Reading: Forgotten Grasslands of the South

Reed Noss' new book, Forgotten Grasslands of the South is quite good. At least the first half.

I try to review books about halfway through because the last half usually drags on. This might prove an exception, but I'll see.

Noss' main point is that grassland of the SE US are the true ancient grasslands of North America.

While the Great Plains were under ice or spruce, the SE was the refugia for much of the grassland biome. And has been for millions of years.

The high levels of endemism of species is a primary line of evidence. For example, he has a short table comparing the number of species of different plants in the Great Plains vs. Florida. Andropogon? 2 species in the plains. 9 in Florida.

Noss also argues for reconsideration of the longleaf pine ecosystem as a grassland.

His best writing is the natural history when he takes you through a tour of places he's visited. Dolomite glades, longleaf pine savannas, ancient salt licks.

I would say that this is one book that deserves to be a documentary. His descriptions are good, but video would do it more justice.

Reading the National Climate Assessment: Riveting

I've been slowly working my way through the draft of the National Climate Assessment Report.

Parts have been riveting.

Chapter 2 on "Our Changing Climate" is an incredible compendium and synthesis.

The key messages read like a list of our worst fears. Temperatures up 1.5°F since 1895. More heat waves, floods, droughts, and hurricanes. Less ice. Sea levels up 8 inches since 1880.

One of the most quotable lines:

"The sum total of this evidence tells an unambiguous story: the planet is warming."

The section on heat waves stood out most for the jump in certainty that has been made:


"The 2011 and 2012 events set records for highest monthly average temperatures, exceeding in some cases records set in the 1930s, including the highest monthly temperature on record (July 2012, breaking the July 1936 record); for the spring and summer months, 2012 had the largest area of record-setting monthly average temperatures, including both hot daytime maximum temperatures and warm nighttime minimum temperatures (Karl et al. 2012). Corresponding with this increase in extreme heat, the number of cold waves has reached the lowest levels on record.

"In the past 3 to 4 decades in the U.S. the ratio of record daily high temperatures to record daily low temperatures has steadily increased (also see Meehl et al. 2009).


"Research has found that the human contribution to climate change approximately doubled the
probability of the record heat in Texas in the summer of 2011 (Hoerling et al. 2012a)."

Compared to even the last IPCC or the last NCA, these syntheses are much stronger.

I guess when you think about it,  what has happened to our climate in just the past 4 years is much stronger.

National Climate Assessment being released for comment

The National Climate Assessment Report is second only to the IPCC report in importance for climate change in the US.

Every four years a number of scientists in the US come together to synthesize new information on the state of climate change for the US.

It's now being released for a 3-month comment period. It can be downloaded at GlobalChange.gov.

It's a long document.

It's also strongly worded in many parts.

Here's the opening paragraph of the Executive Summary:

"Climate change is already affecting the American people. Certain types of weather events have become more frequent and/or intense, including heat waves, heavy downpours, and, in some regions, floods and droughts. Sea level is rising, oceans are becoming more acidic, and glaciers and arctic sea ice are melting. These changes are part of the pattern of global climate change, which is primarily driven by human activity."

Climate change is no longer a future scenario but a present reality.

As I read through this, I'll post anything else that stands out.

Winter bison diet

It goes without saying that you always see something interesting in Nature.

Today's hike at Konza I was kicking over bison fecals. A number of them were just loaded with locust seeds.

Those aren't listed in the typical diet.

The timing of precipitation

Traces of cumulative precipitation (standardized across years) for Konza.  Date range is 145-250 (May 25 - Sep 7), which is the precipitation timing critical climate period.

When rain falls might not seem to be important, but in many ecosystems it is often more important than how much rain falls.

In annual grasslands, the germination of different species if dependent on when rains begin. And the end of the rains set the end of the growing season. Same could be said for many tropical grasslands.

Many deserts are structured by when rain falls as shrubs with deep roots benefit from winter rains and grasses summer rains.

But what about temperate grasslands where the timing growth is dependent more on temperature than rainfall? How important is timing?

Put another way, if climate change causes rains to fall later (or earlier), will it matter.

There has been little work to directly test the importance of timing of rainfall.

I recently dug through the Konza climate data to see how much the timing of precipitation varied among years. Quite a lot. For May-September rainfall, the timing of when the average unit of precipitation fell varied by over six weeks over a 25-y period. In some years, half the rain had already fallen by early June. In other years, it took to mid August.

Does it matter? If plants aren't running out of water and it all goes into the same bucket, how much impact can there be of the bucket filling early or late?

Turns out a lot.

Variation in the timing of rainfall explained as much variation in grass productivity as temperatures and about half as much as the total amount of rain.

From these calculations, shifting precipitation by just 1 week later in the season can reduce productivity by 5-10%. 

For reference, a decrease in productivity of this amount is enough to put most ranchers out of business, all other things equal. 

How to say "It's going to be a hot one"

If there is anywhere where it would be hard to predict the weather, it would have to be here in Kansas. We are just too far from the sea for anything to have strong influence on our weather.

Yet, it seems like it must be a great temptation to try to predict the weather. Because many people have said to me in casual conversation some variation of, "It's going to be a hot summer." Whatever the weather is today (or the past few days) people seem to project forward a couple of months.

Is there any basis for this?

If it's hot this month, is it likely to be hot next month? Or dry?

Here's how you test that.

First you generate climate anomalies. In a seasonal climate like ours, it's simple to know what the climate will be like in a month or 6 months or a year.

If it's June, in the grand scheme of things, it's likely to be hot next month cold in 6 months and hot again in 12 months.

But, what we're interested here is in the climate anomaly and how they are correlated over time.

So we aren't saying it's going to be hot next month, but hotter than average next month.

To evaluate this, you look at past records of data and run autocorrelation analyses. This analysis looks at how different temperature or precipitation over a certain length of time and then examines the correlation between the current period's anomaly (hotter/colder, wetter/drier) vs. next month's.

Here are some data from May weather over the past 50 years for Manhattan KS.

I went to knmi.nl and accessed their climate explorer. The site runs autocorrelations for any weather station in the world.

Here's what the autocorrelation analysis for daily maximum temperatures look like for 30-d periods throughout May and June (start dates of May 1 to May 30).



What is important in this graph is the correlation after 30 d. Essentially it says that the correlation coefficient between the 30-d starting in May and the following 30-d is less than 0.2.

How helpful is that?

Here's data for Konza (close to Manhattan) for 1984-2010, which I've been using for analyses lately.

X-axis is mean maximum temperatures for day of year 120-149 (roughly May) vs. same temperatures in the next 30 d.



Correlation coefficient of this is about 0.3 (similar to autocorrelation analysis).

You can see that there is about 9 °C variation among years in May temperature. If it's the hottest May on record, June is likely to be just 1°C hotter that average. That's not much predictive capacity.

And the 30-d after that? r = 0.2 and no significant predictive capacity. It could be equally likely to be the hottest month on record as the coldest.

For precipitation, it gets even worse.

here's the same autocorrelation analysis for precipitation during that period.

Essentially for the 30-d following May, there is no significant predictive capacity (maybe slightly drier).

 Precipitation between the two 30-d periods at Konza: flat.

As a caveat, i should say that these patterns have nothing to do with general trends over long time periods associated with global warming. Only the structure of seasonal variability.

That said, it's probably more certain to say that the future is going to be hotter than it is to say that the next month is hotter (or colder or wetter or drier).

At least here in Kansas.

Where to send your paper after it's been rejected

Ever wonder where to send your paper after it's been rejected from a journal? 

The authors of a recent Science paper mapped the submission history of over 80,000 scientific articles published from 2006-2008 .

This monumental task allows tracking of the most likely flows of papers through journals. 

The network map provides more than a guide to where to send your next rejected paper.

A few interesting results came out of this. 

First, papers that had been rejected from another journal were cited more. Is this because papers were improved with further revisions? Or is it because papers that are likely to be the most interesting don't often fit into the neat model of a given journal's papers? 

Second, resubmissions often went to journals with lower impact factors. No surprise there, but good to quantify. 

Third, the proportion of papers published that had never been submitted elsewhere was similar across a range of impact factors of journals. The range was really narrow in the grand scheme of things.  

Publishing is tricky. There is an immense amount of wasted effort trying to get papers published. Given that, journals and authors are reasonably efficient in many respects. 

Flows of Research Manuscripts Among Scientific Journals Reveal Hidden Submission Patterns

V. Calcagno et al. Science 338, 1065 (2012)

DOI: 10.1126/science.1227833