Tropical Atlantic Aerosols

Type of Resource: Link

Reviews

No reviews

Be the first to write a review

Description

Students use real satellite data to determine 1) where the greatest concentrations of aerosols are located during the course of a year in the tropical Atlantic region and 2) their source of origin. This is an inquiry-style lesson where students pull real aerosol data and attempt to identify trends among data sets.

Use graphical displays (e.g., maps, charts, graphs, and/or tables) of large data sets to identify temporal and spatial relationships.

Analyze data using tools, technologies, and/or models (e.g., computational, mathematical) in order to make valid and reliable scientific claims or determine an optimal design solution.

Ask questions that arise from careful observation of phenomena, models, or unexpected results, to clarify and/or seek additional information.

Ask questions to determine relationships between independent and dependent variables and relationships in models.

Ask questions that can be investigated within the scope of the classroom, outdoor environment, and museums and other public facilities with available resources and, when appropriate, frame a hypothesis based on observations and scientific principles.

Ask questions to determine relationships, including quantitative relationships, between independent and dependent variables.

Construct an explanation using models or representations.

Make a quantitative and/or qualitative claim regarding the relationship between dependent and independent variables.

Construct and revise an explanation based on valid and reliable evidence obtained from a variety of sources (including students’ own investigations, models, theories, simulations, peer review) and the assumption that theories and laws that describe the natural world operate today as they did in the past and will continue to do so in the future.

Apply scientific reasoning, theory, and/or models to link evidence to the claims to assess the extent to which the reasoning and data support the explanation or conclusion.

Use digital tools (e.g., computers) to analyze very large data sets for patterns and trends.

Develop and/or use a model to generate data to test ideas about phenomena in natural or designed systems, including those representing inputs and outputs, and those at unobservable scales.

Design a test of a model to ascertain its reliability.

Conduct an investigation and/or evaluate and/or revise the experimental design to produce data to serve as the basis for evidence that meet the goals of the investigation.

Plan and conduct an investigation or test a design solution in a safe and ethical manner including considerations of environmental, social, and personal impacts.

Ask questions that challenge the premise(s) of an argument or the interpretation of a data set.

The foundation for Earth’s global climate systems is the electromagnetic radiation from the sun, as well as its reflection, absorption, storage, and redistribution among the atmosphere, ocean, and land systems, and this energy’s re-radiation into space.

Though the magnitudes of human impacts are greater than they have ever been, so too are human abilities to model, predict, and manage current and future impacts.

Weather and climate are influenced by interactions involving sunlight, the ocean, the atmosphere, ice, landforms, and living things. These interactions vary with latitude, altitude, and local and regional geography, all of which can affect oceanic and atmospheric flow patterns.

Through computer simulations and other studies, important discoveries are still being made about how the ocean, the atmosphere, and the biosphere interact and are modified in response to human activities.

Cause and effect relationships can be suggested and predicted for complex natural and human designed systems by examining what is known about smaller scale mechanisms within the system.

Feedback (negative or positive) can stabilize or destabilize a system.

Cause and effect relationships may be used to predict phenomena in natural or designed systems.

Algebraic thinking is used to examine scientific data and predict the effect of a change in one variable on another (e.g., linear growth vs. exponential growth).

Empirical evidence is needed to identify patterns.

Patterns can be used to identify cause-and-effect relationships.

Mathematical representations are needed to identify some patterns.

Systems in dynamic equilibrium are stable due to a balance of feedback mechanisms.

Tropical Atlantic Aerosols

Reviews

Description

Science and Engineering Practices

Disciplinary Core Ideas

Crosscutting Concepts