26  Unit 5: More Hurricanes & Blizzards in NYC? — Unit Opening

It has felt like storms are getting worse. Does the data support that feeling?

HS-ESS2-5 HS-ESS2-8 HS-ESS3-5 Unit Duration: 18–30 days

27 Anchor Phenomenon

27.1 🌀 It Has Felt Like Storms Are Getting Worse

Does the data support that feeling, and will it continue?

In recent years, powerful storms have battered communities across the United States. From deadly blizzards burying the Northeast under feet of snow to Category 5 hurricanes devastating coastal cities, it feels like extreme weather is getting worse. But science doesn’t run on feelings—it runs on evidence.

In this unit, you will investigate whether storms really are becoming more frequent and intense, and use climate models to argue what the future may hold for your community.

28 Unit Driving Question

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

This question will guide your work across all four lesson sequences in this unit. By the end, you will construct an evidence-based argument about how climate change may alter storm patterns in your region.

29 Tell the Story

Before analyzing data and building models, two real events anchor what this unit is about. Watch each video and examine the data that follows — jot down observations, what surprises you, and the questions you want answered.

---

29.1 🌨️ Tell the Story: Blizzards

Every few years, a massive winter storm bears down on the East Coast, shutting down cities and threatening lives. The news report below captures that moment of anticipation and dread right before one such storm arrives. As you watch, consider: Where is this storm heading? Who is in danger? Does this event seem unusual or part of a pattern?

29.1.1 🎬 Watch: A Deadly Winter Storm Heads to the East Coast

▶ Open video Tip: once you find the video, your teacher can embed it directly using its YouTube ID.

29.1.2 📍 US Winter Storm Track Maps: 2018–19 & 2019–20

The maps below show where major winter storms tracked across the United States during two consecutive seasons. Study the paths carefully — notice which regions get hit most often, where storms tend to originate, and how the patterns compare year to year.

29.1.3 📊 NYC Seasonal Snowfall: 2014–2024 (2018–19 & 2019–20 Highlighted)

The bar chart below shows NYC’s total seasonal snowfall for each winter from 2014 to 2024. The two seasons that match your storm track maps are highlighted in blue.

winterSeasons = [
  {season: "2014–15", total: 49.9, highlight: false},
  {season: "2015–16", total: 30.5, highlight: false},
  {season: "2016–17", total: 30.9, highlight: false},
  {season: "2017–18", total: 40.9, highlight: false},
  {season: "2018–19", total: 20.5, highlight: true},
  {season: "2019–20", total: 4.8,  highlight: true},
  {season: "2020–21", total: 38.6, highlight: false},
  {season: "2021–22", total: 10.3, highlight: false},
  {season: "2022–23", total: 20.7, highlight: false},
  {season: "2023–24", total: 8.5,  highlight: false}
]

Plot.plot({
  title: "NYC Seasonal Snowfall (2014–2024)",
  subtitle: "Blue bars = seasons matching your storm track maps",
  width: 700,
  height: 360,
  x: { label: "Season", tickRotate: -25 },
  y: { label: "Total Snowfall (inches)", domain: [0, 60], grid: true },
  marks: [
    Plot.barY(winterSeasons, {
      x: "season", y: "total",
      fill: d => d.highlight ? "#1565c0" : "#90caf9",
      tip: true
    }),
    Plot.ruleY([25], { stroke: "#555", strokeDasharray: "6,4" }),
    Plot.text([{season: "2022\u201323", y: 27}], {
      x: "season", y: "y", text: d => "\u224825\" historical avg", fill: "#555", fontSize: 11
    })
  ]
})

29.1.4 🤔 Initial Observations & Questions — Blizzards

In your science notebook, respond to the following:

1. Video: What specific dangers did the reporter describe? What regions were under the most severe warnings?
2. Storm Track Maps: What patterns do you notice in where storms originate and which direction they travel?
3. Snowfall Graph: The 2018–19 and 2019–20 seasons look very different. What might explain that dramatic difference?
4. Your Wonder: Write one question you have about why blizzards form or why their frequency might be changing.

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29.2 🌀 Tell the Story: Tropical Storms

In September 2017, Hurricane Maria struck Puerto Rico as a Category 4 storm — one of the deadliest hurricanes in recorded US history. Years later, communities were still rebuilding. The video below documents the devastation. As you watch, consider: What made this storm so catastrophic? How does geography matter? Is this a rare event or part of a pattern?

29.2.1 🎬 Watch: Island of Puerto Rico Destroyed by Hurricane Maria

▶ Open video Tip: once you find the video, your teacher can embed it directly using its YouTube ID.

29.2.2 🗺️ North Atlantic Hurricane Tracking Charts: 2018 & 2020

The official NOAA National Hurricane Center charts below show every named storm track in the 2018 and 2020 Atlantic hurricane seasons. Each line is a different storm’s path from formation to dissipation. Compare the two years — 2020 was a record-breaking season.

Use the toggle to compare the 2018 and 2020 Atlantic hurricane seasons. Each colored line represents a named storm’s track from formation to dissipation. 2020 was a record-breaking season.

hurricaneTracks2018 = [
  { name: "Alberto", cat: "TS", points: [[-86,20],[-87,23],[-88,27],[-87,30]] },
  { name: "Beryl", cat: "TS", points: [[-48,10],[-54,12],[-60,14],[-66,16]] },
  { name: "Chris", cat: "H1", points: [[-76,33],[-73,34],[-68,36],[-60,40]] },
  { name: "Florence", cat: "H4", points: [[-24,12],[-35,15],[-48,19],[-60,24],[-70,28],[-77,34]] },
  { name: "Gordon", cat: "TS", points: [[-76,23],[-80,25],[-86,28],[-88,31]] },
  { name: "Helene", cat: "H2", points: [[-20,12],[-28,15],[-34,20],[-36,28],[-30,36]] },
  { name: "Isaac", cat: "H1", points: [[-42,12],[-50,14],[-58,15],[-62,16]] },
  { name: "Joyce", cat: "TS", points: [[-38,20],[-42,23],[-44,28],[-40,32]] },
  { name: "Kirk", cat: "TS", points: [[-30,10],[-38,12],[-45,14],[-50,16]] },
  { name: "Leslie", cat: "H1", points: [[-40,18],[-38,22],[-34,28],[-28,32],[-14,38]] },
  { name: "Michael", cat: "H5", points: [[-84,18],[-85,22],[-86,26],[-85,30],[-82,34],[-76,38]] },
  { name: "Nadine", cat: "TS", points: [[-18,10],[-22,13],[-28,15],[-32,18]] },
  { name: "Oscar", cat: "H1", points: [[-44,22],[-40,26],[-36,30],[-32,34]] }
]

hurricaneTracks2020 = [
  { name: "Arthur", cat: "TS", points: [[-77,28],[-76,32],[-73,35],[-66,38]] },
  { name: "Bertha", cat: "TS", points: [[-79,28],[-80,30],[-80,32],[-78,34]] },
  { name: "Cristobal", cat: "TS", points: [[-90,18],[-91,22],[-90,26],[-89,30]] },
  { name: "Dolly", cat: "TS", points: [[-48,32],[-44,35],[-40,38],[-36,40]] },
  { name: "Edouard", cat: "TS", points: [[-58,28],[-52,32],[-46,38],[-40,42]] },
  { name: "Fay", cat: "TS", points: [[-76,30],[-75,33],[-74,37],[-73,40]] },
  { name: "Gonzalo", cat: "TS", points: [[-50,10],[-55,12],[-60,13],[-62,14]] },
  { name: "Hanna", cat: "H1", points: [[-86,20],[-90,22],[-94,24],[-97,26]] },
  { name: "Isaias", cat: "H1", points: [[-64,14],[-70,18],[-74,22],[-77,28],[-76,34],[-72,40]] },
  { name: "Laura", cat: "H4", points: [[-60,14],[-66,16],[-72,18],[-80,22],[-86,26],[-92,30]] },
  { name: "Marco", cat: "TS", points: [[-80,16],[-84,18],[-88,22],[-90,26],[-90,29]] },
  { name: "Nana", cat: "H1", points: [[-78,14],[-82,16],[-86,17],[-88,17]] },
  { name: "Omar", cat: "TS", points: [[-58,24],[-54,28],[-50,32],[-46,36]] },
  { name: "Paulette", cat: "H2", points: [[-42,16],[-48,20],[-54,24],[-60,28],[-64,32]] },
  { name: "Sally", cat: "H2", points: [[-82,22],[-84,24],[-86,26],[-87,28],[-87,30]] },
  { name: "Teddy", cat: "H4", points: [[-38,12],[-46,16],[-52,20],[-58,26],[-62,32],[-60,40]] },
  { name: "Vicky", cat: "TS", points: [[-22,14],[-26,16],[-30,18],[-34,20]] },
  { name: "Wilfred", cat: "TS", points: [[-24,10],[-28,12],[-32,14],[-36,16]] },
  { name: "Alpha", cat: "TS", points: [[-16,32],[-12,36],[-10,38],[-8,40]] },
  { name: "Beta", cat: "TS", points: [[-90,22],[-92,24],[-94,26],[-96,28]] },
  { name: "Gamma", cat: "H1", points: [[-82,16],[-86,18],[-88,20],[-90,22]] },
  { name: "Delta", cat: "H4", points: [[-76,16],[-80,18],[-84,20],[-88,24],[-92,28],[-92,30]] },
  { name: "Epsilon", cat: "H3", points: [[-56,24],[-54,28],[-50,32],[-46,36],[-42,40]] },
  { name: "Zeta", cat: "H2", points: [[-80,16],[-84,18],[-86,22],[-88,26],[-88,30]] },
  { name: "Eta", cat: "H4", points: [[-76,14],[-80,15],[-82,16],[-84,18],[-86,22],[-84,28]] },
  { name: "Theta", cat: "TS", points: [[-28,26],[-24,30],[-20,34],[-16,38]] },
  { name: "Iota", cat: "H5", points: [[-72,12],[-76,13],[-80,14],[-82,14]] }
]

// Color scale by category
catColor = (cat) => {
  const m = { "TD": "#6ec6ff", "TS": "#00bcd4", "H1": "#ffeb3b", "H2": "#ff9800", "H3": "#ff5722", "H4": "#e91e63", "H5": "#9c27b0" };
  return m[cat] || "#999";
}

viewof hurrYear = Inputs.radio(["2018", "2020"], { label: "Season", value: "2018" })

{
  const topojson = await require("topojson-client@3");
  const world = await fetch("https://cdn.jsdelivr.net/npm/world-atlas@2/land-50m.json").then(r => r.json());
  const land = topojson.feature(world, world.objects.land);
  const tracks = hurrYear === "2018" ? hurricaneTracks2018 : hurricaneTracks2020;
  const seasonLabel = hurrYear === "2018"
    ? "2018 — 15 named storms, 8 hurricanes, 2 major"
    : "2020 — 30 named storms, 13 hurricanes, 6 major (record)";

  return Plot.plot({
    title: `Atlantic Hurricane Season ${hurrYear}`,
    subtitle: seasonLabel,
    width: 750,
    height: 440,
    projection: {
      type: "equirectangular",
      domain: { type: "MultiPoint", coordinates: [[-100, 5], [0, 48]] }
    },
    color: {
      legend: true,
      domain: ["TD","TS","H1","H2","H3","H4","H5"],
      range: ["#6ec6ff","#00bcd4","#ffeb3b","#ff9800","#ff5722","#e91e63","#9c27b0"],
      label: "Category"
    },
    marks: [
      Plot.geo(land, { fill: "#2d5016", stroke: "#1a3009", strokeWidth: 0.5 }),
      Plot.geo({ type: "Sphere" }, { stroke: "#444", fill: "#1b263b" }),
      Plot.geo(land, { fill: "#2d5016", stroke: "#1a3009", strokeWidth: 0.5 }),
      ...tracks.map(t =>
        Plot.line(t.points, {
          x: d => d[0], y: d => d[1],
          stroke: catColor(t.cat),
          strokeWidth: t.cat.startsWith("H") ? 2.5 : 1.5,
          strokeOpacity: 0.85,
          curve: "catmull-rom"
        })
      ),
      ...tracks.map(t =>
        Plot.dot([t.points[0]], {
          x: d => d[0], y: d => d[1],
          r: 3, fill: catColor(t.cat), stroke: "#000", strokeWidth: 0.5
        })
      ),
      ...tracks.map(t =>
        Plot.text([t.points[t.points.length - 1]], {
          x: d => d[0], y: d => d[1],
          text: [t.name], fill: "#fff", fontSize: 8,
          dx: 8, dy: -4, fontWeight: 600
        })
      )
    ]
  });
}

Tropical Storm Cat 1 Cat 2 Cat 3 Cat 4 Cat 5

Data based on NOAA National Hurricane Center seasonal summaries. Track paths are simplified representations.

29.2.3 📊 Atlantic Hurricane Season Frequency: 2015–2023 (2018 & 2020 Highlighted)

The chart below shows how many named Atlantic storms formed each year from 2015 to 2023, with the two seasons that match your tracking charts highlighted in red.

hurrSeasonData = [
  {year: 2015, storms: 11, highlight: false},
  {year: 2016, storms: 15, highlight: false},
  {year: 2017, storms: 17, highlight: false},
  {year: 2018, storms: 15, highlight: true},
  {year: 2019, storms: 18, highlight: false},
  {year: 2020, storms: 30, highlight: true},
  {year: 2021, storms: 21, highlight: false},
  {year: 2022, storms: 14, highlight: false},
  {year: 2023, storms: 20, highlight: false}
]

Plot.plot({
  title: "Named Atlantic Storms per Season (2015–2023)",
  subtitle: "Red bars = seasons shown on the tracking charts above",
  width: 700,
  height: 360,
  x: { label: "Year", tickFormat: "d" },
  y: { label: "Named Storms", domain: [0, 35], grid: true },
  marks: [
    Plot.barY(hurrSeasonData, {
      x: "year", y: "storms",
      fill: d => d.highlight ? "#b71c1c" : "#ef9a9a",
      tip: true
    }),
    Plot.ruleY([14], { stroke: "#555", strokeDasharray: "6,4" }),
    Plot.text([{year: 2016, storms: 15.5}], {
      x: "year", y: 16.5, text: d => "≈14 avg", fill: "#555", fontSize: 11
    })
  ]
})

29.2.4 🤔 Initial Observations & Questions — Tropical Storms

In your science notebook, respond to the following:

1. Video: What kinds of damage did you see? Who were the most affected communities, and why?
2. Tracking Charts: Compare the 2018 and 2020 storm tracks. Which season was more active? Where did most storms form?
3. Frequency Graph: 2020 stands out dramatically. Does this mean hurricanes are definitely getting worse? What other data would you need to be sure?
4. Your Wonder: Write one question the tracking charts raise for you about hurricane patterns or formation.

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30 What Makes This Unit Different?

This unit builds directly on Unit 4: Climate Change, where you analyzed data about Earth’s changing climate. Now you’ll apply that understanding to a question that hits close to home: how might climate change affect the storms that impact your city?

Instead of memorizing weather vocabulary, you’ll:

• 🔬 Build models of how storms form
• 📊 Analyze real data on storm frequency and intensity
• 🗺️ Read weather maps like a meteorologist
• 💬 Construct arguments about future climate impacts

31 Unit Storyline Overview

31.0.1 Four Lesson Sequences — One Big Question

Each 5E sequence below addresses part of the unit driving question. Together they build toward a final performance task where you argue how storms in your region may change in the future.

31.0.2 🌨️ Sequence 1: Blizzards (7–13 days)

Investigative Phenomenon: Winter storm Jonas produced strong enough winds and enough snow to cause significant disruptions to society, damage to property, and harm to human life.

Key Questions: How do severe winter storms form? What causes wind and precipitation?

HS-ESS2-8

31.0.3 🗺️ Sequence 2: The Paths of Severe Storms (6–11 days)

Investigative Phenomenon: Maps from 2018–2020 show that blizzards and hurricanes exhibit clear patterns in where they start and the direction in which they travel.

Key Questions: Why do severe storms follow the paths they do? How might those paths shift with warming?

HS-ESS2-8

31.0.4 🌀 Sequence 3: Hurricanes (5–6 days)

Investigative Phenomenon: In 2005, hurricanes occurred in the North Atlantic Ocean between June and November 30, just like 2018 and 2020.

Key Questions: How do hurricanes form? Why do they occur only at certain times of year?

HS-ESS2-5

31.0.5 📢 Unit Closing: Constructing Your Argument (0–2 days)

Performance Task: Construct an oral argument from data analysis explaining how storms may change in the future in your region.

HS-ESS3-5

32 Storms by the Numbers

Let’s look at some real data to start building your intuition about whether storms are changing.

Plot = require("@observablehq/plot")

// Named Atlantic hurricanes per year
hurricaneData = [
  {year: 1980, count: 11}, {year: 1985, count: 11}, {year: 1990, count: 14},
  {year: 1995, count: 19}, {year: 1996, count: 13}, {year: 1997, count: 8},
  {year: 1998, count: 14}, {year: 1999, count: 12}, {year: 2000, count: 15},
  {year: 2001, count: 15}, {year: 2002, count: 12}, {year: 2003, count: 16},
  {year: 2004, count: 15}, {year: 2005, count: 28}, {year: 2006, count: 10},
  {year: 2007, count: 15}, {year: 2008, count: 16}, {year: 2009, count: 9},
  {year: 2010, count: 19}, {year: 2011, count: 19}, {year: 2012, count: 19},
  {year: 2013, count: 14}, {year: 2014, count: 8}, {year: 2015, count: 11},
  {year: 2016, count: 15}, {year: 2017, count: 17}, {year: 2018, count: 15},
  {year: 2019, count: 18}, {year: 2020, count: 30}, {year: 2021, count: 21},
  {year: 2022, count: 14}, {year: 2023, count: 20}, {year: 2024, count: 18}
]

Plot.plot({
  title: "Named Atlantic Storms per Year (1980–2024)",
  subtitle: "Has there been a trend?",
  width: 750,
  height: 400,
  x: {
    label: "Year",
    tickRotate: -25,
    tickSpacing: 12
  },
  y: {label: "Number of Named Storms", domain: [0, 35]},
  marks: [
    Plot.barY(hurricaneData, {x: "year", y: "count", fill: d => d.count >= 20 ? "#e74c3c" : d.count >= 15 ? "#f39c12" : "#3498db",
        tip: true}),
    Plot.ruleY([14], {stroke: "#999", strokeDasharray: "5,5"}),
    Plot.text([{x: 1985, y: 15.5}], {x: "x", y: "y", text: d => "1991–2020 average: ~14", fill: "#999", fontSize: 11}),
    Plot.linearRegressionY(hurricaneData, {x: "year", y: "count", stroke: "#e74c3c", strokeWidth: 2, strokeDasharray: "8,4"})
  ]
})

32.0.1 🤔 Initial Questions — What Do You Notice? What Do You Wonder?

Take a moment to study the graph above. In your notebook, write down:

1. What patterns do you notice in the number of named storms over time?
2. What questions do you have about why the numbers vary so much from year to year?
3. Do you think storms are getting worse? What evidence from this graph would you use to support your claim?
4. What additional data would you want to see to answer the unit driving question?

32.1 🌥️ Global Cloud Cover by Season

Where the atmosphere is most active, you see clouds. Three patterns drive almost everything in this unit: the Intertropical Convergence Zone (ITCZ), mid-latitude storm tracks (where blizzards live), and monsoon systems. All three shift with the seasons — and that shift is exactly why hurricane season and blizzard season fall when they do.

Toggle through the four seasons and look for: - Where cloud cover is thickest (most active storms / deepest convection) - Which latitudes are clearest (subtropical deserts, sinking air zones) - How the cloud bands connect to when and where hurricanes and blizzards strike

Loading map…

Clear sky

Dense clouds

Modeled seasonal climatology (ISCCP/MODIS) · Equirectangular projection — all regions visible simultaneously

33 Connecting to Unit 4

In Unit 4, you learned that:

• Human activities have increased atmospheric CO₂ from 280 ppm to over 420 ppm
• This enhanced greenhouse effect is raising global temperatures
• Positive feedback loops (ice-albedo, water vapor, permafrost) amplify warming
• Climate models project continued warming throughout the 21st century

33.0.1 🔗 Making Connections

Now consider: How might a warmer atmosphere and warmer oceans change storm behavior?

Write a brief hypothesis in your notebook. We’ll revisit it at the end of the unit to see how your thinking has evolved.

34 What You’ll Figure Out

By the end of this unit, you will be able to:

Sequence	You Will Figure Out…
Blizzards	Wind is caused by uneven heating → pressure differences. Cold and warm air masses collide to produce precipitation. Mid-latitude cyclones become blizzards under certain conditions.
Storm Paths	Global winds driven by uneven solar heating drive storm trajectories. Wind patterns may shift as temperatures rise.
Hurricanes	Hurricanes get energy from warm ocean water. Ocean temperature seasonality explains hurricane season.
Closing	You can construct an evidence-based argument about future storm changes in your region.

35 Getting Started

In the next chapter, we’ll dive into our first investigative phenomenon: Winter Storm Jonas and the science behind blizzard formation. Get ready to think like a meteorologist! 🌨️