Tag: Natural Variability

Climate Models May Underestimate Warming by 25%, New Study Finds

It is the study covered in the New Scientist article discussed previously. The paper addresses an apparent discrepancy in how well climate models match observations and revises upward estimates of the Transient Climate Response (TCR)—the near-term global warming expected from a doubling of atmospheric CO₂.

AI generated by Grok

World may warm by about 25% more than current official projections suggest, according to a New Scientist article (19 August 2026) reporting on new research.

Official climate projections are already severe, but the study indicates they understate future warming.

Lead researcher Gergana Gyuleva (ETH Zurich) states: “For a given emission trajectory, we expect warming to be 25 per cent higher.”

For example, the Climate Action Tracker’s projection of roughly 2.6°C of warming by 2100 under existing policies would rise to about 3.25°C under the new assessment.

How much the planet warms depends on cumulative CO₂ emissions and the climate’s response (climate sensitivity). Models disagree on the transient climate response (TCR)—the near-term warming from a doubling of atmospheric CO₂. Some project ~1.6°C; others up to ~3°C.

For the latest IPCC report, many higher-sensitivity models were down-weighted because they simulated more warming than observed in the recent historical record when driven by past emissions. This led to a TCR range of 1.2–2.4°C with a best estimate of 1.8°C.

Gyuleva’s team took two main steps:

Accounting for natural variability

Using Earth’s energy imbalance

Combining both approaches, the better-performing models give a TCR of 1.9–2.6°C, with a best estimate of 2.25°C (roughly 25% higher than the prior central estimate). The authors note that the results still need confirmation from additional independent lines of evidence.

In short, the research argues that previously discounted higher-sensitivity models better match both adjusted historical temperatures and satellite energy-budget trends, implying substantially more warming for any given emissions path than many current policy-relevant projections assume.

_____________________________________________________________________________________

Recent Temperature and Energy Imbalance Trends Point to Higher Estimates of Future Warming

“Recent Temperature and Energy Imbalance Trends Point to Higher Estimates of Future Warming” is a 2026 paper by Gergana Gyuleva, Erich Fischer, Reto Knutti (ETH Zurich), and Sebastian Sippel (Leipzig University), published in Earth’s Future (DOI: 10.1029/2026EF008356).

It is the study covered in the New Scientist article discussed previously. The paper addresses an apparent discrepancy in how well climate models match observations and revises upward estimates of the Transient Climate Response (TCR)—the near-term global warming expected from a doubling of atmospheric CO₂.

Climate models produce a wide range of TCR values (roughly 1–3 K, or more specifically ~1.3–3 K in the relevant ensembles). Earlier “emergent constraints” based on historical surface temperature trends consistently favored the lower end of this range and tended to down-weight or exclude high-sensitivity (“hot”) models. In contrast, recent observations of Earth’s energy imbalance (EEI—the difference between incoming solar radiation and outgoing long-wave radiation at the top of the atmosphere) show that models with higher TCR better reproduce the observed EEI trends.

The authors reconcile the discrepancy in two main ways:

  • They improve temperature-based constraints by statistically removing the influence of internal (natural) variability from global temperature and EEI records, using spatial surface temperature anomalies. Earlier constraints were biased low because of cooling contributions from variability (e.g., patterns related to La Niña) in recent decades, particularly around 1981–2014.
  • They develop an EEI-based constraint, focusing on trends in the short-wave and long-wave components of the energy imbalance. These show larger inter-model spread than surface temperature alone and therefore have strong potential to constrain future warming.

When both approaches are applied—especially to the more recent period (~2001–2024/2025)—variability-adjusted temperature trends and EEI trends both support higher TCR values.

The paper provides a revised TCR range of 1.9–2.6 K.

This aligns with the New Scientist reporting of a best estimate around 2.25°C (roughly 25% higher than the prior central estimate of ~1.8°C) and the implication that, for a given emissions pathway, expected warming is substantially higher (e.g., shifting a 2.6°C-by-2100 projection under current policies toward ~3.25°C).

Implications and caveats

  • Constraints based on EEI are presented as a valuable additional line of observational evidence alongside temperature trends.
  • Previous TCR constraints need to be revised upward due to the combined effects of accounting for variability and incorporating EEI data.
  • Shorter, more recent trends carry greater uncertainty.
  • The results make it increasingly difficult to exclude high-sensitivity models from the plausible range of future warming.
  • The authors (and external commentators in related coverage) note that further independent confirmation is still needed.

In short, the paper argues that once natural variability is properly filtered and the fundamental planetary energy imbalance is taken into account, both recent temperature and radiation observations point to stronger future warming than many earlier observational constraints had suggested.

Climate sensitivity measures how much the Earth’s global average surface temperature rises in response to a sustained doubling of atmospheric CO₂ (from pre-industrial levels of ~280 ppm to ~560 ppm). It is one of the most important numbers in climate science because it helps translate emissions pathways into expected warming.

Yes, the authors know what they are doing — this is competent, mainstream climate science from a strong group. But a skeptical reading is still warranted, and the paper does not magically close the case on higher future warming.

Reto Knutti (ETH Zurich) is one of the more careful and prominent voices on climate sensitivity, model evaluation, and uncertainty quantification. He has a long track record in IPCC work and has repeatedly warned against over-interpreting single lines of evidence or “hot models.”

The co-authors (Gyuleva, Fischer, Sippel) are active researchers in the same community, working on internal variability, emergent constraints, and observational constraints.

This is not fringe work. It was published in Earth’s Future and presented at EGU. The authors themselves are cautious in places, noting that results need confirmation from independent evidence and that shorter recent trends carry more uncertainty.

What the paper does well

  • It directly confronts a real tension: temperature-based emergent constraints had been pushing TCR downward, while Earth’s energy imbalance (EEI) trends (especially the short-wave/long-wave split) favor higher-sensitivity models.
  • Attempting to remove internal variability (the “hiatus”-era cooling influence) from the temperature record is a legitimate and necessary step. Natural variability can bias short-to-medium-term trend constraints.
  • Using satellite EEI data (CERES era, roughly post-2001) as an additional constraint is scientifically interesting because the energy imbalance is a more fundamental quantity than surface temperature alone.

Legitimate grounds for skepticism

Short observational record for EEI
Reliable continuous satellite EEI data essentially begin around 2000–2001. A ~20–25 year trend is still short in climate terms. Internal variability, potential small instrumental drifts, and the influence of recent large El Niño events can still matter. The paper acknowledges higher uncertainty on the more recent periods.

Method dependence of the variability filter
Statistically “removing” internal variability from global temperature using spatial patterns is clever, but it is not unique or assumption-free. Different statistical choices or different periods can change the residual forced trend. Previous temperature-based constraints that pointed lower were not obviously incompetent; they used different (also defensible) approaches.

Emergent constraints have a mixed track record
These methods can be sensitive to the model ensemble, the exact period, and shared model biases. They have sometimes been revised when new data or better understanding arrived. A single new constraint that moves the range upward should be treated as one update, not a final answer.

Still within (or only modestly above) previous ranges
Their revised TCR of 1.9–2.6°C sits mostly inside or only modestly above the IPCC AR6 very likely range of 1.2–2.4°C. It is not a radical leap into completely new territory. The “25% higher” framing in media coverage is eye-catching but depends on which baseline you choose.

Broader context still matters more than one paper
Climate sensitivity assessments rest on multiple lines of evidence (process understanding, paleoclimate, historical energy budget, models, etc.). AR6’s ECS best estimate of ~3°C and TCR of ~1.8°C reflected that synthesis. One solid paper using recent EEI + adjusted temperatures is useful, but it does not automatically override the rest of the evidence base. Other recent emergent-constraint studies have not all shifted upward by the same amount.

Recent rapid warming and rising EEI are real — but the attribution is not trivial
Earth’s energy imbalance has increased noticeably. Part of this is expected from rising greenhouse gases; part may involve aerosol changes, cloud responses, or pattern effects. Whether it cleanly implies a permanently higher TCR/ECS, or partly reflects transient factors, is still being debated.

The authors are skilled researchers doing serious work on a hard problem. The paper is a legitimate contribution that highlights why earlier temperature-only constraints may have been biased low and why EEI data deserve more weight. It is reasonable to take the upward pressure on TCR estimates seriously.

At the same time, climate sensitivity has resisted precise narrowing for decades.

Short satellite records, statistical filtering choices, model dependence, and the usual difficulties of separating forced response from variability all introduce residual uncertainty.

Treat this as an important data point that shifts the distribution somewhat higher, not as definitive proof that “official projections are 25% too low.”

Healthy scientific skepticism here means:

  • Respect the expertise and the methods.
  • Demand replication and integration with other lines of evidence.
  • Avoid both dismissive rejection and media-style overclaiming.

The field will sort this out the usual way — through further papers, longer records, and the next major assessment.

Published: Earth’s Future

DOI: 10.1029/2026EF008356

Provided: New Scientist (19 August 2026)

Authors: Gergana Gyuleva, Erich Fischer, Reto Knutti (ETH Zurich), and Sebastian Sippel (Leipzig University)

Abstract

Climate models simulate a wide range of 21st century warming for a given forcing scenario. Constraining this uncertainty is a central challenge in climate science because of its implications for climate policy and adaptation. The transient climate response (TCR) is a key idealized metric used to quantify future warming in response to an exponentially increasing CO2 concentration. Climate models span a range of 1.3–3 K for TCR. In attempts to constrain this range, emergent constraints on TCR based on historical temperature trends consistently pointed toward TCR values at the lower end of the range of models. However, recent evidence from trends in the short-wave and long-wave components of Earth’s energy imbalance (EEI) at the top-of-atmosphere suggests that models with higher TCR lie closer to the observed EEI trends. Here, we reconcile this discrepancy and provide a revised range for TCR of 1.9–2.6 K. Using a statistical variability-filtering approach, we show that previous temperature-based constraints were biased low due to internal variability, according to our method. We then provide an EEI-based constraint and demonstrate that short- and long-wave EEI trends have a strong potential to constrain future warming, due to the much larger inter-model spread in EEI compared to surface temperature. When considering the recent 2001–2025 period, our results show that both surface temperature and EEI trends support higher TCR values than previously estimated. This result implies that it is increasingly difficult to exclude high climate sensitivity models from the plausible range of future warming.

Why Winter Rainfall Forecasts Remain Uncertain in a Warming World”

Rainfall (precipitation) prediction remains challenging in a warming world primarily because of uncertainties in how large-scale atmospheric circulation patterns will shift, even as basic thermodynamic (moisture-related) changes are more predictable.

Rainfall (precipitation) prediction remains challenging in a warming world primarily because of uncertainties in how large-scale atmospheric circulation patterns will shift, even as basic thermodynamic (moisture-related) changes are more predictable.

A major 2026 study led by the University of Oxford and ETH Zurich (published in Nature) analyzed Northern Hemisphere winter rainfall (1950–2022) and separated these influences. Models capture thermodynamic changes well but significantly underrepresent circulation-driven trends—for example, simulating only about 10% of the observed circulation-driven rainfall trend in Southern Europe. This gap limits confidence in regional forecasts of floods and droughts.

Regional details (e.g., exact shifts in monsoon strength, mid-latitude storm tracks, or subtropical drying) carry lower confidence, especially for specific locations and seasons. This is why IPCC reports often show wider uncertainty ranges or hatching (low model agreement) for precipitation projections compared to temperature.

_____________________________________________________________________________________

Uncertain dynamic response of mid-latitude winter precipitation

“Uncertain dynamic response of mid-latitude winter precipitation” is the title of a peer-reviewed paper published in Nature on April 29, 2026, led by Lei Gu (University of Oxford, formerly ETH Zurich) along with co-authors including Dominik L. Schumacher, Erich M. Fischer, and Reto Knutti.

Understanding changes in precipitation is crucial for society and ecosystems. Previous studies have examined contributions from anthropogenic forcing versus internal variability, but discrepancies remain between observed and simulated patterns. In Northern Hemisphere winter, mismatches are often blamed on unforced internal variability. However, evidence suggests models may underestimate the total precipitation response to human forcings.

The study shows that thermodynamic contributions (related to moisture and temperature changes) are broadly reproduced by climate models. In contrast, dynamic contributions (shifts in large-scale atmospheric circulation) diverge more substantially. The authors disentangle forced thermodynamic and dynamic components from internal variability in winter precipitation trends (1950–2022).

Key finding in the Mediterranean:

The forced dynamic signal in model simulations explains only about 10% of the observed dynamic trend, making detection challenging. Under continued emissions, projected circulation responses intensify and increasingly resemble observed patterns—though internal variability may still dominate observations. Improving the representation of forced large-scale circulation changes in models is essential for better regional projections.

Core Approach and Methods

  • Decomposition: Researchers separated precipitation trends into:
    • Thermodynamic: Moisture-related effects from warming (e.g., higher atmospheric water-holding capacity).
    • Dynamic: Changes in circulation patterns (e.g., jet stream, storm tracks, pressure systems like the North Atlantic Oscillation) that steer where moisture is delivered.
    • Internal variability: Natural fluctuations unrelated to long-term forcing.
  • They used observations (e.g., ERA5, MSWEP, other reanalyses and datasets), CMIP6 models, large ensembles (e.g., CESM2-LE), and nudged simulations to isolate signals.
  • Analysis focused on Northern Hemisphere mid-latitude winter trends over ~70+ years.

This work directly addresses why regional rainfall projections, especially in mid-latitudes during winter, carry high uncertainty despite clearer global thermodynamic signals.

Dynamic uncertainties stem from:

  • Strong natural decadal variability in circulation.
  • Potential model underestimation of how circulation responds to greenhouse gases, aerosols, or other forcings.
  • Challenges in distinguishing forced trends from noise in relatively short observational records. bioengineer.org

The study suggests an emerging forced dynamic signal under further warming, but confidence remains limited. This has implications for predicting shifts in storm tracks, Mediterranean drying, European rainfall patterns, and risks of extremes (floods/droughts).

It aligns with prior findings on circulation biases in models (e.g., North Atlantic jet stream) and complements research on why mean precipitation and extremes are harder to project regionally than temperature. Data and code are available via Zenodo and OSF repositories for reproducibility.

The full paper is behind a paywall on Nature.com, but the abstract, figures (showing observed vs. simulated trends and decompositions), and supplementary information provide substantial detail. News summaries from Oxford, EurekAlert, and others offer accessible overviews.

This paper advances attribution science and highlights priorities for model improvement ahead of future CMIP phases.

Uncertain dynamic response of mid-latitude winter precipitation

Published: Nature (2026)

Journal information: Nature

peer-reviewed

DOI: 10.1038/s41586-026-10474-y

Provided by University of Oxford

Authors: Lei Gu, 
Dominik L. Schumacher, 
Sebastian Sippel, 
Erich M. Fischer, 
Istvan Dunkl, 
Robin Noyelle, 
Jitendra Singh, 
Lorenzo Pierini & 
Reto Knutti

Abstract

Understanding changes in precipitation is crucial for society and ecosystems1,2. Studies have documented the respective contributions of anthropogenic forcing and internal variability to precipitation trends3,4, yet discrepancies persist between observed and simulated patterns. In Northern Hemisphere winter, these mismatches are often attributed to unforced internal variability that dominates observed trends5. However, growing evidence also indicates that climate models underestimate the total response of precipitation to human forcings6,7,8. Here we show that the thermodynamic contribution is broadly reproduced by climate models, whereas the dynamic contribution can diverge more substantially. Our approach disentangles the anthropogenic forced thermodynamic and dynamic components from internal variability in winter precipitation trends (1950–2022) to investigate their contribution to the trend discrepancies. In the Mediterranean, the forced dynamic signal from model simulations explains only about 10% of the observed dynamic trend, making detection challenging. Under continued anthropogenic emissions, the projected circulation response intensifies and more closely resembles observed trend patterns. Although internal variability in the observed record may contribute to this similarity, the results indicate an uncertain yet potentially emerging role of dynamic response in shaping regional winter precipitation trends. A reliable representation of the forced large-scale circulation response in climate models remains key for increasing confidence in regional precipitation projections.

Scare du jour Marine Heat Waves

Marine heatwaves are influenced by natural climate variability, such as El Niño, Pacific Decadal Oscillation (PDO), and other oceanic and atmospheric processes. These phenomena can cause fluctuations in sea surface temperatures independent of long-term climate change, leading to periods of warmer ocean conditions that may be mistaken for climate-driven trends.

An erupting underwater volcano shooting steam and ash while large waves crash against a rocky formation.

From Science Matters

By Ron Clutz

A tranquil ocean scene with clear blue water and a bright sky, overlaid with bold red text asking if the ocean is boiling.

If you watch legacy media, you must also be wondering after seeing all the current headlines about Marine Heat Waves raising the ocean to its boiling point.

Ocean heatwaves are breaking Earth’s hidden climate engine, Science Daily

The Pacific Ocean is overheated, making fall feel like summer, CBC

The ‘blob’ is back — except this time it stretches across the entire north Pacific, CNN

Record marine heatwaves may signal a permanent shift in the oceans, New Scientist

Global warming drives a threefold increase in persistence and 1 °C rise in intensity of marine heatwaves, PNAS

Etc., etc. etc.

The last one is the paper driving this recent clamor over Ocean SSTs Marcos et al. 2025 From the abstract:

We determine that global warming is responsible for nearly half of these extreme events and that, on a global average, it has led to a three-fold increase in the number of days per year that the oceans experience extreme surface heat conditions. We also show that global warming is responsible for an increase of 1 °C in the maximum intensity of the events. Our findings highlight the detrimental role that human-induced global warming plays on marine heatwaves. This study supports the need for mitigation and adaptation strategies to address these threats to marine ecosystems.

The coordinated media reports are exposed by all of them containing virtually the same claim:

As climate change causes our planet to warm, marine heatwaves are
becoming more frequent, more intense, and longer lasting. 

A global map displaying the Marine Heatwave Category for January 11, 2025, highlighting areas with extreme, severe, strong, and moderate heatwave conditions using a color gradient from yellow to red.
Animation shows locations of moderate to severe MHWs mid-month 2025 January to October. A marine heatwave is defined as one where the measured temperature is within 10% of the maximum values observed (i.e., above the Threshold (90th quantile) , for at least 5 consecutive days. For this, the intensity is compared to the difference between the climatological mean and the 90th percentile value (threshold). A marine heatwave intensity between 1 and 2 times this difference corresponds to a heatwave of moderate category; between 2 and 3 times, to a strong category; between 3 and 4 times, to a severe category; and a difference greater than 4 times corresponds to an extreme category.

First some background context on the phenomena (in italics with my bolds).

Background from perplexity.ai How Do Warm and Cool Ocean Blobs Circulate?

Warm and cool ocean blobs circulate through distinct oceanic and atmospheric processes, often linked to major currents and atmospheric patterns.

Warm ocean blobs, such as the “warm blob” in the northeast Pacific, form due to atmospheric circulation changes triggered by factors like Arctic warming. This leads to a high-pressure system over the region, weakening westerly winds and reducing ocean heat loss, causing surface waters to warm and creating persistent warm anomalies. The formation of these warm blobs involves a feedback loop between weakened winds, reduced ocean-atmosphere heat exchange, and ocean circulation, which retains heat in the mixed layer of the ocean.

Cool ocean blobs, like the North Atlantic “cold blob,” are influenced by weakening of the Atlantic Meridional Overturning Circulation (AMOC). This circulation moves warm, salty water northward, which cools, sinks, and then the cooler deep-water travels southward in a conveyor-belt style flow. The cold blob forms when excess freshwater from ice melt dilutes the salty water, reducing its density and sinking ability, weakening this circulation and causing cooler surface water to persist. This cooling also affects the atmosphere by reducing water vapor, which decreases greenhouse effect locally and amplifies the cold anomaly, creating a coupled ocean-atmosphere feedback loop.

In summary, warm and cool ocean blobs circulate through a combination of ocean current dynamics and atmospheric interactions. Warm blobs form where atmospheric changes reduce ocean heat loss and circulation shifts retain heat, while cool blobs occur where circulation weakens, allowing cooler, less dense waters to persist and affect atmospheric conditions as well.

Illustration comparing hurricane behavior with warm and cool seawater mixing, highlighting how warmer surface waters strengthen hurricanes and cooler waters weaken them.

Then a summary of the issues undermining the alarmists’ claim.

From perplexity.ai What are reasons to doubt climate change is increasing marine heatwaves?

There are several reasons to doubt that climate change is definitively increasing the frequency, intensity, duration, and spatial extent of marine heatwaves, based on some ongoing scientific debates and uncertainties.

Natural Variability and Other Factors

♦  Marine heatwaves are influenced by natural climate variability, such as El Niño, Pacific Decadal Oscillation (PDO), and other oceanic and atmospheric processes. These phenomena can cause fluctuations in sea surface temperatures independent of long-term climate change, leading to periods of warmer ocean conditions that may be mistaken for climate-driven trends.

♦  Some studies emphasize the role of internal ocean variability, which can cause significant short-term temperature anomalies without requiring a direct link to anthropogenic climate change.

Complexity of Attribution

♦  The attribution of marine heatwave trends specifically to climate change involves complex modeling and statistical analysis, which can have uncertainties. Certain models suggest that long-term temperature increases are the primary driver, but the contribution of natural variability remains significant and sometimes difficult to separate clearly from climate signals.

♦  Regional differences and localized oceanic processes can obscure the global patterns, leading some scientists to argue that not all observed phenomena are directly attributable to climate change, particularly in areas with strong natural variability.

Limitations of Climate Models

♦  Climate models predicting future marine heatwave conditions depend heavily on assumptions about greenhouse gas emissions and other factors. These models often have limitations in resolution and in capturing small-scale processes, which could lead to overestimations or underestimations of climate change impacts.

Data Gaps and Uncertainties

♦  Although current observations show increasing trends in marine heatwaves, data gaps exist, especially in remote or deep-sea regions, making comprehensive global assessments challenging. These gaps contribute to uncertainty regarding the full extent and causality of observed changes.

♦  The precise long-term ecological impacts and possible adaptation or resilience mechanisms of marine ecosystems also remain uncertain, complicating the understanding of climate change’s role versus natural variability.

Summary

While a considerable body of evidence supports the role of climate change in increasing marine heatwaves, skepticism persists due to the influence of natural variability, model limitations, regional differences, and data gaps. These factors suggest that attribution is complex, and ongoing research continues to refine our understanding of the relative contributions of human influences and natural climate fluctuations.

Graph illustrating marine heatwaves, showing temperature fluctuations over time, key metrics like start and end dates, peak intensity, and the climatological mean.

Finally, a discussion of a specific example revealing flawed methods supposedly connecting CO2 emissions to marine heatwaves.

Much Ado About Marine Heat Waves

The promotion of this scare was published in 2022 at Nature by Barkhordarian et al. Recent marine heatwaves in the North Pacific warming pool can be attributed to rising atmospheric levels of greenhouse gases.  This post will unpack the reasons to distrust this paper and its claims.  First the Abstract of the subject and their declared findings in italics with my bolds.

Abstract

Over the last decade, the northeast Pacific experienced marine heatwaves that caused devastating marine ecological impacts with socioeconomic implications. Here we use two different attribution methods and show that forcing by elevated greenhouse gases levels has virtually certainly caused the multi-year persistent 2019–2021 marine heatwave. 

There is less than 1% chance that the 2019–2021 event with ~3 years duration and 1.6 ∘C intensity could have happened in the absence of greenhouse gases forcing. 

We further discover that the recent marine heatwaves are co-located with a systematically forced outstanding warming pool, which we attribute to forcing by elevated greenhouse gases levels and the recent industrial aerosol-load decrease.

The here-detected Pacific long-term warming pool is associated with a strengthening ridge of high-pressure system, which has recently emerged from the natural variability of climate system, indicating that they will provide favorable conditions over the northeast Pacific for even more severe marine heatwave events in the future.

Map showing sea surface temperature anomalies in the Pacific Ocean on January 30, 2014, with color-coded areas indicating varying temperature changes.
Background on Ocean Warm Pools

Wang and Enfield study is The Tropical Western Hemisphere Warm Pool Abstract in italics with my bolds.

Abstract

The Western Hemisphere warm pool (WHWP) of water warmer than 28.5°C extends from the eastern North Pacific to the Gulf of Mexico and the Caribbean, and at its peak, overlaps with the tropical North Atlantic.

It has a large seasonal cycle and its interannual fluctuations of area and intensity are significant. Surface heat fluxes warm the WHWP through the boreal spring to an annual maximum of SST and areal extent in the late summer/early fall, associated with eastern North Pacific and Atlantic hurricane activities and rainfall from northern South America to the southern tier of the United States. 

SST and area anomalies occur at high temperatures where small changes can have a large impact on tropical convection. Observations suggest that positive ocean-atmosphere feedback operating through longwave radiation and associated cloudiness is responsible for the WHWP SST anomalies. Associated with an increase in SST anomalies is a decrease in atmospheric sea level pressure.

Chou and Chou published On the Regulation of the Pacific Warm Pool Temperature:

Abstract

Analyses of data on clouds, winds, and surface heat fluxes show that the transient behavior of basin-wide large-scale circulation has a significant influence on the warm pool sea surface temperature (SST). Trade winds converge to regions of the highest SST in the equatorial western Pacific.

The reduced evaporative cooling due to weakened winds exceeds the reduced solar heating due to enhanced cloudiness. The result is a maximum surface heating in the strong convective and high SST regions. The maximum surface heating in strong convective regions is interrupted by transient atmospheric and oceanic circulation.

 Regions of high SST and low-level convergence follow the Sun. As the Sun moves away from a convective region, the strong trade winds set in, and the evaporative cooling enhances, resulting in a net cooling of the surface. We conclude that the evaporative cooling associated with the seasonal and interannual variations of trade winds is one of the major factors that modulate the SST distribution of the Pacific warm pool.

Comparison of sea surface temperature anomalies in the North Pacific Ocean between September 2014 and September 2019, illustrating the development of the 'Blob' and current conditions.
Comment:

So these are but two examples of oceanographic studies describing natural factors driving the rise and fall of Pacific warm pools.  Yet the Nature paper claims rising CO2 from fossil fuels is the causal factor, waving away natural processes.  Skeptical responses were already lodged upon the first incidence of the North Pacific marine heat wave, the “Blob” much discussed by west coast US meteorologists.  One of the most outspoken against the global warming attributionists has been Cliff Mass of Seattle and University of Washington.  Writing in 2014 and 2015, he observed the rise and fall of the warming blob and then posted a critique of attribution attempts at his blog.  For example, Media Miscommunication about the Blob.  Excerpts in italics with my bolds.

Blob Media Misinformation

One of the most depressing things for scientists is to see the media misinform the public about an important issue.

During the past few days, an unfortunate example occurred regarding the warm water pool that formed over a year ago in the middle of the north Pacific, a.k.a., the blob. Let me show how this communication failure occurred, with various media outlets messed things up in various ways.

The stimulant for the nationwide coverage of the Blob was a very nice paper published by Nick Bond (UW scientist and State Climatologist), Meghan Cronin, Howard Freeland, and Nathan Mantua in Geophysical Research Letters.

This publication described the origin of the Blob, showing that it was the result of persistent ridging (high pressure) over the Pacific. The high pressure, and associated light winds, resulted in less vertical mixing of the upper layer of the ocean, with less mixing of subsurface cold water to the surface. Furthermore, the high pressure reduced horizontal movement of colder water from the north. Straightforward and convincing work.

'Warm blob' in Pacific Ocean linked to weird weather across the U.S. - Article summarizing the impact of the temperature anomaly on weather patterns, highlighting warm water off the West Coast.

The inaccurate press release then led to a media frenzy, with the story going viral. And unfortunately, many of the media got it wrong.

A map showcasing sea surface temperature anomalies in the North Pacific Ocean for the year 2010, with various hues indicating temperature ranges.

There were two failure modes. In one, the headline was wrong, but the internal story was correct. . . In the second failure mode, the story itself was essentially flawed, with most claiming that the Blob off of western North America was the cause of the anomalous circulation (big ridge over West Coast, trough over the eastern U.S.). (The truth: the Blob was the RESULT of the anomalous circulations.) That the Blob CAUSED the California drought or the cold wave in the eastern U.S. These deceptive stories were found in major outlets around the country, including the Washington Post, NBC News, and others.

Blob Returns,  Attribution Misinformation

When the Blob returned 2020-2021, Cliff Mass had cause to again lament how the public is misled.  This time misdirection instigated by activist scientists using flawed methods.  His post Miscommunication in Recent Climate Attribution Studies.  Excerpts in italics with my bolds.

A news headline from The Washington Post discussing the link between the Pacific Northwest heat wave and climate change.

This attribution report, and most media stories that covered it, suggested a central role for global warming for the heatwave. As demonstrated in my previous blog, their narrative simply does not hold up to careful examination.

This blog will explain why their basic framing and approach is problematic, leading readers (and most of the media) to incorrect conclusions.

For the heatwave, the attribution folks only examine the statistics of temperatures hitting the record highs (108F in Seattle), but avoid looking at the statistics of temperature exceeding 100F, or even the record highs (like 103F in Seattle). There is a reason they don’t do that. It would tell a dramatically different (and less persuasive) story.

In the attribution studies, the main technology for determining changed odds of extreme weather is to use global climate models. First, they run the models with greenhouse gas forcing (which produces more extreme precipitation and temperature), and then they run the models again without increased greenhouse gases concentrations. By comparing the statistics of the two sets of simulations, they attempt to determine how the odds of extreme precipitation or temperature change.

Unfortunately, there are serious flaws in their approach: climate models fail to produce sufficient natural variability (they underplay the black swans) and their global climate models don’t have enough resolution to correctly simulate critical intense, local precipitation features (from mountain enhancement to thunderstorms). On top of that, they generally use unrealistic greenhouse gas emissions in their models (too much, often using the RCP8.5 extreme emissions scenario) And there is more, but you get the message. ( I am weather/climate modeler, by the way, and know the model deficiencies intimately.)

Vaunted Fingerprinting Attribution Is Statistically Unsound

From Barkhordarian et al.

Unlike previous studies which have focused on linking the SST patterns in the North Pacific to changes in the oceanic circulation and the extratropical/tropical teleconnections2,12,17,18,20,24,26, we here perform two different statistical attribution methodologies in order to identify the human fingerprint in Northeast Pacific SST changes both on multidecadal timescale (changes of mean SST) and on extreme SST events on daily timescale (Marine Heatwaves).

Evidence that anthropogenic forcing has altered the base state (long-term changes of mean SST) over the northeast Pacific, which is characterized by strong low-frequency SST fluctuations, would increase confidence in the attribution of MHWs27, since rising mean SST is the dominant driver of increasing MHW frequency and intensity, outweighing changes due to temperature variability1,2.

In this study, we provide a quantitative assessment of whether GHG forcing, the main component of anthropogenic forcings, was necessary for the North Pacific high-impact MHWs (the Blob-like SST anomalies) to occur, and whether it is a sufficient cause for such events to continue to repeatedly occur in the future. With these purposes, we use two high-resolution observed SST datasets, along with harnessing two initial-condition large ensembles of coupled general circulation models (CESM1-LE28,29 with 35 members, and MPI-GE30 with 100 members). 

These large ensembles can provide better estimates of an individual model’s internal variability and response to external forcing31,32, and facilitate the explicit consideration of stochastic uncertainty in attribution results33. We also use multiple single-forcing experiments from the Detection and Attribution Model Intercomparision Project (DAMIP34) component of Coupled Model Intercomparison Project phase 6 (CMIP635).

A screenshot of academic references and section navigation in a scientific article or paper.
From Barkhordarian et al. References

The IPCC’s attribution methodology is fundamentally flawed

The central paper underpinning the attribution analysis was assessed and found unreliable by statistician Ross McKitrick’s published evaluation. Excerpts in italics with my bolds.

One day after the IPCC released the AR6 I published a paper in Climate Dynamics showing that their “Optimal Fingerprinting” methodology on which they have long relied for attributing climate change to greenhouse gases is seriously flawed and its results are unreliable and largely meaningless. Some of the errors would be obvious to anyone trained in regression analysis, and the fact that they went unnoticed for 20 years despite the method being so heavily used does not reflect well on climatology as an empirical discipline.

My paper is a critique of “Checking for model consistency in optimal fingerprinting” by Myles Allen and Simon Tett, which was published in Climate Dynamics in 1999 and to which I refer as AT99. Their attribution methodology was instantly embraced and promoted by the IPCC in the 2001 Third Assessment Report (coincident with their embrace and promotion of the Mann hockey stick). The IPCC promotion continues today: see AR6 Section 3.2.1. It has been used in dozens and possibly hundreds of studies over the years. Wherever you begin in the Optimal Fingerprinting literature (example), all paths lead back to AT99, often via Allen and Stott (2003). So its errors and deficiencies matter acutely.

Abstract

Allen and Tett (1999, herein AT99) introduced a Generalized Least Squares (GLS) regression methodology for decomposing patterns of climate change for attribution purposes and proposed the “Residual Consistency Test” (RCT) to check the GLS specification. Their methodology has been widely used and highly influential ever since, in part because subsequent authors have relied upon their claim that their GLS model satisfies the conditions of the Gauss-Markov (GM) Theorem, thereby yielding unbiased and efficient estimators.

But AT99:

  • stated the GM Theorem incorrectly, omitting a critical condition altogether,
  • their GLS method cannot satisfy the GM conditions, and
  • their variance estimator is inconsistent by construction.
  • Additionally, they did not formally state the null hypothesis of the RCT nor
  • identify which of the GM conditions it tests, nor
  • did they prove its distribution and critical values, rendering it uninformative as a specification test.

The continuing influence of AT99 two decades later means these issues should be corrected. I identify 6 conditions needing to be shown for the AT99 method to be valid.”

In Conclusion,  McKitrick:

One point I make is that the assumption that an estimator of C provides a valid estimate of the error covariances means the AT99 method cannot be used to test a null hypothesis that greenhouse gases have no effect on the climate. Why not? Because an elementary principle of hypothesis testing is that the distribution of a test statistic under the assumption that the null hypothesis is true cannot be conditional on the null hypothesis being false. The use of a climate model to generate the homoscedasticity weights requires the researcher to assume the weights are a true representation of climate processes and dynamics.

The climate model embeds the assumption that
greenhouse gases have a significant climate impact.

Or, equivalently, that natural processes alone cannot generate a large class of observed events in the climate, whereas greenhouse gases can. It is therefore not possible to use the climate model-generated weights to construct a test of the assumption that natural processes alone could generate the class of observed events in the climate.

Uncertainty In Natural Forcing From Wildfire, Dust Aerosols 2 Times Larger Than Total CO2 Forcing

The radiative effect of natural wildfire aerosol forcing alone can be said to fully cancel out the total accumulated forcing from 170 years of CO2 increases in the current climate.

From NoTricksZone

By Kenneth Richard

The radiative effect of natural wildfire aerosol forcing alone can be said to fully cancel out the total accumulated forcing from 170 years of CO2 increases in the current climate.

It has been estimated that the total change in climate forcing (radiation imbalance) from the 1750 to present CO2 concentration increase has been 1.82 W/m².

Image Source: Feldman et al., 2015

New research suggests today’s climate models omit an “important perturbation to Earth’s energy balance” because they fail to include the natural climate forcing potential from aerosols produced by wildfires and dust loading.

Climate models expressly assume this non-volcanic natural aerosol forcing is not present, or 0.0 W/m², even though it has been estimated that aerosol forcing from wildfire emissions alone – which were much higher during the preindustrial period before declining to today’s levels – deliver a “radiative effect in the current climate of -2 W/m².” Therefore, this natural climate forcing effect can be said to, by itself, fully cancel the surface forcing from the post-industrial CO2 increases.

Furthermore, the estimated 4 W/m² uncertainty in preindustrial-to-present (1850-) aerosol climate forcing from natural wildfire emissions and dust is also over two times larger than 270 years of CO2 concentration increases (1.82 W/m² since 1750). This effectively means that anthropogenic CO2 forcing is too uncertain to clearly separate from natural aerosol forcing.

Image Source: Mahowald et al., 2024

Below are key quotes from this research paper.

1) Regarding the natural aerosol forcing uncertainty missing in the models…

“The uncertainty in aerosol emissions that are usually considered natural, such as from dust storms or wildfires, is likely larger and contributes to larger uncertainties in aerosol radiative forcing.”

“There are no estimates of radiative forcing for important aerosols such as wildfire and dust aerosols in most paleoclimate time periods.”

“…dust deposition records [are] not captured in climate models. These models therefore also predict a dust-climate feedback that is indistinguishable from zero. As such, not explicitly accounting for dust changes as a radiative forcing has the net effect of omitting this potentially important perturbation to Earth’s energy balance.”

“…here we are assuming, without prior information, that the radiative forcing of any paleo-time period relative to the preindustrial is around 0.0 W/m².”

“…we do not know the emissions [from wildfires or dust] well, nor the impact of those emissions on climate. We can envision these uncertainties as mostly unknown.”

2) Regarding the larger radiative forcing impact and uncertainty values of natural aerosols than CO2 forcing…

“In addition, wildfires and open fires represent some of the most important aerosols for direct and aerosol-cloud radiative effects, with a total radiative effect in the current climate of -2 W/m².”

“The total unconstrained uncertainty due to aerosol changes [since 1850] could be estimated as being 4 W/m².”

“…dust [radiative forcing] was likely 2-4 times higher in the Last Glacial Maximum [~25,000 to 17,000 years ago] than it is today.”

“From the limited literature we crudely estimate a paleoclimate aerosol uncertainty for the Last Glacial Maximum relative to preindustrial [1850] of 4.8 W/m².”

“Paleoclimate data from charcoal records suggest a maximum in open fires in the 1850s and a decrease since then. Satellite data show a global decrease in burned area over the last decades…”

“…the 1970s is a time period of Sahel drought, and dust radiative forcing between the 1960s and 1980s changed by perhaps -0.57 ±0.46 W/m².”

acp-24-533-2024Herunterladen