Time Series: Uncovering the Hidden Processes in Science

Type of Resource: Link

Reviews

No reviews

Be the first to write a review

Description

In this activity, students review techniques used by scientists as they analyze a 50-year temperature time series dataset. The exercise helps students understand that data typically has considerable variability from year to year and to predict trends, one needs to consider long-term data.

HS-ESS3-5 Analyze geoscience data and the results from global climate models to make an evidence-based forecast of the current rate of global or regional climate change and associated future impacts to Earth systems.

MS-ESS3-5 Ask questions to clarify evidence of the factors that have caused the rise in global temperatures over the past century.

Construct, analyze, and/or interpret graphical displays of data and/or large data sets to identify linear and nonlinear relationships.

Analyze and interpret data to provide evidence for phenomena.

Apply concepts of statistics and probability (including mean, median, mode, and variability) to analyze and characterize data, using digital tools when feasible.

Consider limitations of data analysis (e.g., measurement error), and/or seek to improve precision and accuracy of data with better technological tools and methods (e.g., multiple trials).

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.

Apply concepts of statistics and probability (including determining function fits to data, slope, intercept, and correlation coefficient for linear fits) to scientific and engineering questions and problems, using digital tools when feasible.

Consider limitations of data analysis (e.g., measurement error, sample selection) when analyzing and interpreting data.

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

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

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

Construct an explanation that includes qualitative or quantitative relationships between variables that predict(s) and/or describe(s) phenomena.

Apply scientific reasoning to show why the data or evidence is adequate for the explanation or conclusion.

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

Compare and critique two arguments on the same topic and analyze whether they emphasize similar or different evidence and/or interpretations of facts.

Compare and evaluate competing arguments or design solutions in light of currently accepted explanations, new evidence, limitations (e.g., trade-offs), constraints, and ethical issues.

Use mathematical, computational, and/or algorithmic representations of phenomena or design solutions to describe and/or support claims and/or explanations.

Evaluate merits and limitations of two different models of the same proposed tool, process, mechanism, or system in order to select or revise a model that best fits the evidence or design criteria.

Integrate qualitative and/or quantitative scientific and/or technical information in written text with that contained in media and visual displays to clarify claims and findings.

Evaluate the validity and reliability of and/or synthesize multiple claims, methods, and/or designs that appear in scientific and technical texts or media reports, verifying the data when possible.

Current models predict that, although future regional climate changes will be complex and varied, average global temperatures will continue to rise. The outcomes predicted by global climate models strongly depend on the amounts of human-generated greenhouse gases added to the atmosphere each year and by the ways in which these gases are absorbed by the ocean and biosphere.

The complex patterns of the changes and the movement of water in the atmosphere, determined by winds, landforms, and ocean temperatures and currents, are major determinants of local weather patterns.

Because these patterns are so complex, weather can only be predicted probabilistically.

Different patterns may be observed at each of the scales at which a system is studied and can provide evidence for causality in explanations of phenomena.

Energy cannot be created or destroyed—it only moves between one place and another place, between objects and/or fields, or between systems.

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

The significance of a phenomenon is dependent on the scale, proportion, and quantity at which it occurs.

Time, space, and energy phenomena can be observed at various scales using models to study systems that are too large or too small.

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).

Energy may take different forms (e.g. energy in fields, thermal energy, energy of motion).

Change and rates of change can be quantified and modeled over very short or very long periods of time. Some system changes are irreversible.

Phenomena may have more than one cause, and some cause and effect relationships in systems can only be described using probability.

Phenomena that can be observed at one scale may not be observable at another scale.

Graphs, charts, and images can be used to identify patterns in data.

Patterns in rates of change and other numerical relationships can provide information about natural systems.

Explanations of stability and change in natural or designed systems can be constructed by examining the changes over time and processes at different scales, including the atomic scale.

Classifications or explanations used at one scale may fail or need revision when information from smaller or larger scales is introduced; thus requiring improved investigations and experiments.

Mathematical representations are needed to identify some patterns.

The observed function of natural and designed systems may change with scale.

Patterns observable at one scale may not be observable or exist at other scales.

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

Time Series: Uncovering the Hidden Processes in Science

Reviews

Description

Performance Expectations

Science and Engineering Practices

Disciplinary Core Ideas

Crosscutting Concepts