NOAA has a page, Sea Ice and Snow Cover Extent, where you can create graphs for snow cover by four regions (Northern Hemisphere, North America and Greenland, Eurasia, and North America) for each month of the year. For example the graph here is for North America in March. The green line is the average and the red the trend. For each graph you can download the associated data or simply download the graph.
Our World in Data has the answer in their post, 50% of the world’s habitable land is used for agriculture. If we all ate like New Zealanders we would need 200% of habitable land, which is supplied in the chart. Simply put, the world all can’t eat like the U.S. The world can’t eat like the countries colored in orange but can with those colored in green. Why?
Livestock takes up nearly 80% of global agricultural land, yet produces less than 20% of the world’s supply of calories. This means that what we eat is more important than how much we eat in determining the amount of land required to produce our food.
There is an association between wealth and diet as can be seen in the chart below, but there are variations.
Nonetheless, there are still large differences in dietary land requirements between countries of a similar income-level. Why, for example, is the requirement for a New Zealander more than double that of a UK citizen, despite them having similar levels of prosperity?
As always Our World in Data includes the data for each of their charts and there are more than the two here. They also allow you to download the graphics which was done for this post.
Here is a post from the International Energy Agency (IEA). Energy Subsidies,with an interactive graph and data in an Excel spreadsheet. The data is in Millions of USD and you’ll see that the subsidies aren’t insignificant.
The value of global fossil-fuel consumption subsidies in 2015 is estimated at around USD 320 billion, much lower than the estimate for 2014, which was close to USD 500 billion.
Interestingly, the U.S. isn’t in the chart or Excel file and so the global subsidies likely don’t include the U.S. Still, the data is useful.
One of the best ways to engage students in sustainability discussions is to use local information. NOAA has you covered with The Climate Explorer. You can type in your zip code and get historical and projected climate data. Today we highlight temperature. For example, the associated graph is the average annual maximum temperature for Tompkins County (home of this blog). The dark gray boxes are historical data. The blue and red lines are projections based on low and high emission scenarios. You can download the graph (just like we did here) and the data. There are numerous choices including average annual minimum temperatures, days above 95 degrees and days below 32 degrees. You can also select monthly or seasonal data. The site is phenomenal and there must be numerous courses that can take advantage of the graph and data.
This blog focuses on data, but we pause periodically to put the data into perspective. When educating about sustainability we want stories along with the data. The BBC provides such a story: The island people with a climate change escape plan. The Guna people live on small islands off Panama.
Most Guna communities live on the archipelago, and have done for centuries, after they were driven offshore by disease and venomous snakes. But now many believe that only a move back to the mainland can secure their future.
They have a plan, but completing the plan isn’t simple.
However, today work on the school and hospital has halted, as a result of a litany of contractual hiccups – and crucially, a failure to plan for adequate supplies of water and electricity. Work never began on the 300 houses.
Along with rising water there are other environmental issues.
“Coral reefs stop wave action. So when you remove the coral, even down to 3m in depth, you have no protection. This has created chaos for people,” says Dr Hector Guzman, a research scientist at the Smithsonian Tropical Institute in Panama City.
This is an excellent story with great photos. Take the time to read it.
NOAA’s Climate Change: Ocean Heat Content page provides a summary of the role the Ocean plays in Climate Change.
Heat absorbed by the ocean is moved from one place to another, but it doesn’t disappear. The heat energy eventually re-enters the rest of the Earth system by melting ice shelves, evaporating water, or directly reheating the atmosphere. Thus, heat energy in the ocean can warm the planet for decades after it was absorbed. If the ocean absorbs more heat than it releases, its heat content increases. Knowing how much heat energy the ocean absorbs and releases is essential for understanding and modeling global climate.
The page is dated July 2015, but the interactive graph and the data, used to create the graph here, is up to date. Connected to this is NOAA’s Hurricanes form over tropical oceans, where warm water and air interact to create these storms.
Recent studies have shown a link between ocean surface temperatures and tropical storm intensity – warmer waters fuel more energetic storms.
Our World in Data’s article Yields vs. Land Use: How the Green Revolution enabled us to feed a growing population includes an excellent set of data. For example, thier data was used to produce the graph here, which includes the index relative to 1961 for land used for cereal (yellow), population (black), cereal yield (red), and cereal production (blue). Notice that as population has increased the land use for cereal production has remained flat, while cereal production has increased.
Most of our improvements in cereal production have arisen from improvements in yield. The average cereal yield has increased by 175 percent since 1961. Today, the world can produce almost three times as much cereal from a given area of land than it did in 1961. As we will explain below, this increase has been even more dramatic in particular regions.
Along with world data there is also regional data. Almost all of the data is useful for linear regression and the article itself has interactive graphs for a QL course. Note also that there is world grain data in the statistics projects section of this blog.
A recent YouGov article, Is anyone ever “asking for it?” Americans seem to think so, provides the pie chart to the left. According to the data, 40% of adults believe that a women wearing revealing clothing is fully or somewhat responsible for unwanted sexual advances. Along with that, another 17% prefer not to say and 6% don’t know. Maybe a better way of reporting the results is that only 36% of adults say that the person is not at all or not very responsible. There is other data in the article as well as a link to the full survey results. This data that is sure to generate a conversation in stats or QL course.
Are you interested in historical temperature trends for your state? NOAA’s State Annual and Seasonal Time Series page has it for you. You can create graphs of annual average min and max temperatures as well as the annual mean temperature, for almost all states (Alaska and Hawaii aren’t listed) . This can be done for annual data or for each of the four seasons. The graphs are from 1805 to 2015. The graph hear is the annual mean temperature for New York State.
These charts present three color-coded time series. The gray line represents the annual (or seasonal) temperature value. The blue line shows the overall trend in a fashion that smoothes out the year-to-year variability in temperature. The light blue shaded area represents the 95% confidence interval for the trend. The smoothed temperature is constructed using a locally estimated scatterplot methodology known as LOESS.
There does not appear to be easy access to the data, but if you contact them (Contact link on the top bar) they may send it to you. Either way, the graphs include confident intervals, useful in stats, and can be used in QL courses. There is also an interactive U.S. temperature map.
The headline from NASA’s Goddard Institute for Space Studies says almost all you need to know, July 2017 equaled record July 2016.
July 2017 was statistically tied with July 2016 as the warmest July in the 137 years of modern record-keeping, according to a monthly analysis of global temperatures by scientists at NASA’s Goddard Institute for Space Studies (GISS) in New York.
Last month (July) was about 0.83 degrees Celsius warmer than the mean July temperature of the 1951-1980 period. Only July 2016 showed a similarly high temperature (0.82 °C), all previous months of July were more than a tenth of a degree cooler.
But, the subtitle of NASA shocker: Last month was hottest July, and hottest month, on record says more
It’s the first time we’ve seen such a record month in the absence of an El Niño boost.
In other words, we are setting records without the help of El Niño. The map here, which you can create here, is interesting because the distribution of temperature anomalies is rather uniform (use in a stats class). You can get the data for the graph below from NOAA’s Climate at a Glance.