Clarification of Monsoon 2024 Outlook

This is not a clarification of my own outlook, which I haven't comitted to (yet), but a clarification of reporting about other outlooks. On the weekend of June 1-2 the Tucson paper published online a story headlined, 2024 Monsoon outlook: Hotter, drier summer in Tucson.

The story repeatedly refers to something it calls the National Weather Service 2024 Monsoon Outlook, which the story says was newly released last week. I'm not familiar with such a product, but stand ready to be educated. I'm guessing that most of the time the reporter is summarizing and interpreting Climate Prediction Center three-month outlooks for Jun-Jul-Aug and Jul-Aug-Sep. I'll comment more about this later.

The fourth paragraph of the Tucson story reads,
Last year was the 17th-driest monsoon across Arizona since 1895, according to the National Weather Service 2024 Monsoon Outlook. Tucson received only 4.73 inches of rain in 2023 or .96 inches less than average, National Weather Service records show.
There's that mysterious (to me) Outlook reference again. Last year's 4.73 inches was the 39th driest in Tucson since 1895 (NWS Tucson -> Monsoon -> Monsoon Stats -> scroll to "Haywood plot"), a far cry from 17th driest. My impression of last year's monsoon in Tucson is in line with how it was summarized by azcentral. After they reported how dry Sky Harbor airport had been during the 2023 monsoon, they wrote:
As a whole, the deviation from the norm for Tucson is not that negative. A typical season usually produces around 5.7 inches of rain for Tucson's airport, coming mainly in July and August. This was mirrored in 2023, as the prime months brought 2 and 2.39 inches, respectively, making up for a zero in the June column and a lackluster September.

It's important to remember that the Climate Prediction Center issues outlooks that provide probabilities for three categories: bottom 1/3, middle 1/3, and upper 1/3. Last year's Tucson monsoon coming in at 39th driest since 1895 puts it in the bottom 1/3 (i.e., 1895-2023 = 129 years, bottom 1/3 = 43 driest years). The current CPC Jun-Jul-Aug outlook tilts the odds just slightly toward the bottom 1/3 (33-40% chance), which leaves 60-66% chance for either middle one-third or upper one-third. The CPC outlook for Jul-Aug-Sep is a little more pessimistic, but still leaves a 50-60% chance for either middle one-third or upper one-third.

I agree with two quotes in the Tucson news story from Michael Crimmins: ... forecasting the monsoon is incredibly hard and ... the summer impact of La Niña on the monsoon is actually quite weak.

My bet, or maybe it's just wishful thinking, is that above normal temperatures that are expected to persist over New Mexico will instead by July have shifted a bit toward north Texas. The circulation around the southwest side of the associated upper-air area of high pressure (what media have taken to calling a heat dome) would favor squall lines on several days sweeping across Tucson during the afternoon and early evening, effectively squeezing out available moisture. That's a pattern that has been lacking in recent years, but it's due.

Tucson Winter Precipitation Update

Starting almost 14 years ago I have been periodically posting here a figure similar to the one below. Last year at this time we had just come out of three straight La Niña winters. Now it's time for the scatterplot to incorporate this past winter, which was dominated by a strong El Niño.

Last year's update was the first in a few years, so in that post I reviewed my reasoning about what is plotted in the figure. Please see last year's update for a more complete discussion. The reason I use a 5-month winter for Tucson is that even in a wet winter an entire month can be well below average, and conversely for a dry winter. It's better in El Niño winters to take note of 4 out of 5 wet months, instead of focusing on mid-winter and so sometimes discounting a precipitation total that results from combining only 2 wet months with 1 dry. As it turns out for this past 5-month winter it was November that was the dry month. A wet March made up for November. So for this past season in particular the net impact of broadening the definition of winter from 3 to 5 months was a wash.

On the horizontal axis, the ONI is a number calculated monthly as an objective way to summarize the status of ocean temperatures in a critical part of the tropical Pacific. Since the calculation is a running mean, the most recent value lags by a month. There is also a persistence requirement, as explained by the Climate Prediction Center in their weekly updates. Generally a value greater than 0.5 means El Niño conditions are present, while less than -0.5 is La Niña. This past winter the ONI edged up to 2.0 in December before dropping back to 1.8 in January. See my last update a year ago for more discussion about what I have plotted here. Though the ONI for this month (April) will probably still be in El Niño territory, model outlooks have it falling rapidly to below -1.0 by this October.

The TUS 5-month total precipitation for this past winter was 6.16 inches, which makes it the wettest winter in 26 years, and ranks it 11th wettest of the last 75 years (i.e., of the 5-month winters 1949-1950 through 2023-2024). Last fall, even though an El Niño of moderate (ONI > 1.0) or strong (ONI >1.5) intensity was expected for the 2023-2024 winter, for the West a minority contrary forecast had a near normal outlook for winter precipitation, apparently based on giving maximum weight to recent trends (subsequent to the 1997-1998 winter). Now that the winter of 2023-2024 is a fact, the underperforming winter of 2015-2016 looks even more like an outlier, not part of a new trend.

Severe Thunderstorm Views, July 28

At 556 pm MST on Fri Jul 28 2023 the National Weather Service issued a severe thunderstorm warning covering much of the Tucson area. The storm was located at the time over Sabino Canyon Recreation Area. The warning stated cell movement as southwest at 15 mph. My place is about 7-8 miles due west of the recreation area. (At the time I only heard a radio host's summary; details of the warning looked at much later.) It was about 6:10 pm before I heard thunder and went out on the patio to take a look. Then it took another 5 minutes to realize that I should go back in and get my phone to take some pictures.

0728061514PM looking east through east-southeast

The format for the image captions is MMDDHHMMSSPM. MST is GMT -7H. The thunder was coming from the anvil overhead. Maybe I was too far away and it was too bright, but I never noticed any cloud-to-ground lightning. The bright white blob on the right screamed hail. I quickly realized that the action was shifting south. The remainder of the images are looking southeast.

0728061638PM

There were widespread reports of inch or more diameter hail on Tucson's east side.

0728061814PM

And 60 mph wind gusts at both airfields on the distant right.

0728062038PM

Awesome arch of darkness.

0728062127PM

On going outside at 6:10 pm I was still in the excessive heat northwest wind. By 6:20 there was occasional moderately buffeting outflow and a few drops of rain. Later about 10-11 pm a secondary band of showers moved through, but for the entire evening I received only 0.01 inch of rain.

Tucson Precipitation, Update for the Last Three Winters

Starting almost 13 years ago I have been periodically posting here a figure similar to the one below. Newer versions of the figure incorporate recent years and occasional refinements. The last update was three years ago, so it's now time to add three more winters.

Since it has been awhile, I'll review my reasoning about what is plotted here. Why November through March? In any winter there are always periods of both wet and dry weather patterns. Though some patterns may be fleeting, others may persist for the better part of a month. A three-month winter could equally end up wet-dry-wet, or dry-wet-dry. I think that five months is a better window for capturing the overall winter. Since on average the months of November and March in Tucson are each drier than any of the other three months, in most years it matters little. But when it does rain in Tucson in those edge months, it is basically a winter pattern. Whatever those two months produce, I think their results deserve to contribute to the winter as a whole. So the vertical axis is Tucson Airport precipitation totaled for five months. Before turning to the horizontal axis, notice that the data point for this past winter of 2022-2023 is labeled 23, corresponding to the end of the five-month period, also to the year in January, the middle of the five-month period. That is what I use to categorize the winters by decade. Selected years are also labeled.

The Climate Prediction Center (CPC) issues a weekly update presentation on ENSO, with each update providing, among many other things, an explanation of and discussion about the Oceanic Niño Index (ONI). Summarizing, the calculation of ONI starts with a climate-adjusted dataset of monthly ocean surface temperature anomalies for a key area of the tropical Pacific. These monthly anomalies are averaged over three months (i.e., the January ONI is an average of the anomalies for the months of December, January and February), and then the ONI is defined to be that average rounded to one decimal place. I've repeated the three-month averaging calculation, but since I've rounded to two decimal places, same as the input dataset, technically what I have plotted is not ONI. The difference amounts to no more than the width of a plotted marker. Notice for the dry winters of 2020-2021 and 2021-2022 (unlabeled, cyan-diamond) the horizontal positions of their markers, plotted here with their (pseudo) ONI value rounded to two decimal places. For both years the January (DJF) official ONI rounds to -1.0.

The expectation that La Niña would rapidly diminish toward the end of this past winter was already well forecast at the beginning of last fall by a consensus of dynamical models. Back then it was already clear that the upcoming winter's La Niña was not going to be the same as the previous two winters. This year's ONI for January (DJF) was down to -0.7. The ONI for February (JFM) is not yet available, but will probably be close to the -0.5 threshold. Barring significant amounts of precipitation during the last two days of this month, the five-month winter of 2022-2023 ranks 23rd wettest among the last 74 winters. The decade of the 2020's, even with the two dry La Niña years, is/will be off to a good start (compared to, for example, the decade of the 2000's). There's every reason to expect that next winter's precipitation will be at least near normal, and maybe even above normal again.

La Niña Nonsense

La Niña itself is not nonsense, nor is the fact that as of Sep 8 La Niña conditions are observed in the tropical Pacific and expected by the National Weather Service Climate Prediction Center to continue through the upcoming Northern Hemisphere winter.

What has been nonsense over the past 2-3 weeks is news coverage of supposed consequences of that expectation. An egregious example appeared in the Tucson paper on Sunday. The article was headlined Another La Niña could be more bad news for the Colorado River. The article quotes two experts. I'll call them Expert 1 and Expert 2. Their views are presented somewhat as a debate. Expert 1 enthusiastically supports the title of the article while Expert 2 says, Some La Niña years have produced near normal or above normal flows while others have seen much below normal flows as we have seen the last two years. So an objective title for the article would have been Experts disagree on whether another La Niña could be more bad news for the Colorado River.

The National Weather Service Climate Prediction Center (the newspaper article links to the same web page that I have linked to above; hereafter CPC) as of Sep 8 quantifies their expectation of a continuation of La Niña conditions as a 91% chance from September through November, decreasing to a 54% chance in January-March 2023. The newspaper article provides those CPC numbers, but misrepresents the probabilities, which actually apply to something the CPC defines objectively. Ocean surface temperature anomalies are determined for a specific portion of the equatorial Pacific, lying roughly south through southeast of Hawaii. There is averaging over time and space to generate a single number. There is an arbitrary threshold, and an additional requirement for duration. The result is an objective answer: La Niña conditions, or not. But the newspaper article describes the 91% and 54% probabilities as chances of a La Niña weather pattern dominating the Northern Hemisphere.

I think of Northern Hemisphere winter weather patterns as rolls of the dice. Pacific Ocean temperatures and associated tropical weather patterns load the dice. If I were to literally roll a single dice (die) once every 15 days this coming winter, I might expect that by the end of the winter each side would have come up once: {1, 2, 3, 4, 5, 6}. Of course by dumb luck one or more sides might come up more than once this winter. Over the long run, if I repeat the experiment every year for many years I would expect the average roll to be 3.50. But let's say I have a second dice loaded in a way that makes it impossible for it to land with the 6 facing up. A potential 6 result will always be turned into a 1. So the set of expectations will be {1, 1, 2, 3, 4, 5}. Over the long run the average roll with the loaded dice will be 2.67, not 3.50. Does that mean that roll 1 dominated the winters when I used the loaded dice, or that I would call each 1 in those years a loaded result? No, because one of the 1's would have happened anyway, and most of the time I still rolled a 2, 3, 4 or 5.

I expect there are people who will subjectively determine that the Northern Hemisphere midlatitudes this fall-winter will have been dominated by a La Niña weather pattern, no matter how the dice turn up. I have no idea how one would determine such a domination objectively, which would allow for a precise probability forecast calculated from historical data. I do know that the CPC issues probabilistic seasonal outlooks for precipitation. As I understand their discussion about those outlooks, they routinely adjust the historical data for recent trends, and that would mean they are somewhat siding with Expert 1 in the newspaper article (i.e., in effect, never mind that some La Niña years have produced above normal flows, look at the last two years with much below). But even with weighting toward recent trends, the CPC is predicting equal chances for the three categories (below-normal, near-normal, above-normal) for western Wyoming for Oct-Nov-Dec 2022, with that equal chances outlook expanding to cover much of the rest of the upper Colorado Basin for Dec-Jan-Feb 2022-2023.

In summary, how would I quantify the word could in the newspaper headline's phrase could be more bad news? I would say more than 50% (where 50% would be "could be bad, could be good") but less than 60% (much less than the tone of the newspaper article). That's based in part on the fact that the 54% chance of La Niña conditions continuing into January-March 2023 is effectively a 46% chance of a return to neutral conditions by then.

Viewing Blend Output

Viewing Blend Output

The National Weather Service provides public access to output from its National Blend of Models. I use the page at NOMADS. There, after selecting directories first for the latest date and then for the latest hour (UTC; latest hour becomes available near the end of that hour or a few minutes into the next hour), I select the "text" directory. Then (using Safari browser) I control-click->Download_Linked_File for whichever file(s) cover(s) the period of interest. In NBM terminology: h=hourly (1 day), s=short-range (3 days), e=extended-range (7 days), x=super-extended-range (8-10 days). Each file takes about 20 seconds to download. Meanwhile I've moved on to other web browsing. Later, offline, in the Terminal application I use the following little shell script to peruse one station at a time.

#!/bin/zsh
# The first $1 is the station identifier, which needs to be provided as an argument to the script.
# The second $1 is the beginning of the awk range pattern, looking for "sta" in field number 1.
awk -v sta="$1" '$1 ~ sta, /SOL/ ' IGNORECASE=1 Downloads/blend_nb[hsex]tx*

The IGNORECASE variable is recognized by the GNU version of awk, installed using Homebrew overriding the pre-installed awk. The setting can be removed for portability, but then one has to remember to capitalize the station (i.e., ./myb DMA vs. lazier fingers ./myb dma).

A list of available stations can be searched here. A complete key to the text bulletins can be found here.

Update on Technical Details

Three months ago, during an extended break in the monsoon, I wrote here Technical Details As Acknowledgements. Much of that post was about the process of installing free software, which I've been using for a variety of things, including producing custom weather maps on my MacBook. One motivation for providing information about the process was that lessons learned might be helpful for anyone interested in doing something similar with various Python packages. Another goal was to acknowledge the effort that people put into making these software packages work and remain freely available.

Most of the details from three months ago remain relevant. A few things have changed, and I'll incorporate those changes into a description of the process starting from scratch on a new MacBook. I had been limping along on a MacBook Air that I bought almost eight years ago, cheaply at the time as a refurbished unit. But recently I succumbed to temptation and bought a new one, which is equipped with a processor chip in the Apple M1 family, known as Apple Silicon, but also as ARM to distinguish from Intel.

I had been reading online discussions about M1, and based on advice in those discussions I expected for now to be able to get only so far with installing things natively on the new Macbook; for some things I would need to keep using my old Macbook. Some online posts advised running Intel versions of software through Apple's Rosetta 2 transition program on the M1 machines. Some recommended that the only hope for doing things natively was to use one of the Conda packages. Those other online recommendations may have been true a few months ago, but it is not so now. Everything that I had been using is now running natively on my new M1 Macbook, with no need for a middle-man software manager like miniconda. (Homebrew is of course a middle-man, but it is much closer to do-it-yourself.) It's time to shut down the eight-year-old Macbook for good.

I have my apple ID registered as a developer. It doesn't cost anything to do just that. That allows me to download the latest versions of Xcode and the Command Line Tools from http://developer.apple.com/downloads/more. Xcode downloads as an .xip file, and the CLT as a .dmg file. To start the install process for either one, double click. It's my understanding that only the CLT are needed by Homebrew. But I always download the latest version of Xcode as well because I need it for a few small stand-alone programs. I always do a separate install of the CLT after installing Xcode, and that seems to ensure that the Homebrew formulas find the CLT in the expected place.

Once again here is the Homebrew installation instruction web page. After completing those instructions on the M1 Macbook, I did brew install gcc and then brew install python@3.9. If either of those failed, there would be no point in trying to go farther. But everything installed natively and automatically with no problems, including several dependencies for each. I then did brew install geos and brew install proj. Those two libraries are needed by the cartopy python package. Three months ago cartopy required an older version of proj, but now cartopy uses the current version. There are a couple of dependencies automatically installed with proj. Everything continued to install natively and smoothly.

A few python packages can be installed either with a Homebrew formula or with the ordinary Python package manager. That was the case several years ago for numpy and scipy, and for awhile I kept them updated with their Homebrew formulas. Then it seemed that these formulas were unsupported, and the recommendation was to install/update numpy and scipy as ordinary Python packages. So I had been doing that recently. But in the online discussions about M1 there were reports of problems with installing these packages, and the recommendation was to use the Homebrew formulas, which have been updated recently. So brew install numpy and then brew install scipy. Again there are several dependencies, and again everything installed natively and smoothly.

Just a few more manual brew install's: hdf5 netcdf eccodes and pkg-config. Then it's on to the python package manager, already installed as pip3 by the Homebrew python@3.9 formula. There is a deprecation warning that is printed with each package installed. It appears that Homebrew will have to change something in the future, but the warning can be ignored for now. I started with pip3 install matplotlib, which automatically installs a number of required packages. Most if not all of these appear to install as native, pre-compiled binary wheels. Then on to
pip3 install shapely --no-binary shapely
pip3 install pyshp
pip3 install pyproj. These three are from the cartopy installation page. Then pip3 install cartopy.

One of the python packages that can be installed with a Homebrew formula is ipython. But the latest version of python, 3.10, was released just last month. The Homebrew formula for ipython is already set to require that recently released version of python as a dependency, while many other packages still depend on python 3.9. So it's easier to just use the alternative, pip3 install ipython. That also installs a number of dependencies, and they all go into Homebrew's 3.9 site packages folder.

The pandas package installed with no problems with pip3, though it took a long time to compile. Then pip3 install cfgrib, pip3 install xarray and pip3 install MetPy, and back in business plotting grib files on my new Macbook.