“But Data”

This square represents a way cast doubt on the quality of measurements, the extent of proxies, and other aspects of the evidence basis supporting AGW. Its main problem is that AGW rests not on one specific data source, but on the consilience coming from various lines of evidence.

Can turn into #ButScapegoat hen a particular organization is targeted (NOAA, NASA, DMI, etc). Also connected to #ButPrediction.


All the climate scientists are faking their data for funding.


The satellite data are the best data we have.


Objections and Replies

Adjustments. All data are adjusted, therefore they’re crap—
☞ Welcome to reality. Embrace crappiness or you’ll remain unhappy {1}.

All Theory. AGW is all theory and no data, and there is no middle ground—
☞ On the contrary, a theory without data is blind, and data without a theory are void. We need both data and theory to do science.


Better. If only we could have a better resolution of—
☞ A pixel-by-pixel representation of the Earth with absolutely exact temps at each point of the time continuum would indeed be great. Until then what we got will have to do {2}


Error. Some Auditor A has uncovered some error E in some dataset D
☞ Happens all the time. Do you have a point?

Fudge. Some organization O tampered with D using fudge F
☞ You mean they corrected an error in the raw data source. Do you have a point? {3}

Future. We do not have data about the future
☞ Agreed. We have enough data to make this bold prediction: you will recycle But Prediction in the near future

Global Temperature. A single temperature for Earth isn’t poss
☞ Only zero-dimensional sanity checks do that.

Lab. In a lab, there is no day and night, and the local humidity level—
☞ Humidity can indeed mask CO2 contribution. In the desert there is still considerable backradiation, however, as can be measured with a pyrgeometer or even a cheap IR thermometer (Vaughan Pratt).


No. There is no data set to complete your claim—
☞ Where have you checked?

Reconstructions. The Auditor found problems with many datasets—
☞ Science marches one funeral at a time. Contrarian zombies are already dead. If you got a better recon, I’m all ears.

Satellites. Satellites are the most accurate measurements—
☞ They’re model-based as they measure brightness, not temperatures. Also, researchers may have misunderestimated warming of the lower troposphere for 40 years.

Sea level. There has not been a long-term distinctive change in sea level—
☞ There has. You’re not listening to Tony, Pierre or Kenneth, are you?

Stations. There were only about 300 to 500 stations reporting data in 1880—
☞ To paraphrase Dick Cheney, you go to press with the data you have, not the data you might want or wish to have at a later time.

Tell. The data tells me that—
☞ Data does not “tell” anything. You’re not a data truth-teller. See #ButEvidence for more.

Time Series. But this or that time series—
☞ The five main ones show significant global warming.

Urban Heat.


{1} Crappiness. Databases are crap. Let’s embrace crappiness instead of channeling impossible demands of perfection.

{2} High Expectation Auditor. It is trivial to cast doubt on less than pristine datasets.

{3} Double Bind. Concerns about error and tampering can create a self-sealing argument: both a corrected and an uncorrected error would indicate misbehavior.

* * *


Climate Data Guide offers data sets, climate indices, reanalyses, and model metrics.

Climate Data Information provides free graphs and data on AGW.

Climate History Databases lists all the main databases.

International Surface Temperature Initiative

2019-10; Explainer: How climate change is accelerating sea level rise.

2018-05; The Pursuit of Crappiness.

2015-10; How well do temperature indices agree?

2015-04; Understanding Adjustments to Temperature Data


2021-06; Using Climate Model Simulations to Constrain Observations; https://doi.org/10.1175/JCLI-D-20-0768.1

2020-10; A theoretical basis for the equivalence between physical and economic climate metrics and implications for the choice of Global Warming Potential time horizon; https://doi.org/10.1007/s10584-019-02486-7

2020-02; Sampling frequency of climate data for the determination of daily temperature and daily temperature extrema; https://doi.org/10.1002/joc.6528

2017-01; A Comparative Analysis of Data Derived from Orbiting MSU/AMSU Instruments; https://doi.org/10.1175/JTECH-D-16-0121.1

2015-03; Removing Diurnal Cycle Contamination in Satellite-Derived Tropospheric Temperatures: Understanding Tropical Tropospheric Trend Discrepancies; https://doi.org/10.1175/JCLI-D-13-00767.1

2013-11; Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends; https://doi.org/10.1002/qj.2297

2013-01; Influence of Urban Heating on the Global Temperature Land Average Using Rural Sites Identified from MODIS Classifications. DOI: 10.4172/2327-4581.1000104

Sample Climateball Episodes

2021-01; Rates of global sea level rise have accelerated since 1900, contrary to bloggers’ claims.

2019-03; Factcheck: What Greenland ice cores say about past and present climate change;
good example of how contrarian networks peddle crap.

2017-02; Climate scientists versus climate data;
when JohnB tried to infer some conspiracy against Karl and the NOAA.

2016-01; Over-reliance on satellite data alone criticized;

2010-09; Comparing Proxy Reconstructions; Zeke shows that none equals today’s warmth.

2007-03. Does a Global Temperature Exist?