Climate, Biomes and Environmental Variation

Earth is closest to the Sun in early January, in the middle of the northern winter. So distance cannot be what makes the seasons. Something else decides how much energy a square metre of ground receives, and that something, together with the way air and water move heat around the planet, sets the climate that every population in the rest of this course lives in. This lesson builds climate from sunlight, then asks how well climate predicts the vegetation. As in how to test an evolutionary explanation, keep pattern and mechanism apart: a match between a climate and a biome is a pattern, not yet a cause.

First, two words. Weather is the state of the atmosphere at a given time and place. Climate is the long-term pattern of weather in an area, summarized by convention as 30-year averages called climate normals (the current World Meteorological Organization standard period is 1991 to 2020).

The idea: sunlight per square metre falls with latitude

Shine a torch straight down at a table and you get a small bright spot. Tilt it and the same light spreads over a larger, dimmer oval. Sunlight striking Earth at a slant does the same: the energy is spread over more ground. The fraction that reaches a horizontal surface is the cosine of the solar zenith angle, the angle between the Sun and the point directly overhead.

energy on a horizontal surface = energy on a surface facing the Sun x cos(zenith angle)

Earth’s axis is tilted about 23.4 degrees to the plane of its orbit. Through the year, each hemisphere in turn leans toward the Sun, getting more direct light and longer days. That, not distance, makes the seasons.

Worked problem, part A. At the top of the atmosphere, a surface facing the Sun receives about 1,360 W m^-2 (watts per square metre).

1. A slanted Sun. With the Sun 60 degrees from overhead (roughly noon at latitude 60 degrees at an equinox), a horizontal surface receives 1,360 x cos 60 = 1,360 x 0.5 = 680 W m^-2, half the head-on value.
2. The whole planet. Earth intercepts sunlight over a disk of area pi r^2 but spreads it, through day and night, over a sphere of area 4 pi r^2. The global mean is 1,360 / 4 = 340 W m^-2.

That 340 is a global average at the top of the atmosphere. About 29% of incoming sunlight is reflected back to space, and no real place receives exactly the mean: the tropics receive more than it, the poles less.

Air in motion: cells, rain belts and deserts

The tropics gain more energy than they radiate away and the poles lose more than they gain, so the atmosphere and oceans carry heat poleward. Near the equator, warm moist air rises. As it rises it expands and cools, its water vapour condenses, and it rains: this is the equatorial rain belt. The now-dry air flows poleward at height and sinks in the subtropics. Sinking air is compressed and warms, its relative humidity falls, and rain is suppressed. Many of the great deserts, the Sahara and the Kalahari among them, lie under this subsiding air.

This loop is the Hadley cell, named for George Hadley, who described it in 1735. The idealized model places three cells in each hemisphere: the Hadley cell in the tropics, the Ferrel cell in mid-latitudes and the polar cell at high latitudes. It is a cartoon. The cells shift north and south with the seasons, which is why many tropical places have a wet season and a dry one, and the real circulation is full of waves and eddies. Do not memorize a latitude for “the desert belt”.

Moving air does not travel in straight lines across a rotating planet. The Coriolis effect deflects moving air and water to the right of their motion in the Northern Hemisphere and to the left in the Southern. Surface air returning toward the equator is turned into the easterly trade winds; air moving poleward in mid-latitudes becomes the westerlies. The effect matters over hundreds to thousands of kilometres; it does not decide which way a sink drains.

Oceans and mountains bend the pattern

Winds drag the ocean surface, and with Coriolis deflection they drive large surface currents that carry warm water from the tropics toward the poles and return cold water toward the equator. Differences in water density, set by temperature and salinity, drive a slower deep circulation as well. Coasts beside warm currents are milder than their latitude suggests, and coasts beside cold currents are often cool and dry.

A favourite example says the Gulf Stream “keeps Europe warm”. It overstates the case. Seager and colleagues argued in 2002 that much of the winter contrast between western Europe and eastern North America comes from the atmosphere: prevailing westerlies bring ocean-moderated air onto Europe’s west coast, and the large-scale wind pattern, shaped partly by the Rocky Mountains, brings cold continental air to eastern North America. The ocean still moves a large share of heat poleward. The simple “conveyor belt” drawing of ocean circulation is a teaching cartoon, not a map.

Mountains add sharp local contrasts. Moist air forced up a slope cools and drops its rain on the windward side; descending the far side it warms and dries, leaving a rain shadow. On mountainous islands such as Hawaii, the windward slopes facing the trade winds are wet and the leeward sides are much drier.

Reading a climate diagram

A Walter-Lieth diagram plots mean monthly temperature and mean monthly precipitation on one chart. The axes are linked so that 10 degrees C sits level with 20 mm. When the precipitation line falls below the temperature line, the month is read as dry; above it, humid. The 10-to-20 ratio is a rule of thumb for when evaporation outruns rain, not a measured water balance. Tools differ on the scale above 100 mm per month: Walter’s own diagrams compressed it, and ClimateCharts.net keeps the 1 degree C to 2 mm ratio only up to 100 mm. Always read the axis labels, the station, its elevation and the years averaged.

Worked problem, part B. In this made-up data set, a lowland station has a mean temperature of 26 degrees C in every month. Mean monthly precipitation, January to December, is 140, 120, 150, 180, 200, 160, 140, 110, 90, 100, 130 and 160 mm. Assume these are 30-year means.

1. Annual total. 140 + 120 + 150 + 180 + 200 + 160 + 140 + 110 + 90 + 100 + 130 + 160 = 1,680 mm.
2. Dry period? On the 10-to-20 scale, 26 degrees C plots at the 52 mm level. The driest month is September with 90 mm, above 52 mm. Every month is humid: no dry period.
3. Köppen-Geiger class, using the thresholds in Beck and colleagues (2018). Aridity is tested first. April to September holds 180 + 200 + 160 + 140 + 110 + 90 = 880 mm (52.4%), and October to March 800 mm (47.6%). Neither half has 70% of the rain, so the precipitation threshold is 2 x 26 + 14 = 66, and the arid cut-off is 10 x 66 = 660 mm. Since 1,680 mm exceeds 660 mm, the station is not arid (not class B). Its coldest month, 26 degrees C, is at least 18 degrees C, so it is tropical (class A). Its driest month, 90 mm, is at least 60 mm, so it is Af, tropical rainforest climate.
4. Biome. Weak temperature seasonality and year-round rain fit tropical wet forest. That is a hypothesis to test against the vegetation actually present.

Monthly temperature, every month 26 °C Precipitation multiplier 1.00 × the lesson’s values

Annual total

Half-years (Apr–Sep / Oct–Mar)

Threshold 2T + 14 (neither half-year has 70%)

Arid cut-off 10 × threshold

Coldest month ≥ 18 °C?

Driest month ≥ 60 mm?

In this sketch: an invented station, the same temperature in every month, the lesson’s twelve monthly means scaled by one multiplier, so the half-year shares stay 52.4% and 47.6% and the 2T + 14 threshold applies. The precipitation axis is linear from 0 to 300 mm in this sketch; Walter’s own diagrams compress it above 100 mm, and the dry-month line here (at most 60 mm) lies below that.

Rule to keep: test aridity first, then the coldest month, then the driest month; the Walter-Lieth dry months (bars below the 2T line) are a separate, rule-of-thumb reading.

In hot, dry biomes many plants use C4 or CAM photosynthesis, variations on the carbon fixation covered in the light reactions and the Calvin cycle.

Biomes are more than climate

A biome is a large region with similar climate and similar dominant vegetation and animals: tropical wet forest, savanna, desert, temperate grassland, chaparral, boreal forest, tundra. Climate predicts biomes roughly. It does not fix them.

Fire and alternative states. Staver, Archibald and Levin (2011) analysed tree cover across the tropics of Africa, Australia and South America. At annual rainfall between about 1,000 and 2,500 mm with a dry season shorter than seven months, tree cover was bimodal: places were mostly either savanna or forest, not in between, and fire was associated with keeping areas open. Savanna and forest may be alternative states under the same climate.

Rainfall timing. Annual totals hide timing. The design problem below takes this up.

Soils. Water-holding capacity and nutrients differ under the same rain, so the same climate can support different vegetation.

Microclimate. A weather station sits in open ground. De Frenne and colleagues (2019) compiled paired temperatures beneath forest canopies and in nearby open habitat at 98 sites on five continents. Understories averaged about 1.7 degrees C cooler in mean temperature and 4.1 degrees C cooler in maximum temperature, and canopies warmed the understory in cold conditions. The temperature an organism experiences is not the station’s.

Observation versus projection. Beck and colleagues (2023) reconstructed that about 5% of land outside Antarctica changed major Köppen-Geiger class between 1901 to 1930 and 1991 to 2020. Under the high-emission scenario SSP5-8.5, their models project that 13% of that land will change major class between 1991 to 2020 and 2071 to 2099. The first is a reconstruction from observations; the second is a model result under a stated scenario. Neither is a biome change, which depends on dispersal, soils, fire and time. Later lessons on niches, ranges and global change take up what happens to species.

Design problem

Two grasslands receive the same mean annual rainfall, but at one it comes in frequent small showers and at the other in a few large storms. Design an experiment to test whether rainfall timing, with the total held constant, changes plant production.

One way to do it

Comparing the two sites cannot separate timing from soils, species pools and history. Manipulate timing within one grassland instead. Build rain-out shelters over replicated plots, assigned at random, and reapply the collected rain by irrigation. Give one treatment the natural pattern and another the same total in fewer, larger events. Add unsheltered plots under natural rain and compare them with the sheltered natural-pattern plots, to check that the shelters themselves (shade, warming) do not change the result. Log soil moisture continuously to confirm the treatment made it more variable, and measure aboveground net primary production and plant species diversity each year, over several years, since one year’s weather can dominate. Knapp and colleagues used rain-out shelters in this way for four years in a native mesic grassland (2002). Fewer, larger events raised soil-moisture variability, lowered production and raised species diversity. The answer applies to that grassland; repeat it at other sites before generalizing.