent phototrophs) could have contributed to iron deposition under global ice cover during Snowball Earth events.
Published:Microbiome, 14, Article 225, 8 October 2026 (open access)
Iron, the most abundant redox-active metal in the Earth’s crust, is coupled to numerous biogeochemical cycles. However, the mechanisms of iron oxidation and the organisms involved remain incompletely understood. Banded iron formations (BIFs) are a major reservoir of iron ore in the Precambrian sedimentary record, yet the biological contribution to their genesis remains a subject of unresolved debate. While large-scale BIF deposition largely ceased after the Proterozoic, microbial activity in modern Holocene sediments under fluctuating redox conditions provides a unique opportunity to examine mechanisms reminiscent of ancient iron cycling. Here, we report the stratigraphic sequence of microbiome profiles recorded in laminated facies with iron-containing crystalline illite in the embayment sediments beneath the Larsen C Ice Shelf (LCIS) in Antarctica during the Holocene.
Blue Oceans Solutions is concerned. “If you drive deep enough into the high desert near Casper, Wyoming,” their post states, “you will find a burial ground that shouldn’t exist.”
It isn’t for people. It’s for the “clean energy” revolution. Rows of massive, fiberglass wind turbine blades—cut into thirds with diamond-encrusted saws—are being stacked thirty feet deep in the earth. From the air, they look like the bleached rib cages of leviathans.
“We are literally filling the scars of the fossil fuel age with the waste of the renewable age. ‘Net Zero’ cannot mean ‘Net Landfill’.” (- Blue Oceans Solutions, below)
Blue Oceans Solutions is concerned. “If you drive deep enough into the high desert near Casper, Wyoming,” their post states, “you will find a burial ground that shouldn’t exist.”
It isn’t for people. It’s for the “clean energy” revolution. Rows of massive, fiberglass wind turbine blades—cut into thirds with diamond-encrusted saws—are being stacked thirty feet deep in the earth. From the air, they look like the bleached rib cages of leviathans.
Continuing:
We built wind turbines to save the future, but we forgot to design them for the end. While 85% of a turbine (steel, copper, electronics) is highly recyclable, the blades are not. They are built to withstand hurricane-force winds for 20 years. They are composites of fiberglass and resin, designed specifically never to break down. When they die, they become “Zombie Waste”. You can’t melt them (they just burn). You can’t crush them easily. So, we bury them.
Big problems are ahead:
The scale is staggering. The U.S. alone is decommissioning roughly 8,000 blades a year—a number that will explode to millions of tons by 2050. As municipal landfills (like Casper’s) run out of space or refuse to take them, regulators in states like Wyoming have opened a new door. We are now permitting the burial of these “green” blades inside abandoned coal mines.
We are literally filling the scars of the fossil fuel age with the waste of the renewable age. “Net Zero” cannot mean “Net Landfill”
“Net landfill” is probably an exaggeration given what landfill technology can do to seal the waste (and build a golf course over it). But if wind turbines are dying out far sooner than what is the claim–say 10-15 years instead of 20-30 years–the scale of disposal is an issue.
This is the uncomfortable truth we need to face: We are currently solving a carbon crisis by creating a material crisis. The “bury it and forget it” era is over. The next green billion-dollar opportunity isn’t in erecting more turbines; it’s in closing the loop.
We need blades that can be chemically dissolved, not just buried. We need “Material Passports” that track every blade from factory to grave. If we don’t solve the back end of the green transition, we aren’t saving the planet. We’re just redecorating the landfill.
Recycling industrial wind turbines (blades, etc.) can add cost to cost for a technology that is not competitive to begin with for grid electricity. Landfill disposal is costly too. The ecological bargain by Big Green to support industrial wind and solar is not looking good, particularly if and when retrofits are uneconomic without an extension of expiring tax breaks.
And if industrial wind is on its own (without the PTC or ITC, as scheduled for the end of next year), will decommissions grow and exceed new capacity additions?
Penguin guano (poop) fertilizes green snow algae along the Antarctic Peninsula, causing denser green blooms that absorb more sunlight and accelerate snowmelt compared to clean white snow.
Penguin guano (excrement) is a major ecological driver in Antarctic and sub- Antarctic ecosystems.
It acts as a powerful natural fertilizer that transfers marine- derived nutrients to land, influencing soil formation, biodiversity, atmospheric chemistry, and even local climate feedback. Impacts range from highly localized (around colonies) to regional, with both positive and complex climate- related effects.
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Penguin guano (poop) fertilizes green snow algae along the Antarctic Peninsula, causing denser green blooms that absorb more sunlight and accelerate snowmelt compared to clean white snow.
A study published in the Journal of Geophysical Research: Biogeosciences (led by researchers including Alia Khan of the University of Colorado Boulder) analyzed 111 snow samples from coastal sites.
Snow algae can appear red or green depending on conditions. When nutrients (especially phosphate from penguin and seabird guano) are scarce, algae produce red protective pigments and go relatively inactive.
With abundant phosphate, supplied when penguins feed at sea on marine organisms and then track or deposit their nutrient- rich droppings on the snow, the algae pack cells with green chlorophyll pigments, become denser, and photosynthesize more actively.
These colored patches darken the snow surface:
Red snow absorbs roughly 20% more solar energy than clean white snow.
Green snow absorbs about 40% more (roughly twice the extra energy absorption of red snow in some descriptions), heating the snow and speeding melt.
As Antarctic temperatures rise, snow algae habitats are expanding along coasts. Penguins and seabirds (such as skuas) link the marine food web to snow ecology by spreading these nutrients, intensifying the green blooms near colonies.
Researchers note that without accounting for this biological darkening, models likely underestimate how quickly coastal snowpacks melt and ice- free terrain expands. Fieldwork near colonies was notably smelly, as penguins track guano widely across the snow.
This is a newly highlighted feedback in Antarctic coastal systems rather than a primary driver of continent-wide ice loss.
Penguin guano (excrement) is a major ecological driver in Antarctic and sub- Antarctic ecosystems.
It acts as a powerful natural fertilizer that transfers marine- derived nutrients to land, influencing soil formation, biodiversity, atmospheric chemistry, and even local climate feedback. Impacts range from highly localized (around colonies) to regional, with both positive and complex climate- related effects.
1. Nutrient fertilization and soil enrichment
Penguins feed primarily on krill, fish, and other marine organisms, concentrating nitrogen (N), phosphorus (P), organic carbon, and trace metals (e.g., iron, copper, zinc) in their guano. Colonies deposit enormous quantities, seabirds (including penguins) globally contribute hundreds of gigagrams of N and P annually, with Antarctic and Southern Ocean coasts receiving some of the highest inputs per unit area.
Soil formation (ornithogenic soils and phosphatization): Guano drives chemical weathering and creates specialized phosphate- rich soils. It forms minerals such as struvite and hydroxyapatite, elevating organic P, N, and organic matter. Abandoned colonies leave lasting “ornithogenic” soil legacies that influence landscape evolution after glacial retreat.
Nitrogen and phosphorus cycling: Guano inputs can exceed agricultural fertilizer rates by orders of magnitude locally (e.g., tens of thousands of kg N ha⁻¹ y⁻¹ in dense colonies). Ammonia volatilizes and spreads inland via wind, enriching soils kilometers away (up to ~240 times the colony area in some estimates). This boosts microbial activity and phosphine (PH₃) production.
2. Impacts on terrestrial biodiversity
Nutrient enrichment dramatically increases primary productivity and supports higher trophic levels in otherwise nutrient- poor Antarctic terrestrial environments:
Mosses, lichens, algae, and microbial communities thrive near colonies.
Invertebrates (e.g., springtails) can be ~10 times more abundant in influenced moss communities than in milder European grasslands.
Overall biodiversity and biomass are elevated in the “halo” around active and abandoned colonies.
3. Effects on snow and ice (recent discovery)
A 2026 study (Journal of Geophysical Research: Biogeosciences) showed that phosphate from penguin (and other seabird) guano fertilizes coastal snow algae along the Antarctic Peninsula.
Nutrient- rich conditions keep algae in a dense, green (chlorophyll- rich) state rather than a red, dormant “sunscreen” state.
Green snow absorbs ~40% more solar energy than clean white snow (vs. ~20% for red snow), accelerating local snowmelt and potentially expanding ice-free areas as temperatures rise and penguin ranges shift.
This links the marine food web directly to snow- surface ecology and is previously under- accounted biological feedback in melt projections.
4. Atmospheric and climate effects
Guano releases large amounts of ammonia (NH₃). Near Adélie penguin colonies, atmospheric ammonia can reach 1,000 times background levels (and remain elevated for weeks after birds leave).
Ammonia reacts with sulfuric compounds from marine phytoplankton, boosting formation of aerosol particles (cloud condensation nuclei) by up to 1,000- 10,000 times.
This enhances low- level cloud and fog formation, which can reflect sunlight and exert a local cooling effect, potentially partially offsetting regional warming. However, the net climate impact depends on cloud location (over ice vs. ocean) and remains an active research area. Declining penguin populations could reduce this cooling feedback.
5. Marine and coastal connections
Guano and runoff return nutrients (including iron) to nearshore waters, stimulating phytoplankton growth and supporting the base of the marine food web.
Trace metals recycled via guano contribute to Southern Ocean productivity.
Historical human harvesting of guano (especially in South America) has disrupted nesting habitats for some penguin species (e.g., Humboldt penguins that burrow in guano deposits).
Broader context and vulnerabilities
Penguin colonies function as biological “pumps” moving nutrients from ocean to land and atmosphere.
Their ecological role is outsized relative to their biomass. However, many penguin populations face pressures from sea- ice loss, shifting prey availability (krill vs. fish), commercial fishing, and disease.
Declines could weaken these nutrient- transfer and climate- modulating services, creating potential positive feedback for local warming or reduced terrestrial productivity.
In summary, penguin guano is far more than waste, it is a keystone process shaping Antarctic biogeochemistry, biodiversity, snowmelt dynamics, and even atmospheric processes.
Research continues to quantify these interconnected effects amid rapid environmental change.
Phosphate Enrichment From Wildlife Inputs Shapes the Distribution and Pigmentation of Green and Red Snow Algae Along the West Antarctic Peninsula
This is the peer- reviewed paper corresponding to the recent news: “Phosphate Enrichment From Wildlife Inputs Shapes the Distribution and Pigmentation of Green and Red Snow Algae Along the West Antarctic Peninsula” by Elise Ryan and Alia Khan, published 7 October 2026 in Journal of Geophysical Research: Biogeosciences.
The full text is available at the link you provided (via AGU/Wiley). Official press materials from AGU and EurekAlert summarize the key findings as follows:
Main findings
Researchers collected 111 snow samples (red and green algal blooms) along the West Antarctic Peninsula coast, spanning sites with and without penguin colonies across about five degrees of latitude.
Snow algae switch pigmentation based on nutrient availability:
Nutrient- scarce conditions → algae produce red (sunscreen- like) pigments and reduce growth. Red snow absorbs ~20% more solar energy than clean white snow, increasing melt.
Nutrient- rich conditions → algae pack cells with green chlorophyll pigments, stay active, and form denser blooms. Green snow absorbs ~40% more energy than clean white snow (roughly double the extra absorption of red snow), further accelerating melt.
Green algal samples contained substantially higher phosphate levels (a key nutrient abundant in penguin and seabird guano) than red samples. Lab work involved counting algal cells under a microscope and extracting pigments and nutrients.
Penguins (and other seabirds like skuas) act as a nutrient vector: they feed on marine organisms, then deposit and track phosphate- rich guano across coastal snow, fertilizing dense green blooms. This directly links the marine food web to snow- surface ecology.
Broader implications
As Antarctic coastal temperatures rise, snow- algae habitats are expanding. Penguin range shifts southward could further spread guano- fertilized green blooms, enhancing local snowmelt and the expansion of snow- and ice -free terrain. Current models of Antarctic melting do not yet include this biological “darkening” feedback; the authors note it is a critical missing component and that more data are needed before it can be fully incorporated into projections.
The work was supported in part by an NSF CAREER award to Alia Khan. Field sampling near colonies was described as particularly unpleasant due to the odor and visual similarity between guano and red algae.
Published:Journal of Geophysical Research: Biogeosciences
Snow algae darken snow through biomass accumulation and pigmentation, yet nutrient controls on algal abundance and pigment composition in Antarctic snowfields remain poorly constrained. Along the West Antarctic Peninsula (WAP), coastal snowfields receive nutrient inputs from marine and wildlife sources that may influence bloom development. We analyzed 111 snow samples collected from nine coastal WAP sites during the 2024–2025 austral summer to assess the relationships between dissolved nutrients and algal cell density, pigment concentration, and pigment composition across red, green, and clean snow. In green snow, median phosphate (PO43−) concentrations were tenfold higher (114 μM) relative to red snow (11.4 μM), and ∼70-fold higher than clean snow (1.54 μM). High-phosphate samples, composed primarily of green snow, exhibited higher cell densities and per-cell pigment concentrations than low-phosphate samples, consistent with enhanced biomass and cellular pigment accumulation under phosphate-rich conditions. These results identify green snow as a bloom state strongly associated with elevated phosphate availability. Green snow was also characterized by lower NO−x :PO43− ratios (NO3− + NO2−: PO43−) and increased photoprotective carotenoids relative to red snow, suggesting that dense green blooms rapidly drawdown bioavailable nitrogen relative to excess phosphate, creating a distinct post-uptake stoichiometric signature in wildlife-influenced regimes. Phosphate concentrations were positively associated with wildlife influence (Spearman’s r = 0.54, p < 0.001), indicating ornithogenic inputs as a key phosphorus source. Collectively, these results link phosphate availability to snow algal biomass and cellular pigment composition and suggest that wildlife-mediated nutrient inputs may intensify algal-driven surface darkening.
I was involved in a weather debate on the so-called Nor’easter this weekend (you will see why I say so-called below), and it was refreshing to see there was no one blaming climate change for that storm.
I believe this storm should have been classified as such because it transitioned from a subtropical to a tropical system. I offered objective analysis to support my point. There was no question it was non-tropical at the start, but the system converted while over abnormally warm water (above the 26c threshold) that was so far north this year. What I do now, to try to avoid bias, is go to the AI, feed it the information, and see what it comes up with.
I believe this storm should have been classified as such because it transitioned from a subtropical to a tropical system. I offered objective analysis to support my point. There was no question it was non-tropical at the start, but the system converted while over abnormally warm water (above the 26c threshold) that was so far north this year. What I do now, to try to avoid bias, is go to the AI, feed it the information, and see what it comes up with.
I was involved in a weather debate on the so-called Nor’easter this weekend (you will see why I say so-called below), and it was refreshing to see there was no one blaming climate change for that storm.
I believe this storm should have been classified as such because it transitioned from a subtropical to a tropical system. I offered objective analysis to support my point. There was no question it was non-tropical at the start, but the system converted while over abnormally warm water (above the 26c threshold) that was so far north this year. What I do now, to try to avoid bias, is go to the AI, feed it the information, and see what it comes up with.
So here is just a bit of the evidence I fed the AI:
Presented with this 850mb (which shows the warm center)
700mb (10,000 feet; also, a warm center)
and 1000-500 mb thickness and surface pattern (circular warm core at the center)
Is this a warm-core feature?
Yes — this is a warm-core feature. The 850 mb map shows a clear warm anomaly in the center of the circulation (yellow core, ~15–18 °C, versus much cooler air wrapping around it over the ocean). The 700 mb map is consistent with that structure: The core is relatively warm compared with the cooler air to the west and shows no cold-core signature. The 1000–500 mb thickness field and surface pattern reinforce this. Thickness values are locally higher near the center of the tight 992 hPa low, and the precipitation shield is wrapped around a compact, quasi-circular circulation rather than being displaced well ahead of a cold-core mid-latitude cyclone.
Note the actual pressure was lower, making the 1000-500 mb thickness even warmer. The higher the thickness, the warmer the column of air from the surface to 500mb (18000 feet)
Taken together, the mid-level temperature fields, thickness, and surface appearance indicate a warm-core (tropical or subtropical) cyclone rather than a cold-core extratropical low.
There has to be a reason the objective FSU cyclone phase evolution actually has a warmer look than Gonzalo.
No Name nor’easter:
Gonzolo:
I use AI to make sure I am not biased.
A recon report could have settled the argument. Yet no recon into a storm, in a year of no storms, to a feature that likely did 1-2 billion dollars of damage. I’m showing you all this to show I had my reasons to say this should have been classified.
Now, let’s look at the sat pics of all the storms this year at their strongest points and see which has the best presentation.
Our tropical season, so far, picture gallery (all are near the time of max intensity):
Arthur
This did have gales on the east side.
Bertha was interesting because its strong winds were associated with a feature that appeared southwest of the center.
Cristobal. Slim pickings.
Dolly. Tried to develop in the Main Development Region, and guess what, homie don’t play that (for all you In Living Color fans); it was dead a day later.
Eduoard. Give it another 6 hours and it would have been a hurricane. Good-looking storm.
Fay. It will be interesting to see if either of these storms in post-analysis get their intensities upped, which would of course mean the record for no hurricanes through Oct 1 would no longer apply if either is upgraded to a hurricane. Point is, both of these looked good.
Gonzalo. This was almost laughable, but again, gales were off to the east in one quad.
Hanna. The center is way behind the thunderstorms.
No name Nor’Easter. Arguably the best-looking of the bunch. Plus, it was impacting people.
We’ve seen the infamous winter bomb cyclones do this, though not as tight as this. But the difference is that a bomb cyclone is not over water that is still 80 degrees; it’s 20-30 degrees lower. The energy over the warmer water, once entrained in a system, can lead to rapid feedback and rapid conversion. Sometimes, when I see the gawking at how fast storms do this, I think it’s a lack of respect for what the weather is capable of. And the fact that it weakened over cooler water was a sign of its true nature.
There have been many storms over the years that fell between the cracks.
But the fact is, no recon went in, so there is plausible deniability. It’s like the whole geothermal argument. I get told I have no proof, yet they have no proof it’s not, because we have no idea how much heat input is coming from the bottom of the ocean from underwater activity. There are no observations there, and saturating the ocean floor near known vents would end the argument one way or the other. Until then, whoever controls the information wins, and the rest of us who question it get pushed aside.
But it was a good old weather fight with people disagreeing with me, and they all had reasons; they weren’t saying anything about climate change. It was all offering ideas that supported the non-naming. But again, because of my reputation as a Warren Zevon Excitable Boy when it comes to the weather, AI’s and actual profiles do a lot to either support or counter the arguments. And it was on my side.
I still think this whole naming thing can be simplified. If you have a closed rotary circulation attaining the required intensity over water at 25 °C or greater, name it and keep the name on it until it dissipates. No classroom arguments, which frankly are like theologians arguing over how many angels they can stick on the head of a needle.
But here is what this proved — something I have been saying for many years. No matter what occasional tiff I have with the NHC, they are completely above board on climate. Think about it: They could have named this. And it would have been the furthest north in that part of the ocean to get named — a real testament to the ocean’s warmth and the changing climate. That it did so much damage would have been a huge day for agenda-driven media, but NHC did not. And to show I am above board, I realize what a naming would have led to with people who simply want to prostitute any weather pattern for their own purpose, yet I thought it should have been named. But the real point is that we both had competing weather ideas, as we should, and it had nothing to do with climate change. They are the hurricane center, so no matter what, I am Western Michigan, and they are Michigan.
Of course, there are the usual rumblings that man-made climate change contributed to the storm being so strong. Here was a tweet Sept 18th on what was coming:
“Let’s see. Big highs moving off the East Coast. A disturbed weather area is developing off Florida this weekend; an upper trough is trying to pick it up next week over warm water. What could possibly go wrong with that? May get pulled back in for at least a nor’easter for the NE next weekend (if not a hybrid howler)”
What is more remarkable is that there have been no landfalling hurricanes on New England or Long Island in 35 years, and in the previous 53 years, there was an average of 1 every 7 years.
I will have another blog on the lack of Atlantic hurricanes this year. Again, we have to think globally. The EPAC is likely to break its ACE record for the most ACE units. People have not been watching the Western Pacific, which has averaged about the same number of below-normal ACE units over the past 5 years (100) as the Atlantic will this year. The point is that a large-scale shutdown pattern is evident and is showing up in other basins. This year, the combination of an unfavorable SST configuration in the Atlantic and, of course, El Niño has shifted it down.
Meanwhile, the EPAC is going wild, and as we said in the preseason, recurving features are helping dent the Southwest drought (the appetizer before winter, as the El Niño will continue to do that). There is likely to be a strong Pacific hurricane later this week hitting Mexico again, which, when it rains itself out, will try to help the drought.
And the Atlantic is down. But I am sure if we get another big hitter in Mexico, no one will say boo.
See, look at that: already two positive aspects of the big bad Niño. Though no one is going to bring that up. They will with Mexico, though.
A new release of the Climate Connectivity Taxonomy (CC Taxonomy), supported by the EU- funded FAIR2Adapt project, was announced on 7 October 2026 by TIB- Leibniz Information Centre for Science and Technology (via Dr. Sandra Niemeyer).
The Climate Connectivity Taxonomy (CC Taxonomy) is an AI- and human- curated collection of terms (concepts and keywords) related to climate change adaptation, mitigation, and disaster risk. It is designed primarily as a bridge across fragmented knowledge rather than a rigid single hierarchy.
AI generated by Grok
A new release of the Climate Connectivity Taxonomy (CC Taxonomy), supported by the EU- funded FAIR2Adapt project, was announced on 7 October 2026 by TIB- Leibniz Information Centre for Science and Technology (via Dr. Sandra Niemeyer).
It addresses the fragmentation of climate change adaptation and disaster risk reduction knowledge, which is scattered across platforms and described with inconsistent terminology.
For example, an adaptation practitioner might refer to “adaptive capacity”, while a disaster risk specialist discusses “coping capacity” or “preparedness”. These related but non- interchangeable terms reflect different disciplines, values, timescales, and perspectives on risk.
Key Features of the CC Taxonomy
It is an AI- and human -curated collection of terms related to climate change and disaster risk.
Rather than imposing a single vocabulary, it maps connections between terms while preserving distinct meanings, enabling discovery of relevant knowledge across disciplinary boundaries.
It draws from authoritative sources such as the IPCC Sixth Assessment Report (AR6) glossary and the United Nations Office for Disaster Risk Reduction (UNDRR) and International Science Council Hazard Information Profiles. Additional sources include items like the Myriad- EU Handbook and the Disaster Resilient Infrastructure Lexicon.
AI- assisted tools continuously harvest content from platforms to identify new terms, which human experts then verify and add.
Unique “scope notes” provide richer contextual information on how the same term is used differently by various actors or disciplines.
An open- access API (version 1 released June 2025) allows other platforms and tools to import and align with the taxonomy. A “submit new concept” feature is forthcoming for user contributions.
The Climate Connectivity Hub
The taxonomy powers the Climate Connectivity Hub (freely accessible at https://connectivity-hub.weadapt.org/), an online search- and- discovery tool curated by the Stockholm Environment Institute (SEI). It aggregates knowledge from platforms including weADAPT, CORDIS, PreventionWeb, Climate-ADAPT, and the Resilience Platform.
Users can explore interactive “knowledge maps” linking keywords/concepts, articles, projects, and organizations. It also supports an interactive glossary on weADAPT (hovering over terms shows definitions). The Hub originated under the PLACARD project and has been further developed through MAIA, DIRECTED, and FAIR2Adapt.
Anne Fouilloux (FAIR2Adapt coordinator, LifeWatch ERIC) noted: “We don’t lack knowledge on climate adaptation. We struggle to connect it… The Climate Connectivity Taxonomy is a great example of how FAIR principles can help turn fragmented knowledge into knowledge that can be found, connected, and reused.”
Sukaina Bharwani (SEI) highlighted long- standing frustrations with platforms unable to interconnect, which inhibits shared learning, and emphasized that supporting interoperability helps build on existing knowledge rather than reinventing it.
About FAIR2Adapt
This Horizon Europe project (grant 101188256; started January 2025, three- year duration) aims to transform data into actionable knowledge for climate adaptation strategies using FAIR principles and EOSC alignment. Partners include LifeWatch ERIC, TIB, SEI, and others across Europe. One case study specifically focuses on enriching weADAPT and the Connectivity Hub.
In short, the taxonomy and Hub aim to reduce knowledge silos, accelerate learning and climate action by making existing work more discoverable and reusable, and foster better cross- community understanding of climate risk terminology.
The Climate Connectivity Taxonomy (CC Taxonomy) is an AI- and human- curated collection of terms (concepts and keywords) related to climate change adaptation, mitigation, and disaster risk. It is designed primarily as a bridge across fragmented knowledge rather than a rigid single hierarchy.
Core Design Philosophy
It does not force all terms into one unified vocabulary. Instead, it:
Maps connections (broader, narrower, and related terms) between concepts.
Preserves distinct disciplinary, cultural, and contextual meanings.
Adds rich metadata so users can understand how and why the same term is used differently by different communities (e.g., “adaptive capacity” vs. “coping capacity”).
This supports FAIR principles (Findable, Accessible, Interoperable, Reusable) and powers the interactive knowledge maps in the Climate Connectivity Hub.
Structural Components of Each Term/Node
Each concept (keyword node) typically includes:
Preferred term (main label).
Definitions (drawn from authoritative sources).
Alternative labels / synonyms.
Broader terms and narrower terms (hierarchical relationships).
Related terms (non- hierarchical connections).
Scope notes, contextual background explaining origins, evolution, disciplinary differences, and usage nuances (these were expanded in later updates, especially for adaptation barriers and enablers, measures and actions, and outcomes and benefits).
Links to tagged content (articles, projects, organizations) in the Hub.
In the Hub interface, terms appear as clickable orange nodes in dynamic network maps, connected to projects (purple), articles (blue), and organizations (green).
UNDRR- ISC Hazard Information Profiles (302 hazards grouped into 8 clusters: meteorological and hydrological, geohazards, biological, technological, societal, etc.).
Integrated Existing Terminologies (sub- sets mapped in) Examples include the Myriad-EU Handbook of Multi- Hazard and Multi- Risk Definitions, Disaster Resilient Infrastructure (DRI) Lexicon, UNICEF Research, Study, Evaluation, Taxonomy, and others.
Thematic / Curated Additions Focused expansions addressing gaps, such as:
Adaptation outcomes and benefits.
Adaptation barriers and enablers.
Adaptation measures and actions.
Transboundary climate risks.
Capacity development and citizen engagement.
Climate finance terms and sector-specific options (e.g., agriculture).
Platform- Derived Tags
Existing tags from weADAPT, CORDIS, PreventionWeb, Climate- ADAPT, Resilience Platform, etc.
New candidate terms harvested by large language models (LLMs) from platform content, then manually reviewed and accepted/rejected by experts via an admin panel (prioritized by frequency or presence in robust sources).
Hierarchy and Relationships
It begins with a relatively flat- to -moderately hierarchical structure (seeded from IPCC and UNDRR sources, which already include some parent and or child links).
Version updates (e.g., v1.0 June 2025 → v1.1 August 2026) have added a more structured network of broader, narrower and related relationships.
Future plans explicitly include introducing more hierarchy and relationships to evolve it toward a fuller ontology or knowledge graph.
Access and Formats
Available via an open- access API (Version 1.0 released 13 June 2025; Version 1.1 on 27 August 2026).
A SKOS- supported web tool and repository allows searching, filtering, and visualizing concepts.
Continuously evolving: AI harvests new terms; experts verify; users can submit new concepts.
In short, the CC Taxonomy functions as a living, interconnected semantic network of climate and disaster risk concepts. It prioritizes contextual connections and interoperability over a single rigid classification tree, enabling discovery across platforms and disciplines while highlighting differences in how communities talk about climate risk.
Scaling knowledge interoperability for accelerated climate action through the Climate Connectivity Hub and Taxonomy
The paper addresses the urgency of the climate crisis by arguing that accelerating learning and implementation requires finding, using, and building on existing knowledge rather than reinventing it. The Climate Connectivity Hub and its underlying Climate Connectivity Taxonomy (CC Taxonomy) visualize and connect dispersed online climate information.
Together, they improve interoperability and discoverability of research while promoting a shared understanding of different knowledge domains and their applicability in policy, research, and practice.
Three key objectives:
Show that useful taxonomies can emerge from existing project results to support interoperability, discoverability, and identification of knowledge gaps.
Visualize the domain expertise of different organizations and increase connectivity between projects.
Demonstrate that a shared interpretation of language and terminology adds value to research, policy, and practice.
The study illustrates how taxonomies advance FAIR (Findable, Accessible, Interoperable, Reusable) knowledge for better platform management, more intuitive tool interfaces (e.g., interactive glossaries), and enhanced climate action by decision-makers, planners, researchers, and policymakers.
Next steps outlined:
Expert verification and validation of the evolving taxonomy (accuracy, relevance, nuances across languages and technical terms).
Adding more hierarchy and relationships to evolve it toward a richer ontology or knowledge graph.
Identifying domain knowledge gaps via orphaned projects and expert consultation.
Key Context from the Paper
Climate knowledge is abundant but often siloed, too technical, or not usable for decision- making, leading to limited uptake, wasted resources, and risks like maladaptation (e.g., short- term flood measures that encouraged development in high-risk areas). Platforms lack common standards for describing content, and terminology varies across disciplines (adaptation vs. disaster risk reduction).
The authors distinguish taxonomies from related concepts (glossaries, thesauri, ontologies) and note the proliferation of recent climate- related taxonomies that often lack coherence. Collaborative tagging systems create further issues (synonyms, inconsistent terms, lack of hierarchy).
The CC Taxonomy was seeded with the IPCC AR6 glossary and UNDRR/ISC Hazard Information Profiles, then expanded via machine learning on sources like CORDIS and weADAPT, expert review of other knowledge structures, and hybrid (AI and human) curation. It does not force a single vocabulary but maps connections while preserving distinct meanings and adding scope notes and context.
The Climate Connectivity Hub aggregates content from platforms (weADAPT, CORDIS, PreventionWeb, Climate- ADAPT, etc.) and uses the taxonomy for interactive knowledge maps linking concepts, articles, projects, and organizations. Version 1 of the taxonomy (with open API) was released in June 2025.
This work builds on prior efforts (e.g., PLACARD roadmap, MAIA project) and aligns with FAIR principles to reduce silos and accelerate climate action.
The full paper is available open access at the link above (including methods, results on taxonomy mapping and visualization, discussion, and supplementary materials).
Journal information:Climatic Change, Volume 179, article number 48 (2026)
Accelerating learning and implementation is necessary to address the urgency of the climate crisis. This requires finding, using, and building on previous knowledge rather than replicating what already exists. The Climate Connectivity Hub and underlying Taxonomy seek to enable this by visualizing and connecting dispersed online climate information. Together, these tools increase the interoperability and discoverability of existing research and promote a shared understanding of different types and domains of knowledge and their applicability in policy, research and practice. To reduce climate knowledge silos and to scale up and accelerate climate action that builds on past experience, this paper has three key objectives: (1) to show that useful taxonomies can emerge from existing project results supporting interoperability, discoverability and the identification of knowledge gaps; (2) to visualize the domain expertise of different organizations and increase connectivity between projects; and, (3) to show that a shared interpretation of language and terminology can add value to research, policy and practice. This study illustrates how taxonomies can advance findable, accessible, interoperable, reusable (FAIR) knowledge in a robust yet dynamic way for: better platform knowledge management and interoperability; more intuitive user interfaces in tool development e.g. through interactive glossaries; and, enhanced climate action by decision makers, planners, researchers, and policy makers. Next steps will include (1) expert verification and validation of the evolving taxonomy, including assessing its accuracy, relevance and the nuances of interpretation in both technical terminology and diverse languages; (2) introducing more hierarchy and potential relationships to aid the taxonomy’s transition towards a more expressive ontology or knowledge graph; and, (3) identifying gaps in domain knowledge through orphaned projects and expert consultation.
Newly available satellite data from NASA’s Surface Water and Ocean Topography (SWOT) mission reveal significant flaws in many global river models, particularly for dammed rivers, arid- region rivers, and Arctic rivers.
The study frames SWOT as an “early microscope” for rivers: it encourages relying more on direct satellite measurements rather than models alone, while also identifying where models need structural improvements to incorporate realistic hydraulics and human influences.
AI generated by Grok
Newly available satellite data from NASA’s Surface Water and Ocean Topography (SWOT) mission reveal significant flaws in many global river models, particularly for dammed rivers, arid- region rivers, and Arctic rivers.
A study led by hydrologist Colin Gleason of the University of Massachusetts Amherst, published in Geophysical Research Letters (title: “SWOT, Empiricism, and River Modeling”), compared state- of- the- art machine- learning river models against SWOT observations. After quality controls, the team analyzed 68,347 river reaches representing about 38% of global discharge.
Key findings include:
Models struggle most with dammed rivers, rivers in arid climates (especially populated ones such as parts of Australia, Central Asia, the southwestern U.S., and Mexico), multi- channel rivers, and many Arctic rivers. Parts of Siberia and China are particularly difficult.
Less than 10% of reaches show “serious error”, but these are often among the most important for water resources, hydropower, irrigation planning, and climate- change projections.
Models perform better on wider rivers and on “normal” single- thread rivers without dams, glacial influence, or estuaries. However, only about 11% of Earth’s rivers fit that idealized description, “a weird river is the norm”.
Gleason notes that errors in these models directly undermine climate predictions and irrigation forecasts. Dammed rivers pose special challenges because models must account for human operations (e.g., pumped- storage hydropower that can change depth by more than a meter per day, as on the Connecticut River). Arid- region difficulties stem largely from delayed, non- obvious groundwater effects. Arctic modeling suffers from sparse training data for machine- learning systems.
The study frames SWOT as an “early microscope” for rivers: it encourages relying more on direct satellite measurements rather than models alone, while also identifying where models need structural improvements to incorporate realistic hydraulics and human influences.
The press release (dated 6 October 2026, by Julia Westbrook of UMass Amherst) and related coverage in outlets such as Science highlight how SWOT’s simultaneous measurements of water level and width expose the true complexity of rivers shaped by dams, braiding, freezing, and human water use.
Global distribution of SWOT-derived SES. Credit: Geophysical Research Letters (2026). DOI: 10.1029/2026gl124323
SWOT, Empiricism, and River Modeling
Answers to questions about freshwater sustainability are answered by modeling rivers, as it is impossible to measure the millions of rivers on the planet in the field.
We use a recently launched satellite (SWOT) designed to measure rivers in a new way to assess an ensemble of global river models. We use SWOT to see which rivers are modeled in an opposite way to what the satellite observes: for example, when SWOT sees the river get deeper than it observed it last week, but the models say the river flow decreased from last week (or vice versa).
We see that models struggle in arid and populated regions, and do relatively well in areas with less human development. However, the less densely populated Arctic is also a challenge. This research reveals the basic spatial pattern of global river modeling errors for the first time, setting the stage for a new, improved generation of models.
The study uses an inductive approach grounded in SWOT’s simultaneous measurements of river water-surface height and extent (via radar interferometry) to evaluate global river models.
These models include traditional physics- based hydrologic and land- surface models coupled with routing schemes, machine -learning (e.g., LSTM) approaches that estimate discharge, and hybrids. Models were assessed only if they produce daily discharge (ideally also depth, width, and velocity) mapped to vector river channels in the SWORD database.
Main methodological approach
High- quality SWOT height observations are compared with modeled discharge on the same day via the SWOT- ensemble- Spearman (SES) correlation (Spearman rank correlation, chosen because the height– discharge relationship is strong, monotonic, and nonlinear).
An ensemble of machine- learning models is preferred; traditional physics- based ensembles fill gaps where ML output is unavailable.
Models were run in a largely “naturalized” mode (human influences appear only indirectly via training gauges) so that satellite data could independently reveal human impacts rather than assuming them a priori.
SWOT height skill is strong (68th- percentile error of ~16 cm in detecting height changes in the validation set).
Broader implications
Current global river models are limited by simplified channel geometry, incomplete representation of dams and water withdrawals, uncertain snowpack timing, and sparse data on human operations.
SWOT provides “detail at scale”, direct observations of real river complexity (dam operations, multi-channel partitioning, abrupt stage changes, infrastructure constraints, etc.) that most models cannot currently reproduce.
The authors argue for a revived empiricism: use SWOT measurements first and trust the observations, while also adapting model structures so they can assimilate realistic hydraulic states rather than relying on oversimplified assumptions.
The paper positions SWOT as a tool that both diagnoses existing model weaknesses on a reach- by- reach basis and supplies the primary data needed to build the next generation of more realistic global river models.
(The full article is open access on the Wiley Online Library page you linked. Supporting Information is also available there.)
Given the insurmountable challenge of measuring all rivers in situ, global river models serve as the foundation of freshwater knowledge past, present, and future. We adopt an inductive empirical framework based on Surface Water and Ocean Topography (SWOT) satellite measurements to assess these models. After controlling for SWOT data quality, we examine 68,347 individual river reaches representing ∼38% of global discharge. We find river models currently struggle in areas of heavy economic development, multi-channel rivers, arid areas, and many Arctic rivers. After controlling for these expected errors, we find better skill as rivers get wider and that parts of Siberia and China are particularly difficult to model. We also find large variability and spatial heterogeneity to model performance, resisting oversimplification. Our results suggest that leveraging SWOT observations within river models will improve them, but river models must adapt their structure to represent realistic hydraulics to do so.
“Bill McKibben is desperate. Instead of accepting reality, he continues his magical thinking of wind and solar dominance. But as James Hansen, the father of the alarm, stated: ‘Suggesting that renewables will let us phase rapidly off fossil fuels in the United States, China, India, or the world as a whole is almost the equivalent of believing in the Easter Bunny and Tooth Fairy’.”
“Bill McKibben is desperate. Instead of accepting reality, he continues his magical thinking of wind and solar dominance. But as James Hansen, the father of the alarm, stated: ‘Suggesting that renewables will let us phase rapidly off fossil fuels in the United States, China, India, or the world as a whole is almost the equivalent of believing in the Easter Bunny and Tooth Fairy’.”
It’s an old story in the futile, wasteful decarbonization effort. Should natural gas be part of the solution, displacing diesel oil and coal in home heating and power generation? Or should natural gas be condemned as a “fossil fuel” just like oil and coal. Enter Natural Allies for a Clean Energy Future, a trade/lobbying group espousing “Our Path to a Clean Energy Future.”
Their tag line reads: “Climate progress, along with energy affordability and reliability, starts with renewables and natural gas working together.” But the climate activists want little to do with this pitch. Bill McKibben, wrote:
At Inside Climate News, Kiley Bense has the scoop on how the natural gas industry is buying Democrats to try and influence the energy debate…. Energy “has to be reliable, it has to be affordable,” [former Philadelphia Mayor Michael Nutter] added, one theme of an argument made throughout the evening that the path to a clean energy future should be built on gas. “It has to be there when people need it. It’s not a sometime thing.”
Continuing,
That messaging is favored by the event’s sponsor, Natural Allies for a Clean Energy Future, a coalition formed in 2020 to “educate and inform about the central role natural gas and natural gas infrastructure play in the clean energy future and as a partner to renewables.” Natural Allies’ goal is to redefine gas as “the most affordable and reliable energy source.”
The fix is in to McKibben.
Natural Allies—whose funders include the fracking company EQT, gas utility Enbridge and Venture Global, a liquefied natural gas provider—woos left-leaning and moderate voters in blue and purple states by hiring Democratic leaders like Nutter to share their message. Nutter’s advisory firm was paid $240,000 in 2024 for his work on behalf of the group, and he sits on its leadership council with other Democratic politicians like former Virginia Gov. Terry McAuliffe and former Ohio Congressman Tim Ryan.
The dreaded word “fossil fuels” is the smoking gun.
Eugene DePasquale, the current chair of the Pennsylvania Democratic Party, is the state chairman for Natural Allies. He appeared on the panel alongside Nutter. “It’s always been focused on, ‘How do we convince Democrat officials to stay onside to support fossil fuels?’” said Charlie Spatz, a research manager at the Energy and Policy Institute, of Natural Allies’ mission. “They exclusively exist to influence Democrats, in my opinion.”
The dash-to-gas is on despite the gas turbine shortage from the misdirection of production of industrial wind turbines. Bill McKibben is desperate. Instead of accepting reality, he continues his magical thinking of wind and solar dominance. But as James Hansen, the father of the alarm, stated:
Suggesting that renewables will let us phase rapidly off fossil fuels in the United States, China, India, or the world as a whole is almost the equivalent of believing in the Easter Bunny and Tooth Fairy.
A new study in Frontiers in Marine Science (published 7 October 2026) estimates that fully restored and protected oyster reefs in the top 10 hotspot countries could deliver a Gross Ecosystem Product (GEP, or “ecopotential”) of about $12 trillion per year in ecosystem services.
The 10 hotspot nations (US, Australia, Chile, China, Japan, South Korea, Argentina, France, Germany, and Italy) account for the bulk of the study’s estimated ~$12 trillion annual gross ecosystem product (GEP, or “ecopotential”) from fully restored oyster reefs.
AI generated by Grok
A new study in Frontiers in Marine Science (published 7 October 2026) estimates that fully restored and protected oyster reefs in the top 10 hotspot countries could deliver a Gross Ecosystem Product (GEP, or “ecopotential”) of about $12 trillion per year in ecosystem services.
Researchers Matteo Convertino and Yuhan Wu (Tsinghua University and Tsinghua Shenzhen International Graduate School) calculated this figure by combining habitat suitability modeling with valuations of services such as oyster production and trade, water filtration, nutrient (e.g., nitrogen) removal, shoreline stabilization and protection, and shell-related carbon sequestration.
Key findings
Habitat suitability and current status: Using presence- only data (from GBIF and iNaturalist) and a MaxEnt machine- learning model with environmental variables (water temperature, salinity, light, nutrients, oxygenation, etc.), they mapped potential oyster reef habitat at ~9.2 km resolution. Sites scoring ≥65% suitability were classed as suitable. In the 10 hotspot countries (USA, Australia, Chile, China, Japan, South Korea, Argentina, France, Germany, and Italy), roughly 295,000 km² (~6% of their coastal zones; ~114,000 square miles in some reports) are suitable. Reefs currently occupy only ~10% of this area, and only ~23% of suitable habitat overlaps marine protected areas. Globally, an estimated 85% of oyster reefs have been lost over the past century due to overexploitation, pollution, habitat modification, and climate pressures.
Hotspots and morphology: Highest ecopotential is concentrated in the 10 countries above. About 72% of reefs occur in estuaries, 21% in lagoons and inlets, and 7% in river deltas. Dominant global drivers of suitability and GEP are seabed light availability and minimum primary productivity (linked to phytoplankton biomass).
Potential services if fully restored:
Water filtration on a massive scale (e.g., ~77.58 quadrillion liters/year in the US, ~69.92 in Australia, ~46.58 in Chile).
Up to ~12.3 million metric tons of carbon sequestered per year.
In the US alone, nitrogen removal, carbon sequestration and water purification valued at ~$25.1 billion/year (other reports cite >$25 billion GEP for the US).
Restoration needs: ~90% of suitable sites would require restoration; ~80% need protection and conservation. Current conditions are described as “very suboptimal”, but the opportunity is large.
Caveats and climate context
These are scenario- based estimates derived from global averages and models; actual outcomes depend on local conditions, population dynamics, and climate change.
Global warming is generally expected to reduce habitat quality and thus overall GEP from the $12 trillion potential, though some regions (e.g., Australia, parts of Indonesia, South Korea, Japan, France) could see expansion under more favorable conditions.
Adjacent blue- carbon benefits (support for seagrass, mangroves, salt marshes via clearer water, sediment retention, etc.) are discussed conceptually but not fully quantified in the carbon valuation.
The work introduces “ecopotential” GEP as a systematic index linking ecological fitness (habitat suitability) to socio- economic value, aiming to guide prioritization of conservation, restoration, and nature- positive design under frameworks like UN SDG 14 (“Life below water”). Digital layers identifying high- GEP habitats are provided to support decision- making.
In short, the study frames well- managed oyster reefs as a high- return natural infrastructure asset for water quality, coastal resilience, biodiversity, fisheries support, and climate- related services- provided large- scale restoration and protection occur.
The 10 hotspot nations (US, Australia, Chile, China, Japan, South Korea, Argentina, France, Germany, and Italy) account for the bulk of the study’s estimated ~$12 trillion annual gross ecosystem product (GEP, or “ecopotential”) from fully restored oyster reefs.
These countries hold the highest combined habitat suitability (HS ≥ 0.65) and restoration opportunity. Across them, high- suitability area totals about 295,000- 295,691 km² (roughly 6% of their delineated coastal zones). Current known reefs occupy only ~10% of suitable habitat overall, and only ~23% of high-HS area overlaps marine protected areas. About 90% of suitable sites would need restoration and ~80% active protection.
The GEP combines potential oyster trade and export value with standardized monetary scenarios for shell-carbon, water filtration and purification, and nutrient (nitrogen) removal. It is a theoretical maximum under full restoration and does not subtract restoration costs.
Global warming is projected to reduce overall GEP, though some of these countries (notably Australia, South Korea, Japan, and France) could see local expansion under changing conditions.
Data are most complete for the top three by physical capacity (US, Australia, Chile). Other countries rank lower on area-scaled physical metrics but vary sharply on trade-value assumptions.
Water filtration capacity (daily, area- scaled under common individual filtration rate of ~189 L/day; not measured real- world clearance):
United States: 77.58 quadrillion liters
Australia: 69.92 quadrillion liters
Chile: 46.58 quadrillion liters
Shell- carbon component (standing stock under common density/weight assumptions; not annual net sequestration, verified credits, or atmospheric CO₂ removal):
United States: 4.92 million metric tonnes C
Australia: 4.43 million metric tonnes C
Chile: 2.95 million metric tonnes C Overall across hotspots, reefs could sequester up to ~12.3 million metric tonnes of carbon per year in related scenarios.
Illustrative ecosystem service monetary values (US example; nitrogen removal, shell- carbon scenario and water purification):
United States: ~$25.1 billion/year combined (approx. $13.8 billion nitrogen removal, $8.8 billion water purification, $2.5 billion shell-carbon scenario). The US and Australia show the largest area-scaled values for these service components.
Potential oyster export and trade value (illustrative gross figures based on country- specific $/kg and assumed production from high- HS area; highly sensitive to price assumptions and not automatically additive to the ecosystem- service scenarios):
Country
$$/kg export
Illustrative export value ($$)
Chile
227.50
~6.35 trillion
Argentina
115.67
~2.74 trillion
USA
59.00
~1.31 trillion
Germany
14.45
~602 billion
Australia
14.37
~282 billion
China
11.30
~228 billion
Japan
10.90
~103 billion
France
8.76
~93 billion
South Korea
8.23
~62 billion
Italy
6.08
~10 billion
Chile and Argentina dominate this trade-value ranking due to high assumed unit prices; the US ranks third. Italy is lowest.
Broader ranking and context
Highest overall physical and area- scaled potential: United States and Australia (largest filtration and carbon components), followed by Chile.
Highest trade- value potential: Chile, then Argentina, then the US.
Remaining countries (China, Japan, South Korea, France, Germany, Italy): Significant high- HS area and restoration opportunity, but lower area- scaled filtration and carbon figures and lower unit export prices in the scenarios. They still contribute meaningfully to the aggregate $12 trillion GEP.
Dominant drivers of habitat suitability and GEP globally: seabed light availability and minimum primary productivity (phytoplankton biomass). Reefs are mostly associated with estuaries (~72%), lagoons/inlets (~21%), and river deltas (~7%).
Caveats: These are model- based screening estimates using MaxEnt habitat suitability, global averages, and standardized parameters. Realized benefits depend on local biology, water quality, successful restoration, protection from human pressures, and climate trends. The figures represent potential under ideal management, not current realized value. The study emphasizes interconnected conservation, restoration, pollution management, and education to unlock this potential.
In short, the US and Australia lead on filtration and carbon- related services, Chile (and Argentina) lead on high-value trade scenarios, and the full set of 10 countries drives the headline $12 trillion figure when services and trade potential are combined under full restoration.
“The gross ecosystem product of oyster reefs” by Yuhan Wu and Matteo Convertino (Tsinghua Shenzhen International Graduate School), published 7 October 2026 in Frontiers in Marine Science.
Core contribution
The authors introduce gross ecopotential (also called Gross Ecosystem Product, or GEP) as a systemic index that combines:
Ecological habitat suitability (HS) for oyster reefs, and
Socio- economic valuation of ecosystem services.
They treat oyster reefs as critical interfacial (ecotone) habitats that support biodiversity, water filtration, shoreline stabilization, and (conceptually) adjacent blue- carbon systems such as seagrass, mangroves, and salt marshes.
Methods (high level)
Occurrence data: ~10,442 presence- only records of the family Ostreidae from GBIF and iNaturalist (not verified reef footprints or species- specific).
Habitat suitability modeling: MaxEnt (AUC ≈ 0.90) using nine benthic Bio- ORACLE v2 predictors at ~9.2 km resolution, clipped to a 45 km coastal buffer.
High- suitability threshold: HS ≥ 0.65 (rounded lower boundary of the highest Jenks natural- breaks class).
Service quantification (standardized global scenario parameters):
Assumed density ≈ 12 oysters m⁻².
Shell carbon component (not a full net carbon budget).
Water filtration capacity.
Nutrient (nitrogen) removal.
Potential oyster trade value (using country-specific export prices, with a conservative adjustment).
Adjacent blue- carbon benefits are discussed conceptually but excluded from the numerical carbon valuation.
Dominant global predictors of suitability were seabed light availability (>40% contribution) and minimum primary productivity.
Key results
Hotspot countries (highest HS and restoration opportunity): USA, Australia, Chile, China, Japan, South Korea, Argentina, France, Germany, and Italy.
High- HS area in these 10 countries: 295,691 km² (~6% of their coastal zones).
Only ~10% of this high- HS area currently coincides with occurrence records; ~23% overlaps evaluated marine and coastal protected areas (leaving the large majority unprotected).
Geomorphic distribution: ~72% estuaries, ~21% lagoons, inlets and bays, ~7% river deltas.
Scaled physical indicators (under the common scenario):
Shell- carbon component largest in USA (~4.92 Mt C), Australia (~4.43 Mt C), Chile (~2.95 Mt C).
Daily filtration capacity: USA 77.58 quadrillion L, Australia 69.92, Chile 46.58.
Illustrative trade- value scenarios (static standing-biomass price, not annual revenue) reach into the trillions for some countries (e.g., Chile ~US$6.35 trillion, Argentina ~US$2.74 trillion, USA ~US$1.31 trillion in the published table).
Combined ecosystem- service scenarios (nitrogen removal, shell-carbon, water purification) are reported separately by country; the USA shows the largest area- scaled values among the service components (e.g., nitrogen removal ~US$13.8 billion in the scenario framework).
The widely reported ~$12 trillion annual GEP figure for fully restored reefs in the top- 10 hotspots originate from the accompanying press materials summarizing the overall potential under the authors’ multicriteria valuation framework.
Limitations emphasized by the authors
Presence- only, family- level data with uneven sampling; high- HS cells without records are candidates for survey, not confirmed absences or guaranteed restoration sites.
Standardized global parameters (density, filtration rate, carbon fraction, prices) enable cross- country comparison but are not local biological constants.
Stock- based (trade and shell carbon) and flow- based (nitrogen removal) values have different temporal boundaries and should not be naively summed.
Realized benefits depend on local substrate, hydrodynamics, larval supply, disease, governance, costs, and climate change (which is expected to reduce overall habitat quality in many places, though some regions may expand).
The model is a screening and prioritization tool, not a mechanistic or site- specific restoration prescription.
Bottom line
The paper argues that oyster reefs represent a large, under- realized socio- ecological asset.
Connecting conservation, restoration, and regeneration at scale, guided by habitat- suitability and GEP layers, could deliver substantial benefits for water quality, coastal resilience, biodiversity, and (to a more limited, scenario- dependent extent) carbon- related services.
The authors supply digital eco- information layers to support prioritization and note that the framework is extensible to other biogenic habitats.
Authors:Yuhan Wu, Ecosystem Intelligence and Design Center (TREES), Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School (SIGS), Shenzhen, China
Matteo Convertino, Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration, Tsinghua Shenzhen International Graduate School (SIGS), Shenzhen, China
Abstract
Oyster reefs are critical interfacial habitats that underpin coastal and marine bio-diversity, and enhance multiple ecosystem services of blue carbon habitats, including water filtration and shoreline stabilization. However, despite this ecological value, oyster reefs have suffered dramatic global declines. The absence of a systemic economic valuation of oyster reef ecosystem services introduces deep uncertainty in the prioritization of restoration areas, thereby constraining coordinated large-scale transboundary conservation, restoration, eco-engineering efforts, and policy. Here, we novelly introduce and estimate the gross ecopotential, or Gross Ecosystem Product (GEP), as a systemic index of reef ecosystem services grounded in ecological fitness. Fitness is assessed by habitat suitability (HS) at a ∼9.2 km resolution globally. The ecopotential is calculated as a multicriteria socio-ecological value function combining potential oyster trade value with monetary scenarios for the quantified shell-carbon component, water filtration and nutrient removal. Potential effects on adjacent vegetated blue-carbon habitats are discussed as conceptual pathways but are not included in the numerical carbon valuation. We highlight ten hotspot countries – the USA, Australia, Chile, China, Japan, South Korea, Argentina, France, Germany, and Italy – where HS and restoration opportunities are the highest. Seabed light availability and minimum primary productivity, reflecting phytoplankton biomass, emerged as the dominant global drivers of oyster reef HS and GEP. At the reference threshold (HS ≥ 0.65), approximately 23% of predicted high-HS habitat overlaps the evaluated marine and coastal protected-area network, and approximately 10% of high-HS cells in the hotspot analysis coincide with occurrence records. Because the presence-only archive is incomplete and unevenly sampled, high-HS cells without an occurrence record are interpreted as candidates for further survey rather than confirmed oyster absences. Hydrogeomorphic analyses indicate that ecosystem morphology drives GEP through distinct routing of ecological flows: ∼72% of reefs occur in estuaries, 21% in lagoons or inlets, and 7% in river deltas. Our findings highlight the considerable GEP of oyster reefs when conservation, restoration, and regeneration are interconnected actions with systemic and global impact. The proposed GEP approach integrates publicly available socio-ecological and economic data to generate decision-relevant priority insights for restoration planning. Digital Eco-Information layers, identifying existing and potential reef habitats with high GEP, are provided, including their hydrogeomorphic flows and cascading ecosystem services. The model is readily tunable to higher-quality data and broadly extensible to other biogenic species and habitats, providing a scalable baseline for blue-carbon ecosystem management and future scenario development.
India’s External Affairs Minister, Dr. Subrahmanyam Jaishankar, used the U.N. General Assembly gathering to say something that climate scaremongers like Al Gore and John Kerry have spent three decades excluding from the conversation: “Energy security is at the heart of development. Availability and prices should not be manipulated for political ends.”
India’s External Affairs Minister, Dr. Subrahmanyam Jaishankar, used the U.N. General Assembly gathering to say something that climate scaremongers like Al Gore and John Kerry have spent three decades excluding from the conversation: “Energy security is at the heart of development. Availability and prices should not be manipulated for political ends.”
The developing world, he said, needs “fair economic and trade practices which enhance our production capabilities and do not reduce us to mere consumers. Our right to industrialize must not be denied.”
Dr. Jaishankar’s statement is a rebuke to climate campaigners who once envisioned India as the poster child of the “green” transition, the manipulators of energy “for political ends.” Those would be the anti-fossil fuel governments that themselves grew rich on coal, oil, and natural gas and now demand that others pursue their own prosperity with inferior energy sources like wind and solar.
The minister knows that a nation without the capability to make steel, cement, fertilizer, and electronics stays a buyer of other people’s goods. That is the future of “mere consumers” that he refuses to accept on behalf of India and the rest of the Global South.
What makes this moment interesting is that India’s actions presaged Dr. Jaishankar’s words. New Delhi has spent years reciting net-zero talking points at climate conferences while quietly building one of the most aggressive expansions of fossil fuel use on the planet.
India also turned to the United States to lock in supplies of oil and natural gas, a move I described as a manifestation of energy sovereignty in the India-U.S. deal. Coal for the grid, oil for transport, and gas for fertilizer and industry. That is the energy mix of a country that intends to grow.
Though India has made progress in tackling poverty, its per capita income remains a fraction of G7 nations whose diplomats lecture climate dogma and expect India’s rural families to wait decades for grid-scale batteries so that a European bureaucrat can announce a climate milestone. That is the outrageous bargain publicly rejected by the subcontinent.
India’s louder voice did not emerge in a vacuum. The current American administration has created more space for public discussion about the economic consequences of aggressive climate mandates. This matters because Washington still shapes global finance, energy diplomacy, and institutional priorities.
When America treats oil, gas, coal, nuclear power and domestic energy production as strategic assets rather than embarrassments, developing countries are better positioned to resist pressure from globalist bureaucracies. Washington’s pro-energy, pro-growth posture empowers India to speak plainly.
Jaishankar made his argument in the context of the “Global South,” and that framing matters. Hundreds of millions of people in sub-Saharan Africa still live without electricity. Many African governments sit on vast gas and coal reserves they are expected to leave in the ground. India, with its economic weight and diplomatic reach, can say what smaller nations fear to utter at the negotiating table.
The climate movement has built its moral authority on the claim that it speaks for the world’s poor. India has dismantled that assertion on the U.N.’s own stage. The poor, it turns out, want electricity that works, fuel they can afford, and jobs in factories that run on something more dependable than good intentions.
Originally published at PJ Media, October 2nd, 2026.
Vijay Jayaraj is a Science and Research Associate at theCO2 Coalition, Fairfax, Virginia. He holds an M.S. in environmental sciences from the University of East Anglia and a postgraduate degree in energy management from Robert Gordon University, both in the U.K., and a bachelor’s in engineering from Anna University, India. He served as a research associate with the Changing Oceans Research Unit at University of British Columbia, Canada.
El Niño and tropical Atlantic warming can provide early- warning signals for extreme heat in the Amazon, with some precursors detectable up to seven months ahead, according to a new study.
The study characterizes lagged dependence between large- scale climate modes and Amazon temperature and precipitation extremes, then evaluates whether monthly precursor signals enable early warning of compound hot- dry events.
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El Niño and tropical Atlantic warming can provide early- warning signals for extreme heat in the Amazon, with some precursors detectable up to seven months ahead, according to a new study.
Researchers from the ARC Centre of Excellence for 21st Century Weather (led by scientists at UNSW Sydney, with collaborators from the Australian National University, University of Melbourne, and Brazil’s National Institute of Space Research/INPE) published the findings in the journal Earth’s Future.
They analyzed monthly climate data from 1950- 2023 across six climatic sub- regions of the Amazon Basin, examining links between large- scale climate modes and temperature and rainfall extremes (assessed over three- month periods).
Key findings
Hot extremes in the Amazon are more likely after unusually warm conditions linked to El Niño (positive ENSO phase in the tropical Pacific) and warming in parts of the tropical Atlantic. These relationships are stronger and more widespread for heat than for cold extremes or rainfall extremes.
The clearest long- range signal is for March- May: extreme heat across much of the Amazon is associated with positive climate- mode anomalies observed as much as seven months earlier.
Seasonal variations:
September- November: Hot extremes primarily linked to earlier tropical North Atlantic warming.
December- February: Extreme heat more often tied to recent tropical South Atlantic warming (across much of the basin), with longer- lead ENSO signals in southern and western areas (e.g., Planalto Amazon and Andes- Amazon foothills).
Compound hot- dry events
The team also assessed simultaneous extreme heat and rainfall deficits (compound hot- dry events), which raise wildfire risk, stress ecosystems, threaten health, food and water security, and can reduce the Amazon’s carbon uptake.
Using statistical (copula) methods focused on extremes plus machine learning to test predictability from lagged climate indices (ENSO, tropical North and South Atlantic SSTs, and North Atlantic Oscillation):
Useful early- warning potential exists in some seasons and regions, especially northern and lower central Amazon.
Predictability is dominated by ENSO, peaking in December- February (notably western Guiana highlands) and March- May (lower central Amazon). Skill is weakest in June- August and parts of the western Amazon.
Reliable skill typically requires at least one climate index at short (e.g., 1- month) lead; the multi- month heat precursor signal does not translate into reliable seven- month forecasts of compound events.
Rainfall extremes show weaker, less widespread dependence on these large-scale ocean patterns than temperature does, partly because rainfall is more strongly influenced by regional weather, land- atmosphere feedback, and local conditions.
Context and caveats
El Niño alters tropical circulation in ways that can suppress Amazon rainfall and increase surface heating; tropical Atlantic warming can reinforce dryness and heat by shifting the Intertropical Convergence Zone. These mechanisms are well- known for seasonal means, but the study highlights their imprint on extremes, including surprisingly long lead times for heat in some seasons.
The results indicate potential predictability and identify promising regions and seasons for monitoring Pacific and Atlantic conditions. They do not constitute an operational forecast system.
Translating the findings into practical early- warning tools will require further work with dynamical climate models and additional predictors (e.g., soil moisture, vegetation state).
Lead author Dr Sanaa Hobeichi and co-author Associate Professor Andrea Taschetto (UNSW) noted that machine learning helped isolate the most informative signals while also showing where ocean indices alone are insufficient.
This research is relevant amid rising frequency and intensity of Amazon hot- dry extremes, which are expected to worsen with continued global warming and possible amplification by deforestation.
Amazon region and climate indices used in this study; (b) climatic sub-regions identified by K-means clustering of annual and seasonal precipitation totals and mean temperatures. Regions from wetter to drier are shown in darker to lighter blue shades. Credit: Earth’s Future (2026). DOI: 10.1029/2026ef008496
Comparison of Hobeichi et al. (2026) with related Amazon climate studies
Hobeichi et al. (2026, Earth’s Future) focuses on lagged statistical dependence (via copulas, emphasizing tail and extreme dependence) between major climate modes (primarily ENSO/Niño3.4, TNA, TSA, and NAO) and Amazon temperature and precipitation extremes, plus machine- learning assessment (Random Forest) of the predictability of compound hot-dry events from those lagged indices.
It divides the basin into six climatic sub-regions, uses 1950- 2023 observational data, and highlights a particularly long- lead (up to 7-month) precursor signal for March- May heat extremes, with more limited and shorter- lead skill for compounds (strongest in northern and lower- central regions in DJF/MAM; weakest in JJA).
This sits within a large body of work on Amazon hydroclimate teleconnections, extremes, and predictability. Below is a structured comparison across key dimensions.
1. Established teleconnections (ENSO and tropical Atlantic)
Classic and recent studies establish the physical basis that Hobeichi et al. build upon:
ENSO: El Niño weakens the Walker circulation, promotes subsidence over northern/central Amazonia, suppresses convection/rainfall, and elevates temperatures. Major droughts (e.g., 1926, 1983, 1998, 2015- 16) are strongly linked to El Niño. La Niña tends to produce the opposite. Northern Amazonia is especially sensitive.
Tropical Atlantic: Warm TNA (often lagged after El Niño via atmospheric bridge mechanisms) shifts the Intertropical Convergence Zone northward, delaying or weakening the wet- season onset (especially March- May in northeast Brazil and southern Amazonia) and reinforcing dryness and heat. The 2005 drought is a classic TNA- dominated example. TSA anomalies and the Atlantic Meridional Mode (AMM) also play roles. Southern Amazonia is often more Atlantic-influenced.
Combined Pacific- Atlantic effects frequently amplify extremes; interactions with the Indian Ocean Dipole (IOD) appear in some multi-year drought analyses.
How Hobeichi et al. differ and advance: Most prior work uses linear correlations, composites, regressions, or wavelet methods focused on seasonal means or specific historical events. Hobeichi et al. explicitly target extremes via tail dependence (copulas), quantify lag dependence systematically (up to 7 months), and test predictive skill for compounds with ML. They confirm the expected physical mechanisms while showing temperature extremes have stronger and more widespread lagged links than precipitation extremes (which are noisier due to local processes).
2. Focus on extremes and compound hot-dry events
Multiple studies documents increasing frequency, intensity, duration, and spatial extent of Amazon heatwaves, droughts, and compounds (especially since ~2000 or 2010). Extreme heat almost always co- occurs with dryness in the most severe cases. The 2023- 2024 period stands out as record-breaking in compound dry-hot conditions, low soil moisture, and high vapor-pressure deficit, affecting large fractions of the basin.
Heatwaves are amplified under dry conditions via land- atmosphere feedback (reduced evaporative cooling, increased sensible heat). Synoptic patterns (e.g., enhanced South Atlantic subtropical high, reduced moisture influx) contribute.
Projections (CMIP5 and CMIP6) robustly show rising compound hot- dry risk under warming, with Amazonia as a hotspot; deforestation can further intensify them via feedback.
Hobeichi contribution: Moves from documentation and attribution of past events and future projections toward statistical early- warning potential using remote climate modes alone. It shows compounds are harder to predict than heat alone, and skill is highly region- and season- dependent, consistent with known spatial heterogeneity (northern vs. southern Amazonia).
3. Predictability and early- warning efforts
Some prior work has proposed Atlantic SST- based early- warning indicators for central Amazon droughts, with claimed lead times of many months (up to ~18 months in network- based approaches focused on specific ocean regions).
Seasonal forecasting systems and empirical methods already use ENSO and Atlantic indices operationally, but skill for extremes (especially compounds) is limited and variable.
Multi- mode analyses (ENSO, IOD and TNA) highlight coupled tropical interactions for multi- year droughts and note forecasting potential by monitoring oceanic indices.
Hobeichi contribution: Provides a more rigorous, basin-wide, lag- explicit quantification using modern tools (copulas for dependence and Random Forest for predictability).
It tempers expectations: the impressive 7- month heat signal does not equate to reliable long- lead compound forecasts; useful skill usually requires shorter leads (often concurrent or 1-month) and is strongest in specific region- season windows. NAO contributes little.
4. Methodological and regional novelty
Sub- regional analysis is common (northern vs. southern, or specific sub-basins), but Hobeichi’s k-means clustering into six climatologically coherent regions is systematic.
Lead author Hobeichi has prior expertise in ML applications to drought metrics, extremes predictability, and climate- mode explanations of precipitation variability (mostly Australian- focused previously), so the paper extends that toolkit to the Amazon.
Related recent work explores two- way interactions (e.g., Amazon drought itself amplifying subsequent TNA warming via land- atmosphere- ocean feedback) and emerging “hyper tropical” conditions under continued warming.
Summary of positioning
Hobeichi et al. (2026) is evolutionary rather than revolutionary: it confirms well- established ENSO and tropical Atlantic teleconnections while advancing the quantitative characterization of extreme (especially heat) dependence at long lags and the practical limits of using those modes for compound- event early warning.
Strengths relative to much of the literature include the explicit extremes focus (copulas), ML predictability testing, long consistent observational record, and clear regional and seasonal differentiation. Limitations shared with many observational studies include reliance on large- scale indices alone (local land- surface and atmospheric processes matter, especially for rainfall and western Amazonia) and the challenge of translating statistical relationships into operational systems.
It complements event- based analyses (e.g., 2023- 24 compounds), process and mechanistic studies, and climate- projection work by highlighting where monitoring Pacific and Atlantic conditions offers the most actionable lead time for heat risk, and where additional predictors will be essential for compounds.
Climate-Mode Precursors and the Predictability of Amazon Hot and Dry Extremes
Authors and affiliations
Lead and corresponding author:Sanaa Hobeichi (ARC Centre of Excellence for the Weather of the 21st Century; Climate Change Research Centre & UNSW AI Institute, University of New South Wales). Co-authors include Lincoln Muniz Alves (INPE, Brazil), Andréa S. Taschetto (UNSW/ARC Centre), Wil Laura (ANU), Nayan Talmale (University of Melbourne), and Lisa V. Alexander (UNSW). First published online 27 August 2026.
Abstract and Key findings
The study characterizes lagged dependence between large- scale climate modes and Amazon temperature and precipitation extremes, then evaluates whether monthly precursor signals enable early warning of compound hot- dry events.
Methods: Copula- based analysis of tail dependence (focusing on extremes rather than average relationships) between climate indices and local anomalies at multiple lags. Machine- learning models (Random Forest classification) then test predictability of binary compound hot- dry months from lagged indices.
Climate drivers examined: Niño3.4 (ENSO), Tropical North Atlantic (TNA), Tropical South Atlantic (TSA), and North Atlantic Oscillation (NAO).
Data: CRU TS v4.08 monthly temperature and precipitation (1950- 2023) over the Amazon, divided into six climatic sub- regions via k -means clustering on seasonal precipitation and temperature climatologies. Indices from NOAA sources.
Main results:
Hot extremes are more likely following positive ENSO phases and tropical Atlantic warming; these relationships are stronger and more widespread than for cold extremes.
Strongest and most persistent signal: March- May (MAM) temperatures. Hot extremes are more likely across much of the Amazon after positive climate -mode anomalies at lags of up to 7 months → substantial long- lead early- warning potential for heat.
September- November: Hot extremes mainly associated with prior TNA warming.
December- February: Short- lag TSA warming across the basin; longer ENSO lags in the Planalto Amazon and Andes and Amazon foothills.
Precipitation extremes show limited tail dependence with the climate indices. NAO generally shows weak dependence with individual hot or dry extremes.
Compound hot- dry predictability: Dominated by ENSO. Highest skill in DJF (especially western Guiana highlands and northern regions) and MAM (lower central Amazon). Lowest skill in June-August. Highest success rates in specific region- season pairs (e.g., ~81% in one central region for MAM, ~79% in a northern region for DJF). Reliable predictions typically require at least one climate index at ~1-month lead.
Plain- language summary (from the paper)
Unusually high or low phases of major climate modes (especially ENSO and tropical Atlantic variability) months in advance are linked more strongly to extreme heat than to rainfall deficits. The clearest long- lead heat signal is in March- May (up to 7 months). Forecast skill for simultaneous heat and dryness peaks in DJF and MAM and is generally strongest when ENSO information is included, but useful forecasts usually need relatively short lead times.
Context and aims
Hot-dry compound events threaten Amazon ecosystems, carbon uptake, fire risk, health, and food and water security. They have become more frequent and intense and are projected to worsen with continued warming (and possible amplification by deforestation). While mean- state teleconnections of ENSO and Atlantic SSTs with Amazon climate are well known, the paper focuses specifically on extremes, lagged relationships, and quantitative early- warning potential using modern statistical and ML tools.
The paper emphasizes that the 7- month signal is a long- range precursor for heat extremes, not evidence of reliable multi- month forecasts of compound events. Further work with dynamical models and additional local predictors (soil moisture, vegetation, etc.) would be needed for operational systems.
We characterize the dependence between lagged large-scale climate modes and Amazon climate extremes and assess whether monthly precursor signals enable early warning of compound hot-dry events. A copula-based framework analyses tail dependence between major climate drivers and precipitation and temperature anomalies at multiple lags, identifying where and how far in advance extreme phases of modes of variability likely precede local extremes. Machine learning models then evaluate the predictability of compound hot-dry events from lagged climate indices, determining which modes and lead times yield skillful forecasts. Copula modeling shows hot extremes are more likely following positive phases of El Niño-Southern Oscillation (ENSO) and tropical Atlantic variability, with relationships more prevalent than for cold extremes. The most persistent signal occurs for March–May temperatures, when hot extremes are more likely across the Amazon following positive climate-mode anomalies at lags up to 7 months, indicating substantial long-lead early-warning potential. In September–November, hot extremes are associated with prior tropical North Atlantic warming. During December–February, they occur at short lags with tropical South Atlantic warming across the basin, and at longer ENSO lags in the Planalto Amazon and Andes–Amazon foothills. In contrast, precipitation shows limited tail-dependence with climate indices, and North Atlantic variability exhibits weak dependence with individual hot and dry extremes. Predictability of compound hot-dry events is dominated by ENSO and peaks in December–February, especially in the western Guiana highlands, and in March–May in the lower central Amazon, with lowest skill in June–August. Reliable predictions typically require at least one climate index at 1-month lead.
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