Category Archives: Climatology

Climate Oscillations and GISS Temperature Anomaly Trends

In this post, I examine the combined impacts of Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO) and El Nino – Southern Oscillation (ENSO)   on the long-term GISS Land and Ocean Temperature Anomaly (LOTA) trend.

Introduction

Professor Don Easterbrook of Western Washington University  has stated …

“The PDO cool mode has replaced the warm mode in the Pacific Ocean, virtually assuring us of about 30 years of global cooling, perhaps much deeper than the global cooling from about 1945 to 1977.”  source

Easterbrook’s PDO theory is repeated  here and here. Clearly he believes that the shift in PDO phase from warm to cool will have a significant impact on global temperatures for the next 30 years.

In this post I take a closer look at PDO, AMO and ENSO indexes to see how they are related to the GISS anomaly trends.

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Visualizing Arctic Sea Ice Extent Trends

Reader GH sent me an e-mail asking about a previous Arctic sea ice extent trend post (click). GH asked ….

“Why is there such a difference between this type of representation and the chart at link ? What you’ve written above seems to imply that the definitions of extent are the same.   Just looking at 2002 – present,  I’m not clear why the JAXA chart doesn’t appear to demonstrate the same clear trend. ..”

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Tracking Nino34 Trends with ImageJ

Here’s a NOAA/NESDIS image of sea surface temperature anomalies (SSTA).

This image shows the land areas in black and has color codes for SSTAs, ranging from -5 to + 5 C. The yellow – orange color range shows positive anomalies while the blue – purple range show negative anomalies.

Many climate data sites show these NOAA images. Lucia at Blackboard, for example,  compared Oct, Nov and Dec 2007 and 2008 by displaying an image montage.

Lucia  said “..

“I have to admit I always have trouble integrating color images to estimate whether the net effect is a positive or negative anomaly. But, it is fun to look at the images…”

I have the same problem. While the images are great for giving the reader a sense for the spatial distribution of SSTAs,  it sure would be nice to be able to evaluate the anomalies in a defined areas like NINO34 or even better to specify an area and see the trend over  time!

There’s a free tool that I think is great for that!

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RCimate Script: RSS Global Temperature Anomaly Trends

This RClimate Script lets users retrieve and plot the latest data file on monthly global RSS temperature anomaly data based for the lower troposphere.   The trend chart shows monthly and annual anomalies since 1979.

RSS Temperature Anomalies – Lower troposphere

I’ve discussed RSS temperature anomalies in this earlier post.

Here’s Remote Sensing Systems (RSS)  global land and ocean temperature  anomaly data series trend since 1979.

This chart shows the monthly and annual mean satellite based global temperature anomalies s and highlights the latest monthly anomaly.

Here are the data and RClimate Script links:

RClimate Script: Sea Surface Temperature (SST) Anomaly Trends

This RClimate Script lets users retrieve and plot the National Climatic Data Center’s monthly Sea Surface Temperature (SST) Anomaly dat series .  Links to the NCDC data file and my RClimate script are included. Users can run my script with a simple R source() statement.

NCDC  SST Anomaly

I’ve discussed the Hadley SST anomaly trends in this earlier post.

Here’s NCDC’s global SST anomaly data series trend since 1880.

This chart shows the decadal means and highlights the latest monthly SST anomaly.

Here are the data and RClimate Script links:

RClimate Script: NINO 3.4 SST Anomaly Trends

This RClimate Script lets users retrieve and plot the weekly NOAA NINO 3.4 SST  anomaly data for 1990 to the most recent value.  Links to the NOAA data file and my RClimate script are included. Users can run my script with a simple R source() statement.

NINO 3.4 SST Anomaly

I’ve discussed ENSO and NINO 3.4 in this earlier post.

SST’s in 4 equatorial Pacific zones are closely monitored to assess the status of El Nino – Southern Oscillation (ENSO).

NINO 3.4 SST anomaly provides a quick and effective indication of ENSO conditions. NOAA updates the NINO 1,2,3,3.4 and 4 SST and SSTA series weekly.

Here’s theweekly NINO 3.4 trend from 1990 to the most recent weekly reading. Click image to enlarge.

Here is the Data Link.

Here is the R script that I used to generate this chart.



## set link & read data
 #link <- "http://www.cpc.noaa.gov/data/indices/wksst.for"
link <- "http://www.cpc.ncep.noaa.gov/data/indices/wksst8110.for"
 nino_34 <- read.fwf(link, widths=c(10,-31,4,4,-17),skip = 4, header=F)
 names(nino_34) <- c("cdt", "sst", "ssta")

## calc yr_frac
 dt <- as.Date(nino_34$cdt, format="%d%b%Y")
 yr <- as.numeric(format(dt, format="%Y"))
 mo <- as.numeric(format(dt, format="%m"))
 dy <- as.numeric(format(dt, format="%d"))
 yr_frac <- as.numeric(yr + (mo-1)/12 + (dy/30)/12)
 nino_34 <- data.frame(dt,yr_frac, nino_34[,2:3])

## Determine Month, Year for last reading
 c <- nrow(nino_34) # Find number of data rows
 ldt <- nino_34[c,1]
 ldt_c <- paste(mo[c],"/", dy[c],"/", yr[c],sep="")
 l_yr_frac <- nino_34[c,2]
 l_nino_34 <- nino_34[c,4]
 lp_note <- paste(ldt_c, " @ ",l_nino_34,"C",sep="")

## subset data to add color for LaNina & El Nino cnditions
 la_nina <- subset(nino_34, ssta < 0)
 el_nino <- subset(nino_34, ssta>= 0)

## Plot titles
 title = "NINO 3.4 SST Anomaly Trend (Baseline: 1950-1979) \n NOAA: Weekly Data Centered on Wed"
 y_lab <- expression(paste("SST Anomaly ",degree,"C (1950-1979)", sep=""))

p_fun <- function() {
## set plot pars
 par(ps=10); par(las=1); par(oma=c(3.5,1,0,1)); par(mar=c(2,4,2,0))

plot(nino_34$yr_frac, nino_34$ssta, type = "n", main=title, xlab="", ylab = y_lab,
 cex.main=0.95, cex.lab=0.95)
 points(la_nina$yr_frac, la_nina$ssta, col = "blue", type = "h")
 points(el_nino$yr_frac, el_nino$ssta, col = "red", type = "h")
 points(l_yr_frac, l_nino_34, pch=19, col = "black",cex=0.75)
 points(1991.5, -2, pch=19, col="black", cex=0.75)
 text (1992, -2, lp_note, adj=0, cex=0.8)

## Generate and add bottom footer KOD, source, System date notes
 source_note <- paste("Data Source: ", link)
 mtext("D Kelly O'Day - http://chartsgraphs.wordpress.com", 1,1, adj = 0, cex = 0.8, outer=TRUE)
 mtext(format(Sys.time(), "%m/%d/ %Y"), 1, 1, adj = 1, cex = 0.8, outer=TRUE)
 mtext(source_note, 1,0,adj=0.5, cex=0.8, outer=T)
 }

RCimate Script: Recent Total Solar Irradiance (TSI) Trends

This RClimate Script lets users retrieve and plot the latest data file on daily total solar irradiance (TSI) data from NASA’s  Solar Radiation and Climate Experiment (SORCE) Mission. The trend trend chart shows daily values from 2/25/03 to about one week before when the R script is run.

Recent TSI Trends

Here’s the 1/21/10 plot of NASA’s SORCE TSI data.

Here are the data and RClimate Script links:

RClimate Script: CO2 Trends

This RClimate Script lets users retrieve the latest data file on monthly Mauna Loa  CO2 levels and generate a trend chart with the latest reading highlighted.

CO2 TrendsKeeling Curve

Here’s the Mauna Loa Observatory CO2 trend from 1958 to Dec., 2009.

Here are the data and RClimate Script links:

Understanding the Science of CO2’s Role in Climate Change: 3 – How Green House Gases Trap Heat

This post, the 3rd (1st here, 2nd  here) in the series on Understanding the Science of CO2’s Role in Climate Change, discusses how water vapor, CO2, CH4, O3 and N2O  absorb and emit the Earth’s longwave radiation, changing the Earth’s energy balance.

I’ve made a 5 panel chart that shows spectra data for 5  greenhouse gases (GHG). Molecules of these gases in the atmosphere absorb and emit the Earth’s infrared radiation at specific frequencies, trapping some of the Earth’s  radiation, warming the planet.

I’ve included a link to my R script so that readers can access the online spectra data and generate your own GHG spectra.

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Understanding the Science of CO2’s Role in Climate Change: 2 – Electromagnetic Radiation and Earth’s Climate

This post, the 2nd (1st post here) in the series on Understanding the Science of CO2‘s Role in Climate Change, discusses several electromagnetic radiation topics:  1) electromagnetic spectrum basics, 2) essential climate related electromagnetic radiation physics, and 3) the Sun  and Earth’s electromagnetic radiation spectra.

I present the basic formulas and 3 R based charts that I have developed to help readers get a sense for the underlying physics and to provide a basic  foundation for understanding the climate related properties of greenhouse gases and the energy balance models presented in upcoming posts.I have also included an Excel workbook with these basics formulas.

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