Lesson 6: AI in Climate Science and Prediction

Name:   Date:   Period: _____

Part 1: Data Exploration Station

Station Selected: Temperature Trends Extreme Weather Arctic Ice Carbon Dioxide

Initial Observations (5 minutes)

1. What type of data are you examining? (Be specific)

1. What is the time range covered by this dataset?

From:   To:  

1. What patterns or trends do you notice immediately?

Part 2: Detailed Data Analysis (10 minutes)

Quantitative Analysis

1. Record at least 5 specific data points from your exploration:

Year/Date	Measurement	Unit	Notes

1. Calculate the change over time:

Beginning value:   Ending value:  

Total change:   Rate of change per decade:  

Pattern Recognition

1. Describe the overall trend in this data:

Increasing steadily

Decreasing steadily

Increasing at an accelerating rate

Decreasing at an accelerating rate

Fluctuating with no clear trend

Other:  

1. Are there any anomalies, outliers, or unexpected variations in the data? Describe them:

1. Do you notice any repeating patterns or cycles? (Example: seasonal variations, decade-long trends)

Part 3: AI Applications in Climate Data

Understanding AI's Role

1. How much data would a human scientist need to analyze manually to identify the patterns you observed?

A few hours of work

Several days of work

Weeks or months of work

Would be practically impossible for one person

Explain your reasoning:

1. How could artificial intelligence help scientists analyze this type of climate data more effectively? (List at least 3 ways)

a.  

b.  

c.  

1. What patterns might AI detect in this data that would be difficult for humans to notice?

Part 4: Predictions and Implications

Making Evidence-Based Predictions

1. Based on the trends you observed, what prediction can you make about this climate variable in 10 years?

What evidence supports your prediction?

1. How confident are you in this prediction?

Very confident (80-100%)

Moderately confident (50-79%)

Somewhat confident (25-49%)

Not very confident (<25%)

What factors affect your confidence level?

Real-World Impact

1. What are the potential consequences if the trend you observed continues? (Consider environmental, economic, and social impacts)

Environmental impacts:

Economic impacts:

Social/Human impacts:

1. How might AI-powered climate predictions help society prepare for or prevent these consequences?

Part 5: Critical Thinking About Data Quality

Data Reliability and Limitations

1. Where did this data come from? (What instruments or methods were used to collect it?)

1. How reliable do you think this data is?

Very reliable

Mostly reliable

Somewhat reliable

Need more information to judge

What factors influence data reliability?

1. What are some limitations or uncertainties in this data?

1. Could this data be biased in any way? (Consider geographic coverage, time period, measurement methods)

Part 6: Connections and Synthesis

Relating Different Climate Variables

1. How might the climate variable you studied be connected to other environmental factors? (Temperature, precipitation, ice coverage, CO2 levels, etc.)

1. If you could access unlimited computing power and AI capabilities, what additional climate questions would you want to investigate?

Part 7: Reflection

Personal Learning

1. What surprised you most about the climate data you explored?

1. How has this activity changed your understanding of climate science or AI?

1. What questions do you still have about AI in climate science?

Extension Challenge (Optional)

Compare your findings with a partner who explored a different data station. How are the climate variables related? Can you identify any cause-and-effect relationships?

Teacher Use Only:

Completion: Minimal Partial Complete Thorough

Data Analysis Quality: Developing Proficient Advanced

Critical Thinking: Basic Good Excellent

Comments:

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