NASA CERES dataset shows observed 'net climate feedback' is significantly less than climate model assumptions.

Can this explain why climate models are running too hot?

Climate models in a nutshell…

Climate models are computer-based mathematical models that simulate the Earth's climate system. They are used to study the climate system's behavior and make predictions about how it will change over time. These models are complex and simulate the interactions between the atmosphere, oceans, land surface, and ice, taking into account many different physical, chemical, and biological processes.

Climate models use mathematical equations to represent the physical processes that drive the climate system. These equations describe how energy is transferred between different parts of the Earth's system, such as how the sun's radiation heats the Earth's surface, how water evaporates from the oceans and forms clouds, and how carbon dioxide is exchanged between the atmosphere and the oceans.

The models are run on supercomputers, which use the equations to simulate the behavior of the climate system over time. This allows scientists to study how the system responds to changes in various factors, such as greenhouse gas concentrations, solar radiation, and volcanic activity.

To validate the models, scientists compare their outputs to historical observations of the climate, such as temperature records and sea-level measurements. They also use the models to make predictions about future climate change under different scenarios, such as varying levels of greenhouse gas emissions.

Source: https://en.wikipedia.org/wiki/Climate_model

The following steps outline the general process by which a climate model works:

1. Defining the domain and resolution: The first step in building a climate model is to define the spatial and temporal resolution of the model. This involves dividing the Earth's surface into a grid of cells, each of which represents a specific region or location. The resolution of the grid determines how detailed the simulation will be, with higher resolutions providing more detail but requiring more computational resources.

2. Initializing the model: Once the domain and resolution have been defined, the model is initialized with initial conditions that represent the state of the Earth's climate at a specific point in time. This involves setting the values of various climate variables such as temperature, humidity, and wind speed.

3. Incorporating physical processes: The model then incorporates a range of physical processes that drive the Earth's climate, such as solar radiation, the greenhouse effect, and atmospheric circulation. These processes are represented by a set of mathematical equations that describe how energy and matter are transferred between different parts of the Earth's system.

4. Incorporating biogeochemical processes: Climate models also incorporate biogeochemical processes such as the carbon cycle and nitrogen cycle, which describe how carbon and nitrogen are exchanged between the atmosphere, oceans, and land surface.

5. Running the model: Once the model has been initialized and the physical and biogeochemical processes have been incorporated, the model is run on a supercomputer. This involves solving the set of equations that describe the behavior of the Earth's climate system over time. The model is typically run for several decades or even centuries to simulate long-term climate trends.

6. Validating the model: To validate the model, scientists compare the output of the model to historical observations of the climate, such as temperature records, satellite measurements, and ocean buoy data. If the model accurately reproduces these observations, it provides confidence that the model can be used to make predictions about future climate change.

7. Using the model for predictions: Climate models can be used to make predictions about future climate change under different scenarios, such as varying levels of greenhouse gas emissions.

How accurate are climate models?

Climate models are scientific tools that are subject to some level of uncertainty, as they involve making predictions about complex systems based on incomplete knowledge and assumptions.

One source of uncertainty in climate models is related to the complexity of the Earth's climate system. The climate system is governed by a wide range of physical, chemical, and biological processes, many of which are poorly understood. Additionally, there is a great deal of natural variability in the climate system, such as changes in solar radiation and volcanic activity, that can make it difficult to predict future climate trends.

To address these uncertainties, climate models incorporate a range of different scenarios or "emissions pathways," which represent different possible levels of greenhouse gas emissions in the future. By running the model under different scenarios, scientists can estimate the range of possible future climate outcomes and the likelihood of different scenarios.

Recently climate models appear to be running hotter than the observations.

Users beware: a subset of the newest generation of models are ‘too hot’² and project climate warming in response to carbon dioxide emissions that might be larger than that supported by other evidence^3–7. Some suggest that doubling atmospheric CO₂ concentrations from pre-industrial levels will result in warming above 5 °C, for example. This was not the case in previous generations of simpler models.

Source: https://www.nature.com/articles/d41586-022-01192-2

Source: https://www.drroyspencer.com/2020/06/cmip6-climate-models-producing-50-more-surface-warming-than-observations-since-1979/

What is The Clouds and the Earth’s Radiant Energy System (CERES)?

The Clouds and the Earth’s Radiant Energy System (CERES) is a project led by NASA that focuses on measuring the Earth's energy budget and its relationship with clouds. The project's main objective is to improve our understanding of the role that clouds and other atmospheric phenomena play in regulating the Earth's climate.

CERES uses a suite of instruments that are mounted on satellites to measure the amount of energy that is absorbed and emitted by the Earth's surface and atmosphere. The instruments measure both shortwave and longwave radiation, which together make up the Earth's energy budget.

One of the key focuses of the CERES project is on understanding the role that clouds play in regulating the Earth's energy budget. Clouds can reflect incoming sunlight back into space, which can help to cool the Earth's surface. However, they can also trap outgoing longwave radiation, which can lead to warming. CERES data is used to improve our understanding of how clouds affect the Earth's radiation balance, and how changes in cloud cover and properties may impact the Earth's climate.

CERES data has been used in a range of scientific studies, including research on the effects of aerosols on the Earth's radiation budget, the impact of land-use changes on climate, and the role of clouds in global warming. The data is also used by climate modelers to improve the accuracy of climate simulations and predictions.

The CERES project has been ongoing since the late 1990s, with several iterations of instruments and satellites launched over the years to improve the quality and quantity of data gathered. The project has provided valuable insights into the Earth's energy budget and the role of clouds in climate regulation, and it continues to be a key resource for scientists studying the Earth's climate.

Let’s compare CERES data with the most recent climate models…