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Mapping the after-effect of rain to determine where and when bio-aerosols influence rainfall


This blog post summarizes how and why I have pursued making maps of the after-effect of rainfall across continental US (3000 sites) and Western Europe and the Mediterranean basin (500 sites). As described in the posting below, I believe that maps of the intensity of rainfall feedback are a useful tool to clarify the contexts under which aerosols markedly influence the outcome of atmospheric processes that lead to rainfall.  The maps, and the tools to make and analyze the maps, are freely available:



Aerosols play a vital role in the formation and amount of precipitation because they can influence the number and rate at which eventual rain drops form. In other words, there are conditions under which, without certain types of aerosols, rainfall might not occur. However, because aerosols are ever-present in the atmosphere and their effects cannot be easily separated from those of the synoptic conditions, it is a challenge to determine how important they are in the outcome of events leading to rainfall. Furthermore, there are many different sources of aerosols, and their types and abundances vary over space and time. This lack of uniformity further complicates quests to understand their significance in atmospheric processes and to generalize results from field studies.

Decades ago, the atmospheric physicist Keith Bigg started to suspect that he could capitalize on the temporal variability of aerosols to assess how they influence rainfall. Early in his career he observed that the atmospheric concentration of INPs (ice nucleating particles) increased right after rainfall and continued to accumulate for up to about 3 weeks (Bigg, 1958). He reasoned that marked changes in the rare aerosols – under relatively constant synoptic conditions – could reveal how these aerosols influence rainfall. Compared to the very abundant cloud condensation nuclei, INPs are relatively rare. So, perhaps the shifts in their abundance after rainfall could provide insight into the extent to which INPs influence the outcome of processes leading to rainfall. Keith started to explore rainfall data from weather stations across Australia to search for patterns that would be consistent with a lingering increase in INPs.  But, he was nevertheless perplexed about the mechanism that brought about this increase. He couldn’t think of any physical process that could sustain INP production over weeks. And furthermore, he had the impression that the curves of INP increase that he plotted looked like classic growth curves of organisms.

The discovery that certain microorganisms can catalyze the freezing of super-cooled water came about 20 years later – in the late 1970’s (as summarized by Chris Upper and Gabor Vali, 1995). This discovery sparked new research questions among physicists about the behavior of microorganisms as atmospheric INPs and among plant pathologists about their role in frost damage to the plants on which they lived. It took yet another 30 years for physicists and biologists to start consistently working together on questions of how the growing list of biological INPs can influence atmospheric processes (see Decade of Interdisciplinarity). All the while, Keith was ingesting this new perspective on ice nuclei and he was cogitating.

Since the early 1980’s, when I was a graduate student at the University of Wisconsin (in the lab where one of the two independent discoveries of biological ice nucleation activity was made), I had heard about a couple of eclectic scientists who had novel ideas about the role of microorganisms in rainfall. One of these was David Sands, a plant pathologist from Montana State University with whom I eventually linked up to initiate the research network described above. The other was Keith Bigg, an Australian atmospheric physicist who had some precise ideas about the interaction of microorganisms with rain and who was trying to do some experiments to gain support for them. The opportunity to meet Keith came in 2011 when Uli Pöschl (Max Planck Institute, Mainz, Germany) asked me to help him organize a session on bio-aerosols at the annual meeting of the IUGG (International Union of Geodesy and Geophysics) in Melbourne, Australia. By 2011 Keith had long retired from his official association with a research institute, but I managed to contact him at his home in Sydney and asked if he would be willing to come to Melbourne to attend the session. On the day of the session, I sat in one of the available seats near the front of the room for the talks just before our bio-aerosol session. When the gentleman seated next to me asked a particularly insightful question to the speaker, I was convinced that this was Keith. And indeed it was.

This was the beginning of a long intellectual journey. Keith described to me the methods he developed to find a feedback signal in historical rainfall data from Australia. He mapped the signals of rainfall feedback across Australia in search of how land use might influence the signal because land use can affect aerosol sources. To a microbiologist and epidemiologist such as myself, the notions of variability of aerosol types and of their abundance over space and time sounded very much like population dynamics and genetic diversity of microorganisms. These are basic staples for research in disease epidemiology.  But I was not sure that I understood the mathematics that he used. It seemed like a sort of time series analysis. And I wanted to see the confidence intervals associated with the signal that he measured. Keith agreed that I could engage some additional help.

At the research center where I work (INRA’s research center in Avignon, France) there is a strong program in spatial statistics and a team of scientists who have learned the pedagogic tools for communicating with biologists (INRA’s BioSP research unit). I knocked on the door of Samuel Soubeyrand who, still early in his career, had a reputation for excellent pedagogic and communication skills and lots of patience. I put him to the test with my approximate explanations of Keith’s ideas. Eventually, after 3 years of a very technical discussion between Samuel and Keith via email – with me in the middle asking lots of dumb questions in hopes of catching up in my understanding – we produced a tool to calculate an index that represents the rainfall feedback signal and to calculate its confidence intervals (Soubeyrand et al, 2014), and we mapped the index based on 100-year daily rainfall data across about 100 sites in Australia (Bigg et al, 2015).

The discovery of the ice nucleation activity of microorganisms that live on plants allowed Keith to suspect that the lingering after-effect of rainfall on subsequent rainfall is due to changes in amounts of bio-aerosols from rain-induced growth. This growth could transform negligible amounts of INPs into critical quantities that could influence atmospheric processes. As such, the Rainfall Feedback Index (RFI) indicates the extent to which atmospheric processes are sensitive to aerosols. Therein lies a key to capitalizing on the variability of aerosols over time to reveal their importance for rainfall.  Mapping RFI across continents would, in turn, provide the means to capitalize on spatial variability to uncover its influence on rainfall.

When I finally understood that the primary importance of the RFI is as a proxy for the dynamics of biological aerosols (and not for the amount of rain generated by feedback), I became somewhat obsessive about mapping this phenomenon. This obsession has its roots in my training in plant pathology where I learned about the importance of pathogen dynamics for epidemiology and disease management. But you might wonder how the management of plant health is related to the physics of rainfall formation.  It is related to the ideas that have been sparked about the practical applications of the microorganism-rainfall interaction.

There is more and more discussion about how microorganisms could be leveraged to “make rain” as scientists and the public learn about a possible role for microorganisms in rainfall. While that is a noble goal that should be considered, we cannot lose sight of the fact that some of the most active and wide spread bio-INPs can also cause disease to crops. The losses caused by these microorganisms can be of considerable importance economically and for food security. These dual-role microorganisms include the rust fungi such as Puccinia spp. (Morris et al, 2013), various strains of the bacterium Pseudomonas syringae (Berge et al, 2014) and several species of the fungus Fusarium including F. avenaceum and more that will likely be revealed soon. Questions about “making rainfall” are part of the battle to combat the consequences of climate change. Fortunately, this battle rallies a strong force. But it can also be blind to other efforts to protect the environment. This was illustrated clearly at the 2011 IUGG meeting I attended in Melbourne where a plenary speaker highly recommended that production of wheat and other large scale annual crops be intensified with heavy fertilizer inputs to assure that the plants sequester sufficient carbon. I sprung up during the question session to ask if he was aware that most of the major agricultural research institutes, especially those in Europe, were now mandated to work on de-intensifying the inputs of synthetic fertilizers into agriculture out of concern for deleterious effects on the environment. Clearly, the disciplines of Earth Sciences and of Agronomy had had little communication up to that date. Any effort to tweak plant-associated microorganisms to influence rainfall will need to assure that trade-offs between dual roles of microorganisms have been considered. By mapping the RFI, I felt that I could contribute to 1) clarifying where and when microbial INPs are beneficial for rainfall, 2) identifying specifically which microorganisms are implicated and 3) how they are related to crops. If there is corroborative evidence from field observations that potential plant pathogens are strong suspects as decisive actors in the formation of rainfall, then we will need to assess a critical epidemiological question about the quantities of the pathogen that are involved: Do these quantities pose significant threats to crop health or are they in a range where plant health can be managed by inherent resistance of the plant and/or agronomic practices?

In October 2014 I started to download daily rainfall data from the website of NOAA’s National Centers for Environmental Information. I was looking for sites in the US with roughly 100 years of daily data like Keith had used for his study of Australia. At first I was timid about downloading because I was not sure if the data were open access. But little by little I understood that this was an open service. Being quite content with the 1250 sites I found in the western US, I convinced Samuel that mapping the RFI for these sites would lead to fascinating results. After I manually formatted the data to fit to the R programing code that he developed to calculate the RFI, he kindly complied with my request.  I also convinced him that it would be great to have a website where everyone can see the maps, access the RFI results and learn how to make their own maps (see the previous blog post on Rainfall Feedback maps). With the help of Keith, David Sands and Jessie Creamean, an early career atmospheric chemist who participated in the MILAF mentoring workshops that David and I had organized (see The MILAF meetings), this effort also led to a publication in BAMS last year that describes the tools and the trends across the western US (Morris et al, 2017). The acceptance of our work for publication in a highly respected meteorological journal was reassuring that our tool had potential to give novel insight into the role of aerosols in rainfall formation.

Although I was encouraged to continue mapping the RFI, I was hoping that other scientists would get excited and want to make maps to ease my work. I also felt that I had made many demands on Samuel’s time and that maybe I should cool down for a while. But then the results of the 2016 US presidential election changed everything. By early December 2016 there was a strong concern in the community of Earth Systems scientists that the new US government would close or limit access to data bases related to climate change research. And so, on 15 December 2016, I started a marathon of screening the NOAA website for the data needed to calculate the RFI across the rest of the continental US and in Western Europe and the Mediterranean basin – all while keeping in mind that I needed to “run this marathon” in my spare time such as evenings and weekends. Six months later I had data from 1700 additional weather stations in the US and 500 across Europe, northern Africa and the Middle East and had formatted some of them.  I also had developed very painful carpal tunnel syndrome in my right hand (that I managed to eventually overcome without an operation). To help ease my hand and the amount of data manipulation it was doing, one of the scientists in my team, Christelle Lacroix, wrote a program with R software to automate the data formatting and the extraction of the metadata concerning the location of the weather stations and the range of dates of the data at each site. Christelle also helped me de-bug the R package that Samuel installed on a computer in my office so that I could make the calculations of RFI myself. And eventually, I didn’t bother anyone about the maps until early July 2017 when I finished the calculations for Europe and again last week (early March 2018) when I finished the calculations for the 1700 sites in the eastern half of the US.

I pursued the making of the maps posted at for several years. But Keith has been working on the foundation of these maps for the whole duration of my life, i.e. for 60 years. That is an incredible achievement as a scientist. After meeting Keith in 2011, I traveled back to Australia twice to visit him again and to learn as much as I could about his insights on rainfall feedback and to imbibe in his historical perspective. It is a rare opportunity when knowledge can be directly transmitted across multiple generations, and it gave me a great sense of responsibility to receive such a gift. I hope that I have done justice to what I have learned. And I hope that the next generation is listening and interested.



Berge O., Monteil C.L., Bartoli C., Chandeysson C., Guilbaud C., Sands D.C., Morris C.E.   2014. A user’s guide to a data base of the diversity of Pseudomonas syringae and its application to classifying strains in this phylogenetic complex. PLoS One 9(9): e105547. doi:10.1371/journal.pone.010554


Bigg EK. 1958. A long period fluctuation in freezing nucleus concentrations. J. Meteorology 15: 561-562.


Bigg E.K., Soubeyrand S., Morris C.E. 2015. Persistent after-effects of heavy rain on concentrations of ice nuclei and rainfall suggest a biological cause.  Atmos. Chem. Phys. 15: 2313-2326


Morris C.E., Sands D.C., Glaux C., Samsatly J., Asaad S., Moukahel A.R., Gonçalves F.L.T., Bigg E.K. 2013. Urediospores of rust fungi are ice nucleation active at > −10 °C and harbor ice nucleation active bacteria.  Atmos. Phys. Chem. 13:4223-4233.


Morris C.E., Soubeyrand S., Bigg E.K., Creamean J.M., Sands D.C. 2017. Mapping rainfall feedback to reveal the potential sensitivity of precipitation to biological aerosols. Bull. Amer. Meteorol. Soc.  (June 2017:1109-1118)


Soubeyrand S., Morris C.E., Bigg E. K. 2014. Analysis of fragmented time directionality in time series to elucidate feedbacks in climate data. Environmental Modeling and Software 61:78-86


Upper C.D., Vali G. 1995. The discovery of bacterial ice nucleation and its role in the injury of plants by frost. In Biological Ice Nucleation and its Applications, edited by R. E. Lee, Jr., G. J. Warren and L. V. Gusta. St. Paul: APS Press.

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