a) Weather and Climate
Weather and climate are related but they differ in the time scales of changes and their predictability. They can be defined as follows.
Weather is the instantaneous state of the atmosphere around us. It consists of short-term variations over minutes to days of variables such as temperature, precipitation, humidity, air pressure, cloudiness, radiation, wind, and visibility. Due to the non-linear, chaotic nature of its governing equations, weather predictability is limited to days.
Climate is the statistics of weather over a longer period. It can be thought of as the average weather that varies slowly over periods of months, or longer. It does, however, also include other statistics such as probabilities or frequencies of extreme events. Climate is potentially predictable if the forcing is known because Earth’s average temperature is controlled by energy conservation. For climate, not only the state of the atmosphere is important but also that of the ocean, ice, land surface, and biosphere.
In short: ‘Climate is what you expect. Weather is what you get.’
b) The Climate System
Earth’s climate system consists of interacting components (Figure 1). The atmosphere, which is the air and clouds above the surface, is about 10 km thick (more than two thirds of its mass is contained below that height). The ocean covers more than two thirds of Earth’s surface and has an average depth of roughly 4 km. Contrast those numbers with Earth’s radius which is approximately 6,400 km and you’ll find that Earth’s atmosphere and ocean are very thin layers compared to the size of the planet itself. In fact, they are about 1,000 times thinner. They are comparable perhaps to the outer layer of an onion or the water on a wet soccer ball. Yet all life is constrained to these thin layers. The major ocean basins are the Pacific, the Atlantic, the Indian, and the Southern Ocean. Ice and snow comprises the cryosphere, which includes sea ice, mountain glaciers and ice sheets on land. Sea ice is frozen sea water, up to several meters thick, floating on the ocean. Ice sheets on land, made out of compressed snow, can be several kilometers thick. The biosphere is all living things on land and in the sea from the smallest microbes to trees and whales.
Figure 1: The blue marble. A composite image of Earth from space. It shows all four components of Earth’s climate system. The atmosphere with its complex cloud patterns. The ocean, which covers about 70% of Earth’s surface. The cryosphere is visible as the white areas on the top: sea ice covering the Arctic Ocean and the Greenland ice sheet. Green colors on land and turquoise shades along the ocean’s margin indicate the biosphere as forests and phytoplankton blooms. Notice in the lower left the thin layer of the atmosphere surrounding Earth. From nasa.gov.
The components interact with each other by exchanging energy, matter (e.g. water), and momentum thus creating a deliciously complex coupled system. Imagine water evaporating from the tropical ocean heated by the sun (Figure 1). The air containing that water rises and cools. The water condenses into a cloud. The cloud is carried by winds over land where it rains. The rain sustains a forest. Trees are dark, having a low albedo. This influences the amount of sunlight absorbed by the Earth. Dark surfaces absorb more sunlight and get warmer compared to bright surfaces such as desert sand or snow. Air warmed by the surface rises and affects the wind.
Figure 2 illustrates some of the important processes that contribute to the complex interactions within the climate system. Earth’s energy source is the sun. Both solar and terrestrial radiation are affected by gases, aerosols, and clouds in the atmosphere. Thus, the atmospheric composition affects the heating and cooling of the earth. Heating and cooling affect the temperature and circulation of the atmosphere and oceans. Circulations of the air and sea affect temperatures and precipitation over both ocean and land, which impact the biosphere and cryosphere. Atmosphere and oceans exchange heat, water (evaporation and precipitation), and momentum. Wind blowing over the ocean pushes the surface water ahead. Air temperatures and snow fall affect the growth and melting of glaciers and ice sheets. Water from melting ice flows through rivers into the ocean affecting its salinity, its density and movement. Variations in solar irradiance can cause global climate to change. Volcanoes can eject large amounts of aerosol into the atmosphere with climatic implications. Humans are influencing the climate system through emissions of greenhouse gases, aerosols, and land use changes.
Figure 2: Schematic view of components of the climate system and processes involved in their interactions. From Le Treut et al. (2007).
The complexity makes studying the climate system challenging. Scientists from many disciplines contribute such as physicists, chemists, biologists, geologists, oceanographers, atmospheric scientists, paleoclimatologists, mathematicians, statisticians, and computer scientists. To me, the challenges and interdisciplinary nature of climate science are fascinating and fun. I learn something new about it every day.
Le Treut, H., R. Somerville, U. Cubasch, Y. Ding, C. Mauritzen, A. Mokssit, T. Peterson and M. Prather, 2007: Historical Overview of Climate Change. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
- What are the two main differences between weather and climate?
- How big is Earth? How thick is the atmosphere, how deep the ocean?
- What are the components of Earth’s climate system?
- List processes that cause interactions between the components: atmosphere-ocean, atmosphere-biosphere, atmosphere-cryosphere, ocean-biosphere, ocean-cryosphere.
- Name three processes (forcings) that can cause global climate to change.
Go to one of the following websites and explore temporal variations of surface temperature observations in a location of your interest. Plot today’s data, yesterday’s, last week’s, last month’s, last year’s, the last decade’s, as far back as you can and make some notes on what you observe. Note regular, predictable cycles such as the diurnal and annual cycle in contrast with irregular, unpredictable variations such as day-to-day and year-to-year fluctuations. What causes the regular cycles? Try to order the variations with respect to their amplitude (strength). Discuss with fellow students and instructor.
Hourly and daily data are available at: https://www.wunderground.com/history/. Enter a location of your interest. Click on the “Weekly”, “Monthly”, or “Custom” tabs to select different time periods. Unfortunately, when I tried it Jan. 30th, 2017 the “Custom” option didn’t allow one to plot more than one year of data. However, you may be able to identify diurnal (daily) cycles in temperature. Figure 3 shows an example. Although some nights can be warmer than some days and sometimes days and nights have the same temperature, on average days are warmer than nights. Thus the average diurnal cycle is part of climate. It is forced by the diurnal cycle of solar irradiance. Does pressure have a diurnal cycle? Answer: no. What is the typical timescale of pressure variations? Often it is about a week or so. This timescale is associated with the transition of weather systems (high and low pressure systems) passing by.
Figure 3: Weather data from Corvallis, Oregon from January 2017. In the upper panel we can see diurnal cycles in temperature. Pressure varies on longer timescales. Low pressure is associated with storms (high winds and wind gusts).
Clicking on “local”, choosing the “Local Data/Records” tab, and clicking on “Daily Records for cities in Oregon” brings me to the website of the Western Regional Climate Center http://www.wrcc.dri.edu/summary/climsmor.html where I can choose a city. Now I choose “Corvallis Oregon State University” and scroll down on the left side menu to “Graph and Lister” under “Daily Data”. Now select the time period (I choose 20120101 to 20160101) and click on the “Daily Precipitation” button and then “Create Graph”.
Figure 4: Local weather data (daily minimum and maximum temperature and daily precipitation) from Corvallis, Oregon, from 2012 to 2016. Large variations in temperatures from ~40°F in winter to ~65°F in summer indicate the seasonal (or annual) cycle. Precipitation, which is very episodic, also shows a large annual cycle with lots of rain in winter and less rain in summer. The mean seasonal cycle is shown in Figure 5. From wrcc.dri.edu
Averaging many years of these weather data yields a climatology. At the WRCC website you can see plots of climatologies over 30 years by scrolling back up the left side menu and clicking on one of the “Daily Temp. & Precip.” links. For Corvallis it shows average temperatures around 40°F (4°C) in winter and 65°F (18°C) in summer. It also shows a larger diurnal cycle in summer than in winter. What could be the reason for that? Answer: less cloud cover in summer. Clouds lead to cooler days and warmer nights, thus decreasing the diurnal cycle. As expected, precipitation is low in summer and high in winter. Curves are smooth in the climatology. In contrast to Figure 4 the weather variations have been averaged out.
Figure 5: Climatology of temperature and precipitation in Corvallis, Oregon. The difference between the red and blue curve denotes the average diurnal cycle.
Now try a forecast of temperature for next August and next January. What is your guess? Answer: a good guess would be the climatology, i.e. 65°F for August and 40°F for January. Thus the average seasonal cycle is climate and it is predictable because it is forced by the seasonal cycle of solar irradiance. However, deviations from the climatology for a certain day next year are unpredictable weather.
Statistical properties of climate can be summarized in a frequency histogram. Let’s explore those with an example. Download from the WRCC website daily average temperature data from a certain day of year, e.g. May 1st. You can do this by clicking on the “Lister” link in the left menu. Next choose start and end days (e.g. 19851001 and 20160101) to create a list of 30 years of data. Enter “wrcc35” as the password. Let’s choose “Average Temperature” as the element to list. Now select a day of the year (e.g. May 1st) for both “Starting Date” and “Ending Date”, and click the “Get Listing” button. This gives the following list (or vector mathematically speaking) of N=30 temperatures T = ( T 1, T 2, … , T N) = (52, 52, 44, 52, 52, 54, 48, 52, 50, 56, 59, 46, 64, 58, 55, 48, 54, 54, 63, 54, 51, 54, 42, 52, 52, 48, 48, 48, 66, 56). Here N = 1 corresponds to 1985, N = 2 to 1986 and so on. In order to construct the frequency histogram we first need to choose bin sizes. I propose to choose 6 bins: 40-44, 45-49, 50-54, 55-59, 60-64, and 65-70. Now simply count how many years fall into each bin. I get 2, 6, 14, 6, 2, 1. Using python I produced the following graph (Figure 6).
Climate change can be expressed as a change in the mean, which would correspond simply to a shift of the whole histogram to warmer or colder temperatures. However, it can also change the shape of the histogram. For example by making the distribution wider or narrower. This would increase or decrease the occurrences of extreme events. And, of course, it can both change the mean and the width. Most of the time, including in this text, discussions of climate change consider only changes in the mean. But we should keep in mind that changes to the tails of the distribution may be equally important because it is those tails that can have large impacts (heat waves, cold spells, droughts, floods, etc).
Figure 6: Histogram of 30 year (1985-2016) temperatures in Corvallis on May 1st. Most years (14 out of the 30) the temperature is between 50 and 55°F. The mean of the distribution is 52.8°F, its standard deviation of σ =5.4°F represents its width (about 2/3 of all years have temperatures within ±1 σ of the mean). The histogram, although it approximates well a Gaussian (normal) distribution, is slightly asymmetric such that very warm years (65-70) occur slightly more often than very cold extremes (35-40). The tails of the distribution are the upper and lower bins. They represent rare or extreme events. Only one year was warmer than 65°F. This year (2014) can be regarded as an extreme event. It was a record warm year in Oregon.
Now let’s explore effects of spatial scales on climate variations. Go to https://www.ncdc.noaa.gov/cag and start with the U.S. tab selection. We want to compare the annual average temperature change in a city of your choice (e.g. Salem in Oregon), averaged over the larger region of a state, for the U.S. as a whole, and the globe (use “Globe” tab and plot “Land and Ocean”, “Land”, and “Ocean” separately). For “Timescale” select “Annual”. Check the “Display Trend” box. This will include a trend line on the graph. Compare the trends and year-to-year variations. Are the trends larger for the global ocean or for the global land?
Compare the year-to-year variations and trends in regionally and globally averaged temperatures (Figs. 7-12) to the local diurnal cycle, day-to-day weather fluctuations (Figure 3), and to the seasonal cycle (Figs. 4-5). Even though the global temperature trends (~0.1°C/decade) are much smaller than diurnal, day-to-day or seasonal fluctuations, which can be 10°C or more, we will see in the remainder of this course that global temperature changes of a few degrees C will have important impacts on natural systems and human societies.
Figure 7: Annual average temperature changes in Salem, Oregon. Note the large year-to-year variations of 2-3°C and the small overall trend (blue line).
Figure 8: Averaged over the state of Oregon temperature fluctuations from year-to-year are reduced (1-2°C) compared with the figure from Salem and there is a strong (0.1°C/decade) warming trend.
Figure 9: Averaged over the contiguous U.S. the year-to-year variability has slightly decreased compared to Oregon. The warming trend is similar but slightly lower.
Figure 10: This figure shows anomalies (differences) with respect to the 1901-2000 average of global averaged land temperatures. Averaged over all of Earth’s land areas the year-to-year variations are further reduced (~1°C) and the trend is with 0.1°C/decade slightly larger than that in the U.S.
Figure 11: Averaged over the global ocean the year-to-year variations are still smaller (~0.5°C) than over land and the trend is also smaller (0.06°C/decade).
Figure 12: Averaged over land and ocean the increase in Earth’s temperature over the past 100 years has been approximately 0.7°C (0.07°C/decade). This result is very robust. Several groups around the world have analyzed the available data in different ways and came to the same conclusion. This is a key figure.