I dug into raw temperature date, looked at the role of water vapor, and compared historical temperature patterns. I realized the story is more than the simplified slogans. This post is about my observations, explains the scientific context (where people often get tripped up), and gives a checklist for anyone who wants to reproduce the analysis. I plotted raw station data, learned how water vapor acts as a greenhouse gas, and compared historical temperature trends. Here’s a clear, practical look at why context and methods matter — and how to check the data yourself.
1) Water vapor (clouds) is a major greenhouse factor
My observation
When I reviewed lists of greenhouse gases, I noticed water vapor (clouds) have the largest direct impact on heating and cooling. If clouds dominate the greenhouse effect, then focusing on CO₂ felt misleading. Water vapor is the most abundant greenhouse gas and has a strong effect on radiative balance and temperature.
2) Historical temperature trends are not a single straight upward line
My observation
I saw historical temperature plots showing a relative cooler period (a “mini ice age” type dip) and noticed a dip around the early industrial era. CO₂ from fossil fuel burning did not suddenly rise sharply during the industrial revolution. I expected a straightforward stepwise warming spike which didn’t match the idea that C02 is the reason for an increase in global temperature.
Important context and common pitfalls
- Long-term climate has variability. Volcanic activity and solar variations (sun spot activity) can create dips and peaks.
- A single station is not a global thermometer. Urban development can raise nighttime minimums (urban heat island) without changing global maximas.
“Global warming” rebranded to “Climate change”
- This shift in language reflects that belief that climate includes more than mean temperature increases. That’s fine. Let’s be clear - climate change is not due to human actives and CO2 produced by human actives.
- This shift has been characterized as an attempt to “cover up” data. It’s meant to communicate a broader idea. That’ fine but “climate change” is not due to man’s activities - it’s due to mother nature.
Plotting raw station data yourself is powerful
I downloaded raw temperature records, selecting Phoenix Sky Harbor as a station with continuous data, plotted 100 years, and found no increase in daily maximum temperature. It might appear there is a slight increase in the nightly minimums (how cool it gets at night in Phoenix). This suggests urban development/land-use changes, rather than a global daytime increase in temperature.
Strengths of this approach
- Transparency: working with raw data avoids black-box summaries, It lets you check assumptions and see things for yourself.
- Station-level insight: single stations reveal local effects (growth) and allows you to check the data for instrumentation changes and station location changes.
- Focus on Tmax vs Tmin: Tmin (nighttime minimum) often rises with urbanization, Tmax may be influenced by regional factors. Or not.
Key methodological caveats (so your data and conclusions are robust)
If you want a robust interpretation, check these factors:
- Station metadata (station moves, instrument changes, location siting): changes in station location or instruments can create artificial jumps/trends. Homogenization methods are used by climate centers to correct for these, changes not disclosed to those who use homogenized date . By downloading your own RAW data you can see everything yourself.
- Urban Heat Island (UHI): local development (airports, pavement, buildings) may raise nighttime temperatures and bias local trends.
- Homogenization vs raw: curated datasets (NOAA GHCN, NASA GISS, HadCRUT) apply homogenization algorithms to account for non-climatic station changes, missing data points (days the weather station was down) and delete data points it considers to be “flyers”, anomalies. This editing, considered by some to be a legitimate statistical process, is not transparent and prevents common man from “seeing it for yourself.” It’s not disclosed what gets adjusted and why - you just see someone else’s plot or the data they created.
- Record length and sample size: one station over 100 years is more valuable and logical than global conclusions that use many stations and spatial averaging. You can never have enough weather stations around the world. No such data exists going back a century or more.
Practical reproducible checklist: how to validate your analysis
- Consider station metadata: instrument changes and station moves.
- Compare raw vs homogenized datasets: download the raw station record data and compare that to its homogenized counterpart (NOAA GHCN-Daily, NASA GISS station series, etc.).
- Plot more than maximum temperature. Plot the Tmax, Tmin, daily mean.
- Document everything: publish code/scripts and data sources so others can reproduce your work.
This article helps you think clearly in a noisy world, cut through misinformation, and find the solutions as applied to climate change and global warming.