Climate Modelling

Weather simulation—being governed by chaos theory—is inherently unpredictable beyond a short-term horizon, for even relatively simple models. And contrary to contemporary claims, you cannot improve precision by reducing granularity of simulation—even if you do call it climate forecasting.

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Climate modeling relies on two core mechanisms: the Navier-Stokes¹ equations and Active Adaptive Management². The former describes the behaviour of incompressible fluids with time, adopted in more specialised circumstances to climate modelling—but it essentially comes back to the same mathematics. Adaptive management—through continuous prediction, execution, monitoring, and adjustment—is designed to iteratively refine its accuracy, but if this actually works, or just creates increasingly larger black swans³… well, that’s the question.

But while Navier-Stokes in general works—at least to some extent—for fluid dynamics at a local scale, applying them to global climate systems introduces serious complexities, especially given turbulence, nonlinear feedback loops, and chaotic elements. Climate models rely heavily on parameterisation, approximating small-scale processes that can’t be directly resolved, thus introducing a degree of uncertainty that adaptive management tries to correct through iterative feedback.

However, the assumption underlying adaptive management is that errors can be continuously reduced through better data collection, improved computing power, and refined models. But if the system itself is chaotic, small miscalculations can compound unpredictably⁴, leading to black swans rather than smoother adaptation. The biggest risk is that policymakers rely on these models as if they were deterministic, even though they inherently involve deep uncertainty.

The real question, then, is whether the feedback mechanism in active adaptive management actually enhances predictive accuracy or if it merely reinforces systemic biases and hidden errors, leading to greater overconfidence in projections. If the latter, then the very tools meant to mitigate risk could be accelerating unintended consequences.

The Earth system is so incredibly complex⁵ that even with state-of-the-art computing power, we are forced to take drastic shortcuts.

For the Earth system to be modelled even close to realistic, we are talking about more than 10^40 separately modelled particles, set in a reflexive environment where the continuous roll of waves and waving trees—though affected by the winds, also affect said winds back in return. And even if a model existed to handle this level of complexity, no computer could realistically perform these calculations in anywhere close to real-time. In fact, we aren’t even close, especially as the fourth dimension—time—presents yet another issue; that of time step resolution. Because at what resolution should discrete time steps be run at? Make it too short and complexity of calculation explodes. Too long and precision declines, with error accumulating rapidly. Consequently, shortcuts are taken. And in that regard, let’s consider the complexity of modelling in bullet form:

• Spatial precision matters greatly⁶.
  While nature operates with near-infinite locality, computer simulations are constrained by finite precision arithmetic, typically using 32- or 64-bit representations per dimensional component. This introduces limitations, particularly when there are differences in precision between position and velocity data, which can lead to cumulative rounding errors over time. In large-scale simulations like climate modeling, where billions of particles (grid points) interact, these errors compound rapidly, distorting long-term accuracy and contributing to numerical drift.

• Temporal precision also matters greatly⁷.
  As discrete time steps approach zero, computational expenditure often increases exponentially. Conversely, increasing time step size reduces precision, leading to numerical diffusion, instability, and a decline in simulation quality. Striking a balance is crucial, as too fine a resolution makes calculations infeasible, while too coarse a resolution allows error accumulation and loss of physical accuracy over time.

• The halfway house between reducing time steps (which increases computational cost) and keeping them too large (which reduces precision) is adaptive resolution, where velocity fields dictate granularity in both spatial and temporal precision. Higher velocities result in finer spatial grids and smaller time steps, while slower-moving regions operate at coarser resolutions. However, this approach introduces nonlinear error propagation, particularly at the interfaces between high- and low-resolution regions, where mismatches in time-stepping and spatial refinement create instabilities, numerical dissipation, and artificial reflections. These transition zones accumulate errors rapidly, as small phase shifts in wave propagation and asynchronous feedback loops can lead to cascading distortions over time.

• The quantity and density of particles (or components) in a simulation directly determine its accuracy, as finer granularity allows for more precise modeling of physical processes. However, increasing the particle count significantly raises computational costs, both in terms of memory and processing power. Additionally, higher spatial resolution often necessitates finer temporal resolution to maintain numerical stability, further compounding computational complexity and making large-scale simulations increasingly expensive.

• Reflexive arguments pertain to the dynamic nature of the environment, where even minor interactions—such as waves shifting, trees swaying, or a migratory bird dropping excreta in different locations—can introduce subtle but cumulative variations in outcomes. These seemingly insignificant factors contribute to feedback loops that influence larger atmospheric and climatic patterns. Accounting for such complexities may necessitate environmental simulations that incorporate finer-scale interactions, further compounding computational costs and adding another layer of uncertainty to predictive models.

• But even if we somehow managed to account for every atom on the planet, external influences such as gravitational perturbations from celestial bodies or even quantum-scale effects would still introduce uncertainties. While these factors may seem negligible in the short term, their impact can accumulate over time, leading to subtle but compounding deviations that further challenge the long-term accuracy of any simulation.

• All of the above illustrates why long-term weather prediction remains inherently limited in accuracy. While short-term forecasts (a few days ahead) can maintain some reliability within a range of error, beyond a week, the chaotic nature of the system causes distributions to scatter unpredictably, making predictions increasingly unreliable. Despite repeated claims of improving forecast precision, recent years have shown⁸ that even hurricane trajectories remain highly uncertain⁹, with significant deviations often occurring despite advanced modeling techniques¹⁰.

• The issue becomes even more stark when considering tornado paths¹¹, which remain virtually impossible to predict with any significant level of certainty¹². While hurricanes can be forecasted with some degree of reliability, these predictions are ultimately probabilistic, not deterministic. The fundamental limitation is tied to wind speed—as velocity increases, so does unpredictability. Once wind speeds exceed 50 mph, the system's chaotic nature rapidly amplifies errors, causing forecast accuracy to decline sharply, making precise trajectory predictions unreliable.

• A common argument for improving simulation stability involves filtering neighboring particles to smooth numerical instabilities. However, this approach is another error-introducing operation, often trading short-term accuracy for long-term stability—much like downscaling image quality to reduce visual noise at the cost of detail. This redistribution of precision is inherently destructive, as it does not eliminate errors but rather spreads and distorts them over time, leading to an overall decline in simulation quality. At best, it represents a trade-off between stability and accuracy, but nothing is gained without a corresponding loss.

Global Weather Models, like ECMWF¹³ or ICON¹⁴ typically operate on 3D datasets with a horizontal resolution ranging from 10 to 50 km in latitude and longitude, while regional simulations refine this to around 2–3 km per dimension. Time steps are often adaptive, adjusting based on local conditions—higher wind speeds and dynamic events necessitate finer temporal resolution. However, even with these refinements, the granularity remains extremely coarse compared to the video lip simulation , with common time steps ranging from 1 to 10 minutes, limiting the ability to capture rapid, small-scale atmospheric changes in full detail.

Climate models further scale these resolutions by roughly a factor of 10 in each dimension, including spatial and temporal precision. Given computational constraints, this means that in practice, no more than one simulated particle (grid point) per cubic kilometer is used—and often far fewer. Time steps are similarly coarse, typically no lower than one update per minute, making fine-scale interactions effectively unresolved.

Beyond these inherent limitations, boundary conditions introduce additional errors. Even if a simulation were perfectly modeled internally, flows transferred from the external environment carry inaccuracies, compounding uncertainty and disrupting the integrity of long-term predictions. This issue is particularly pronounced in regional climate models, which rely on data from global simulations that are themselves subject to parameterisation and uncertainty.

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And as for Climate Predictions:

• The process of converting weather data into climate data involves a significant reduction in overall data quality. As short-term fluctuations and high-frequency variations are smoothed out to establish long-term trends, fine-scale details are lost, leading to a net loss of information. This filtering process reduces granularity and precision, ultimately diminishing the accuracy of simulations by sacrificing localised variability in favor of broader statistical patterns.

• This conversion process is therefore a simplification—and a lossy one at that. By averaging out short-term variability, high-frequency details are discarded, reducing precision. While this—under some conditions—could help in identifying broader trends, it comes at the cost of losing localised complexity, which can significantly impact the accuracy of long-term projections.

• The simplified climate data effectively transforms into a bell curve, adopting a fundamentally different distribution from the original high-resolution weather data. While accuracy can be adjusted—either preserving finer details at the cost of broader trends or vice versa—the general loss of precision across the system is already significant at this stage. As a result, no guarantees can be made regarding the fidelity of long-term projections, as the underlying variability has been smoothed out, potentially obscuring critical dynamics.

• While it may appear more precise in the short term, this precision is illusory, as it is achieved by eliminating vast amounts of high-frequency information from the system. Since climate models simulate over much longer periods of time, the accumulated loss of detail introduces significant uncertainty.

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Validity of Data:

• Heat islands are not removed from climate datasets; instead, their effects are ‘normalised’ through modeled estimates. This approach almost inevitably introduces errors at best, while at worst, it provides yet another avenue for manipulation, enabling potential model-driven data fraud with complete impunity. A far more rigorous and transparent approach would be to exclude heat island data entirely, yet this option is not even considered—a fact that, frankly, speaks volumes about the integrity of the process.

• A significant percentage of temperature sensors are ‘proprietary’, meaning their data is not publicly accessible. This lack of transparency presents yet another open invitation for abuse. When conducting climate modeling with global implications, all data should be freely available for the widest possible dissemination and scrutiny. The existence of restricted, proprietary datasets in such critical modeling is indefensible—there should be absolutely no proprietary data in the database whatsoever.

• In fact, not only do these proprietary datasets comprise a large quantity, but the exact percentage remains undisclosed, and their specific locations are not publicly available. This lack of transparency raises serious concerns, as it suggests that large regions of climate data could be entirely fabricated through closed-source modeling with no way to verify accuracy. Without full disclosure, there is no way to ensure data integrity, leaving the system vulnerable to manipulation and unchecked biases.

• Metadata, in general, is not open-source either, creating yet another open invitation for abuse. Without full transparency, critical variables—such as the height of sensors above ground, sensor placement changes, or calibration adjustments—can be manipulated to distort average temperature readings. This lack of openly accessible metadata makes it impossible to independently verify whether adjustments are scientifically justified or simply a means of influencing climate model outputs.

• The quantity and locations of faulty sensors remain unknown, with no historical record documenting which sensors have been removed from the dataset, the reasons for their exclusion, when issues were detected, or how long a sensor may have been operating with faulty readings before its removal.

• There is also no publicly available track record detailing sensor and device calibration. Without access to this data, it is impossible to verify whether measurements have been consistently accurate, properly adjusted, or even tampered with. Calibration errors or inconsistencies can significantly impact recorded temperatures, yet the lack of transparency means that potential biases or faults in the dataset remain hidden from independent scrutiny.

• The density and distribution of sensors present another major issue, particularly in earlier years, when temperature monitoring was hyper-concentrated in the Western world. This geographical imbalance has resulted in significant historical data gaps, forcing climate models to rely heavily on synthetic, modeled data to fill in missing regions. Such interpolation introduces another layer of uncertainty, as these estimations are not direct measurements but algorithmic reconstructions, potentially amplifying biases in long-term climate records.

• Climate models are also not typically open-source, meaning their inner workings remain inaccessible to independent researchers and the public. Consequently, they can be extensively manipulated without external oversight, as there is no way to scrutinise or verify how inputs are processed, how adjustments are made, or whether biases have been introduced.

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Common excuses presented by IPCC acolytes:

• Open-Source Models and Hidden Parameters: Even when core algorithms and methodologies are disclosed, there can still be critical parameters or ‘tuning’ constants that remain undisclosed or poorly documented. These hidden adjustments can significantly influence outcomes, allowing for fine-tuned manipulations that shape model projections while maintaining the appearance of transparency. Without full disclosure of all variables, weightings, and calibration factors, even so-called open-source models may still function as black boxes, where key decisions remain opaque to external scrutiny.

• Peer Review Bias: The peer review process is intended to mitigate biases by subjecting research to scrutiny from experts with diverse perspectives—yet, in practice, this often does not happen. Instead of fostering genuine critical evaluation, peer review in climate science can become an echo chamber, where contrarian viewpoints are excluded, funding pressures influence outcomes, and dissenting research struggles to get published. This results in a self-reinforcing cycle, where pre-approved narratives remain dominant, and challenging assumptions becomes professionally risky rather than scientifically encouraged..

• Intermodel Comparisons: Comparing multiple models is intended to reveal common patterns and divergences in predictions, which can be informative even when models share underlying assumptions. However, these ‘comparative models’ often do not fully disclose their models, configuration data, or tuning parameters, rendering the comparison effectively meaningless.

• Transparency and Accessibility: The only genuine solution to these issues is full disclosure of all data, models, configuration settings, and tuning parameters—down to the smallest epsilon. Without complete transparency, external verification is impossible, and the risk of bias, manipulation, and selective data interpretation remains unchecked. Any attempt to conceal or withhold any part of the dataset, model structure, or calibration parameters should be considered grounds for immediate disqualification. Science relies on verifiability and reproducibility—without them, the entire foundation collapses into a matter of trust, rather than evidence.

• Replication and Validation: The dynamic nature of climate models, where core parameter data is continuously updated through ‘iterative refinement’, means that data from an earlier year is not reproducible using models from a later year. This lack of fixed reference points raises serious questions about the validity of past datasets, as model outputs are not static but subject to ongoing, opaque adjustments. Consequently, this years dataset will also become unverifiable with time, as future refinements introduce modifications that cannot be traced or independently replicated, undermining the integrity of historical climate records.

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Historical Records:

• Ice core samples are often presented as definitive records of past climate, yet they are far from infallible. While they provide valuable insights, their interpretation relies on assumptions about deposition rates, gas diffusion, contamination, and compression over millennia—all of which introduce uncertainties and potential biases. Moreover, differences in sampling locations, methodologies, and calibration techniques can lead to inconsistent reconstructions.

• Air Bubble Mixing: During periods of thaw or when ice approaches melting conditions, there is a risk of air bubbles mixing or gases diffusing through the ice matrix, potentially altering the historical atmospheric record. To minimise this risk, scientists typically extract ice cores from regions where temperatures remain well below freesing year-round and employ careful handling and analysis techniques to account for potential contamination. However, despite these precautions, uncertainties remain, as diffusion effects can still occur over long timescales, and even small shifts in temperature, pressure, or external contamination can influence the composition of trapped gases, impacting the reliability of climate reconstructions.

• Calibration Challenges: The calibration of ice core records involves aligning them with known historical or geological events, such as volcanic eruptions that leave distinct chemical signatures in the ice, or using radiometric dating of volcanic ash layers for cross-validation. While these methods help improve accuracy, they are not without inherent uncertainties—errors in dating, contamination, and regional variability can all affect reliability.

• Over time, ice cores act as a natural filter, where short-term climate variations become increasingly smoothed out due to ice compression, gas diffusion, and layer merging. As a result, while recent centuries may retain annual or even seasonal resolution, going back 100,000 years or more reduces temporal precision to multi-decadal or even centennial averages. This loss of fine detail means that short-lived climatic fluctuations, abrupt warming or cooling events, and extreme anomalies may be entirely erased or blurred, making long-term reconstructions inherently limited.

• Claiming that any specific month is ‘the hottest in 150,000 years’ is an outright misrepresentation, as there is absolutely no way to verify such a statement with the available data. Ice core records and other paleoclimate proxies lack the temporal resolution necessary to identify individual months or even specific years beyond recent history.

• As snow accumulates over time, the increasing weight of upper layers compresses deeper ice, gradually thinning annual layers until they are no longer distinguishable as distinct yearly markers. This compression effect reduces temporal resolution, making it increasingly difficult to precisely date past climate events. The farther back in time the record extends, the more these layers merge, often limiting climate reconstructions to decadal or even centennial scales, thereby filtering out short-term variability and obscuring finer climatic fluctuations.

• Over time, gases trapped within ice core bubbles can diffuse through the ice matrix, particularly in deeper and older layers where the ice has been under prolonged pressure. This diffusion effect gradually blurs the atmospheric record, making it increasingly difficult to precisely link gas compositions to specific years or even decades.

• Loss of high-resolution climate data due to ice layer melting is commonly compensated for not through direct observation, but through closed-source climate models, which can easily lead to gaps in the record, filled using model-based reconstructions, which introduce assumptions, interpolations, and statistical adjustments that are not independently verifiable.

• Beyond temporal melting, spatial melting further complicates climate reconstructions, as ice loss occurs unevenly across different regions. This results in gaps in the physical record, which—much like temporal gaps—are filled using climate models rather than direct observations.

• We have no direct historical data on oceanic temperatures, atmospheric CO₂ levels, or other critical climate variables beyond recent instrumental records. Most long-term reconstructions rely on proxies (such as ice cores, sediment layers, and tree rings), but these provide indirect and highly averaged estimates rather than precise, time-specific measurements. Even reported ancient CO₂ concentrations often originate from model-derived outputs rather than raw empirical data, meaning the accuracy of these values is contingent on the assumptions built into the models themselves.

• We have virtually no historical record of oceanic temperatures, particularly when stratified by depth and location. While modern instruments (such as ARGO floats and satellite measurements) provide detailed oceanic data, long-term reconstructions rely on proxies like foraminifera shells, sediment cores, and coral isotopes, which offer highly averaged, indirect estimates rather than precise, location-specific temperature readings.

• Historically, consistent and widespread measurement of ocean temperatures began much later than land-based weather recording. While surface temperatures were sporadically recorded by merchant and naval ships in the 19th century, these were highly localised, inconsistent, and lacking depth-specific data. Systematic, depth-stratified ocean temperature measurements only started in earnest late in the 20th century, with the development of modern oceanographic tools such as expendable bathythermographs (XBTs), moored buoys, and satellite remote sensing. Even today, comprehensive global ocean temperature data is limited, particularly for deep-sea regions, making long-term reconstructions heavily reliant on models and proxy data rather than direct observations..

• The lack of detailed historical data across various ocean depths makes it extremely difficult to construct a comprehensive picture of past oceanic conditions. This limitation is highly significant because oceans act as Earth's primary heat reservoir, absorbing and redistributing vast amounts of thermal energy that influence weather patterns, atmospheric circulation, and long-term climate variability. Without high-resolution, stratified temperature records, much of what is assumed about historical ocean behavior relies on models and proxy estimations rather than direct empirical evidence..

• When calls for expanded oceanic measurements were made in 1979, it took more than 20 years before any large-scale, systematic efforts were implemented—hardly the response of a high-priority scientific endeavor. Comprehensive ocean monitoring, particularly with ARGO floats and global ocean observation networks, did not become operational until the early 2000s, meaning critical decades of potential data collection were lost. This delay underscores the historical neglect of oceanic data collection, despite the ocean's dominant role in regulating Earth's climate—raising questions about the true urgency behind climate science priorities.

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Conclusion

The unpredictability of weather and climate models—governed by chaos theory and constrained by computational limitations—underscores significant uncertainties in long-term climate projections. While adaptive management and iterative refinement are touted as solutions, they cannot eliminate compound errors, biases, and resolution limitations that trouble these models. The reliance on closed-source models, undisclosed tuning parameters, and opaque data processing further erodes confidence in the accuracy of climate forecasts. Additionally, the transformation of weather data into climate data introduces significant information loss, with averaging techniques smoothing out short-term fidelity. Given these constraints, the certainty with which long-term climate projections are presented to policymakers and the public is deeply problematic.

Beyond computational and theoretical concerns, data integrity and transparency remain major unresolved issues. Proprietary temperature datasets, lack of open calibration records, and selective use of interpolated proxy data raise serious questions about the reliability of the climate record itself. Historical oceanic temperatures, stratified by depth and location, are virtually nonexistent prior to the late 20th century, and ice core samples—while valuable—suffer from compression, diffusion, and calibration uncertainties that limit their precision. Yet, rather than addressing these weaknesses with full transparency, climate science continues to operate behind institutionalized gatekeeping, shielding critical datasets and methodologies from independent scrutiny.

Without complete open-source access to models, data, and configuration parameters, climate science will remain susceptible to bias, manipulation, and overconfidence in predictions that could well rest on inherently fragile assumptions.

A Climate Chronology

A UNFCCC/CBD synthesis.

The Climate Consensus

The Keeling Curve