Year 10 Science · Unit 4 · Lesson 1
Challenge Worksheet
Learning Goals
Read the graph
Study the climograph for Darwin, Northern Territory. Answer the three questions below using the data shown.
Darwin Monthly Climate, Average Temperature and Rainfall
Data: Bureau of Meteorology, Darwin Airport climate statistics (1941–2020 averages).
(a) Using the graph, identify Darwin's climate zone. Name the months that make up the wet season and the dry season.
(b) What is the approximate temperature range for Darwin across the year? How does this compare to what you would expect in Sydney, which has a temperate climate?
(c) What does the combination of Darwin's rainfall pattern and nearly constant temperature tell you about how the sun's energy drives its climate? Why does the temperature change so little compared to rainfall?
Evaluate the claim
Someone claims...
"Because weather is unpredictable, we can't even get the five-day forecast right half the time, we definitely can't trust long-term climate change predictions. If scientists can't tell me if it will rain next Thursday, how can they claim to know what global temperatures will be in 50 years?"
(a) What part of this claim is supported by science? Explain why short-term weather prediction is genuinely difficult.
(b) What is misleading about this claim? Use ENSO as an example of a climate pattern that is predictable months in advance, even though individual storm days are not.
(c) What is the key difference between predicting tomorrow's weather and projecting long-term climate trends? What type of evidence would you look for to decide if a 50-year climate projection is reliable?
Wrap Up
In one sentence, what was the main idea of this lesson?
Warm Up, Read the graph
(a) Tropical. The graph shows a sharply divided year: a wet season from November to April, with monthly rainfall climbing from about 140 mm in November to a peak near 380 mm in January, and a dry season from May to October, where several months record almost nothing — June, July and August are only a few millimetres each. Temperature stays close to 30°C throughout. High rainfall concentrated in one half of the year, with consistently high temperatures, is the tropical pattern. [2 marks: 1 for identifying the zone as tropical, 1 for both season ranges. Accept December–March for the wet season if the student justifies it from the graph, since April is transitional at about 100 mm.]
(b) Reading the temperature line, the coldest month is July at about 24°C and the warmest is November at about 30°C, so the annual range is only about 6°C. Sydney, being temperate, has a far wider range — roughly 12°C in July against about 23°C in January, so something like 11°C, close to double Darwin’s. A temperate climate has genuinely distinct seasons; Darwin’s seasons are wet and dry rather than hot and cold. [2 marks: 1 for a range near 6°C read from the graph, 1 for a larger Sydney range with the seasonal reason. Accept 5–7°C for Darwin and any sensible larger figure for Sydney.]
(c) Darwin sits deep in the tropics, so the sun is high in the sky all year round and day length barely changes between June and December. The incoming solar energy is therefore nearly constant, and with no large seasonal swing in energy input the temperature has little reason to change — which is exactly what the flat red line shows. What does change is where the moisture comes from: the monsoon reverses the prevailing winds, drawing warm moist air off the ocean through the summer months and dry air from the continental interior through winter. So in the tropics the seasons are driven by wind and moisture rather than by temperature, and the graph shows that directly — rainfall swings from near zero to nearly 400 mm while temperature moves 6°C. [2 marks: 1 for near-constant solar input from the high sun angle and stable day length, 1 for the rainfall variation coming from a seasonal shift in wind and moisture rather than from temperature.]
Go Deeper, Evaluate the claim
(a) The supported part is that short-range weather prediction really is hard and really does degrade with time. The atmosphere is chaotic: a tiny difference in today’s measurements grows rapidly, so forecasts lose skill after about a week and no amount of extra computing removes that limit. A forecaster genuinely cannot tell you whether it will rain next Thursday. [2 marks: 1 for conceding the point honestly, 1 for a reason — sensitivity to initial conditions, or error growing with forecast length.]
(b) The claim treats weather and climate as the same problem, and they are not. Weather asks what happens on one day in one place; climate asks what the average and the odds look like over a season or decades. Being unable to answer the first says nothing about the second. ENSO is the example the question asks for: the BOM issues El Niño and La Niña outlooks months ahead and they carry real skill, because they track slow, large ocean and atmosphere conditions rather than one storm. A forecaster can say with confidence that eastern Australia faces a wetter than average winter without having the faintest idea which Thursday it will rain. [2 marks: 1 for separating a single day from a long-run average, 1 for ENSO as a worked example of something predictable months ahead while individual days are not.]
(c) The key difference is what is being predicted. A weather forecast is an initial-value problem — it needs to know today’s exact state and follow it forward, and small errors grow until the forecast is useless. A climate projection is a boundary-condition problem — it asks what happens to the long-run balance when you change something persistent, such as the concentration of greenhouse gases, and that balance is far more stable than any individual day. A useful comparison: nobody can say who will win a particular hand of cards, yet the house’s long-run take is very predictable. Evidence that would make a 50-year projection trustworthy: whether models reproduce the climate of the past century when fed the real historical emissions; whether earlier projections have since been checked against what actually happened; whether independent models built by different groups agree; and whether the projection rests on physics that can be measured in a laboratory. [3 marks: 1 for the initial-value versus boundary-condition distinction, however worded; 1 for why that makes the long run more predictable than the short; 1 for a genuine test of reliability — hindcasting, checking past projections, or independent model agreement.]