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Related Concept Videos

Global Climate Change01:50

Global Climate Change

Throughout its ~4.5 billion year history, the Earth has experienced periods of warming and cooling. However, the current drastic increase in global temperatures is well outside of the Earth’s cyclic norms, and evidence for human-caused global climate change is compelling. Paleoclimatology, the study of ancient climate conditions, provides ample evidence for human-caused global climate change by comparing recent conditions with those in the past.
Heating and Cooling Curves02:44

Heating and Cooling Curves

When a substance—isolated from its environment—is subjected to heat changes, corresponding changes in temperature and phase of the substance is observed; this is graphically represented by heating and cooling curves.
For instance, the addition of heat raises the temperature of a solid; the amount of heat absorbed depends on the heat capacity of the solid (q = mcsolidΔT). According to thermochemistry, the relation between the amount of heat absorbed or released by a substance, q, and its...
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
Modeling in Therapy01:26

Modeling in Therapy

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Participant Modeling
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Related Experiment Videos

Climate model tuning with adaptive supermodeling.

Jordan Seneca1, Suzanne Bintanja2, Frank M Selten1

  • 1Koninklijk Nederlands Meteorologisch Instituut, De Bilt 3730 AE, Netherlands.

Chaos (Woodbury, N.Y.)
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

Adaptive supermodeling tunes internal climate model parameters, reducing biases and computational costs. This new approach achieves performance comparable to a perfect model, enhancing climate science research.

Related Experiment Videos

Area of Science:

  • Climate science
  • Computational modeling
  • Earth system science

Background:

  • Climate model tuning is computationally intensive due to high dimensionality and long integration times.
  • Supermodelling dynamically couples multiple models, training coupling strength to reduce biases.

Purpose of the Study:

  • Introduce adaptive supermodeling, a novel approach to tune internal parameters of climate models within a supermodel framework.
  • Evaluate the effectiveness of adaptive supermodeling against direct parameter optimization and classical supermodeling.

Main Methods:

  • Performed three experiments: direct internal parameter optimization of a single climate model.
  • Optimized coupling weights between two models in a classical supermodel setup.
  • Implemented adaptive supermodeling, tuning internal parameters of supermodel members.

Main Results:

  • Adaptive supermodeling achieved performance comparable to a perfect model.
  • The new approach effectively addressed challenges that hindered previous methods.
  • Demonstrated significant potential for bias reduction in climate simulations.

Conclusions:

  • Adaptive supermodeling offers a computationally efficient and effective method for climate model tuning.
  • This technique holds promise for improving the accuracy and reliability of climate projections.
  • Advances in climate modeling are crucial for understanding and mitigating climate change.