Tag Archives: data source

The EIA article U.S. renewable energy consumption surpasses coal for the first time in over 130 years by Mickey Francis (5/28/2020) has the data.

In 2019, U.S. annual energy consumption from renewable sources exceeded coal consumption for the first time since before 1885, according to the U.S. Energy Information Administration’s (EIA) Monthly Energy Review. This outcome mainly reflects the continued decline in the amount of coal used for electricity generation over the past decade as well as growth in renewable energy, mostly from wind and solar. Compared with 2018, coal consumption in the United States decreased nearly 15%, and total renewable energy consumption grew by 1%.

The obvious question is are we using less electricity and if not what other source is picking up the slack? The eia Electricity explained page has an interactive time series of electricity generation.  Natural gas has picked up the slack.

The first article has two other charts and links to data.

For those of us living in the northeast or a good part of the U.S. we might have felt that April was cold and it was. It is easy to use that as evidence that climate change is “fake news” yet it is good to keep in mind that if it is cold where you are it is likely much warmer somewhere else. The map here is from NASA’s GISS Surface Temperature Analysis page where similar maps can be made for a variety of time periods.  Here we can see that for April 2020 parts of the U.S. and Canada where one of the  few cold spots in the world. The rest of the planet was warmer.

NOAA’s Global Climate Report – April 2020 notes:

Averaged as a whole, the global land and ocean surface temperature for April 2020 was 1.06°C (1.91°F) above the 20th century average of 13.7°C (56.7°F) and the second highest April temperature in the 141-year record. Only April 2016 was warmer at +1.13°C (+2.03°F). The eight warmest Aprils have occurred since 2010. April 2016 and 2020 were the only Aprils that had a global land and ocean surface temperature departure above 1.0°C (1.8°F).

Time series data is available on the NOAA page. Note that NASA uses 1951-1980 as their baseline while NOAA is using the 20th century. This accounts for the slight differences in their calculations on April’s anomaly from the baseline.

The NASA article Can’t ‘See’ Sea Level Rise? You’r looking in the Wrong Place by Alan Buis (5/13/2020) combines the quantitative facts of sea level rise with stories of places feeling the impact.

“Thanks to satellite and tide gauge data, we know that sea level is rising about 3.3 millimeters (0.13 inches) a year, a rate that grows by another 1 millimeter (0.04 inches) per year every decade or so,” Willis said. “Each year, global warming is currently adding about 750 gigatonnes of water to the ocean – enough to cover my home state of Texas about 1 meter (more than 3 feet) deep. We can’t really eyeball a few millimeters of sea level rise a year just by looking at the ocean because of waves, tides, etc. But we can definitely see the effects of it, both short- and long-term.”

Ocean Isle Beach NC:

I passed a woman walking her dog and asked her about the homes. “There used to be two streets of houses in front of these homes,” she told me. “Now they’re oceanfront.”

Norfolk, VA:

Over the past couple of decades, high tide flooding here has accelerated rapidly, and now occurs about 10 days a year, causing flooding in downtown Norfolk.

Sea Level data from NASA’s Sea Level Page.

The CDC’s new webpage Excess Deaths Associated with COVID-19 provides one method to measure pandemic related deaths:

Estimates of excess deaths presented in this webpage were calculated using Farrington surveillance algorithms (1). For each jurisdiction, a model is used to generate a set of expected counts, and the upper bound of the 95% Confidence Intervals (95% CI) of these expected counts is used as a threshold to estimate excess deaths. Observed counts are compared to these upper bound estimates to determine whether a significant increase in deaths has occurred. Provisional counts are weighted to account for potential underreporting in the most recent weeks. However, data for the most recent week(s) are still likely to be incomplete. Only about 60% of deaths are reported within 10 days of the date of death, and there is considerable variation by jurisdiction.

The interactive graphics allows the user to choose a jurisdiction and different data types. The graph here is for the U.S. and weekly excess deaths. All data can be downloaded as a csv file.

TPM put together a number of graphs comparing the NYC metro area to the rest of the country in their article Distinguishing the NYC Metro Outbreak from the Rest of the Country by Josh Marshall (5/6/2020).

The NYC metro area was hit early and hard by COVID-19, but will it end up a unique hot spot in the U.S. or will the rest of the country be hit similarly? Given the size of the U.S. we really can’t even compare U.S. states to European countries. Time will tell, but this data is worth keeping in mind.

The data for COVID-19 deaths by county in the U.S. from Johns Hopkins is here. The data is updated daily.

The Census Bureau report Last Census Population Estimates of the Decade (4/6/2020) has a great interactive graphic of U.S. population change for each state. The image here is what it looks like and if you scroll over a state the data is highlighted in the other graphs. Overall,

The U.S. population was at 328.2 million on July 1, 2019, up 0.48% since July 1, 2018. Growth has slowed every year since 2015, when the population increased 0.73% relative to the previous year.

The three states with the most growth were Texas (3,849,790), Florida (2,673,173) and California (2,257,704). The District of Columbia had the highest percentage change (17.3%), followed by Utah, Texas and Colorado.

Four states have lost population since the 2010 Census: Vermont (-1,748), Connecticut (-8,860), West Virginia (-60,871) and Illinois (-159,751).

The article and graphic are an excellent QL resources. Also, the data is cited at the bottom and the Census Bureau posts all of its data.

From the NOAA Global Climate Report – March 2020:

Averaged as a whole, the global land and ocean surface temperature for March 2020 was 1.16°C (2.09°F) above the 20th century average of 12.7°C (54.9°F) and the second highest in the 141-year record. Only March 2016 was warmer at 1.31°C (2.36°F). The 10 warmest Marches have all occurred since 1990, with Marches of 2016, 2017, 2019, and 2020 having a global land and ocean surface temperature departure from average above 1.0°C (1.8°F). The March 2020 global land and ocean surface temperature departure tied with February 2020 and December 2015 as the third highest monthly temperature departure from average in the 1,683-month record. Only February and March 2016, when a strong El Niño was present in the tropical Pacific Ocean, had higher temperature departures.

Data for the graph here.

The Climate.gov article Nature’s archives: piecing together 12,000 years of Earth’s climate story by Alison Stevens (4/15/2020) provides an overview of paleoclimate proxies and links to a new database of these records.

Paleoclimate proxies indirectly record climate and atmospheric conditions present when they formed or grew; air bubbles in ice cores sample past carbon dioxide levels, pollen and undecayed plant matter reveal growing conditions, and the ratio of oxygen isotopes in marine fossils indicate ocean temperatures. Proxies can come from all over the world — from glaciers in Antarctica to the tropical oceans — and compiling them into datasets can help place today’s warming climate into the context of a longer history.

The article links to the Nature post A global database of Holocene paleotemperature records with the data source at NOAA’s Temperature 12k Database.

The EPI article State of Working America Wages 2019 by Elise Gould (2/20/2020) provides a detailed summary of wage growth. For example, copied here is the third of over 20 charts. Note that he bottom 10 percent is barely above 0 and only recently got there. A related fact from their previous  chart:

 As shown in Figure B, the top 1% of earners saw cumulative gains in annual wages of 157.8% between 1979 and 2018—far in excess of economywide productivity growth and over six times as fast as average growth for the bottom 90% (23.9%). Over the same period, top 0.1% earnings grew 340.7%.

Each chart has avaialbe data.