31  Unit 5: Closing — Constructing Your Argument

Will there be more frequent and more intense severe storms in the future?

HS-ESS3-5 Time: 0–2 Days

📢 Your Voice, Your Evidence, Your Argument 📢

32 The Performance Task

32.1 🎯 Unit Driving Question

Will there be more frequent and more intense severe storms in the future?

32.1.1 Your Task

Construct an oral argument from data analysis that addresses how severe storms (hurricanes and/or blizzards) may change in frequency, intensity, or path in the future for your region (New York City / Northeast US).

Your argument must:

• ✅ Make a clear claim about future storm changes
• ✅ Support your claim with at least three pieces of evidence from data analyzed in this unit
• ✅ Include scientific reasoning that connects your evidence to your claim using the mechanisms you’ve learned
• ✅ Address at least one counterargument or limitation of your claim
• ✅ Be delivered as a 2–3 minute oral presentation to your class

33 Building Your Argument: The CER Framework

Your argument should follow the Claim–Evidence–Reasoning (CER) framework.

33.0.1 📌 CLAIM

A clear, specific statement that answers the driving question for your region.

Example format: “In the future, [specific storm type] in [your region] will likely become [more/less frequent / more/less intense / shift in timing or path] because [brief reason].”

Strong claim example: “Hurricanes affecting the Northeast US will likely become stronger and produce more rainfall by 2050, though they may not increase in total number.”

Weak claim: “Storms will get worse.” ← Too vague! What type? In what way? Where?

33.0.2 📊 EVIDENCE

Specific data points, trends, or observations from your investigations in this unit. You need at least three pieces of evidence from at least two different lesson sequences.

Good evidence is: - Specific (numbers, dates, locations) - From reliable sources (NOAA data, scientific studies, your own data analysis) - Relevant to your claim (directly supports or informs it)

33.0.3 🧠 REASONING

The scientific explanation that connects your evidence to your claim. This is where you use the mechanisms you learned:

• How does the Clausius-Clapeyron relation connect warmer temperatures to storm intensity?
• How does Arctic amplification affect the jet stream and storm paths?
• How does sea surface temperature affect hurricane formation and intensity?
• How do convection cells and the Coriolis effect determine where storms form and travel?

34 Evidence Review: What You’ve Learned

Let’s review the key evidence from each lesson sequence.

34.0.1 🌨️ From Blizzards Sequence

Available Evidence:

Data Point	What It Shows
Winter Storm Jonas stats	27.5” snow in NYC, 60 mph winds, $3B damages
Clausius-Clapeyron relation	~7% more atmospheric moisture per 1°C warming
Extreme snowfall trends	More extreme events per decade even as average snowfall decreases
Arctic amplification & jet stream	Weakened gradient → wavier jet → cold outbreaks can be more extreme
Pressure gradient & wind	Greater temperature/pressure contrasts → stronger winds

34.0.2 🗺️ From Storm Paths Sequence

Available Evidence:

Data Point	What It Shows
Storm trajectory maps (2018–2020)	Blizzards and hurricanes follow predictable paths
Global circulation cells	Hadley, Ferrel, Polar cells driven by uneven heating
Westerlies & jet stream	Mid-latitude storms steered SW→NE by prevailing winds
Jet stream projections	Warming may shift storm tracks poleward
Precipitation by latitude	Rising air zones = wet; sinking air zones = dry

34.0.3 🌀 From Hurricanes Sequence

Available Evidence:

Data Point	What It Shows
SST threshold: 26.5°C	Minimum ocean temperature for hurricane formation
Seasonal lag	Ocean peaks ~2 months after solar maximum (high heat capacity)
2005 season: 28 named storms	Record-breaking season with 0.55°C SST anomaly
SST trend: +0.65°C anomaly by 2024	Tropical Atlantic warming steadily
Rapid intensification: +150% increase	Storms strengthening faster → less warning time
Named storm count trend	Slight upward trend from ~11 (1980) to ~18+ (2020s)

Plot = require("@observablehq/plot")

35 Data Synthesis: The Big Picture

synthesisData = [
  {year: 1980, temp: 0.26, storms: 11, category45: 1},
  {year: 1985, temp: 0.12, storms: 11, category45: 1},
  {year: 1990, temp: 0.45, storms: 14, category45: 1},
  {year: 1995, temp: 0.45, storms: 19, category45: 2},
  {year: 2000, temp: 0.39, storms: 15, category45: 2},
  {year: 2005, temp: 0.68, storms: 28, category45: 5},
  {year: 2010, temp: 0.72, storms: 19, category45: 3},
  {year: 2015, temp: 0.90, storms: 11, category45: 1},
  {year: 2017, temp: 0.92, storms: 17, category45: 4},
  {year: 2020, temp: 1.02, storms: 30, category45: 3},
  {year: 2023, temp: 1.17, storms: 20, category45: 3},
  {year: 2024, temp: 1.29, storms: 18, category45: 3}
]

Plot.plot({
  title: "Global Temperature Anomaly vs. Atlantic Storm Activity (1980–2024)",
  subtitle: "As temperatures rise, are storms changing?",
  width: 700,
  height: 380,
  x: {label: "Year"},
  y: {label: "Count / Anomaly (°C)"},
  color: {legend: true},
  marks: [
    Plot.barY(synthesisData, {x: "year", y: "storms", fill: "#3498db", fillOpacity: 0.4, title: d => `${d.storms} named storms`,
        tip: true}),
    Plot.dot(synthesisData, {x: "year", y: d => d.temp * 20, fill: "#e74c3c", r: 6, title: d => `${d.temp}°C anomaly`,
        tip: true}),
    Plot.line(synthesisData, {x: "year", y: d => d.temp * 20, stroke: "#e74c3c", strokeWidth: 2,
        tip: true}),
    Plot.dot(synthesisData, {x: "year", y: d => d.category45 * 3, fill: "#f39c12", r: d => d.category45 * 2, symbol: "diamond",
        tip: true}),
    Plot.text([{x: 2000, y: 28}], {x: "x", y: "y", text: d => "🔴 Temp anomaly (×20)", fontSize: 10, fill: "#e74c3c"}),
    Plot.text([{x: 2000, y: 25}], {x: "x", y: "y", text: d => "🔵 Named storms", fontSize: 10, fill: "#3498db"}),
    Plot.text([{x: 2000, y: 22}], {x: "x", y: "y", text: d => "🔶 Cat 4-5 storms (×3)", fontSize: 10, fill: "#f39c12"})
  ]
})

35.0.1 📝 Data Analysis Questions

Before writing your argument, analyze this synthesis graph:

1. Is there a clear correlation between global temperature and the number of named storms? Why or why not?
2. Is there a stronger relationship between temperature and the intensity of the strongest storms?
3. Why might the relationship between warming and storm count be more complicated than the relationship between warming and storm intensity?
4. What does this mean for your claim? (Hint: “more storms” vs. “stronger storms” are different claims!)

36 Argument Structure Guide

36.0.1 📋 Oral Argument Outline (2–3 minutes)

Opening (15–20 seconds): State your claim clearly and specifically.

Evidence Block 1 (30–45 seconds): Present your first piece of evidence. Explain what the data shows using specific numbers.

Evidence Block 2 (30–45 seconds): Present a second piece of evidence from a different lesson sequence. Explain the trend or pattern.

Evidence Block 3 (20–30 seconds): Present a third supporting piece of evidence.

Reasoning (30–45 seconds): Explain the scientific mechanism that connects your evidence to your claim. Use key vocabulary.

Counterargument (15–20 seconds): Acknowledge one limitation, uncertainty, or opposing viewpoint. Explain why your claim still holds.

Closing (10–15 seconds): Restate your claim and its significance for your community.

37 Addressing Counterarguments

A strong scientific argument acknowledges complexity. Consider these common counterpoints:

Counterargument	How to Address It
“Natural variability causes storm cycles, not climate change”	True that multi-decadal cycles exist (AMO), but the long-term warming trend is overlaid on top of natural variability. The mechanisms (more moisture, warmer SST) add fuel regardless of cycle phase.
“Some studies show fewer total storms in a warmer world”	Correct — some models project fewer but more intense storms. Distinguish between frequency and intensity in your claim.
“We can’t predict individual storms”	True — but we can project statistical changes in average conditions. Like saying “summers will be hotter” without predicting the temperature on July 14, 2047.
“Technology and preparation can prevent damage”	Important point, but infrastructure has limits. The physics of stronger storms still matters even with better warning systems.

38 Peer Review & Feedback

38.0.1 👥 Peer Feedback Protocol

After each oral argument, provide feedback on:

Criterion	Rating (1–4)	Comments
Claim is clear, specific, and answerable
At least 3 pieces of evidence from data
Evidence is specific (numbers, dates, sources)
Scientific reasoning connects evidence to claim
At least 1 counterargument addressed
Vocabulary used accurately
Delivery is clear and within time limit

39 Reflection: How Has Your Thinking Changed?

39.0.1 🔄 Revisiting Your Hypothesis

At the start of this unit, you wrote a hypothesis about how warmer atmosphere and oceans might change storm behavior.

Take it out now and answer:

1. How has your thinking changed since writing that initial hypothesis?
2. What was the most surprising thing you learned about storms and climate?
3. What new questions do you have that weren’t answered in this unit?
4. How confident are you in your argument? What additional evidence would make you more confident?
5. What does this mean for your community? Should NYC be preparing differently for future storms?

40 Unit 5 Summary: What You Figured Out

Driving Question	What You Figured Out
What causes wind?	Uneven heating → pressure differences → air flows from H to L pressure
How do blizzards form?	Mid-latitude cyclones form at cold/warm air mass boundaries; blizzard conditions on cold side
Why do storms follow specific paths?	Global circulation cells + Coriolis effect create prevailing westerlies that steer storms
What steers storms?	The jet stream — a high-altitude river of air at cell boundaries
What powers hurricanes?	Latent heat from condensation of warm ocean water (≥26.5°C)
Why is there a hurricane season?	Thermal lag: ocean peaks ~2 months after solar maximum
Could storms change in the future?	Warmer air = more moisture; warmer oceans = more energy; weakened jet = more extremes

40.1 🎓 You Did It!

You’ve just completed a deep investigation into the science of severe storms. You can now:

• ✅ Explain the physics behind wind, precipitation, and storm formation
• ✅ Read and interpret weather maps and climate data
• ✅ Connect climate change to changes in storm behavior
• ✅ Construct evidence-based scientific arguments

These are the skills of a real climate scientist. The data is clear — the question now is: what will we do about it?