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.
Several people have noted that the decreased total albedo in the CERES dataset not only provides the additional energy necessary to explain a quarter-century of warming. It also gives changes in the total absorbed solar radiation (ASR, incoming solar radiation minus reflected solar radiation) that match up very nicely with the warming.
Several people have noted that the decreased total albedo in the CERES dataset not only provides the additional energy necessary to explain a quarter-century of warming. It also gives changes in the total absorbed solar radiation (ASR, incoming solar radiation minus reflected solar radiation) that match up very nicely with the warming.
Figure 1. Normalized ASR and surface upwelling longwave radiation. “Normalized” means set to have an average of zero and a standard deviation of one.
Now, a problem with the CERES data is that it only covers the last 25 years. So in place of albedo, I thought I’d look at “sunshine hours” instead. This is the percentage of the daylight hours of a day, week, month, or year during which the sun is shining. It’s not albedo, but it’s related. Some research showed that the longest dataset we have is from Oxford in the UK. Here is that record.
Figure 2. Annual percentage of the daylight hours during which the sun is shining in Oxford, UK. Yellow line is a CEEMD smooth of the data.
Hmmm, sez I … most interesting. Although this isn’t the same as ASR, it’s most definitely increasing.
Next, I looked at Europe. Copernicus has a dataset showing sunshine hours there. It’s shorter, starting only in 1983. Figure 3 shows the Copernicus data.
Figure 3. Average annual percentage of the daylight hours during which the sun is shining in Europe. Yellow line is a CEEMD smooth of the data.
So I kept looking. My next dataset was from the US. Before ~ 1900, coverage is sparse, but after 1900, there are over 100 stations that record sunshine hours. So I didn’t try to area- weight them, I just took a straight average.
Figure 4. Average annual percentage of the daylight hours during which the sun is shining in the US. Yellow line is a CEEMD smooth of the data.
[UPDATE] An alert reader, JohnC, located a sunshine hours graph for the entire UK here. This is their graph.
Having seen that the percentage of sunshine hours was increasing in all the datasets I could find, and having seen that the CERES absorbed solar radiation was increasing, I turned back to the CERES dataset. Recall that “ASR”, the absorbed solar radiation, is the top-of-atmosphere solar radiation minus the solar reflections from the clouds, aerosols, and the surface. Here’s what’s happening with the ASR, shown along with the theoretical increase in forcing due to CO2.
Figure 5. Global monthly absorbed solar radiation (ASR, solar radiation minus reflected solar radiation), and the theoretical increase in “forcing” (downwelling radiation due to CO2) over the same period.
Now, bear in mind that one of the core arguments of the advocates of the idea that CO2 alone causes the recent global warming is that there is no natural explanation for the warming. Here’s the IPCC making that argument, that if the models are only given natural forcings, they can’t replicate the increase in temperature.
Figure 6. Models with natural forcings (blue) and natural plus anthropogenic forcings (red)
But as the ASR data above shows, the change in albedo provides enough solar power to explain the temperature increase without greenhouse gases.
However, note that this does NOT mean that greenhouse gases have no role in setting the temperature. It only means that they are merely part of the story. As I showed in my recently published peer-reviewed study entitled “Computational implementation and empirical validation of a Constructal climate model“, you need both the albedo and the greenhouse gases to explain the changes in the Earth’s temperature.
Blue springtime skies today, lots of sunshine hours, and plenty of outside maintenance calling me … my best regards to all on this most awesome planet.
w.
As Is My Wont: I ask that when you comment, you quote the exact words you are discussing. It keeps the thread from getting lost in misunderstandings.
During the cold Arctic winter, drill holes through existing sea ice floes and use pumps (powered by gasoline in the small tests, with plans for renewables like wind or hydrogen in larger concepts) to bring up seawater from below and spread it across the surface.
During the cold Arctic winter, drill holes through existing sea ice floes and use pumps (powered by gasoline in the small tests, with plans for renewables like wind or hydrogen in larger concepts) to bring up seawater from below and spread it across the surface.
The frigid air freezes the water quickly, adding a layer of ice on top. This also compacts overlying snow, which can reduce insulation and promote additional ice growth from below as heat conducts upward more efficiently.
Proponents, including startups like Real Ice and Arctic Reflections, and researchers at the University of Cambridge’s Centre for Climate Repair, frame it as “restoration” rather than full geoengineering—enhancing a natural process to make ice thicker and potentially more likely to survive summer melt.
The goal is to increase albedo (reflectivity) and slow the ice-albedo feedback loop: Less ice means more dark ocean absorbs sunlight, warming the region further and accelerating melt.
Two small tests delivered proof-of-concept results:
Svalbard lagoon (Norway): Researchers used a gasoline-powered pump delivering ~3,500 liters of seawater per minute. They flooded a limited area and saw rapid surface freezing plus extra bottom-up growth as the added layer compacted snow and improved heat conduction.
Cambridge Bay, Nunavut (Canada): Real Ice’s team (with partners) used battery-powered pumps to flood up to 250,000 m² (about 62 acres) of sea ice. They added roughly 30 cm (12 inches) of new ice thickness compared with nearby control sites—equivalent to decades of natural thinning caused by warming. Drone and satellite data even showed the engineered patches were initially ~40% brighter.
By June, the thickened ice had melted at the same rate as untouched areas. Ocean heat from below, surface slush, and ponding erased the gains. The engineered patches broke up when the surrounding bay ice fractured—exactly like the controls.
Independent glaciologists called even these tiny-scale efforts “enormous.” Scaling to cover meaningful portions of the Arctic (millions of square kilometers) would require fleets of floating, renewable-powered pumps operating through the polar night—logistically and energetically daunting. One researcher described it as “using a teaspoon to hold the ocean back.”
Arctic sea ice has been shrinking dramatically for decades (summer minimum extent down ~13% per decade). These experiments show the technique works locally in winter, but they also highlight the gap between small tests and climate impact. Broader reviews of polar geoengineering (including this approach, reflective glass beads, and underwater curtains) conclude none are currently viable at the speed or scale needed—and some carry environmental risks.
The results were published last week in the Journal of Geophysical Research: Oceans and a preprint in Earth’s Future.
Here are the two specific studies referenced in the Science article (published around April 17, 2026):
1. Journal of Geophysical Research: Oceans (published April 2026)
Title: Field Observations of Sea Ice Thickening by Artificial Flooding
Authors: Lead author includes Hammer et al. (Svalbard team, involving Arctic Reflections and collaborators like The University Centre in Svalbard and Delft University)
Conducted in Vallunden Lagoon (Van Mijenfjord), Svalbard, from March to June 2024.
They artificially flooded a small area (~1,500 m²) with seawater pumped from below during April 11–15, 2024.
Results: Added ~26 cm of ice thickness on top of the original ~90 cm first-year ice. The flooding heated and increased salinity in the upper ice layers. Surface albedo changed due to slush, snow drift, and melt events.
Positive short-term effects: Thicker ice and a 6-day delay in “rotten ice” formation.
Limitation: The extra thickness did not delay the final disappearance of the ice in summer compared to the non-flooded reference site. Ocean heat and surface processes erased the gains.
This is the smaller-scale Svalbard lagoon test mentioned in the coverage.
2. Preprint in Earth’s Future (now accepted for publication)
Title: Artificial flooding leads to thicker and brighter Arctic sea ice (or similar; full title describes the Cambridge Bay fieldwork campaign)
Conducted in Cambridge Bay, Nunavut (Canada), over the 2024/2025 sea ice season (Real Ice collaboration with University of Cambridge Centre for Climate Repair and others).
Larger test: Flooded up to ~250,000 m² using battery-powered pumps in multiple sessions.
Results: Flooded areas were up to 30 cm thicker than control (unflooded) sites by early May, with slightly thinner snow cover. Sites flooded twice thickened more than those flooded once. Drone/satellite data showed the thickened ice was initially ~40% brighter (higher albedo).
During melt: Brighter appearance and initially slower melt rates, but the ice ultimately broke up when the surrounding bay ice fractured in summer—similar fate to controls.
Both studies demonstrate that artificial flooding can thicken sea ice and temporarily increase reflectivity in winter/early spring, but the added thickness did not persist through summer melt under the tested conditions.
Access notes
The JGR: Oceans paper is open access (full PDF available directly via the Wiley link above).
The preprint is freely downloadable from ESS Open Archive.
If you have institutional access or are willing to pay for the final Earth’s Future version once fully published, that will be the polished peer-reviewed form.
These are the exact publications behind the recent Science news piece. The findings align with what was summarized: promising local winter thickening (~20–30 cm, equivalent to reversing decades of thinning), but no lasting summer survival advantage in these small trials, highlighting the massive scaling challenges for any meaningful Arctic-wide impact.
From the “not that yellow snow” department. Research into light and snow interactions provides new insights into how black carbon pollution can affect vegetation growth and impact ecosystems. As I’ve always suspected, it plays a central role in some of the melts we see on surface ice in the Arctic. This study confirms what I’ve said and written in the past about it but takes it further and applies it to plant growth. -Anthony
From the “not that yellow snow” department. Research into light and snow interactions provides new insights into how black carbon pollution can affect vegetation growth and impact ecosystems. As I’ve always suspected, it plays a central role in some of the melts we see on surface ice in the Arctic. This study confirms what I’ve said and written in the past about it but takes it further and applies it to plant growth. -Anthony
As Canada experiences record snowfall, new research from the University of Waterloo suggests that tiny amounts of industrial pollution trapped in snow can change how sunlight reaches the ground below and significantly alter fragile environments.
The culprit is black carbon, a sooty form of pollution produced when fossil fuels burn incompletely. It can come from vehicle exhaust, industrial emissions and other combustion sources. While black carbon is already known to contribute to warming, the Waterloo research highlights another, less visible effect: how it alters the “light environment” under snow in ways that affect plant growth.
Even at the height of winter, some sunlight is transmitted through snow and reaches soil, seeds and vegetation beneath. Snow doesn’t let all light through equally. As it propagates certain wavelengths of light and absorbs others that are vital for biological processes, like seed germination, cold activation tolerance and chlorophyll production, it can affect the development of vegetation that’s just below the snow. Conversely, snow reflects light at specific wavelengths that can actually disrupt plant dormancy and stimulate stem growth, influencing how higher plants develop.
“Our research indicates that black carbon can have a significant effect on vegetation growth patterns even when there are only a few parts per billion in snow,” said Dr. Gladimir Baranoski, a professor of computer science at the University of Waterloo. “It can alter which wavelengths of light make it through snow, throwing off finely tuned natural cycles.”
The researchers used computer simulations to examine how different amounts of black carbon change the light that snow reflects into the atmosphere and transmits down to the ground. Their results show distinct changes at specific wavelengths, changes that align with “greening,” a phenomenon observed in some high-latitude and high-altitude regions where vegetation appears to be expanding or emerging earlier than expected.
In places that normally remain snow-covered well into the season, plants may begin growing sooner, or different types of vegetation may gain an advantage. The team says their findings are consistent with reports of forested areas expanding in some northern landscapes, while some low-lying plants may struggle to develop under altered conditions.
Those shifts matter because northern and alpine ecosystems are finely tuned to short growing seasons and predictable snow cover. If plants start growing earlier, or if certain species begin to outcompete others, the effects can ripple outward, influencing biodiversity, habitat and how much carbon the landscape stores or releases.
The work builds on a detailed model of how light interacts with snow, developed by Baranoski and Dr. Petri Varsa, a recent PhD graduate in computer science. Using field measurements collected by scientists worldwide, the team has built a versatile model that can be adjusted to predict changes in the light emitted by snow, a major driver of climate change.
This research reflects the University of Waterloo’s Sustainable Futures work—bringing together experts across disciplines to better understand climate-driven environmental change and develop evidence that supports resilient ecosystems and communities.
While this research focused on black carbon, the next step is to investigate brown carbon, a pollutant produced by burning organic matter, such as during forest fires.
The Earth’s reflectivity is measured by albedo (a latin word meaning ‘whiteness’). Albedo measures the proportion of solar irradiation that is reflected back to space, either by the ground or in the atmosphere.
All life on Earth (somehow) originates from the Sun’s heat. But how much of the solar irradiation reaches us Earthlings? The question is highly relevant, as the amount of reflected sunlight displays a remarkable decreasing trend over time. As will be shown, this trend alone explains most of the warming we’ve had in recent decades.
The Earth’s reflectivity is measured by albedo (a latin word meaning ‘whiteness’). Albedo measures the proportion of solar irradiation that is reflected back to space, either by the ground or in the atmosphere. If albedo is 100 percent, all light is reflected, and 0 percent means no light is reflected. Albedo is very different for different materials: pure snow can be as high as >90%, dirty snow can be as low as 20%. Water has albedo <10% (depending on the angle of reflection, see figure 1).[1] Clouds vary greatly and are between 30% and 80% depending on the type of cloud. The Earth’s albedo averages 28%.
Figure 1. What reflects more sunlight, clouds, snow, ice, or water?
If we start from the top, we have a pretty much constant solar irradiance. As soon as the radiation enters the atmosphere, photons begin to be reflected by ozone in the stratosphere, by clouds, water vapor and aerosol microparticles. Of the remaining solar irradiation that reaches the Earth’s surface, an additional part will be reflected away. The amount that is reflected varies greatly and depends on numerous factors such as the geographical location, ground conditions (land/sea/rural/urban), which season we’re in, temperature, altitude, and not least the weather at the location.
On a global scale, all that local randomness evens out, and we have a stable irradiation on a monthly basis. Figure 2 shows how much net solar irradiation that flows in, as a global average, per square meter: it is solar irradiation at the top of the atmosphere, about 340 W/m2, minus reflected shortwave irradiation of about 98 W/m2. Net approximately 242 W/m2.
Figure 2. Net solar irradiation that reaches Earth, watts/m2, calculated as incoming solar radiation minus reflected shortwave radiation. Seasonally adjusted monthly averages for each of the hemispheres. Thick lines are Loess smooths, March 2000 – September 2023. Data: Ceres Ebaf4.
There is a significant positive trend during the period 2000–2023. The increasing amount of solar irradiation reaching Earth is due to a trendwise decrease in reflected shortwave irradiation by 1.5 W/m2. On the other hand, solar irradiation decreased by approximately 0.2 W/m2 during the same period due to the solar cycle becoming less active.
We have thus had an increase in net solar radiation over the period of approximately 1.3 W/m2 according to the Ceres data. The long-term positive trend in shortwave radiation has been confirmed to exist since 1983 (with other satellite data).[2] That there is an established trend for 40 years makes it climatologically significant.
Figure 3. The trend of increase in shortwave radiation from 1983-2001. Copy of figure 1 in [2].
Albedo change explains the increase in net solar irradiation
Albedo is highly variable in time and space. Nevertheless, there is a great deal of stability on a global scale. In Figure 4, the development is shown separately for the Northern Hemisphere (NH) and the Southern Hemisphere (SH). They are very close to each other; the difference is stable and mostly less than 0.5 percentage points. SH has the slightly lower level, which is explained by the fact that it is dominated by sea (with ~80 percent of the surface area, compared to the NH’s ~60 percent). If we’re looking only at the nature of the Earth’s surface, the difference would be significantly higher due to the land/sea ratio – but the reflection from the clouds acts to even out the hemispherical differences.
Figure 4. Albedo for the period March 2000 – September 2023, calculated as reflected shortwave radiation measured at the TOA relative to incoming solar radiation. Seasonally adjusted monthly averages. Thick lines are Loess smooths. Data: Ceres Ebaf.
We have a continuing, and significant negative trend. This means that more solar irradiation enters and warms the Earth. That the albedo trend is negative is well established, and has been demonstrated with different methods: Ceres satellite data, and also by measuring the Earth’s back-radiation towards the Moon.[3][4][5]
What causes the albedo decrease? Several factors contribute: a decreasing cloud cover (both its extent and optical depth) has been shown to be the primary factor [3][4]; increasing vegetation (‘global greening’) is also a well-established secular trend of significant importance. Decreasing amounts of aerosols are of less importance. This can be imputed from the stable difference between NH and SH in figure 4, as aerosols have a local, short-term effect that mainly affects the NH.[6]
The climate is very sensitive to a reduced albedo. Scientists at NASA claim that a reduction in albedo of 1 percentage point produces a warming effect equal to a doubling of CO2 in the atmosphere.[7] Accordingly, the albedo decrease we have had in the last 24 years corresponds to almost 2.0 W/m². The increase in CO2 corresponds to only 0.7 W/m². See calculations below.
Conclusion: The ongoing, longterm reduction in albedo has produced more than twice as much warming effect in recent decades as the radiative forcing from CO2.
Calculations
1. Forcing from CO2
The IPCC states 3.9 W/m2 increased forcing from a doubling of CO2.[8] Mathematically, we search a factor that gives 3.9 from a doubling, expressed as a logarithm, ln(2/1). That factor is 5.63:
3.9 = x * ln(2/1) è x = 5.63
We can now insert start and end values for CO2 instead of the numbers ‘2’ and ‘1’. In March 2000 we had about 370 ppm and in September 2023 about 420 ppm. The theoretical forcing from CO2 for the period would then be:
5.63 *ln(420/370) = 0.71 W/m²
2. Forcing from albedo decrease
a. Using the data. Estimating the global albedo decrease from March 2000 to September 2023 with OLS yields a negative slope of -0.0019 percentage points (pp) per month, and the total decline for the period is 0.54 pp.
Assuming a linear relationship during the period, a reduced albedo of 0.54 pp gives an increased solar irradiation of: 0.0054 * 340 W/m2 = 1.84 W/m2
b. Using the NASA rule of thumb. Alternatively, calculated according to the NASA researchers’ claimed relationship of the warming effect of a 1 percent albedo reduction mentioned above, and using IPCC:s value for forcing from CO2 of 3.9 W/m2, the theoretical forcing of the global albedo reduction for the 24 years is: [7]
3.9 * 0.0054/0.01 = 2.1 W/m²
In both variants a. and b., the resulting figure is more than 2.5 as large as the forcing from CO2. The calculations have not taken into account the slightly decreasing solar radiation over time, but this affects the result only marginally.
[6] Divergent global-scale temperature effects from identical aerosols emitted in different regions, Persad and Caldeira, Nature 2018, https://doi.org/10.1038/s41467-018-05838-6
In order to create the diagrams, I first downloaded data from Ceres (NetCDF4 format). The data were then preprocessed in the program CDO (Climate Data Operators) in Linux (in my case Ubuntu under Windows). All the time series data were calculated as surface-weighted monthly values in CDO. After that, the resulting data files were read into R/Rstudio where the figures were created with GGplot. All data and software are free to use/open source.
Gabriel Oxenstierna is a PhD at Stockholm University and one of the Clintel signatories.
You must be logged in to post a comment.