Climate Modeling Essay

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Climate modeling u ses simple to highly complex mathematical formulas and computing power to simulate climate system processes. Climate-related processes often occur beyond the physical or temporal scale for laboratory experiments. Climate events such as El Nino occur over millions of acre-feet of water, or the retreat of entire continental ice sheets may take hundreds of years. Climate modeling simulates the behavior found in climate processes using state-of-the-art computer technology to provide timely results for analysis. The analyses of the climate model outputs use observed climate data (i.e., temperature or ice-core records) for comparison and basic knowledge of the climate system to understand the results. Climate modeling is used to understand past and current climates, or to predict future climates.

Essential components of climate modeling are boundary conditions (initial conditions), which are derived from observed climate data such as sea surface temperatures. Climate modeling uses forcing where the boundary conditions are changed (i.e., sea surface temperatures increased or mountain ranges removed) to simulate how a climate system will respond to these changes. The simulations and climate forcing use equations based on the known physical laws that drive climate. For example, increasing land-surface snow cover will increase the reflection of incoming solar radiation contributing surface cooling in the model output. A final step in climate modeling is the analysis of the climate data from the model output and a comparison of the results to a research hypothesis.

Challenges to Climate Modeling

A challenge with climate modeling is that in the real world, many processes of the climate system occur on different spatial or temporal scales. For example, the uplifting of mountain ranges may occur over millions of years while land-surface heating and cooling occur over seasonal and diurnal periods. In order to deal with different spatial and temporal scales, researchers must determine what type of climate model to apply to simulate the climate processes and time period of interest. To simulate a climate process such as a monsoon, researchers may run the model for a time scale of several months or 100 years over specific geographic region. To simulate changes with significant movements of the continents, researchers may run the model to simulate for millions of years for the entire globe.

Climate modeling became a researching tool in the mid-1960s. The most simple climate models are zero-dimensional models, or radiative equilibrium models. These models are used to gain an idea of a planet’s radiative temperature based on an assumed constant amount of incoming solar radiation and the planet’s mass. For earth, these models calculate a radiative surface temperature of 255 K (0 degrees F). These models omit some of the known climate processes such as warming from greenhouse gases, hence the actual average surface temperature for earth is actually 288 K (59 degrees F).

There are two general types of one-dimensional climate models: radiative-convective models and energy balance models. The radiative-convective models were developed in the mid-1960s to analyze the thermal equilibrium of the atmosphere. These models calculate the temperature for each layer of the atmosphere based on the incoming solar radiation, surface temperatures, surface reflectivity, cloud cover, atmospheric pressure, and moisture content. Radiative-convective models are useful to our understanding of climate processes of temperature decreases with altitude or local temperature inversions. They are also useful in understanding local climate processes such as thunderstorm development. Energy balance models were developed in the late 1960s to calculate the amount of solar radiation absorbed or reflected by clouds and the earth’s surface. These calculations are based on the amount of incoming solar radiation, cloud cover, and reflectivity at different latitudes. Energy balance models demonstrate how temperature decreases with increasing distance from the earth’s equator.

In the early 1970s came the development of two-dimensional climate models. These models were a combination of radiative-convective models and energy balance models to simulate more realistic climate processes of the atmosphere. Two-dimensional climate models can represent a horizontal area representing the earth’s surface or a horizontal and vertical surface representing a cross-section of the atmosphere. The two-dimensional climate models simulate climate processes of energy transport from the equator to the poles and the patterns of quasistationary high pressure and low pressure systems.

The climate models that aim to represent the spatial dimensions of the entire climate system are the general circulation models (GCMs). Boundary conditions for GCMs are set at points over a horizontal and vertical grid, representing the earth’s surface and atmosphere. When simulations are run, mathematical equations relating to climate processes are solved for each point. The GCMs consider combinations of conditions related to climate such as seasonal incoming solar radiation, surface friction, cloud formation, coastlines, and mountain ranges. Although GCMs have more realistic simulations for the climate system, they do have limitations, such as the computational power for the simulation of model runs. The calculation of millions of numbers at grid points in a timely manner is a challenge for even the fastest computers. Advancements in computer technology at the world’s leading atmospheric research institutions address these limitations.

The first GCMs were being developed concurrently with other early climate models of the 1960s. The early GCMs were derived from the numerical models used in the 1950s for short-term weather forecasting. A fundamental addition to GCM climate modeling was the development of ocean GCMs (OGCMs). The outputs from OGCM simulations are sea surface temperatures, sea-ice extent, and salinity, which provide boundary conditions for atmospheric GCMs. There has also been the development of atmosphere-ocean GCMs or AOGCMs, which combine the dynamic ocean processes (i.e., currents, temperature, and sea ice) with climate system processes to simulate climates over the globe. Because there are many other physical components and processes such as oceans that significantly affect climate, other types of models have been developed to be incorporated with GCMs. Vegetation models can simulate the impact a rainforest or deforestation has on local, regional, or global climates. Ice-sheet models can provide boundary conditions for the simulation of GCMs for glacial and inter-glacial periods for earth’s climate history or future climate scenarios..


  1. G. Barry and R.J. Chorley, Atmosphere, Weather and Climate, 8th ed. (Routledge, 2003). M. Beniston and M.M. Verstraete, Remote Sensing and Climate Modeling: Synergies and Limitations (Kluwer Academic Publishers, 2001);
  2. Beniston, From Turbulence to Climate: Numerical Investigations of the Atmosphere with a Hierarchy of Models, (Springer, 1998);
  3. V. P. Dymnikov, Mathematics of Climate Modeling (Basel, 1997);
  4. N. Hewitt and A. V Jackson, Handbook of Atmospheric Science: Principles and Applications (Blackwell Publishing, 2003);
  5. W. Robinson, Modeling Dynamic Climate Systems (Springer, 2001);
  6. W.F. Ruddiman, Earth’s Climate: Past and Future (W. Freeman and Company, 2001);
  7. E. Trenberth, Climate System Modeling (Cambridge University Press, 1992);
  8. W.M. Washington, An Introduction to Three Dimensional Climate Modeling (University Science Books, 2005).

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