SUMMARY

The Lawrence Livermore National Laboratory  has produced two dozen individual climate models simulating changes in the Earth's surface temperature and sea-ice extent at past and future years 1919, 2002, 2061, and 2099. Results are averages from the output of the two dozen individual climate models.

In keeping with its mission to "enhance the energy and environmental security of the nation" , The Lawrence Livermore National Laboratory promotes many climate and carbon science research and development efforts. These efforts involve teams of both environmental and computer scientists, as well as diverse support personnel, who work together to achieve scientific and technical innovations directed toward pressing national and international problems in these fields.

GOALS

Scientists at Livermore Laboratory investigate prospective climate change using a variety of scientific methods:

• Devising statistical methods for the detection and attribution of climate change

• Developing new climate model capabilities important for simulating climate change (e.g. representation of fine-resolution atmospheric/oceanic circulation features, transports of chemical constituents and aerosols, etc.)

• Modeling potential mechanisms for triggering abrupt climate change such as the sudden release of methane (a powerful greenhouse gas) from thawing of high-latitude ocean clathrates or permafrost.

• Validating/diagnosing climate model simulations of current climate relative to global observational data in order to identify needed model improvements

• Implementing atmospheric model testbeds for detailed study of simulated physical processes (e.g. radiation, convection, cloud/precipitation formation) compared with field observations or satellite measurement

ACTIVITY DESCRIPTION AND TEACHING MATERIALS

Computer simulated changes in the Earth's surface temperature and sea-ice extent at past and future years 1919, 2002, 2061, and 2099. Results are averages from the output of nearly two dozen individual climate models.

Explore:  Climate and Carbon Science Mission

TEACHING NOTES / CONTEXT FOR USE

Climate simulation models are used to conduct controlled numerical experiments to comprehensively study the possible consequences of introducing different forcings (e.g. from solar variations, natural and anthropogenic particulates, greenhouse gas emissions, etc.) on the global climate system.

To corroborate the model simulations of climate change that are a focus of research at LLNL and at many other institutions, it is a priority to develop innovative D&A methods that are appropriate for both global and regional scales. 

Scientists at Livermore Laboratory investigate prospective climate change using a variety of scientific methods:

• Devising statistical methods for the detection and attribution of climate change

• Developing new climate model capabilities important for simulating climate change (e.g. representation of fine-resolution atmospheric/oceanic circulation features, transports of chemical constituents and aerosols, etc.)

• Modeling potential mechanisms for triggering abrupt climate change such as the sudden release of methane (a powerful greenhouse gas) from thawing of high-latitude ocean clathrates or permafrost.

• Validating/diagnosing climate model simulations of current climate relative to global observational data in order to identify needed model improvements

• Implementing atmospheric model testbeds for detailed study of simulated physical processes (e.g. radiation, convection, cloud/precipitation formation) compared with field observations or satellite measurements

ASSESSMENT

Assessment is at the discretion of the educator in how these models are applied.

The Lawrence Livermore National Laboratory practices the following assessment of the actual simulations:

Assessing a models simulation of the current climate is crucial to gaining confidence in the its ability to accurately predict a prospective climate change. At LLNL, three types of model assessment are practiced:

• Intercomparison of different model simulations of climate with available observations--and with one another--in order to identify common model errors that need to be corrected;

• Using a model "testbed" to compare the simulation of high-frequency, fine-scale physical processes (e.g. solar/infrared radiative fluxes or clouds) with observations that are also measured on short time scales;

• Quantifying the uncertainty in a model's simulation of climate change by measuring the spread across many model runs made with different parameter values.

SHORT DESCRIPTION

Climate change refers to any significant variation in the statistical distribution of the Earth's weather over periods of time ranging from decades to millions of years.

One way the Earth's atmosphere maintains a life-sustaining surface temperature is by absorbing and reflecting the Sun's heat in a process of thermal infrared radiation. Research has shown that excessive concentrations of carbon dioxide and other heat-trapping gases in the atmosphere can impede the critical thermal infrared radiation process and contribute to climate change.It is predicted that even small shifts in the thermal infrared radiation process will impact the environment—both positively and negatively.

For example, in higher-latitude regions a milder climate may reduce the cost of snow-removal in urban communities, lessen the use of energy to heat homes, increase the population of certain species, extend the growing season of vegetation, and reduce deaths from influenza and other cold-weather health problems.Negative effects of climate change may include an increase in extreme weather events (e.g. heat waves, droughts, floods), higher sea level and coastal inundation, strained energy and water resources, poor air quality, and stressed ecosystems.

The problem of identifying human-induced climate change is typically addressed using   "detection and attribution" and techniques, where climate change is viewed as a statistical "signal in noise" problem. Detection is the process of demonstrating that an observed change in climate is unlikely to be explained by background ‘climate noise’, while attribution aims at demonstrating that an observed signal is consistent with a given combination of human and natural forcings.