Let there be light? Estimating the impact of geoengineering on crop productivity using volcanic eruptions as natural experiments (Guest post by Jonathan Proctor)

[This is a guest post by Jonathan Proctor, a Doctoral Fellow at the Global Policy Lab and PhD candidate in the Ag and Resource Econ department here at Berkeley]

On Wednesday I, and some notorious G-FEEDers, published a paper in Nature exploring whether solar geoengineering – a proposed technology cool the Earth by reflecting sunlight back into space—might be able to mitigate climate-change damages to agricultural production. We find that, as intended and previously described, the cooling from geoengineering benefits crop yields. We also find, however, that the shading from solar geoengineering makes crops less productive. On net, the damages from reduced sunlight wash out the benefits from cooling, meaning that solar geoengineering is unlikely to be an effective tool to mitigate the damages that climate change poses to global agricultural production and food security. Put another way, if we imagine SRM as an experimental surgery, our findings suggest that the side effects are as bad as the cure.

Zooming out, solar geoengineering is an idea to cool the earth by injecting reflective particles --usually precursors to sulfate aerosols -- into the high atmosphere. The idea is that these particles would bounce sunlight back into space and thus cool the Earth, similarly to how you might cool yourself down by standing in the shade of a tree during a hot day. The idea of such sulfate-based climate engineering was, in part, inspired by the observation that the Earth tends to cool following massive volcanic eruptions such as that of Pinatubo in 1991, which cooled the earth by about half a degree C in the years following the eruption.

Our visualization of the stratospheric aerosols (blue) that scattered light and shaded the planet after the eruption of Mount Pinatubo in 1991. Each frame is one month of data. Green on the surface indicates global crop lands. (The distance between the aerosol cloud and the surface is much larger than in real life.) 

A major challenge in learning the consequences of solar geoengineering is that we can’t do a planetary-scale experiment without actually deploying the technology. (Sol’s questionably-appropriate analogy is that you can’t figure out if you want to be a parent through experimentation.) An innovation here was realizing that we could learn about the impacts of solar geoengineering without incurring the risks of an outdoor experiment by using giant volcanic eruptions as natural experiments. While these eruptions are not perfect proxies for solar geoengineering in every way, they give us the necessary variation we need in high atmosphere aerosol concentrations to study some of the key effects on agriculture. (We expand on how we account for the important differences between the impacts of volcanic eruptions and solar geoengineering on agricultural production later in this post). This approach builds on previous work in the earth science community which has used the eruptions to study solar geoengineering’s impact on climate.  Here’s what we found:

Result 1: Pinatubo dims the lights

First, we find that the aerosols from Pinatubo had a profound impact on the global optical environment. By combing remotely sensed data on the eruption’s aerosol cloud with globally-dispersed ground sensors of solar radiation (scraped from a Russian website that recommends visitors use Netscape Navigator) we estimate that the Pinatubo eruption temporarily decreased global surface solar radiation (orange) by 2.5%, reduced direct (i.e. unscattered, shown in yellow) insolation by 20% and increased diffuse (i.e. scattered, shown in red) sunlight by 20%.

Effect of El Chichon (1982) and Mt Pinatubo (1991) on direct (yellow), diffuse (red) and total (orange) insolation for global all-sky conditions.

These global all-sky results (i.e. the average effect on a given day) generalize previous clear-sky estimates (the effect on a clear day) that have been done at individual stations. Like a softbox or diffusing sheet in photography, this increase in diffuse light reduced shadows on a global scale. The aerosol-scattering also made redder sunsets (sulfate aerosols cause a spectral shift in addition to a diffusion of light), similar to the volcanic sunsets that inspired Edvard Munch’s “The Scream.” Portraits and paintings aside, we wanted to know: how did these changes in sunlight impact global agricultural production?

Isolating the effect of changes in sunlight, however, was a challenge. First, the aerosols emitted by the eruption alter not only sunlight, but also other climatic variables such as temperature and precipitation, which impact yield. Second, there just so happened to be El Nino events that coincided with the eruptions of both El Chichon and Pinatubo. This unfortunate coincidence has frustrated the atmospheric science community for decades, leading some to suggest that volcanic eruptions might even cause El Niños, as well as the reverse (the former theory seems to have more evidence behind it).

To address the concern that volcanoes affect both light and other climatic conditions, we used a simple “condition on observables” design – by measuring and including potential confounds (such as temperature, precipitation and cloud cover) in the regression we can account for their effects. To address the concurrent El Nino, we do two things. First, we directly condition on the variables though which an El Nino could impact yields – again temperature, precipitation and cloud cover. Second, we condition on the El Nino index itself, which captures any effects that operate outside of these directly modeled channels. Essentially, we isolate the insolation effect by partitioning out the variation due to everything else – like looking for your keys by pulling everything else out of your purse.

The above figure schematically illustrates our strategy. The total effect (blue) is the sum of optical (red) and climatic components (green). By accounting for the change in yields due to the non-optical factors, we isolate the variation in yields due to stratospheric aerosol-induced changes in sunlight.

Result 2: Dimming the lights decreases yields

Our second result, and the main scientific contribution, is the finding that radiative scattering from stratospheric sulfate aerosols decreases yields on net, holding other variables like temperature constant. The magnitude of this impact is substantial – the global average scattering from Pinatubo reduced C4 (maize) yields by 9.3% and C3 (soy, rice and wheat) yields by 4.8%, which is two to three times larger than the change in total sunlight. We reconstruct this effect for each country in the figure below:

Each line represents one crop for one country. These are the reconstructed yield losses due to the estimated direct optical effects of overhead aerosols.

My young cousins dismissed the sign of this effect as obvious – after all plants need sunlight to grow. But, surprising to my young cousins, the prevailing wisdom in the literature tended to be that scattering light should increase crop growth (Sol incessantly points out that David even said this once). The argument is that the reduction in yields from loss of total light would be more than offset by gains in yield through an increase in diffuse light. The belief that diffuse light is more useful to plants than direct light stems from both the observation that the biosphere breathed in carbon dioxide following the Pinatubo eruption and the accompanying theory that diffusing light increases plant growth by redistributing light from the sun-saturated leaves at the top of the canopy to the light-hungry leaves below. Since each leaf has diminishing photosynthetic productivity for each incremental increase in sunlight, the theory argues, a more equal distribution of light should promote growth.

Aerosols scatter incoming sunlight, which more evenly distributes sunlight across the leaves of the plant. We test whether the loss of total sunlight or the increase in diffuse light from aerosol scattering has a stronger effect on yield.

While this “diffuse fertilization” appears to be strong in unmanaged environments, such as the Harvard Forest where the uptake of carbon increased following the Pinatubo eruption, our results find that, for agricultural yields, the damages from reduced total sunlight outweigh the benefits from a greater portion of the light being diffuse.

We cannot tell for sure, but we think that this difference between forest and crop responses could be due to either their differences in geometric structure (which could affect how deeply scattered light might penetrate the canopy):

Or to a re-optimization towards vegetative growth at the cost of fruit growth in response to the changes in light:

Two radishes grown in normal (left) and low light (right) conditions.  Credit: Nagy Lab, University of Edinburgh

This latter re-optimization may also explain the relatively large magnitude of the estimated effect on crop yield.

Result 3: Dimming damages neutralize cooling benefits from geo

Our final calculation, and the main policy-relevant finding of the paper, is that in a solar geoengineering scenario the damages from reduced sunlight cancel out the benefits from warming. The main challenge here was figuring out how to apply what we learned from volcanic eruptions to solar geoengineering, since the climate impacts (e.g. changes in temperature, precipitation or cloud cover) of a short-term injection of stratospheric aerosols differ from those of more sustained injections (e.g. see here and here). To address this, we first used an earth system model to calculate the impact of a long-term injection of aerosols on temperature, precipitation, cloud cover and insolation (measured in terms of aerosol optical depth). We then apply our crop model that we trained on the Pinatubo eruption (which accounts for changes in temperature, rainfall, cloud cover, and insolation independently) to calculate how these geoengineering-induced changes in climate impact crop yields. This two-step process allows us to disentangle the effects of solar geoengineering on climate (which we got from the earth system model) and of climate on crops (which we got from Pinatubo). Thus, we can calculate the change in yields due to a solar geoengineering scenario even though volcanic eruptions and solar geoengineering have different climatic fingerprints. Still, as with any projection to 2050, caveats abound such as the role of adaptation, the possibility of optimized particle design, or the possibility that variables other than sunlight, temperature, rainfall and cloud cover could play a substantial role.

Estimated global net effect of a geo-engineering on crop yields through four channels (temperature, insolation, cloud cover, precipitation) for four crops. The total effect is the sum of these four partial effects.

So, what should we do? For agriculture, our findings suggest that sulfate-based solar geoengineering might not work as well as previously thought to limit the damaging effects of climate change. However, there are other sectors of the economy that could potentially benefit substantially from geoengineering (or be substantially damaged, we just don’t know). To continue the metaphor from earlier, just because the first test of an experimental surgery had side effects for a specific part of the human body does not mean that the procedure is always immediately abandoned. There are many illnesses that are so harmful that procedures known to cause side effects are sometimes still worth the risk. Similarly, research into geoengineering should not be entirely abandoned because our analysis demonstrated one adverse side effect, there may remain good reasons to eventually pursue such a strategy despite some known costs. With careful study, humanity will eventually gain a better understanding of this powerful technology. We hope that the methodology developed in this paper might be extended to study the effects of sulfate aerosol injection on ecosystem or human health and would be open to collaborate on future studies. Thanks for reading, and I’m excited to hear any thoughts the community may have.

Some scintillating satellite studies

We’ve had a couple of papers come out that might be of interest to readers of the blog. Both relate to using satellites to measure crop yields for individual fields. This type of data is very hard to come by from ground-based sources. In many places, especially poor ones, farmers simply don’t keep records on production on a field by field basis. In other places, like the U.S., individual farmers often have data, and government agencies or private companies often have data for lots of individuals, but it’s typically not made available to public researchers.

All of this is bad for science. The less data you have on yields, the less we can understand how to improve yields or reduce environmental impacts of crop production. We now know exactly how much a catcher’s glove moves each time they frame a pitch, exactly how well every NBA player shoots from every spot on the floor, and exactly how fast LeBron James flops to ground when he is touched by an opposing player. And all of this knowledge has helped improve team strategies and player performance.

But for agriculture, with all the talk of “big data”, we are still often shooting in the dark. And in some cases it’s getting worse, such as this recent post at farmdoc daily about how the farmer response rate to area and production surveys is fading faster than my hairline (but not quite as fast as Sol’s):

As we’ve discussed before on this blog (e.g., here and here), satellites offer a potential workaround. They won’t be perfect, but anyone who has done phone or field surveys, or even crop cuts within fields, knows that they have problems too. Plus, satellites are getting cheaper and better, and it’s also becoming easier to work with past satellite data thanks to platforms like Google Earth Engine.

Now to the studies. Both employ what we are calling SCYM, which stands either for Scalable Crop Yield Mapper or Steph Curry’s Your MVP, depending on the context. In the first context, the basic idea is to eliminate any reliance on ground calibration by using simulations to calibrate regression models. 

The first study (joint with George Azzari) applied SCYM to Landsat data in the Corn Belt in the U.S. for 2000-2015, and then analyzed the resulting patterns and trends for maize and soybean yields. Here’s the link to the paper, and a brief animation we had made to accompany the story. Also, we’ve made the maps of average yields available for people to visualize and inspect here. Below is a figure that summarizes what I thought was the most interesting finding: that maize yield distributions are getting wider over time.

Overall maize yields are still increasing, but mainly this is driven by growth in high yielding areas. This is true at the scale of counties, and also within individual fields. The paper discusses reasons why (e.g. uptake of precision ag) and possible implications. Maybe I’ll return to it in a future post. A short side note is that since that paper was written we’ve expanded the dataset to a nine state area, and made some useful tweaks to SCYM to improve performance.

The second paper (joint with Marshall) looks at yield mapping with some of the newer high-res sensors in smallholder systems. In this case, we looked at two years of data in Western Kenya, using detailed surveys with hundreds of farmers to test our yield estimates, which were derived using 1m resolution imagery from Terra Bella. The results were pretty good, especially when you consider only fields above half an acre, where problems with self-reported yields are not as big. Just like in the US, the satellites reveal a lot of yield heterogeneity both within and between fields (the top image shows the true-color image, the bottom shows the derived yield estimates for maize fields):

One interesting result is that in many ways it looks like the satellite estimates are no less accurate than the (expensive) field surveys. For example, when we correlate yields with inputs that we think should affect yields, like fertilizer or seed density, we see roughly equal correlations when using satellite or self-report yields.

None of this is to say that satellite-based yields are perfect. But that was never the goal. The goal is to get insights into what factors are (or aren’t) important for yields in different settings. Both studies show how satellites can provide insights that match or even go beyond what is possible with traditional yield measures. We hope to continue to test and apply these approaches in new settings, and plan to make the datasets available to researchers, probably at this site.  

Measuring temperature from space

[We are going to try to bring G-FEED out of its fall/winter hibernation with a few posts on papers that various of us have coming out.  Below is the first of these, written by our excellent post-doc Sam Heft-Neal.]

Can we use satellites instead of ground stations to measure temperature and estimate climate response functions?

Guest post by Sam Heft-Neal

Temperature data are commonly used to estimate the sensitivity of societally relevant outcomes to ongoing climate changes. In order to derive a temperature measure, researchers typically interpolate nearby ground station data or use one of the publicly available global gridded data products put out by groups like CRU or UDel. However, Max and Sol and Wolfram and Adam have shown that there are a number of pitfalls associated with using interpolated station data to measure temperature in areas with sparse coverage. Temperature estimates far from weather stations, or in areas where stations go on and offline frequently, are subject to substantial measurement error that can bias impact estimates. This problem is particularly acute in tropical regions, where many of the largest climate impacts are expected to occur, but where we still have a limited understanding of key climate/society relationships. 

David, Marshall and I have a new paper that evaluates whether using temperature measures derived only from satellites can be used to estimate climate response functions. Several satellites measure surface emission of thermal energy, which can be converted into estimates of skin surface temperature (Ts) – a product that MODIS has provided at 1km resolution daily for nearly a decade. Past studies have evaluated agreement between MODIS and air temperature measured by weather stations (Ta) on daily time scales, often finding weak correlations for daytime temperatures because factors other than Ta, such as cloudiness and soil moisture, can affect Ts. However, these results could be of limited relevance for our purpose, since the climate response functions we care about usually rely on year-to-year variations in seasonally aggregated measures of temperature exposure, and correlations between station and satellite data tend to increase as the period of aggregation lengthens.

                                                                                     

In order to test whether Ts could be used in place of Ta to study temperature impacts, we revisited three previous studies (each led by a different G-Feed blog contributor) that had used standard measures of Ta to study climate impacts. For each study we replicated the analysis with both Ta and Ts and compared model performance. The first study examined temperature effects on maize yields in Africa using historical field trial data from plots under optimal management and plots under drought management. The second study looked at county level maize yields in the U.S. and the third study, which we selected to look at an application outside of agriculture, estimated temperature effects on county-level GDP in the U.S..

                                                                    

In order to assess model performance we calculated out-of-sample prediction error by repeatedly estimating the models on randomly selected 75% subsets of locations, predicting values for the 25% of locations that had been excluded from estimation, and calculating the RMSE of out-of-sample predicted values relative to actual values. In each case we found similar relationships between temperature and the outcome of interest (panels 1 and 2 below). For both types of African maize field trials we actually found that models with satellite temperature had lower prediction error than models with ground station temperature. However, when we compared models in the U.S. (panel 3) with its high density of stations, the models had very similar predictive powers. This finding suggests that Ts is more useful in regions with poor station coverage where Ta is measured with significant levels of error.               

 

Response functions for maize yields in Africa (left 2 plots) and maize yields in US (right plots).  Orange is surface temperature from MODIS, blue is air temperature from stations. 

While Ts predicts crop yields well, it is less clear whether it could be used to estimate response functions for non-agricultural applications like GDP. The GDP study also differs from the agricultural studies because instead of using seasonal averages for temperature we used temperature bins. The satellite data we used were 8-day composites so for every observation that fell into a given temperature bin we assigned eight days to that bin (in other words we assumed constant – or at least within a constant bin - temperature for each 8-day period). Even with this assumption, the model with Ts (somewhat surprisingly to us) reproduced a similar non-linear response function over most of the temperature support, particularly at the upper end of the temperature distribution where income appears to be most sensitive to temperature.  

 

Replication of Deryugina and Hsiang (2014), using ground stations (left) and MODIS (right).

Lastly, as a final comparison, we compared the aggregated impacts from 1◦C warming estimated with both models. In doing so we again find similar estimates for all applications.

 

Impact of +1C warming for different models. 

This overall consistency is perhaps somewhat surprising, given the often low correlations between anomalies in Ts and Ta at the daily or 8-day time scale. In the paper we argue that there are at least four reasons Ts could outperform Ta:

1.   Some of the “noise” in Ts vs. Ta relationships stems from errors in the Ta measures, particularly in regions such as Africa where Ta is often interpolated from anomalies at stations tens of kilometers away. So just because Ts and Ta don’t always agree it doesn’t necessarily mean Ts is wrong.

2.   Much of the noise likely cancels out when aggregating temperatures to the monthly or seasonal time scales that are used in regressions that relate outcomes to temperature. For applications that require finer temporal resolution of temperature measures, the noise in Ts may become more important – although again, whether it is larger than noise in high-temporal-resolution Ta remains an empirical question.

3.  Unlike ground measurements, satellite data come from a consistent sensor. Relative spatial variations could therefore be captured more precisely with satellites than with ground measurements from different instruments.

4.   There is reason to believe that Ts could be more appropriate than Ta for agricultural applications. In vegetated areas much of the noise in the daytime Ts vs. Ta relationship arises from anomalous canopy transpiration rates, with stressed canopies often several degrees warmer than Ta whereas healthy canopies are typically several degrees below Ta. Thus, Ts provides a more direct measure of crop condition than Ta, and this represents an advantage of Ts for agricultural applications that may compensate for some of its deficiencies.

Overall this exercise increased our optimism that Ts can serve as a replacement for Ta in some applications. One of the primary downsides of using satellite data is that the records do not go back as far as ground station records so this approach will not be appropriate for many studies with longer timescales. There are also some issues surrounding the transformation of Ts units into Ta units that we discuss in the paper. Despite these caveats, for studies covering recent years our results suggest that Ts is a viable option for replacing Ta, and that in areas with poor ground station coverage, using satellites to measure temperature may in fact be the better option.