data visualization

Climate change and 400 ppm carbon dioxide

January 14, 2015 by Brian Magi

In the great carbon cycle that is at work on our planet, carbon dioxide (CO2) gas concentration in our atmosphere, as measured in the most famous observation site in the world (Mauna Loa, Hawaii, home of the Keeling Curve), has risen again above 400 parts per million, or 400 ppm for short. This happened in 2014 before CO2 dipped back below 400 ppm, and while 400 ppm is an arbitrary choice to focus on, round numbers typically get more attention than, say, 397 ppm. Think about a baseball player’s batting average, which is hits divided by at bats. Somehow a 0.299 (or “299”) batting average is perceived as worse than a 0.300 (300) batting average, but really, it’s the difference of a few hits (or at bats) in the course of a season. Ted Williams hit 406 in 1941. 185 hits in 456 at bats. 3 fewer hits, and he would have hit 399, and the world would’ve sighed. 3 hits! Back to CO2. I’ll suggest, like many others, that 400 ppm is a good place to step back and think.

What is the carbon cycle?

IPCC AR5 Figure 6.1 is a nearly perfect capture (as it should be given the expertise that developed the figure!), but I boiled away the beauty to a more practical figure for my classes. carbon-cycle-boiled The reason that CO2 goes up and down in any given year is mainly because the Earth breathes in and out. When the Earth breathes in, plants draw CO2 from the air and convert it to plant carbon via photosynthesis. As a result CO2 concentration in the atmosphere goes down. When the Earth breathes out, plants release CO2 into the air via that respiration, the process of decomposition that acts in the opposite direction of photosynthesis. CO2 concentration in the atmosphere then goes up. The breath results in a steady rise in CO2 concentration from October to May, and a steady decrease from June to September. As you would expect, the rise and fall are essentially reversed in when they occur in the Southern Hemisphere, and this is evident in the data as well. As you might also surmise, in the Northern Hemisphere, the enormous number of seasonal plant growth/decay results in a bigger “breath” than in the Southern Hemisphere. Check the graph here to see that hemisphere difference.

The Keeling Curve, and CO2 concentration in general, is a way to “see” a part of the Earth’s carbon cycle, which are all the physical/chemical/biological/geological (biogeochemical, for short) processes that exchange carbon. The exchanges between carbon “reservoirs” (for example, the atmosphere and the land in the figure above) happen at different rates and magnitudes. Oceans store enormous amounts of carbon from CO2, and rocks store even more. The atmosphere is relatively carbon-free, but we are burning carbon from rock reservoirs (fossil fuels), and burning is a combustion chemical reaction that produces many carbon-containing gases and particles, but most fundamentally water vapor and CO2. This CO2 goes into the atmosphere and stays there for a long time. Water vapor goes into the atmosphere too, but leaves the atmosphere within a couple of weeks via precipitation. As a result, the year to year variability shows the Earth’s breath (land-atmosphere exchange), but the long-term trend shows that CO2 concentration itself is increasing when you compare the average from one year to one from a previous year. That long-term trend is showing how more and more carbon from CO2 is being stored in the atmosphere reservoir of the carbon cycle.

We are FORCING the carbon cycle to change by changing the amount of carbon in the atmosphere. That 400 ppm concentration value is a measure of how much carbon from CO2 (in units of mass, like kilograms or pounds) is in the atmosphere. The change in concentration is a measure of how much carbon from CO2 has been put into the atmosphere (again, in units of mass). The pre-industrial concentration of CO2 was about 280 ppm, so 120 ppm has been added to the atmosphere reservoir in the carbon cycle. It’s relatively easy to show that +120 ppm is equal to 284 billion tons of carbon added to our atmosphere.

Most of that 120 ppm is from human activities of fossil fuel burning (moving carbon from rock reservoir) and from deforestation (moving carbon from land reservoir), and 400 ppm is, as far as humans are concerned, completely unprecedented. At no time in the past 800,000 years, through several ice ages and enormous climate changes (figure at bottom), has the planet had concentrations of anything close to 400 ppm. Furthermore, it is quite clear from scientific and anthropologic evidence (at least!) that human civilization has evolved in a period of relative stability in Earth’s climate history. CO2 concentration has largely remained around 280 ppm until the last 100 years or so. Evidence that scientists have collected suggest that CO2 and temperature track each other. This is fundamentally why most climate scientists, and most scientists in general, are concerned about short and long term futures.

Humans can adapt and we will have to adapt to some degree, but the changes we are imposing on the planet through the carbon cycle are much faster than anything that we have an analog for in the past through naturally-driven climate changes. This is where carbon mitigation strategies are so critical, and why everyone is talking about the EPA Clean Power Plan, COP20 Lima, China-USA negotiations, and the upcoming COP21 Paris negotiations. These negotiations are about whether humans can live on the world without altering it in ways that more than likely is detrimental before being beneficial. Right now, the science says we are not very good tenants. With 400 ppm CO2, we are breathing air with more CO2 in it than any other human or proto-human has ever breathed. It’s not poisoning us directly, but the increased CO2 is changing how the Sun and Earth-Atmosphere system are interacting with each other. We are forcing the planet to warm as more electromagnetic radiation is absorbed by the unusual excess of greenhouse gases in the atmosphere. The warmth is changing everything, and it will continue.
co2-800k-present

Tags: adaptation carbon climate CO2 data analysis data visualization global warming mitigation

Robust features in the 2014 USA forecast

February 22, 2014 by Brian Magi

Building on a previous discussion about a seasonal forecast product from NOAA Climate Prediction Center (CPC), I am still really curious about how robust the features in the seasonal weather patterns in the USA are. “Weather” in this case is referring to temperature and precipitation (T and PCP), and features refer to 3-month boxcar averages of T and PCP anomalies compared to the corresponding 3-month climatologies. So this is not the normal day-to-day weather or even the recent weather. Here are some new figures, which I explore below in terms of features that seem to be “robust” and features that seems to be “ephemeral”.

First, temperature in two plots:

Then, precipitation in two plots:

Watch the figures carefully. All the animations start with a forecast for 3-month averaged T and PCP for March-April-May (MAM). Then, they step forward to April-May-June (AMJ)The CPC data product seems intended to provide an idea of whether T and PCP will be above or below average for the USA (including Alaska). In a previous discussion, I looked at CPC outlooks for 2014 and early 2015, and their figures and analysis were produced using actual mid-January 2014 conditions.

New data

Now another month of data is in and CPC has updated their seasonal forecast to begin with mid-February 2014 conditions. A natural expectation is that the seasonal forecast would be better earlier in the overall forecast period. In other words, as the animation above progresses, the confidence in the forecast should decrease with time. Sometimes, however, larger patterns of atmospheric variability that emerge somewhere else in the world can exert some level of control on weather patterns (T and PCP) in the USA. El Nino-Southern Oscillation (ENSO, or sometimes just “El Nino”) is the best known example.

There could be all sorts of speculative lines of thinking in terms of causes, so for now, I’ll focus on the features that seem to hold up after another month of data. I’ll call these robust, and point out one overall theme that is worth watching as winter releases its grip on much of the USA.

Robust Features

The Southwestern USA and often the Western USA in general is facing what will likely be a warmer than average year until about October. I think this is a pretty safe prediction. There is almost no evolution after more data was considered, except perhaps that the Pacific Coast tends towards higher probability of above average warmth. Upper Alaska is also holding up to the earlier forecast of warmth, especially in the northernmnost reaches. Both these regions are well known as fire prone under unusual warmth. Uh-oh. By November-December, the above average warmth shifts to the mid-Atlantic and the Southeast USA. The Northeast USA drifts towards unusual warmth starting in the summertime, maybe July, and ending about, oh, early next calendar year. For precipitation, much of the USA seems to be a normal water year. The problem is that in the near term, California remains dryer than average. Other features in a featureless prediction are that the deep South is dry in the spring, while the Ohio River Valley is wetter than average. Northern Florida and the coastal SE USA tend towards dry late in the calendar year.*

Summary and What the AVERAGE Year Looks Like

Overall, the story remains clear: The USA should experience another warmer than average year. Warmer than average is a relative term. Remember that NOAA (and CPC) define the normal temperature and precipitation amounts by the 1981-2010 30 year average. This is a particularly irritating 30 year timeframe mainly because climate is clearly warming most rapidly during the 1970 to present day period. It is what it is, but sometimes the simpler message is lost. The CPC forecast is for a year that is warmer than the 1981-2010 average. So what is the 1981-2010 average?? This is what the 1895-2013 temperature and precipitation trends for the contiguous USA (no Alaska) from NOAA NCDC with the baseline average 1981-2010 average temperature overlaid. contiguousUSA-1895-2013-annual-T

The precipitation is not the story, in my mind. The story is that we should expect a warmer than 1981-2010 year. The average of 1981-2010, without doing any math, is clearly warmer than most of the years this past century. Quickly eyeballing this number says that 82 of the 100 years in the last century are colder than the 1981-2010 average. This is really important in terms of perception of the significance of a warmer than “average” year. 1981-2010 is not a very good choice for the “average”. Gonna be a warm year according to CPC. Nowhere is there a robust and spatially significant feature suggesting below average temperatures, by the way.

*There are a few features that are not that robust, by my admittedly weak definition. For example, it’s not that clear whether the NE USA or the NW USA will tend warmer than average in the early summer. And precipitation has about the same number of features that are robust as not robust.

Tags: climate data analysis data visualization NOAA spatiotemporal analysis USA

Forecasting the USA temperature and precipitation tendency for 2014

February 13, 2014 by Brian Magi

Where we are this calendar year

Currently, the USA as a whole and the Southeastern USA are both cooler than normal this year 2014-02-12-YearTDeptUS

and precipitation is slightly below average for the Eastern USA, above average for Colorado-Wyoming-Idaho, and well below average for the Southwestern USA. 2014-02-12-YearPNormUS

Where we are right now

Thinking about the upcoming year in weather while in the midst of a crippling snow/ice storm in the Carolinas (discussion via #NWSGSP, over 2,000 outages by end of 12 Feb 2014 mostly in Lancaster, Greenville, and Pickens Counties in SC, and Macon and Caswell Counties in NC*, flights cancelled, Rayleigh-Durham turning into a parking lot like Atlanta only two weeks ago, etc.) is the perfect time to test whether you can separate a trend from variability (teach me, dog walker).

*updated on 13 Feb 2014 midday is 36,400 outages; more than 14,000 in Mecklenburg, 4000 in Cabarrus, 2700 in Gaston, 1600 in Rowan, 1300 in Lincoln, and 1100 in Durham County, NC. About 5000 in Lancaster and 2900 in Chester County, SC. Wow. Second band of snow falling in North Mecklenburg dropped maybe another 6″ on the 5-6″ we had yesterday. Double wow. Snow and ice totals should be impressive after the analysis is complete.

Trend, variability, and perception

The temptation is that your opinion is tempered by what you are currently experiencing. The old and boring argument that “Hey, it’s cold. What’s up with global warming?” The short-story (pun intended) is that weather is always variable, and the #SEStorm snow and ice storm is no exception.

What is the trend? Globally, it’s simple. Temperature is increasing (NASA GISS, UK Met Office CRU, NOAA NCDC). For the USA, and states within the USA, it’s less simple. Variability in the weather tends to average out less and less over smaller and smaller spatial scales. What does this mean? The ups and downs we expect from weather like our February snow/ice storm and the preceding week with beautiful warm temperatures become less and less noticeable at larger spatial scales because while North Carolina might be down in temperature, somewhere else on Earth is certainly up on temperature. They average out unless there is an overriding trend, like the trend imposed by increases in greenhouse gases. That’s why the global temperature trend is so important. If something is making the entire Earth warm above what is considered a range of natural variability, then some very powerful mechanism is at work.

Where we might be this calendar year

Back to the question at hand though. Can science address near-term (say, over the next 3-12 months) temperature and precipitation? The answer is yes, and this prediction is studies using an analysis called seasonal forecasts. I was shocked by what is suggested for temperature for the rest of 2014 and slightly into the beginning of 2015. NOAA Climate Prediction Center (CPC) updates their seasonal forecasts about the middle of every month, and I put this animated version of their graphics together below. temperature-2014-01-16

where, once you wrap your head around what I call the “geography” of the figure, you see that NOAA CPC is predicting whether the temperature over successively farther 3-month periods (Feb-Mar-Apr, Mar-Apr-May, etc.) will be above, at, or below the average temperature for 1981-2010 (the climate normal). Clearly, NOAA CPC analysis is suggesting that the USA is due to experience an above-average year for temperature. In particular, the Southwest and Alaska are pummeled by warmer-than-average temperatures until October (a hot summer in the Southwest is not pleasant, and hot summers in Alaska may be suggestive of a bad fire year). Furthermore, by about October-November, the forecast for the Southeastern USA is to be above average temperature even after the rest of the country goes to even chances for above or below-average temperature. That translates to a nice Halloween and Thanksgiving in the short-view, and yet another warm blip on the global warming trend in the long-view.*

Precipitation seems to be less interesting in terms of climatological deviations, but the Southwest does seem to at least move away from below-normal, dry conditions that are plagueing California right now. precipitation-2014-01-16

*I’ll revisit the seasonal forecasts again in a couple of weeks after NOAA CPC updates their analysis, and then also look at how well the forecasts capture reality at the end of the year using NOAA NCDC archived temperatures. This verification is mainly because I haven’t spent much time with these seasonal forecasts, but I am always seeking out new media for the classroom. A natural question about the NOAA CPC products is: Are they any good? We’ll see.

Summary

A lot to digest, and time will tell, but don’t let this cold early part of 2014 deceive you. Global warming is a major trend that is imposed on every weather system in the world. No single weather events is very likely attributable to global warming because of the complexity in parsing out all the causes and effects that modulate a weather system as it tracks through the USA (think of how tricky the forecast of ice vs snow was for this Feb 11-13 storm, and then try to say what it was that caused that specific location of the border between the two – hard!). But the average weather is slowly changing, and the average weather is climate. In the meantime, back to staring at the sleet that is falling and wondering when UNC Charlotte will open again for classes!

Tags: climate CPC data visualization NOAA North north carolina USA

Climate in the Southeast in January 2014

February 01, 2014 by Brian Magi

Scientists studying the Earth’s climate system are supported by an immense and rich array of data. Sometimes it seems like you only have to be comfortable working with all this information. Programming languages help (matlab, R, python, NCL, for example). But even more accessible are incredible web resources. The USA High Plains Regional Climate Center updates their climate and weather relevant maps on a daily basis. Here are some figures showing where the country and the southeast USA stands. From NCDC time series plotter, the contiguous USA (no Alaska and Hawaii) was the 37th warmest year in the last 119 years, as shown in the graph below.
contiguous-USA-2013-T
In itself, 2013 wasn’t unusual. In recent memory, 2008 and 2009 were really similar to 2013. However, compared to the fanfare around the hottest year on record for the USA in 2012, it does seem different. Who can remember ought-8 and ought-9, right?

But even more to the point is what we feel where we live. Science and statistics are fine, but just like no one on Earth experiences the average global temperature, no one in the USA experiences the average USA temperature either! Let’s look at the Southeast. In 2013, drawing from the HPRCC link above for the figures below, the temperatures were cooler than average.
AnnDec13TDeptSERCC-2013-12-31
In the last 120 years, 2013 in the Southeast was about the 67th warmest. Most of the years in the past 120 years have been warmer! But this is really not that ususual. 4 of the last 10 years in the Southeast have been cooler than more than half the past 120 years. 10 years is a limited view, but I chose it because it’s a round number and because we remember the last 10 years. Going back to the USA, *none* of the last 10 years have been cooler than more than half of the past 120 years. Not really even close. You can verify this with NCDC data tools. What global warming? Well, that’s where perception matters. The Earth is warming, even if the Southeast seems to be avoiding the problem we’ve created with CO2 emissions.

Looking to the more recent period, we can also glean a little bit about our winter months with a 3 month average (Nov-Dec-Jan) using HPRCC again
Last3mTDeptSERCC-2014-01-31
We see that except for Florida, the Southeast is largely cooler than average. Here HPRCC is comparing against the 1981-2010 average temperature (temperature anomaly). Appalachia and further to the west are in a deep recession of the warmth we expect when we think of global warming.

Finally, we can look at January 2014 using HPRCC tools.
Last1mTDeptSERCC-2014-01-31
The Southeast is cold! Even poor Florida, which over the last 3 months is anomalously warm compared to the rest of the Southeast, is in a deep cold this past January. If we eyeball-average the data on the figure, we get a number of about 6-7 degrees F below the 1981-2010 average. Jeez. Where can we go for unseasonable warmth (retrospectively)? The West is certainly above average, and more importantly below average on precipitation, as the figures show below.
Last3mTDeptUS-2014-01-31
Last3mPNormUS-2014-01-31
Be thankful the Southeast is so stubbornly refusing to budge on global warming… but I worry that as a result of this stubborness, our legislators will forget this is a problem. North Carolina will be affected even if we are a hold out for now. Think global whenever you think of climate. Or, if you want, think of Bob Marley (one love, one heart). This figure from the recent IPCC report (WGIAR5-SPM_Approved27Sep2013) shows that there are only a couple of non-red areas on Earth (ie. they are not following the warming trend). The Southeast is one of them! But that is one scorched Earth otherwise.
ipcc-ar5-wg1-spm-fig1

Tags: climate data visualization NCDC NOAA north carolina USA

Ramping up for teaching with NOAA NCDC

August 07, 2013 by Brian Magi

Summer is a time of dedicated research for me. Finished one project, waiting for peer reviews on that manuscript, tinkering with twitter, planning out research conference travel in the next school year, and working on a grant proposal to NSF. The season of the classroom is nearly here though, so I’m slowly re-allocating my hours to teaching. A great early-career workshop for university and college faculty that I attended the last week of July helped me get into gear with teaching again. I need a workshop like that every summer!

Another way I start to think about teaching is to begin to browse through the data that I want to bring into the classroom. One site I haven’t visited in months, but that I prolifically visit throughout past school years, is the NOAA NCDC time series plotter. I had the pleasure of visiting the numbers again tonight and remain very impressed by NCDC outreach and transparency efforts. The new addition to the time-series plotter (which you can use to produce climate-relevant analysis at different spatial and temporal scales) is a slightly more friendly user-interface, and a few features that I think most stats people will really appreciate. Yes, it’s not a super fancy analysis package, but the statistical analysis you can do just via the webpage now includes two new options. One is the option to display the anomaly against a different base period rather than always using the 20th Century average. In other words, you can choose a base period of 1951-1980 like NASA GISS tends to use or you can play around and see what the effect of a different base period is. The other new option is a display of a trend line for any period. The first thing you can do with this is see how temperature (for example) trends in the early part of the century compare to the trends in the latter part of the century. Or you can mimic the cherry picking that climate data is sometimes a victim to and choose very specific start and end points to produce a trend that amplifies an argument you are making (“look, it’s getting colder!” or “look, it heating up super fast”). one exception to all this great online analysis is that it only applies at the “super” level for data in the contiguous USA. someday, i’ll ask NCDC scientists why this can’t be done for Alaska and Hawaii, and why the global analysis tools are more limited. either way, an exciting development in my virtual friendship with NOAA NCDC.

Tags: climate data analysis data visualization NCDC NOAA north carolina teaching

Earth observations gallery

July 07, 2013 by Brian Magi

I used to really like photography. A big chunk of my senior year in high school and freshman year in college were spent in a dark room experimenting with developing processes and photograph creation. Film cameras are a thing of the past essentially (although I still have my Nikon and old photo gear that was woefully incompatible with the new Digital SLR bodies), but imagery is not. Data visualization is a huge force on the web, and imagery is constantly thrown our way from the more traditional camera and from cameras in space. The exploratory power of all this information is staggering (one example here). All that being said, I think it’s important to step back and just view some of the imagery emerging from these sources. I don’t have webpage links for these, and I’ve enhanced contrast and color in some cases but take a look. Which evokes the most visceral sense that you are connected to a global community? Data, nature, both?

[Show as slideshow]

The images are all cropped to an aspect ratio of 16:9 or HDTV. Let me know if you want more information about the photos/images.

Tags: data visualization images

Beijing air quality and agricultural fires

June 28, 2013 by Brian Magi

As I browsed through my favorite twitter feeds which includes @BeijingAir and the other US Embassies, I saw there was some really really poor air quality in Beijing. The US Embassies in China tweet particulate matter 2.5 (PM2.5) concentrations in the atmosphere on an hourly basis and also provide 24 hour average PM2.5 per the (USA) EPA regulatory methods. Namely, 24 hour PM2.5 is what’s regulated by the EPA in our country, and since US Embassies are US territory (as I understand it from The Simpsons Bart vs Australia episode), then US-relevant metrics are tweeted in addition to the hourly data. I tweeted about the very poor air quality in Beijing today

#BeijingAir 6-28-13 19:00 1hour PM2.5 is 466 ug/m3!! On #EPA AQI scale, that is 477 or "Hazardous" for a 24 hr exposure. that is really bad.

— Brian Magi (@brianmagi) June 28, 2013

and

Holy cow. 24 hour PM2.5 #BeijingAir is 294.5 ug/m3 or 345 AQI or Hazardous! 500 AQI is as bad as #EPA goes. Please cease all breathing now.

— Brian Magi (@brianmagi) June 28, 2013

The second tweet was an tribute to an Onion article saying something like EPA tells people to stop breathing but I couldn’t find a link. Ok. So after I sent out that 2nd tweet, I began to wonder: What the heck is going on in Beijing? PM2.5 is above 400 ug/m3* for hours on end during the day and the 24 hour average PM2.5 just got tweeted as nearly 300 ug/m3. This is extremely hazardous, both on the EPA scale of Air Quality Index (AQI) and on the scale of I-Cannot-Breathe-Long-Enough-To-Finish-This-Sente… (no link to that scale). As the title of the post implies, the answer is related to burning practices – and I think it’s worth more that a couple of tweets. I’m asserting that the main problem is emissions from local/regional agricultural fires. This touches on my own research into fires and the peculiar human-influenced fire seasonality as a function of where you are in the world. Now, take it easy, I tell myself. Why? Every scientist loves to talk about science, but we especially love talking about our own research. I’ll try not to go on and on, is what I’m saying.

How can we do a first-order (read as “informal”) test of this hypothesis? First, let’s check satellites. There is some very accessible information from NASA that can be used to study problems that aren’t in the data-rich part of the world or in the parts of the world where I don’t even know what the characters are for “fire”. NASA has a satellite called Terra reporting data since November 2000. From this page, I googled the lat-lon of Beijing (40 N, 116 E) and created my own custom satellite image of Eastern China with the approximate location of Beijing marked.

Satellite image from MODIS sensor on the NASA Terra satellite for June 28 2013. Beijing location is approximate.

Right away, you see there are clouds. But there are also signs of gray-ish haze very similar to my research page header up at the top (smoke pouring off of southern Africa). So smoke is a distinct possibility. Now we can use data from the same NASA satellite (Terra) and same sensor (MODIS) but using a different wavelength of electromagnetic radiation. Namely, the parts that we feel/sense as heat – or thermal infrared radiation. Turns out, NASA has a whole team of scientists looking at this data and there is a data product called the Thermal Anomaly product. Something more “operational” (meaning it’s available at a semi-regular and rapidly updated way, like weather data is for weather forecasting models) for global fires is available at the same NASA website as I used to get the image above. Here’s the global view of fires

Global fire activity from the last 10 days ending on June 28 2013.

Clearly, fires are active in Eastern China – so we’re almost at the bottom of the mystery of why @BeijingAir is not the place for breathing deeply right now. You can download a map file showing fires from the last 48 hours for different regions by going to the KML tab and opening the KML file in Google Earth. I downloaded the “Russia and Asia” KML file and produced this

Active fires from the MODIS sensor on the NASA Terra for the 48 hours ending on June 28 2013.

where you can see that my Google Earth has the Beijing Embassy location saved as a placemark. Regardless of the clouds, the pollution from the fires is certainly pouring into the atmosphere over Beijing and affecting surface air quality to the point that the AQI values are nearly off the scale again, but this time, it is not because of the combination of meteorology and emissions from fossil fuel consumption.

The winter and spring months – the months related to the very poor air quality referred to in the report above and here – are plagued by deep near surface temperature inversions that act to inhibit mixing. What does this mean? Well, if pollution is emitted from cars and factories in Beijing in the winter-spring, it will tend to stay in the first 500 meters (1800 ft) above the ground – roughly. The pollution gets trapped. On a day without a temperature inversion, the pollutants emitted are probably about the same, but mix into a much deeper atmosphere (say about 2000-3000 meters, or about 4-6 times deeper layer). The pollution is thus more dilute. I haven’t checked meteorology in the case of todays very poor air quality, but I suspect the effect of meteorology (even if mixing is deep and efficient) is overwhelmed by the emissions from all the fires southeast of Beijing.

What kind of fires? Or why are they burning? Great question! Are these forest fires like the lightning-triggered fires plaguing the Western USA right now? No! When you mask out the Terra MODIS fire data in a way that you only look at data from land that is mostly cropland (agriculture) in Eastern China, then you find something related to our findings in the Biogeosciences paper.

Fire season for land that is mostly agriculture (cropland in Eastern China) and land that is mostly non-agriculture (forests, grasslands). This is based on the average over 10 years of data from MODIS. The region considered is roughly Mongolia and China. Other parts of the world look much different – more analysis like this in the link to my paper below.

The figure above shows that while the land with a low fraction of cropland (less than 20%) tends to burn in July-August, the land with a high fraction of cropland (greater than 80%) tends to burn in (you guessed it) June-July. As the caption states, these “average” seasonalities are based on over 10 years of Terra MODIS fire observations. When you average 10+ years of Junes for the low and high fraction of cropland, you get the data point in month six for blue and green curves above. In other words, the fires are right on schedule, Eastern China! Hopefully for the citizens of Beijing, the burning will be short-lived and meteorology will transport the smoke away and dilute it down with clean air in the process.

All that being said, a full scientific analysis of the air quality requires much more than this post offers. Sensitivity, ground-based analysis, meteorological analysis, and actual counting of the fires among other things would be required to prove with a much higher degree of confidence that my hypothesis does not fail, but usually scientists make hypotheses because they observe an event/phenomenon that is consistent or inconsistent within some sort of framework. In this case, what I saw in China air quality was inconsistent with what I understood about the meteorology there (for this time of year) and consistent with the work I did with colleagues regarding fire seasonality.

*The unit of concentration for PM2.5 is micrograms per cubic meter which is often written as ug/m3 even though the “u” should be the Greek letter “mu” which itself means “micro” which is one millionth and “m3” should be “m” with a superscript “3” to indicate “cubed”)

Tags: air quality data analysis data visualization fire fire seasonality NASA publications twitter

May 2013 climate in North Carolina and the world

June 24, 2013 by Brian Magi

With global warming and all of the impacts, it’s very important to constantly consider the question of time and space scales. May 2013 is a good example for those of us living in the Southeastern USA or North Carolina. Namely, North Carolina’s normal-to-cool spring is not at all indicative of how the global temperature is evolving. Let’s see how we can quickly use NOAA NCDC graphs to figure this out.

Global warming refers to the increase in average temperature of the entire Earth. The last part – the entire Earth – is the spatial scale. And that’s a huge spatial scale! When a scientist talks about global warming or that global warming has been detected, you have to step back and say WOW. What on Earth could warm an entire planet? coal_fired_power_plant Over long time scales, of course there are a number of possible reasons (changes in the Sun, Earth’s orbital shape/proximity around the Sun, plate techtonics), but these take so long, they aren’t relevant to the concept of global warming. Even my statement that What on Earth could warm an entire planet? should be more precise and say something like What on Earth could warm an entire planet over a relatively short time period? The simplest, if somewhat incomplete, answer is the combination of greenhouse gases and aerosols emitted into the atmosphere from human activities. Period.

May 2013 analysis of global temperatures are trickling out. NOAA NCDC as always has a wonderfully complete report of climate news for May and for all previous months. My favorite part is the plethora of hyperlinks. NOAA NCDC should really be commended for their public outreach! Here is one of the figures from that webpagewhere you can see how different the Southeast USA is from the world in May 2013 – the world is shades of red, while the Southeast USA is shades of blue (cooler than normal). We’ve had a very pleasant spring in North Carolina. Pull back on the temporal (time) scale to see the March-April-May seasonal average and you can see that the cool spring extends well beyond May in terms of the anomaly. By this, I mean that the blues become deeper when you consider a three month period (March-April-May) and that implies without any quantitative work that March-April were more cooler-than-average. Pull back slightly further to the year-to-date rankingsand here you see that the Southeastern USA and in fact most of the USA and even Alaska have been right at the climatological normal (which for NCDC is the average temperature from 1981-2010). The short story is that North Carolina below average temperatures for the period from January to May, March to May or just plain old May are not indicative of global temperatures. The real question is why?

Tags: climate data analysis data visualization global environmental change global warming NCDC NOAA north carolina USA

CO2 trends from around the world

June 03, 2013 by Brian Magi

Time series are profilic in climate science. This is a dataset that shows the how a measurement changes over some period of time. The best known in our world is the global warming time series displayed as the globally-averaged surface temperature trend, which is compiled from thermometer measurements. A few research groups worldwide maintain this analysis (NASA GISS, UK Met Office, NOAA NCDC). Since CO2 is in the news, and since there is variability from one measurement location to another, it is useful to see how the best-known station in Mauna Loa, Hawaii (source of the data shown in the Keeling curve graph). Once you navigate the shifting axes (y-axis on the right and left, and the time series begin at different points in the past) and digest the information visualized here, the graph below is very useful in quickly understanding variability in CO2 concentration from the northernmost latitudes to the southernmost, noting the latitude is listed under the three-letter station identifier but that the graph is arranged north to south. co2-globaltrends There is clearly a bias toward higher CO2 in the northern hemisphere compared to the southern hemisphere – CO2 is about 10-12 ppm higher near the north pole. This piece of information – this data – reflects the higher abundance of sources of CO2 in the northern hemisphere and the relatively slow transport times required for air to move across the equator (like a slow drip compared to the winds we feel every day in the USA). The graph also effectively conveys another dimension of information: Regardless of the specific location of CO2 measurement, the long-term trend is essentially the same worldwide, indicating that CO2 continues to accumulate in the atmosphere worldwide at about the same pace. The trend could relatively easily be quantified, but sometimes qualitative analysis is enough. From the webpage where I found the figure, the station identifiers are PTB = Point Barrow, LJO = La Jolla, MLO = Mauna Loa Observatory, CHR = Christmas Island, SAM = Samoa, and SPO = South Pole. You can also find some commonality in the stations at NOAA’s website. All in all, a great data visualization that can be done entirely in black-and-white!

Tags: CO2 data visualization global warming NOAA Scripps spatiotemporal analysis

CO2 time line for May 2013

June 02, 2013 by Brian Magi

The month of May is officially over, and perhaps the Earth is about to take a big breath in and begin to draw down CO2 from its year 2013 peak. The last tweets by @Keeling_curve showed a relatively (emphasis on relatively!) sharp decrease from May 29 to May 30 with CO2 falling from 400.33 ppm to 398.41 ppm, and then May 31 had variability that was too high as tweeted here. Funny side note was that for whatever reason, this “data too variable” drew the attention of one well-known (but not well-respected) blog, to which @Keeling_curve replied “see here“. Geez, you’d think seasoned bloggers would click a couple of web links before tweeting a question like that. The values of CO2 should start their annual decrease from the peak value in the Northern Hemisphere as the plant life in temperate and polar zones comes to life, but in the mean time, we’re living in the age of a 400 ppm CO2 world, which is very unusual in recent geological history, as discussed here and shown here. Here’s the time line of CO2 concentrations for this historic May 2013 co2-2013-05 which shows the weekly-averaged CO2 from the daily-averaged values posted on Twitter (ok, tweeted). The straight horizontal purple line is the monthly-averaged CO2 of 399.82 ppm (wow!), and the straight red line is the mystical 400 ppm CO2. I calculated the weekly-average as the value of the previous 7 days up. For example, May 15 weekly-average is the average of values from May 9 through May 15. The weekly-average ideally is 7 data points, but occasionally a daily-averaged value is not tweeted due to high variability in the data. From the figure you can see that we reached our first weekly-averaged CO2 concentration greater than 400 ppm on May 19. I actually thought that would be it for the year, but from May 24 to May 29, daily values were again well over 400 ppm. This brought the number of weekly-averaged values greater than 400 ppm up to 5. Roughly, about 33% of the days in May 2013 had CO2 greater than 400 ppm. The decline should begin soon with the annual minimum in September-October reaching values of about 394-395 ppm, noting that the annual minimum for 2013 will probably be very close to the maximum from only 2 years ago. Below is the data shown in the graph above. An impressive May, and one that will be recorded in the history books.

                       carbon dioxide (ppm)
year    month   day     daily   weekly
2013	5	1	*	399.61
2013	5	2	399.29	399.40
2013	5	3	*	399.40
2013	5	4	399.68	399.49
2013	5	5	399.54	399.50
2013	5	6	399.52	399.51
2013	5	7	399.71	399.55
2013	5	8	*	399.55
2013	5	9	399.73	399.64
2013	5	10	399.4	399.60
2013	5	11	399.46	399.56
2013	5	12	399.41	399.54
2013	5	13	400.16	399.65
2013	5	14	399.91	399.68
2013	5	15	399.74	399.69
2013	5	16	400.25	399.76
2013	5	17	400.04	399.85
2013	5	18	399.8	399.90
2013	5	19	400.15	400.01
2013	5	20	399.73	399.95
2013	5	21	399.91	399.95
2013	5	22	399.85	399.96
2013	5	23	399.88	399.91
2013	5	24	400.09	399.92
2013	5	25	400.2	399.97
2013	5	26	400.53	400.03
2013	5	27	400.27	400.10
2013	5	28	400.06	400.13
2013	5	29	400.33	400.19
2013	5	30	398.41	399.98
2013	5	31	*	399.97

*data was too variable over the course of the day. no value was reported on twitter.

Tags: carbon CO2 data visualization global environmental change global warming NOAA

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data visualization

What is the carbon cycle?

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First, temperature in two plots:

Then, precipitation in two plots:

New data

Robust Features

Summary and What the AVERAGE Year Looks Like

*There are a few features that are not that robust, by my admittedly weak definition. For example, it’s not that clear whether the NE USA or the NW USA will tend warmer than average in the early summer. And precipitation has about the same number of features that are robust as not robust.

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Where we are this calendar year

Where we are right now

Trend, variability, and perception

Where we might be this calendar year

Summary

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*The unit of concentration for PM2.5 is micrograms per cubic meter which is often written as ug/m3 even though the “u” should be the Greek letter “mu” which itself means “micro” which is one millionth and “m3” should be “m” with a superscript “3” to indicate “cubed”)

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