A newly published study has identified changes in precipitation patterns in the US Northeast, which are likely caused by human pollution of the atmosphere with greenhouse gasses, which has resulted in global warming. According to the study, there has been an increase in extreme precipitation events, and an increase in the clumping across time of precipitation, with longer or more intense rainy periods, and longer dry periods.

Generally, climate and weather watchers have noticed that arid regions are drier, wetter regions are wetter, and many feel this is a consequence of global warming. Increased temperatures may increase the intensity of precipitation; this is a matter of physics. As air temperature increase, the air is able to hold more water, and this increase is not linear; a little more heat means a lot more moisture.

Also, the overall pattern of movement of air currents seems to be affecting the distribution of precipitation. For example, the main jet stream that influences weather in the Northern Hemisphere seems to be more often wavy and slower moving. This causes low pressure systems that bring precipitation to move more slowly, so a given area may have both more intense rainfall and rainfall over a longer period of time. Nonetheless, while an increasing number of climatologists are thinking that global warming is changing the weather, it has only been happening for a few years, and it is a system with a high level of natural variability. This means the basic observational data may be difficult to bring to bear on understanding what is going on. The physics predict these changes. Modeling of climate has demonstrated a high likelihood of these changes. Direct observations are beginning to show these changes.

In a recent paper, “Quasi-resonant circulation regimes and hemispheric synchronization of extreme weather in boreal summer,” Dim Coumou, Vladimir Petoukhov, Stefan Rahmstorf, Stefan Petri, and Hans Joachim Schellnhuber noted the emergence of more frequent “Rossby Waves” in the jet stream, indicating that these waves have become more common and more persistent. They said, “We show that high-amplitude quasi- stationary Rossby waves, associated with resonance circulation regimes, lead to persistent surface weather conditions and therefore to midlatitude synchronization of extreme heat and rainfall events. Since the onset of rapid Arctic amplification around 2000, a cluster of resonance circulation regimes is observed involving wave numbers 7 and 8. This has resulted in a statistically significant increase in the frequency of high- amplitude quasi-stationary waves with these wave numbers. Our findings provide important new insights regarding the link between Arctic changes and midlatitude extremes.” (I elaborate on this finding here: More Research Linking Global Warming To Bad Weather Events.)

Climate Scientist Jennifer Francis, writing in Scientific American, notes,

One thing we do know is that the polar jet stream—a fast river of wind up where jets fly that circumnavigates the northern hemisphere—has been doing some odd things in recent years. Rather than circling in a relatively straight path, the jet stream has meandered more in north-south waves. In the west, it’s been bulging northward, arguably since December 2013—a pattern dubbed the “Ridiculously Resilient Ridge” by meteorologists. In the east, we’ve seen its southern-dipping counterpart, which I call the “Terribly Tenacious Trough.”

Different research teams differ somewhat in their explanation of this phenomenon, some seeking explanations in the warming Arctic, others in sea surface temperatures in the Pacific. Either way, the phenomenon seems to be real and important. I asked Justin Guilbert, lead author of the paper under consideration here, about this, and he noted, “The current very persistent atmospheric setup consists of a ridge in the west and a trough in the east. This setup is causing drought in the west and extreme cold and storminess in the east. All of which is consistent with recent studies suggesting that amplified planetary waves contribute to persistence. Such conditions tend to lead to persistent surface weather conditions because it is thought that high-amplitude waves do not move laterally as fast as lower-amplitude waves. The real weather story this year and last is the combination of persistent cold and repeated storms affecting the northeast. While we did not explore temperature persistence in the record, our analysis of the data shows that such setups may be on the rise concurrent with recent climate change.” So, the phenomenon of changes in precipitation patterns in the Northeastern US is yet another example, it seems, of warming induced changes in weather patterns. This applies as well to the cold many of us have been experiencing during our northern Winter.

So, now on to the details of the new paper just out in the American Geophysical Union addresses change in weather resulting from anthropogenic global warming. This study looks specifically at precipitation in the Northeastern United States. The paper is timely (though only by accident, the timing of peer reviewed publication and that of news cycles are entirely unconnected!) because of the recent heavy snows in New England. The study concludes that there is “… evidence of increasing persistence in daily precipitation in the Northeastern United States that suggests global circulation changes are affecting regional precipitation patterns… Precipitation in the northeastern United States is becoming more persistent; Precipitation in the northeastern United States is becoming more intense; [and these] Observed trends constitute an important hydrological impact of climate change.”

The paper is “?Characterization of increased persistence and intensity of precipitation in the Northeastern United States” by Justin Guilbert, Alan Betts, Donna Rizzo, Brian Beckage and Arne Bomblies.

The study used data from 222 weather stations in the US Northeast (defined as Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New York, Pennsylvania, Vermont, West Virginia, and the District of Columbia, but in the end excluding DC and Maryland because the data did not meet the study criteria). They used data from stations that had over 50 years of measurements and ran to past January 1, 1990, and excluded station data missing too many years of observation. The various data sets go back in time to as far as 174 years, with a mean coverage of about 84 years.

How do you count rain?

It is hard to count rain. If it rains on Friday and Saturday, you will get two records in a weather database, one for each day. But isn’t that just one storm? Maybe. Maybe not. Say it starts raining. It stops. It is still cloudy. It starts raining again, the same day. Or the next day. Is that one or two precipitation events? Is the rain from one low pressure system all one storm? Probably. So, OK, go back to 1882 and look at the rain gauge data for a particular town. It rained Monday and Tuesday. Were those the same low pressure system? Well, just check the satellite data for those days. But wait, satellites were still science fiction then! This is why most climate scientists a) don’t like the Weather Channel naming storms; you often can not define the boundaries of a given weather event unless it is something very compact like a hurricane or tornado, and even then, it can be a problem; and b) often have little hair.

The method used in this study is complicated but appropriately so. To measure precipitation extremes, they took precipitation data and subjected it to two streams of processing. First, they looked at the lower 75th percentile of daily precipitation values, and second, they looked at the upper, remaining, tail. Various appropriate distributional statistical analysis were applied. The data were then looked at using a moving 30 year window, so any given representation would have plenty of data to dampen out variation caused by low sample sizes. (Remember, the station data varies in density across time and space.) This information was then characterized as a median trend (typical rainfall) and extreme (high rainfall events). Then time trends were tested for. The research team did not find large changes in average precipitation, but they did identify increases in extreme events.

More than … two-thirds … of the 222 stations show positive trends for [extreme precipitation events] in the months of October through May and at least half of the stations display significant (p<0.01) positive trends during every month except July and September. The strongest regional trend in the 95th percentile of daily precipitation was observed in April when the average trend was +0.7 mm per day per decade. ... these trends are not spatially uniform. The entire region experienced an average trend of +0.5mm per decade in annual 95th percentile daily precipitation while Connecticut was found to have the greatest increase with a trend of +1.1mm per day per decade in annual 95th percentile daily precipitation . No trend was found for West Virginia in annual 95th percentile daily precipitation.

How dry I am

The other weather pattern the study looked at was, essentially, clumping of rain. We seem to see this a lot lately. Here in Minnesota, we experienced what Paul Douglas called a “Flash Drought” a few years ago. Not enough dry to make a full on drought, but the rain falling across the larger region seemed to be clumped in time and space such that there was very little in the Upper Midwest corn belt. Last summer, by contrast, it rained nearly every day in Minnesota from just before the start of June up through the end of June. We got totally clumped on by rain. (See: Minnesota’s Current Weather Disaster — Don’t worry we’ll be fine.)

The research team figured out a way to characterize this by looking at the relationship between two simple questions: Is it raining/not raining now? Is it raining/not raining the next day? That is an oversimplification of their methods, but I think it gets the point across. Imagine that today’s conditions with respect to precipitation is used to predict tomorrow’s, based on experience. If so, changes in the distribution across time of events would change the way that prediction would work out. The researchers found that “For daily precipitation events, the warmer months show the greatest increase in wet persistence, the colder months show larger increases in the magnitude of extremes, and dry persistence increases in early spring and decreases in early fall. … on an annual basis, it is likely that the study region will experience increasingly persistent and intense precipitation events.”

These findings confirm observations made by many people in the weather industry. They also may relate to patterns we see in things like snowfall in New England. Prior to the late 1970s, New England seemed to have the occasional large scale snow storm or blizzard (they are not exactly the same thing). Since then, the frequency of these events seems to have risen to about one every other year, at least in Southern New England. This year seems to be exceptionally snowy even by those standards. The concern here is that places like Boston have an infrastructure adapted to the occasional debilitating winter storm, but the storms may not remain occasional. One can imagine the T (that’s what they call the public transit system there) welding snow plows on to the front of the trollies.

I asked Guilbert if his team could put a time frame on these changes. Did alterations in precipitation patterns start at a certain point in time, or is there an acceleration in the rate at which these changes are happening? He told me, “Unfortunately the record is not long enough to robustly explore this question. I used a linear model to represent all the changes that were discussed so that a positive or negative symbol could be assigned to trends in persistence and intensity of precipitation. I looked back at the data on an annual level across the entire region to see if there was any evidence of non-linear behavior happening or if there appeared to be a ‘start time’ of which I found no evidence for either. However, this does not mean that there hasn’t been an acceleration in our metrics, it’s just that we haven’t been able to detect anything yet.”

A few days ago, using data from Jeff Master’s blog at Weather Underground, I plotted out the major snow storm events at four locations in the general vicinity of Boston, and got this graph:

There certainly were major storms before the cluster you see here, but early enough, or located in the wrong place, so that they don’t show up at these weather stations. So even though the study being discussed here does not directly address the question of “start time” there is an indication of this being a relatively new phenomenon with timing suggestive of a global warming related cause. We also know that weather related natural disasters in the US have been on the increase in recent decades. This graph is of events, not costs of events (that would go up just with inflation):

Note that the snow event graph above ends before the last few large events in New England. Note also that the natural disaster graph is not fully up to date. Also note that the Guilbert et al study reported here does not run up to the present. One gets the impression that the changes we are observing in weather patterns are happening quickly, a bit too quickly for longer term, carefully done studies, to keep up with. That simply means that whatever you were thinking based on the peer reviewed research, changes are, global warming’s effects are coming on faster than previously thought.