Problem Solving: Determining Growing Season Start and End Date

Figure 1:

Figure 1: The growing season is getting longer in the near-shore Chesapeake Bay area.

A while back, you may remember my quest to calculate the start and end date of the growing season length in the near-shore Chesapeake Bay area.

Our previous work has found strong confidence that the growing season has gotten longer (Fig. 1), but how it is getting longer is important (i.e. earlier, later, both?).

This post will highlight my approach, road blocks, and solutions to this interesting question!

First Approach: Using the single mean time series

Did you know

Did you know that changes in the growing season could affect the first appearance and metamorphosis of butterflies? Pictured here is my favorite butterfly, the Eastern Tiger Swallowtail, which can be found in the Chesapeake Bay area! credit

In order to look at dates, and not length, we have to use the original time series of daily temperature used to calculate the growing season length. I have two versions (this will be important!)

Time series 1: All 18 individual GHCN-Daily stations as separate time series for daily temperature

Time series 2: A single time series that is the mean daily temperature for Chesapeake Bay (average of these 18 stations)

Both time series compose the same information, but the order of which the aggregated mean is taken will be important.

My first approach was to keep it simple: use time series 2, that single time series of daily average temperature to determine the start (as this first span of 6 consecutive days when Tmean > 5°C) and the end (as this first span of 6 consecutive days after July 1st when Tmean < 5°C) date of the growing season.

Did you know

Did you know that changes to the growing season could cause over-wintering birds to migrate later to Chesapeake Bay, such as this winter’s late arrival of Canadian Geese. credit *Note: The birds pictured here are Barnacle Geese, not Canadian Geese. Thank you to Jeff Shendot for catching this! You can read about the late Canadian Geese arrival here!

 

To do this, I created a simple loop in R. My approach was to treat the growing season temperature indicator (5°C) as a binary system (that’s only 1’s and 0’s!).

Let me explain this approach for determining the start of the growing season. If we set any temperature ≤ 5°C to 0, then any number >5°C to 1, we can easily find the first span of 6 days above 5°C! How? If we take a moving mean of a 6 day window (January 1-6, January 2-7, January 3-8…), the start of the growing season is the first time that mean is 1!

Note: Looping is not very efficient in R, but for this approach, a loop was the easiest approach I could come up with. And since our time series is short, we do not have to worry about R’s slowness here!

Data Check: Using the single mean time series

Figure 2:

Figure 2: Data check…how good is our manually calculated growing season length? An R-squared of 0.80 is very good, but not satisfying to me!

Before we get to look at the results, of course we have to check to see how good our ‘manually calculated’ growing season length calculation is!

Here, the growing season is the difference between the end date and start date. It should be pretty close to our growing season length calculated using the R package climdex.pcip.

From Figure 2, you can see that we have a pretty good and significant fit….but an R2 of 0.80 is not satisfying to me!

Why the ‘just okay’ fit?

The likely cause…..the order of which the mean was taken!

In our start date calculation above, we used a daily mean temperature time series that already had been aggregated by mean. However, when we calculated the growing season length using the climdex.pcip package, we calculated the length for each of the 18 stations, THEN took the mean.

Figure 3: An example of missing data in the GHCN-Daily weather data.

Figure 3: An example of missing data in the GHCN-Daily weather data.

This subtle difference has a huge impact on our data because of how it handles missing data. In our growing season length calculation with climdex.pcip, we rejected any years that had >15% or 3 full months of missing data.

This missing data, especially at the start or end of a year, could throw off our growing season start and end!

This ‘discard’ of years with a lot of missing data would not be factored in the already averaged time series.

The solution: repeat the process above for all 18 GCHN-Daily weather stations AND calculate the amount of missing data to determine data points we can throw out the same way as climdex.pcip (Figure 3).

Did it work with time series 1?

Figure 4: New and improved growing season calculation!

Figure 4: New and improved growing season calculation!

You bet! We improved the R2 fit to a 0.93 (Figure 4). There are still a few, very minor, differences. For example, in my method, years with <15% missing data with an NA for the end date are automatically set to 365; in other words, the growing season never technically ended. I am realizing just now that 365 would be wrong for leap years!

Nonetheless, I am happy with this improved fit! Now I can more confidently inspect the start and end date of the growing season!

That post will be coming soon!

 

 

Kari Pohl

About Kari Pohl

I am a post-doctoral researcher at NOAA and the University of Maryland (Center for Environmental Science at Horn Point Laboratory). My work investigates how climate variability and extremes affect the diverse ecosystems in Chesapeake Bay. I received a Ph.D. in oceanography from the University of Rhode Island (2014) and received a B.S. in Environmental Science and a B.A. in Chemistry from Roger Williams University (2009). When I am not busy being a scientist, my hobbies include running, watching (and often yelling at) the Boston Bruins, and taking photos of my cat.
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5 Responses to Problem Solving: Determining Growing Season Start and End Date

  1. Victoria Coles says:

    Yeah for persistence!

  2. Sarah Nuss says:

    Any way we can parse out just the Virginia data? Working on a project where we will be discussing growing season length but just for Virginia, not Bay wide.

    • Kari Pohl Kari Pohl says:

      Sarah: You got it! I made growing season length plots for just “south” Chesapeake (VA). I can email you more details (and make this the next post!)

  3. Jeff Shenot says:

    Kari – fyi, the picture of a flock of geese (“Did you know that changes to the growing season could cause over-wintering birds to migrate later to Chesapeake Bay, such as this winter’s late arrival of Canadian Geese”) are not Canada Geese, they are Barnacle Geese! Where did you get the picture? I assume it was taken in Europe, since even a single Barnacle Goose in the U.S. would be a mega-rarity.

    The picture is titled correctly (Branta Leucopsis)

    • Kari Pohl Kari Pohl says:

      Jeff, Thank you! oops! I use pictures that are free use (to avoid any potential for publication problems). I was searching for a nice migratory picture and it popped up as Canadian Geese. I should have checked the species! I’ll add a note in now! Thank you!

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