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Earth Just had its 400th Consecutive Warmer-Than-Average Month Thanks to Global Warming

posted by martyb on Friday May 18 2018, @10:55AM   
from the gonna-need-a-MUCH-bigger-parasol dept.

DeathMonkey writes:

It was December 1984, and President Reagan had just been elected to his second term, Dynasty was the top show on TV and Madonna's Like a Virgin topped the musical charts.

It was also the last time the Earth had a cooler-than-average month.

Last month marked the planet's 400th consecutive month with above-average temperatures, federal scientists from the National Oceanic and Atmospheric Administration announced Thursday.

[...] "We live in and share a world that is unequivocally, appreciably and consequentially warmer than just a few decades ago, and our world continues to warm," said NOAA climate scientist Deke Arndt. "Speeding by a '400' sign only underscores that, but it does not prove anything new."

https://www.usatoday.com/story/news/world/2018/05/17/global-warming-april-400th-consecutive-warm-month/618484002/?csp=chromepush

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Original Submission

• Data source (Score: 5, Informative) by Anonymous Coward on Friday May 18 2018, @12:48PM

  by Anonymous Coward on Friday May 18 2018, @12:48PM (#681129)

  Looks like this is the data source: https://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt [nasa.gov]

  Here is a plot by month: https://image.ibb.co/daWWfd/TempByMo.png [image.ibb.co]

  Looks interesting... I'd say there are three phases (pre-1940, 1940-1980, and post-1980)

  Here is a plot of the number of stations over time: https://data.giss.nasa.gov/gistemp/history/output/numStations.png [nasa.gov]

  I'd say there are three phases (pre-1950, 1950-1990, and post-1990), so almost a perfect 10 yr lag.

  More details on the methods:

  Documentation and Assessment of Results

  The analysis method was fully documented in Hansen and Lebedeff (1987), including quantitative estimates of the error in annual and 5-year mean temperature change. This was done by sampling at station locations a spatially complete data set of a long run of a global climate model, which was shown to have realistic spatial and temporal variability. This however only addresses the error due to incomplete spatial coverage of measurements.

  As there are other potential sources of error, such as urban warming near meteorological stations, many other methods have been used to verify the approximate magnitude of inferred global warming. These methods include inference of surface temperature change from vertical temperature profiles in the ground (bore holes) at many sites around the world, rate of glacier retreat at many locations, and studies by several groups of the effect of urban and other local human influences on the global temperature record. All of these yield consistent estimates of the approximate magnitude of global warming, which reached about 0.8°C in 2010, twice the magnitude reported in 1981.

  Further affirmation of the reality of the warming is its spatial distribution, which has largest values at locations remote from any local human influence, with a global pattern consistent with that expected for response to global climate forcings (larger in the Northern Hemisphere than the Southern Hemisphere, larger at high latitudes than low latitudes, larger over land than over ocean).

  More recent documentation (Hansen et al. 2010) compares alternative analyses and addresses questions about perception and reality of global warming; various choices for the ocean data are tested; it is also shown that global temperature change is sensitive to estimated temperature change in polar regions, where observations are limited. A multi-year smoothing is applied to fully remove the annual cycle and improve information content in temperature graphs. Despite large year-to-year fluctuations associated with the El Niño-La Niña cycle of tropical ocean temperature, the conclusion could be made that global temperature continued to rise rapidly in the 21st century, new record heights being reached in every decade.
  GISS Homogenization (Urban Adjustment)

  One of the improvements — introduced in 1998 — was the implementation of a method to address the problem of urban warming: The urban and peri-urban (i.e., other than rural) stations are adjusted so that their long-term trend matches that of the mean of neighboring rural stations. Urban stations without nearby rural stations are dropped. This preserves local short-term variability without affecting long term trends. Originally, the classification of stations was based on population size near that station; the current analysis uses satellite-observed night lights to determine which stations are located in urban and peri-urban areas.

  https://data.giss.nasa.gov/gistemp/ [nasa.gov]

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