Category Archives: R Climate Data Analysis Tool

Plotting Atmospheric Temperature Structure and Lapse Rate

This post presents an RClimate script to develop a temperature sounding profile and calculate the ambient lapse rate using University of Wyoming atmospheric sounding data. Understanding atmospheric structure and lapse rate is essential to a full understanding of the role of  greenhouse gases in global warming.

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RClimate Tools for Do It Yourself Climate Trend Analysis – Nov, 2010 Update

I have made several updates to  RClimate tools for do-it-yourself  climate scientists.  The downloadable monthly climate trends file  (link to csv file) now includes the 5 major global land-ocean temperature anomaly time series (GISS, HAD, NOAA, RSS, UAH) as well as  PDO, AMO and NINI34 indexes.  Stay tuned, I plan to add several more series in the next few weeks. Do you have any suggestions?

I have also added several functions to my on-line RClimate.txt file to help DIY  citizen climate scientists to quickly and easily retrieve up to date climate trend data so that they can spend their time analyzing the  temperature anomaly and climate oscillation trends rather than slugging through data downloads and reformatting.

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September Anomaly Trends Show Global Warming Continues: Update 1

In this post I present a trend chart which shows the September anomaly trends for the 5 major global temperature anomaly series and a table that shows how September 2010 ranks over the entire record for each series. The source data and RClimate script file links are provided.

Update 1: In a comment,  ChristianP  suggested the addition of a loess regression fit to the trend line chart. Thanks ChristianP.

Click to Enlarge

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Arctic Sea Ice Extent Trends: 1979-2014; Update 2

Now that the 2010 2013 Arctic sea ice melt season is over, we can see how 2010  2013 fits into the long-term trends Arctic  Sea Ice Extent. This post shows an R Climate chart that I have made to look at the annual  NSIDC Arctic Sea Ice Extent maximum, minimum and seasonal melt trends for the 35 year period, 1979 to 2013. Data and RClimate scripts are provided.
Update 1 (10/6/10) Added trend lines to plots based on suggestion from reader. Update 2: Extended to 2014, included R script.

Here’s my RClimate script trend chart of 1979-2010  NSIDC Arctic Sea Ice Extent data.  I have plotted NSIDC’s maximum and minimum sea ice extent for each year and my calculated value for seasonal melt (maximum – minimum). (Click image to enlarge)

Arctic_sea-ice_extent

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RClimate Tools for Do It Yourself Climate Trend Analysis

In this post I introduce my RClimate functions which allow R users to easily download and plot monthly temperature anomaly data for the 5 major global temperature anomaly data series: GISS, HAD, NOAA, RSS, UAH.

Consolidated LOTA Data File

In this previous post I introduced my global  Land Ocean Temperature Anomaly (LOTA) monthly csv file that Excel and R users can download to conduct climate trend analysis.

In this post, I introduce my RClimate.txt R scripts that users can source() to simplify access to the LOTA data.  Please note that I have used the “.txt” descriptor  for my file type to avoid download problems encountered when I use the standard R file descriptor.

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Time Series Regression of Temperature Anomaly Data: 1 – Don’t Use OLS

Phil Jones Statement (February , 2010)

Phil  Jones of the  Climate Research Unit (CRU) responded to a series of questions from the BBC in early February, 2010 (link). Question B dealt with global warming trends in the 1995 – 2009 period. Here’s the BBC question and Phil Jones answer:

BBC Question B: B – “Do you agree that from 1995 to the present there has been no statistically-significant global warming?”
Phil Jones Answer: “Yes, but only just. I also calculated the trend for the period 1995 to 2009. This trend (0.12C per decade) is positive, but not significant at the 95% significance level. The positive trend is quite close to the significance level. Achieving statistical significance in scientific terms is much more likely for longer periods, and much less likely for shorter periods.”

Phil Jones’ statement provided a time series regression learning moment for  many of us citizen climate observers who quickly checked his statement with our Excel, R or other handy regression analysis tools.  I sure did. Two readers, J and S, contacted me with questions – comments:

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Tracking Long Term and Recent UAH Channel 5 Anomally Trends

In this post I present  combined long term and recent trend charts of UAH Channel 5 temperature anomalies using R’s figure inside figure capability.  This approach provides a better picture of global temperature trends than UAH’s day-of-year plots.

Introduction

The University of Alabama Huntsville (UAH) provides a daily update to their Channel 5 global average temperature at 14,000 feet. I have previously posted about this data set here. The source data file link is here.

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