Just as ocean tides are explained by the cyclic forces of the Sun and Moon, we must assume the same effect occurs on the ocean's thermocline, thus impacting the Earth's climate via behaviors such as El Nino and La Nina in the Pacific Ocean.
Climate is an example (the example) of what Physical Complex Systems is. Generally speaking, climatic phenomena involve a large number of interdependent parameters which evolve in space and time in a wide spectrum of scales ranging from the scale of movement of the leaves of small plants under the effect of the wind (mm, seconds); to the scales of the orientation of the axis of the earth or of solar activity (a few tens of years to millennia); passing through intermediate scales those of the general circulation of movements oceanic and cyclonic (from a few weeks to a few months).
Climate brings into play the phenomena of transport, transfer, and transformation within the hydrosphere, the atmosphere, the lithosphere, and the biosphere. All these phenomena are dynamic and intercorrelated in a non-linear manner. This should lead us to admit that, given the state of our recognition of these phenomena and their interactions, more research is needed to improve our knowledge of the driving forces that control the Climate System Dynamics in order to build more accurate General Circulation and Climate Models.
Jamel Chahed all true from a declarative POV. The question is whether some or any of these subsystems can be solved.
Modeling the Carbon cycle, water cycle, nutrients cycle, phosphorus cycle, solar activities, and transfers at the interfaces of the geosphère, hydrosphère, biosphère, lithosphere, cryosphère... all are well-posed problems known by scientists in the different areas of geosciences. As all these knowledge topics participate in Climate Science, any scientific progress will give momentum to Climate modeling. This will take time, research effort, and willingness to do. Fortunately, there are passionate researchers, far from any controversy, working hard to put a brick in the wall (Dixit Pink Floyd) as did Manabe (Nobel Prize, 2021) and all other researchers who worked from the sixties to provide us with the current General Circulation Models (GCMs) and Regional CM (RCMs) used in meteorology for daily weather forecasting. The ongoing research effort on Earth System Models (ESMs), will, in the same way, improve actual inaccurate Climate models and will provide future generations with better tools to understand and predict climate phenomena.
Jamel Chahed That's all good information, but I think there are fundamentals in geophysics that can be addressed and which will simplify climate models. Consider that the great progress made in Artificial Intelligence recently has mainly to do with the enormous context that questions, queries, and prompts (a la ChatGPT) are placed in. New and fresh context is that part of information that is used to solve innovative research problems. So I think that AI and machine learning will soon uncover the sources of natural climate variability via other unexplored external factors, with my bet it being due to tidal and annual solar factors.
https://pukite.substack.com/p/machine-learning-validates-the-enso
The GitHub Gist site allows contribution of charts to open discussion. Here is a start: https://gist.github.com/pukpr/cb5c09d42489fcdbdc3fd8ffb796be27
Just came across this article on LOD and ENSO
Article Analysis of Relationships Between ENSO Events and Atmospheri...
What a Digital Twin implies for Climate Science
https://geoenergymath.com/2023/05/14/a-digital-twin-of-enso/
This paper, tries to answer the following question: is it possible to predict Water Resources only with GCMs, without downscaling, and what would be the resulting uncertainties? Besbes M., Chahed J. "Predictability of water resources with global climate models. Case of Northern Tunisia", Published online: 12 June 2023, Les Comptes Rendus. Géoscience. https://doi.org/10.5802/crgeos.219 Available on: Article Predictability of water resources with global climate models...
Fine as far as it goes ... need to be able to cross-validate models as in the following : https://geoenergymath.com/2023/06/17/canonical-cross-validation/
How well does it do? The dLOD calibration and the training fit + cross-validation extrapolation
from https://gist.github.com/pukpr/0708cf8a38867973b45da9e1ba9672ee
Progress in understanding Climate Change and its effects needs advances in modeling Climate Phenomena. "IPCC Models" "Climate Models" "General Circulation Models", appellations are of no importance. It is in all cases Physics-Driven Models developed within multi-disciplinary scientific teams worldwide to describe the evolution of weather phenomena (at short time scales) and climate phenomena that involve long time-scale processes, more complex to analyze, as part of these phenomena are not yet well understood.
This is why, despite the enormous progress already achieved, the predictability of Climate Models (The Earth System Models, ESMs), are not yet sufficiently accurate. The standard deviations between the different models remain of the same order of magnitude as the mean values and huge biases on regional levels are noticed and well documented in technical and scientific references of each of the models.
This should lead us to admit that more research is needed to improve our knowledge of the driving forces that control the climate in order to build more accurate predictive climate models, as scientists do well for weather prediction
See also:
https://www.researchgate.net/post/Climate_Change_and_Climate_Models_Progress_and_Limits
"as part of these phenomena are not yet well understood."
That's why I'm working on it. As an example, the Atlantic Multidecadal Oscillation (AMO) is poorly understood -- long after Michael Mann coined the term AMO, he had second thoughts and has since asserted it may not actually be an oscillation. Yet, if we carefully model the AMO as a semi-annual impulse modulated by the lunar tidal force (calibrated from LOD) we can straightforwardly capture the ~60 year cycle in the time-series. The forcing is the middle panel in the chart above, the long period arising as a consequence of the 2 primary tidal factors Mf and Mm nearly aligning with the annual cycle, but not quite so that the phase gradually cycles between reinforcing and cancelling over a multidecadal time-frame. Moreover, the finer detail in the tidal model, when incorporated in a Laplace's Tidal Equation LTE) formulation, also captures the faster cycling of the AMO -- that is the top panel. For completeness, the spectral characteristics and LTE modulation is shown in the bottom panel.
What does all this imply? For one, it's likely that climate scientists, geophysicists, and oceanographers have all overlooked what should be an obvious explanation for the temperature variability of an ocean basin. The long period tides, interacting with annual impulses, and acting on the subsurface thermocline are forceful enough to alternately mix the deeper cooler water with the surface and thus modulating the sea-surface temperature on an inter-annual basis. Secondly, it implies that rapid progress in the understanding will certainly occur in the coming years.
On Climate Models: From General Circulation Models (GCMs) and Earth System Models (ESMs). General Circulation Models (GCMs)which are the core of weather forecasting Models appeared in the 1960s with the pioneer's work of Manabe (2021 Nobel Prize in Physics). A fundamental point is that is difficult to speak about GCMs and even less of Climate Models without a minimum review starting from Atmosphere Dynamics Models genesis in the 1960s to the actual Earth System Models (ESMs) that participated in the last "CMIP6". These represent the State-of-art of universal knowledge about Climate and its modeling. The results published in 2021 covers 80 ESMs from as many research teams throughout the world. Nowadays, Climate Science and Modelling have attained an international critic-mass never reached in any other domain.
ESMs include a number of components that try to describe the evolution of intercoupled phenomena that govern Climate Phenomena. To understand how this works, one has to know about the progress achieved and still-opened questions related to Climate Models. Mathematically the resolution of the dynamic and the transport equations of physical quantities on more or less important scales provide accurate predetermination in a relatively short time. This is what meteorologists do to deliver us every day their newsletter. This is what the same meteorologists are trying to do with scientists from all sides to build climate models in the long term, sure inaccurate today, exactly as was the 1960s weather model of Manabe, Nobel Prize in Physics 2021, the pioneer of general circulation modeling. The very first general circulation models were based on atmosphere-only physical models (Manabe et al., 1965, Nobel Prize in Physics, 2021), which were quickly improved to take into account the hydrologic cycle and its role in the general circulation of the atmosphere (Smagorinsky et al. 1965). From there, climate modeling has made considerable progress by gradually integrating the many positive or negative feedback processes that occur at different scales between the different components of the system: ocean circulation (Manabe and Bryan, 1969), land hydrological processes (Sellers et al., 1986), sea ice dynamics (Meehl and Washington, 1995), and aerosols (Takemura et al., 2000), biophysical and biogeochemical processes (Cox et al., 2000). Models with these latter components are often called Earth System Models (ESMs) and more recent such models include land and ocean carbon cycle, atmospheric chemistry, dynamic vegetation, and other biogeochemical cycles (Watanabe et al., 2011, Collins et al., 2011). It should be noted that as a whole and for the same reasons, the horns of ESMs, which are based on physical formulations similar to those employed in general circulation models applied in meteorology, have not evolved much, except for the increase in the resolution of the calculations made possible thanks to the increase in the computing capacity or their capacity to assimilate increasingly abundant and precise data; in particular global satellite data, which complements and connects measurements on the ground or at low altitude.
Manabe, S., Smagorinsky, J., & Strickler, R. F. (1965). Simulated climatology of a general circulation model with a hydrologic cycle. Monthly Weather Review, 93(12), 769-798.
Smagorinsky, S. Manabe, and J. L. Holloway, “Numericd Results From a Nine-Level General Circulation Model of the Atmosphere,” Monthly Weather Review, vol. 93, No. 12, Dec. 1965, pp. 727-768.
Manabe, S., & Bryan, K. (1969). Climate calculations with a combined ocean-atmosphere model. J. Atmos. Sci, 26(4), 786-789.
Sellers, P. J., Mintz, Y. C. S. Y., Sud, Y. E. A., & Dalcher, A. (1986). A simple biosphere model (SiB) for use within general circulation models. Journal of the atmospheric sciences, 43(6), 505-531.
Meehl, G. A., & Washington, W. M. (1995). Cloud albedo feedback and the super greenhouse effect in a global coupled GCM. Climate dynamics, 11(7), 399-411.
Takemura, T., Okamoto, H., Maruyama, Y., Numaguti, A., Higurashi, A., & Nakajima, T. (2000). Global three‐dimensional simulation of aerosol optical thickness distribution of various origins. Journal of Geophysical Research: Atmospheres, 105(D14), 17853-17873.
Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A., & Totterdell, I. J. (2000). Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature, 408(6809), 184-187.
Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., ... & Kawamiya, M. (2011). MIROC-ESM 2010: Model description and basic results of CMIP5-20c3m experiments. Geoscientific Model Development, 4(4), 845-872.
Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., ... & Woodward, S. (2011). Development and evaluation of an Earth-System model–HadGEM2. Geoscientific Model Development, 4(4), 1051-1075.
See Also:
Besbes, M., & Chahed, J. (2023). Predictability of water resources with global climate models. Case of Northern Tunisia. Comptes Rendus. Géoscience, 355(S1), 1-22. Available on:
Article Predictability of water resources with global climate models...
https://www.researchgate.net/post/Climate_Change_and_Climate_Models_Progress_and_Limits
The climate indices of interest are low dimensional structures, such as ENSO which occupies a waveguide along the equatorial Pacific. These require simplified systems for analysis, much simpler than full-blown GCMs.
The famous article [1] by Solanki et al. 2004, on the Unusual activity of the Sun during recent decades (1132 citations), reported that the Sun was responsible for all the global warming prior to 1970, at the most 30% of the strong warming since then can be of solar origin". This means that less than a third of global warming can be attributed to the Sun activity. What about the two remaining thirds?
To understand the question let's consider that the climatic parameter T (temperature), depends on Sun Activity (SA), GHG (so on Partial Pressures of all gases that compose the atmosphere, say for simplification x) and on a set of other climate parameters T(x,y, z...), (y, z...) being, for example, seismic activity, photosynthesis (all elements of carbon Nitrogen, phosphorus Cycles ..), etc. What is accessible to measurement is the total differential DT, which is written as a function of the partial differentials (dT) in the form:
DT=(dT/d(SA))D(SA)+(dT/dx)Dx+(dT/dy)Dy+(dT/dz)Dz+.....
The authors of the paper have achieved reconstructions of solar total and spectral irradiance as well as of cosmic ray fluxes. Let's consider that the parameter (SA) evolves and the calculation of its effect on (T) is DTSA we have thus:
DTSA=(dT/d(SA))D(SA)
By comparing with surface temperature records DT, the authors found that DTSA is at the most 30% of DT. So the 70% of DT which corresponds to DT-DTSA is given by:
DT-DTSA=(dT/dx)Dx+(dT/dy)Dy+(dT/dz)Dz+.....
Further research is needed in order to determine as much as possible the remaining partial derivatives. At the state of our knowledge, it is almost impossible to close the equation because some of the partial derivatives are not even understood.
[1] Solanki, S., Usoskin, I., Kromer, B. et al. Unusual activity of the Sun during recent decades compared to the previous 11,000 years. Nature 431, 1084–1087 (2004).
See also:
https://www.researchgate.net/post/Adaptation_and_Resilience_to_Climate_Change_Temporal_Paradox_versus_Chronology_Protection_Conjecture
The Sun's annual/seasonal cycle interacting with lunar+solar gravitational tides generates all of the climate variability via ocean dynamics. All you have to do is present a better match to the data that one can do via solving Laplace's Tidal Equations and applying a calibrated forcing.
This is an ensemble average of 25 model fits to the Darwin measurement of SOI, which is index to El Nino/La Nina cycles caused by tidal forces acting on the reduced effective gravity along the Pacific Ocean's equatorial thermocline. The cross-validation interval is untainted from training.
I await your better model cross-validation to the data
Iceland volcano erupts on Reykjanes peninsula (BBC, 4 hours ago). Volcanic eruptions, always Fascinating in Beauty and Majesty, remind us in a spectacular way of essential factors in the heat balance of the globe: the transfers at the Visible Lithosphere-Atmosphere Interface in the form of Seismic and Volcanic Activities and the transfers at the Lithosphere-Hydrosphere interface, Invisible because they occur at the bottom of the oceans. Unlike the GHE, the effects of these activities on Climate Change are not well analyzed, at least in Climate Models, including those used in IPCC projections.
Illustration Source: ICELANDIC MET OFFICE:
https://www.bbc.com/news/world-europe-67756413
See Also:
https://www.researchgate.net/post/Adaptation_and_Resilience_to_Climate_Change_Temporal_Paradox_versus_Chronology_Protection_Conjecture
https://www.researchgate.net/post/Climate_Change_and_Climate_Models_Progress_and_LimitsArticle Predictability of water resources with global climate models...