On May 5, the Bren School of Environmental Science & Management continued its Bren Seminar series with Postdoctoral Researcher Julie Edwards. Edwards’ lecture “Climate Change Detection and Attribution Using Tree Rings,” provided a new perspective on the types of tools used to detect changes in climate from both internal and external forces.
Her research emphasizes climate science and variability, extreme events and the consequences of destructive volcanic eruptions on climate. Tree rings are one tool she uses in addition to historical data and climate simulations, focusing on detailed images with high resolution and measurements of wood anatomy structures.
Climate change has been a common topic of discourse in decision-making, environmental policies and overall management of our planet. Therefore, it is vital to distinguish natural internal climate variability from anthropogenic forces and expand our understanding beyond the historical data.
Edwards highlighted that using tree-ring paleoclimate data “lets us extend our baseline for variability because this gets us information about the climate on time scales longer than the instrumental record.” Unknown to most, Edwards noted that the instrumental record of climate change dates back to the 1700s, whereas regions like Latin America only have robust continuous data records from the 1980s to now. So, her research bridges this gap in data and allows scientists to have access to information we would not otherwise have.
Edwards noted that the main goal of her research is “using both the general circulation models and paleoclimate proxy data to make up for each others’ discrepancies and understand why they might disagree with each other on what the climate is doing.” Consequently, climate scientists are able to compare the differences in variability between the two models from a structural standpoint and determine a more accurate conclusion of the climate history in a region.
But what is the difference between general circulation models (GCMs) and paleoclimate proxy data? On one hand, GCMs are computer programs that use mathematical equations to simulate how the Earth’s atmosphere and oceans move and interact, helping researchers understand and predict climate. Paleoclimate proxy data “are proxy observations, or indirect records” of what the previous climate in a region would look like.
Before diving into her research, Edwards made it a point to differentiate between general circulation models and paleoclimate proxy data with their respective strengths and weaknesses, underscoring the importance of tree-ring data in climate change research methodologies.
To start, Edwards mentioned that GCMs allow scientists to use simulations and reenact different scenarios on a global scale in order to analyze the different responses. In addition, the model is based on fundamental physics and provides a “mechanistic understanding of climate processes like radiation.”
But like most scientific research models, it also has its respective weaknesses. Edwards noted that GCMs have an uncertain range of internal variability as well as structural biases, thus there is an increased opportunity for over- or underestimations of certain processes or the timing of them. There is also limited spatial resolution, in which Edwards said, “for small scale processes like cloud formation, which are super important to the way climate works, they can not be modeled by these large global scale simulation models.”
As previously mentioned, paleoclimate proxy data are indirect records taken from natural sources like tree rings. Using this data allows for the “validation of models from different climate periods,” therefore allowing researchers to investigate their results based on instrumental observations and develop a more comprehensive understanding of “the range of our climate systems and how accurately they are represented by climate models.”
Paleoclimate proxy data still has its shortcomings with limited spatial and temporal coverage since Edwards collects terrestrial tree ring data only during the summertime when her research group coordinates to travel. Proxy data is also influenced by forces in addition to climate since they are a product of biological and geochemical processes. She mentioned that paleoclimate proxy data is “not just capturing the climate signal, there is all this other information that goes into constructing them that can bias your interpretation of them.”
Both models are successful tools to help both scientists and the public better understand climate change, but more specifically, Edwards uses these mechanisms to help answer important questions about climate change detection: why climate models and proxy data disagree, and the causes of the discrepancy between the two.
To address these uncertainties, Edwards shared three case studies in which she utilized both climate models and proxy data to settle the gap in data between them.
The first case study introduced looked at arctic temperature over the last millennium to address the main issue of climate models underestimating pre-industrial regional temperature variability. Edwards’ main research question investigated the most “true” range of slow, long-term changes in climate, also known as low-frequency variability, at relevant time periods.
For this study, the paleoclimate proxy that Edwards used was tree-ring wood density since it is a stronger recorder of past temperatures from the high latitude northwestern North America. Using wood anatomical data helped reconstruct temperatures and compare high- versus low-resolution anatomical density measurements.
Low-resolution images are less detailed than high-resolution and exhibit a warmer temperature during the pre-industrial period. Edwards’ study found that high resolution was a better match than low resolution for the data, showing that “when we use high-resolution wood cell anatomical density data, the climate models do not appear to underestimate low-frequency variability over this region.”
By focusing on a specific geographic location, it also reduces the amount of false positives of climate change detection and attribution when used in conjunction with the underestimations in previous pre-industrial data. Edwards highlights that “efforts are needed to improve the climate simulations of natural variability and climate models, accompanied by further refining of paleoclimate proxy data used to understand pre-industrial variability.”
The second case study looked at the 1783 Laki volcanic eruption to examine the difference between what the proxies and the models say about the climate’s response to the eruption and determine if models overestimate the effect of volcanic eruptions as an external factor.
Here, Edwards is able to measure the cell characteristics throughout the growing season in which tree-ring density reveals a bigger picture. Her team inferred that since the annual tree-ring boundary is determined by early summer temperatures, the “low-density portions of the tree ring reflect that ephemeral September cold snap” as a result of an abrupt stunt in growth from the eruption.
This timing allows a specific time focus to compare proxies and models, and aided Edwards and her team “to solve the problem of the mismatching seasonal windows, at least.” However, there still needs to be more research on the biological processes that create tree rings and volcanic eruptions themselves to better understand the internal variability of climate change.
Finally, the last and current case study Edwards presented was her research on atmospheric rivers in Southern California and the regional variability of precipitation extremes. She emphasized “the detection of extremes in particular is heavily dependent on accurate constraints of internal variability,” so in order to achieve more accurate results, they aim to “quantify and constrain uncertainty relating to internal climate variability and extreme precipitation in California.”
In this case, Edwards and her team utilized a specific type of general circulation model known as Single Model Initial-Condition Large Ensembles (S.M.I.L.E.s). This helps them to have multiple variations of responses “to isolate forced from internal responses and also let [them] see the underlying ocean-atmosphere circulation dynamics” known to trigger extreme precipitation episodes.
By comparing the internal variability to the S.M.I.L.E. results, Edwards found that the standard deviation of heavy precipitation from two large ensemble climate models was drastically different. This allowed them to detect a difference in heavy precipitation between natural variability and the observed results.
Overall, there are still many questions remaining about natural and external forces on climate change, but Edwards is still on the pursuit of “closing the proxy/model gap for better climate change detection.”
A version of this article appeared on p.10 of the May 15, 2025 edition of the Daily Nexus
